diff --git a/applications/jupyterhub/deploy/templates/configmap.yaml b/applications/jupyterhub/deploy/templates/configmap.yaml index ee05f5736..3fc89b76c 100644 --- a/applications/jupyterhub/deploy/templates/configmap.yaml +++ b/applications/jupyterhub/deploy/templates/configmap.yaml @@ -3,7 +3,7 @@ apiVersion: v1 metadata: name: "jupyterhub-notebook-config" labels: - app: jupytehub + app: jupyterhub data: {{- (.Files.Glob "resources/jupyterhub/applications/*").AsConfig | nindent 2 }} --- diff --git a/applications/jupyterlab-minimal/Dockerfile b/applications/jupyterlab-minimal/Dockerfile index 6e28c36c7..fcb2462fb 100644 --- a/applications/jupyterlab-minimal/Dockerfile +++ b/applications/jupyterlab-minimal/Dockerfile @@ -1,4 +1,4 @@ -FROM jupyter/base-notebook:hub-1.5.0 +FROM quay.io/jupyter/base-notebook:hub-4.0.2 USER root RUN apt-get update && apt-get install git -y diff --git a/applications/jupyterlab/Dockerfile b/applications/jupyterlab/Dockerfile index 3d867b4e1..8ace49ff9 100644 --- a/applications/jupyterlab/Dockerfile +++ b/applications/jupyterlab/Dockerfile @@ -1,4 +1,4 @@ -FROM quay.io/jupyter/base-notebook:latest +FROM quay.io/jupyter/base-notebook:hub-4.0.2 USER root @@ -35,12 +35,12 @@ USER root RUN echo -e '\n\nalias cd..="cd .." \nalias h=history \nalias ll="ls -alt" \n' >> ~/.bashrc ### Set up jnml, reusing pynml jar -RUN echo -e '#!/bin/bash\n#Reusing the jNeuroML jar from the pip installed pyNeuroML for the jnml command\n\njava -classpath /opt/conda/lib/python3.12/site-packages/pyneuroml/lib/jNeuroML-*-jar-with-dependencies.jar org.neuroml.JNeuroML $@' >> /opt/conda/bin/jnml +RUN echo -e '#!/bin/bash\n#Reusing the jNeuroML jar from the pip installed pyNeuroML for the jnml command\n\njava -classpath /opt/conda/lib/python3.11/site-packages/pyneuroml/lib/jNeuroML-*-jar-with-dependencies.jar org.neuroml.JNeuroML $@' >> /opt/conda/bin/jnml RUN chmod +x /opt/conda/bin/jnml ENV JNML_HOME=/opt/conda/bin ### Set up lems, reusing pynml jar -RUN echo -e '#!/bin/bash\n#Reusing the jNeuroML jar from the pip installed pyNeuroML for the lems command\n\njava -classpath /opt/conda/lib/python3.12/site-packages/pyneuroml/lib/jNeuroML-*-jar-with-dependencies.jar org.lemsml.jlems.viz.VizMain $@' >> /opt/conda/bin/lems +RUN echo -e '#!/bin/bash\n#Reusing the jNeuroML jar from the pip installed pyNeuroML for the lems command\n\njava -classpath /opt/conda/lib/python3.11/site-packages/pyneuroml/lib/jNeuroML-*-jar-with-dependencies.jar org.lemsml.jlems.viz.VizMain $@' >> /opt/conda/bin/lems RUN chmod +x /opt/conda/bin/lems RUN cat ~/.bashrc @@ -93,10 +93,10 @@ RUN cd /tmp && \ cd nest && \ mkdir $NEST_HOME && \ apt-get install libgsl-dev -y && \ - cmake -DCMAKE_INSTALL_PREFIX:PATH=$NEST_HOME -DPYTHON_EXECUTABLE:FILEPATH=/opt/conda/bin/python -DPYTHON_INCLUDE_DIR=/opt/conda/include/python3.12 . && \ + cmake -DCMAKE_INSTALL_PREFIX:PATH=$NEST_HOME -DPYTHON_EXECUTABLE:FILEPATH=/opt/conda/bin/python -DPYTHON_INCLUDE_DIR=/opt/conda/include/python3.11 . && \ make -j7 && \ make install -ENV PYTHONPATH=$NEST_HOME/lib/python3.12/site-packages +ENV PYTHONPATH=$NEST_HOME/lib/python3.11/site-packages ENV PATH=$PATH:$NEST_HOME/bin USER jovyan @@ -106,7 +106,7 @@ RUN pip install backports.tarfile>=1.2 # temp fix for error: ImportError: cannot RUN pip install -r requirements.txt --upgrade --no-cache-dir # Compile NEURON mod files for PyNN -RUN cd /opt/conda/lib/python3.12/site-packages/pyNN/neuron/nmodl && nrnivmodl +RUN cd /opt/conda/lib/python3.11/site-packages/pyNN/neuron/nmodl && nrnivmodl # Install NeuroML Schemas etc. RUN git clone https://github.com/NeuroML/NeuroML2 @@ -121,7 +121,7 @@ ENV PATH=$PATH:$XPP_HOME ######################################################################### # fix for https://github.com/jupyter/notebook/issues/7048 -RUN pip install traitlets==5.9.0 +# RUN pip install traitlets==5.9.0 COPY --chown=jovyan:users overrides/* /opt/conda/share/jupyter/lab/static/ WORKDIR /opt/workspace diff --git a/applications/jupyterlab/overrides/index.html b/applications/jupyterlab/overrides/index.html index 7d8cf46ae..17ced3ec6 100644 --- a/applications/jupyterlab/overrides/index.html +++ b/applications/jupyterlab/overrides/index.html @@ -1,18 +1,9 @@ - - - -JupyterLab{# Copy so we do not modify the page_config with updates. #} {% set page_config_full = page_config.copy() %} {# Set a dummy variable - we just want the side effect of the update. #} {% set _ = page_config_full.update(baseUrl=base_url, wsUrl=ws_url) %}{% block favicon %}{% endblock %} {% if custom_css %}{% endif %} - - - - - - {% block favicon %}{% endblock %} {% if custom_css %}{% endif %} - + if (url === location.href) { + return; + } - \ No newline at end of file + window.history.replaceState({ }, '', url); + })(); \ No newline at end of file diff --git a/applications/netpyne/Dockerfile b/applications/netpyne/Dockerfile index 909d168b0..b6230141b 100644 --- a/applications/netpyne/Dockerfile +++ b/applications/netpyne/Dockerfile @@ -14,12 +14,12 @@ COPY $APP_DIR/webapp . RUN yarn build-dev ### Download on a separate stage to run in parallel with buildkit -FROM quay.io/jupyter/base-notebook:latest as downloads +FROM quay.io/jupyter/base-notebook:hub-4.0.2 as downloads USER root RUN wget --no-check-certificate -O /nyhead.mat https://www.parralab.org/nyhead/sa_nyhead.mat ### -FROM quay.io/jupyter/base-notebook:latest +FROM quay.io/jupyter/base-notebook:hub-4.0.2 ARG APP_DIR=dependencies/NetPyNE-UI ENV NB_UID=jovyan ENV FOLDER=netpyne diff --git a/applications/nwb-explorer/Dockerfile b/applications/nwb-explorer/Dockerfile index eea35a236..3fc1b667c 100644 --- a/applications/nwb-explorer/Dockerfile +++ b/applications/nwb-explorer/Dockerfile @@ -14,7 +14,7 @@ RUN yarn build ### -FROM quay.io/jupyter/base-notebook:latest +FROM quay.io/jupyter/base-notebook:hub-4.0.2 ENV NB_UID=jovyan ENV FOLDER=nwb-explorer USER root diff --git a/applications/nwb-explorer/dependencies/nwb-explorer b/applications/nwb-explorer/dependencies/nwb-explorer index ff44c209b..b0839b2e1 160000 --- a/applications/nwb-explorer/dependencies/nwb-explorer +++ b/applications/nwb-explorer/dependencies/nwb-explorer @@ -1 +1 @@ -Subproject commit ff44c209b71b119a1d9ede41550d555e362c76e8 +Subproject commit b0839b2e1d902a42955a32a3981823aab36017c7 diff --git a/libraries/client/README.md b/libraries/client/README.md new file mode 100644 index 000000000..b25846a97 --- /dev/null +++ b/libraries/client/README.md @@ -0,0 +1,51 @@ +# Scripts for getting lists of repositories on OSBv2 and associated databases + +These scripts can be used to get a cached list of the current repositories on OSBv2 and v2dev, and also keep them up to date with the contents of DANDI, ModelDB, BioModels etc. + +0) A GitHub access token must be created so scripts using the python `github` package can be run. + + Save in `github.auth` locally. + +1) Update all current cached lists: + + ``` + ./info_all.sh -q # Runs a quick check of contents of OSBv1, OSBv2, OSBv2dev, OSB repos on Github & DANDI Archive. + ./info_all.sh # Same as above, but with BioModels & ModelDB + ``` + + Contents of these caches will be saved in JSON files in `cached_info/` + +2) Check/update OSBv1 projects + + The following command will regenerate the cached list of current OSBv1 projects using the OSBv1 API: + + ``` + python osbv1_info.py + ``` + + It saves the list to `cached_info/projects_v1.json`. + + If there has been a new project created on OSBv1 recently (and so the json cache has changed), which hasn't been added to v2/v2dev, run: + + ``` + python loadosbv1.py -v2dev -dry # this does a dry run and prints info on which projects/repos it still needs to add + ``` + + Get an access token by logging in to http://v2dev.opensourcebrain.org, opening the Web Developer console, loading a page, copying the network access token (e.g. abcxxx123) and using this to add the repo via the api: + + ``` + python loadosbv1.py abcxxx123 -v2dev # add new repos + + python osb_info.py -v2dev # regenerate cached list of all OSBv2 repos + ``` + + Then do the same using `-v2` instead of for `-v2dev` for the live version of OSBv2. + +3) Check/update the cached info for OSB projects on GitHub + + This will generate a cached list of all repositories under https://github.com/opensourcebrain into `cached_info/osb_gh.json`. Note: most (~2K) of these are forks of ModelDB GitHub repos, many of the rest are repos which were used on OSBv1. + + ``` + python osb_gh_info.py + ``` + diff --git a/libraries/client/biomodels_info.py b/libraries/client/biomodels_info.py index 6bb0d446f..49bb8f70e 100644 --- a/libraries/client/biomodels_info.py +++ b/libraries/client/biomodels_info.py @@ -1,23 +1,38 @@ """ -Script to get Biomodels project info +Script to get BioModels project info """ +import requests import json import pprint -from loadbiomodels import get_model_identifiers, get_model_info verbose = True # verbose = False info_all = {} +API_URL: str = "https://www.ebi.ac.uk/biomodels" +out_format = "json" + + +def get_model_identifiers(): + response = requests.get(API_URL + "/model/identifiers?format=" + out_format) + response.raise_for_status() + output = response.json() + return output + + +def get_model_info(model_id): + response = requests.get(API_URL + "/" + model_id + "?format=" + out_format) + response.raise_for_status() + output = response.json() + return output + if __name__ == "__main__": min_index = 0 - max_index = 20 - index = 0 - - from loadbiomodels import get_model_identifiers + max_index = 10000 + index = 1 model_ids = get_model_identifiers()["models"] @@ -29,73 +44,33 @@ % (index, len(selection), index + min_index, model_id) ) - model_link = f"[{model_id}](https://www.ebi.ac.uk/biomodels/{model_id})" - info = get_model_info(model_id) - model_name = info["name"] - print(f" {model_id}: \n {pprint.pformat(info['name'])}--") + model_url = f"https://www.ebi.ac.uk/biomodels/{model_id}" + model_link = f"[{model_id}]({model_url})" + try: + info = get_model_info(model_id) + if info["curationStatus"] != "CURATED": + print( + " **** Not adding, as curationStatus = %s" + % info["curationStatus"] + ) + else: + model_name = info["name"] + print(f" {model_id}: \n {pprint.pformat(info['name'])}--") - info_all[model_id] = info - """ - son.loads(get_page('https://modeldb.science/api/v1/models/%s'%model)) + info_all[model_id] = info + except Exception as e: + msg = f"Error retrieving model at {model_url}: {e}" - print(' %s'%info[model]['name']) - if 'gitrepo' in info[model] and info[model]['gitrepo']: - with_gitrepo+=1 - print(' gitrepo: %s'%info[model]['gitrepo']) - else: - print(' gitrepo: %s'%False) + print(" ******* %s" % msg) - expected_forks = 0 - possible_mdb_repo = 'ModelDBRepository/%s'%(info[model]['id']) - try: - mdb_repo = gh.get_repo(possible_mdb_repo) - - repo_to_use = mdb_repo - print(' Exists at: %s (def branch: %s; forks: %i)'%(mdb_repo.html_url, mdb_repo.default_branch, mdb_repo.forks)) - - possible_osbgh_repo = 'OpenSourceBrain/%s'%(info[model]['id']) - try: - osb_repo = gh.get_repo(possible_osbgh_repo) - msg = ' Exists at: %s (def branch: %s; forks: %i), order %i'%(osb_repo.html_url, osb_repo.default_branch, osb_repo.forks, index+min_index) - on_osbv2.append(msg) - print(msg) - repo_to_use = osb_repo - expected_forks+=1 - - info[model]['osbv2_gh_repo'] = repo_to_use.html_url - info[model]['osbv2_gh_branch'] = repo_to_use.default_branch - except: - print(' Missing fork: %s, forking now: %s'%(possible_osbgh_repo, fork_if_missing)) - if fork_if_missing: - print(' Forking to: %s...'%possible_osbgh_repo) - org = gh.get_organization('OpenSourceBrain') - org.create_fork(mdb_repo,default_branch_only=False) - msg = ' Forked to: %s...'%possible_osbgh_repo - print(msg) - forked_now.append(msg) - - else: - msg = ' Yet to be forked: %i, order %i; %s'%(info[model]['id'], index+min_index,info[model]['name']) - print(msg) - to_be_forked.append(msg) - - - if (not mdb_repo.forks==expected_forks) and (not (info[model]['id'] in known_to_have_other_forks)): - msg = ' Unexpected forks for %i (%s != %s)...'%(info[model]['id'], mdb_repo.forks,expected_forks) - print(msg) - many_forks.append(msg) - - except: - msg = ' Problem locating repo for: %i (%i/%i) %s'%(info[model]['id'],index, len(selection), info[model]['name']) - print(msg) - errors.append(msg)""" + info_all[model_id] = {"error": msg} index += 1 if verbose: infop = pprint.pprint(info_all, compact=True) - print("\nThere were %i models checked\n" % (len(info))) + print("\nThere were %i models checked\n" % (len(info_all))) filename = "cached_info/biomodels.json" @@ -104,4 +79,4 @@ fp.write(strj) -print("Data on Biomodels (%i models) written to %s" % (len(info), filename)) +print("Data on Biomodels (%i models) written to %s" % (len(info_all), filename)) diff --git a/libraries/client/cached_info/biomodels.json b/libraries/client/cached_info/biomodels.json index b789a4543..7519bc74b 100644 --- a/libraries/client/cached_info/biomodels.json +++ b/libraries/client/cached_info/biomodels.json @@ -5877,5 +5877,315906 @@ "publicationId": "BIOMD0000000020", "submissionId": "MODEL6617668482", "vcsIdentifier": "aaa" + }, + "BIOMD0000000021": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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We analyse the occurrence of chaos and birhythmicity by means of bifurcation diagrams and locate the different domains of complex oscillatory behavior in parameter space. The relative smallness of these domains raises doubts as to the possible physiological significance of chaos and birhythmicity in regard to circadian rhythm generation. Beyond the particular context of circadian rhythms we discuss the results in the light of other mechanisms underlying chaos and birhythmicity in regulated biological systems. Copyright 1999 Academic Press.", + "title": "Chaos and birhythmicity in a model for circadian oscillations of the PER and TIM proteins in drosophila ", + "type": "PubMed ID", + "volume": "198", + "year": 1999 + }, + "publicationId": "BIOMD0000000021", + "submissionId": "MODEL6617834203", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000022": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Bruce Shapiro: Generated by Cellerator Version 1.0 update 3.0303 using Mathematica 4.1 for Microsoft Windows (June 13, 2001), April 2, 2003 16:49:13, using (PC,x86, Microsoft Windows,WindowsNT,Windows)

Bruce Shapiro: Corrected 29 March 2005

Nicolas Le Nov\u00e8re: Added Dbt and Cyc species, and the corresponding reactions. 23 April 2005


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"institution": "ERATO Kitano Symbiotic Systems Group, Suite 6A, M31, 6-31-15 Jinguumae, Shibuya, Tokyo, 150-0001, Japan. hiro@m.u-tokyo.ac.jp", + "name": "H R Ueda", + "orcid": "0000-0001-8342-9176" + }, + { + "name": "M Hagiwara" + }, + { + "name": "H Kitano" + } + ], + "issue": "4", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/11403560", + "month": "6", + "pages": "401-406", + "synopsis": "A mechanism for generating circadian rhythms has been of major interest in recent years. After the discovery of per and tim, a model with a simple feedback loop involving per and tim has been proposed. However, it is recognized that the simple feedback model cannot account for phenotypes generated by various mutants. A recent report by Glossop, Lyons & Hardin [Science286, 766 (1999)] on Drosophila suggests involvement of another feedback loop by dClk that is interlocked with per-tim feedback loop. In order to examine whether interlocked feedback loops can be a basic mechanism for circadian rhythms, a mathematical model was created and examined. Through extensive simulation and mathematical analysis, it was revealed that the interlocked feedback model accounts for the observations that are not explained by the simple feedback model. Moreover, the interlocked feedback model has robust properties in oscillations.", + "title": "Robust oscillations within the interlocked feedback model of Drosophila circadian rhythm.", + "type": "PubMed ID", + "volume": "210", + "year": 2001 + }, + "publicationId": "BIOMD0000000022", + "submissionId": "MODEL6617909980", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000023": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

SBML Level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS .

Run this model online at http://jjj.biochem.sun.ac.za .

To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

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Despite numerous studies, the factors affecting sucrose accumulation are still poorly understood, and no consistent pattern has emerged which pinpoints certain enzyme activities as important controlling steps. Here, we develop an approach based on pathway analysis and kinetic modelling to assess the biochemical control of sucrose accumulation and futile cycling in sugar cane. By using the concept of elementary flux modes, all possible routes of futile cycling of sucrose were enumerated in the metabolic system. The available kinetic data for the pathway enzymes were then collected and assembled in a kinetic model of sucrose accumulation in sugar cane culm tissue. Although no data were fitted, the model agreed well with independent experimental results: in no case was the difference between calculated and measured fluxes and concentrations greater than 2-fold. The model thus validated was then used to assess different enhancement strategies for increasing sucrose accumulation. First, the control coefficient of each enzyme in the system on futile cycling of sucrose was calculated. Secondly, the activities of those enzymes with the numerically largest control coefficients were varied over a 5-fold range to determine the effect on the degree of futile cycling, the conversion efficiency from hexoses into sucrose, and the net sucrose accumulation rate. In view of the modelling results, overexpression of the fructose or glucose transporter or the vacuolar sucrose import protein, as well as reduction of cytosolic neutral invertase levels, appear to be the most promising targets for genetic manipulation. This offers a more directed improvement strategy than cumbersome gene-by-gene manipulation. The kinetic model can be viewed and interrogated on the World Wide Web at http://jjj.biochem.sun.ac.za.", + "title": "Analysis of sucrose accumulation in the sugar cane culm on the basis of in vitro kinetic data.", + "type": "PubMed ID", + "volume": "358", + "year": 2001 + }, + "publicationId": "BIOMD0000000023", + "submissionId": "MODEL6618063111", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000024": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The similarities between model outcomes and empirical findings establish that circadian rhythmicity at the cellular level can plausibly emerge from interactions among molecular systems which are not in themselves rhythmic.", + "title": "A mathematical model for the intracellular circadian rhythm generator.", + "type": "PubMed ID", + "volume": "19", + "year": 1999 + }, + "publicationId": "BIOMD0000000024", + "submissionId": "MODEL6618241436", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000025": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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M. Keck Center for the Neurobiology of Learning and Memory, The University of Texas-Houston Medical School, Houston, TX 77225, USA.", + "authors": [ + { + "institution": "Department of Neurobiology and Anatomy, W. M. Keck Center for the Neurobiology of Learning and Memory, The University of Texas-Houston Medical School, Houston, TX 77225, USA.", + "name": "Paul Smolen" + }, + { + "name": "Douglas A Baxter" + }, + { + "name": "John H Byrne" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/12414672", + "month": "11", + "pages": "2349-2359", + "synopsis": "Although several detailed models of molecular processes essential for circadian oscillations have been developed, their complexity makes intuitive understanding of the oscillation mechanism difficult. The goal of the present study was to reduce a previously developed, detailed model to a minimal representation of the transcriptional regulation essential for circadian rhythmicity in Drosophila. The reduced model contains only two differential equations, each with time delays. A negative feedback loop is included, in which PER protein represses per transcription by binding the dCLOCK transcription factor. A positive feedback loop is also included, in which dCLOCK indirectly enhances its own formation. The model simulated circadian oscillations, light entrainment, and a phase-response curve with qualitative similarities to experiment. Time delays were found to be essential for simulation of circadian oscillations with this model. To examine the robustness of the simplified model to fluctuations in molecule numbers, a stochastic variant was constructed. Robust circadian oscillations and entrainment to light pulses were simulated with fewer than 80 molecules of each gene product present on average. Circadian oscillations persisted when the positive feedback loop was removed. Moreover, elimination of positive feedback did not decrease the robustness of oscillations to stochastic fluctuations or to variations in parameter values. Such reduced models can aid understanding of the oscillation mechanisms in Drosophila and in other organisms in which feedback regulation of transcription may play an important role.", + "title": "A reduced model clarifies the role of feedback loops and time delays in the Drosophila circadian oscillator.", + "type": "PubMed ID", + "volume": "83", + "year": 2002 + }, + "publicationId": "BIOMD0000000025", + "submissionId": "MODEL6618294363", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000026": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model corresponds to the schemas 1 and 2 of Markevich et al 2004, as described in the figure 1 and the supplementary table S1. Phosphorylations and dephosphorylations follow distributive ordered kinetics. The phosphorylations are modeled with three elementary reactions:
E+S<=>ES->E+P
The dephosphorylations are modeled with five elementary reactions:
E+S<=>ES->EP<=>E+P

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stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.", + "title": "Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades.", + "type": "PubMed ID", + "volume": "164", + "year": 2004 + }, + "publicationId": "BIOMD0000000026", + "submissionId": "MODEL6618379269", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000027": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Markevich2004 - MAPK double phosphorylation,ordered Michaelis-Menton
The model corresponds to the schemas 1and 2 of Markevich et al 2004, as described in the figure 1 andmodelled using Michaelis-Menten like kinetics. Phosphorylations anddephosphorylations follow distributive ordered kinetics. Itreproduces figure 3 of the main article.

This model is described in the article:

Markevich NI, Hoek JB, Kholodenko BN.
J. Cell Biol. 2004 Feb; 164(3): 353-359

Abstract:

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model is hosted on BioModels Database and identified by: BIOMD0000000027.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"accession": "14744999", + "affiliation": "Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, 1020 Locust St., Philadelphia, PA 19107, USA.", + "authors": [ + { + "institution": "Department of Pathology, Anatomy, and Cell Biology, Thomas Jefferson University, 1020 Locust St., Philadelphia, PA 19107, USA.", + "name": "Nick I Markevich" + }, + { + "name": "Jan B Hoek", + "orcid": "0000-0001-7127-4218" + }, + { + "name": "Boris N Kholodenko", + "orcid": "0000-0002-9483-4975" + } + ], + "issue": "3", + "journal": "The Journal of cell biology", + "link": "http://identifiers.org/pubmed/14744999", + "month": "2", + "pages": "353-359", + "synopsis": "Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.", + "title": "Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades.", + "type": "PubMed ID", + "volume": "164", + "year": 2004 + }, + "publicationId": "BIOMD0000000027", + "submissionId": "MODEL6618496308", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000028": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table S2. Phosphorylations follow distributive random kinetics, while dephosphorylations follow an ordered mechanism. The phosphorylations are modeled with three elementary reactions:
E+S<=>ES->E+P
The dephosphorylations are modeled with five elementary reactions:
E+S<=>ES->EP<=>E+P
The model reproduces figure 5 in the main article.

The model is further described in:
Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. Markevich NI, Hoek JB, Kholodenko BN. J Cell Biol. 2004 Feb 2;164(3):353-9.
PMID: 14744999 ; DOI: 10.1083/jcb.200308060
Abstract:
Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table S3, and modelled using Michaelis-Menten like kinetics. Phosphorylations follow distributive random kinetics, while dephosphorylations follow an ordered mechanism.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2007 The BioModels Team.
For more information see the terms of use .

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.", + "title": "Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades.", + "type": "PubMed ID", + "volume": "164", + "year": 2004 + }, + "publicationId": "BIOMD0000000030", + "submissionId": "MODEL6618676537", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000031": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model describes the double phosphorylation of MAP kinase by an ordered mechanism using the Michaelis-Menten formalism. Two different enzymes, MAPKK1 and MAPKK2, successively phosphorylate the MAP kinase, but one and the same phosphatase dephosphorylates both sites.
The model reproduces figure S9 in the supplemental material of the article.

The model is further described in:
Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. Markevich NI, Hoek JB, Kholodenko BN. J Cell Biol. 2004 Feb 2;164(3):353-9.
PMID: 14744999 ; DOI: 10.1083/jcb.200308060
Abstract:
Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modelling the dynamics of the yeast pheromone pathway.
Kofahl B, Klipp E Yeast[2004 Jul; Volume: 21 (Issue: 10 )] Page info: 831-50 15300679,
Abstract:
We present a mathematical model of the dynamics of the pheromone pathways in haploid yeast cells of mating type MATa after stimulation with pheromone alpha-factor. The model consists of a set of differential equations and describes the dynamics of signal transduction from the receptor via several steps, including a G protein and a scaffold MAP kinase cascade, up to changes in the gene expression after pheromone stimulation in terms of biochemical changes (complex formations, phosphorylations, etc.). The parameters entering the models have been taken from the literature or adapted to observed time courses or behaviour. Using this model we can follow the time course of the various complex formation processes and of the phosphorylation states of the proteins involved. Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone.


The model was updated on 21st October 2010, by Vijayalakshmi Chelliah.
The following changes were made: 1) The model has been converted to SBML l2v4.2) The model has been recurated and the curation figure was updated (units are in nanoMolar; but the publication has units in microMolar). Simulations were done using Copasi v4.6 (Build 32).3) Notes have been added.4) Annotation for one of the species has been corrected (Complex M).



SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

The following are the four major differences between the original publication by Kofahl et al and the model that actually is able to replicate the results as depicted in the publication (those corrections have been made in agreement with the authors):
1. Bar1 is the inactive protease present inside the cell but the publication wrongly mentions that Bar1 is also the protease that is present on the extracellular surface.
The model correctly names the protease in it's different forms by calling inactive Bar1 within the cell as Bar1, active Bar1 within the cell as Bar1a and extracellular Bar1 as Bar1aex
2. The initial amount of Alpha-factor is given as 1000nM but the model uses a value of 100nM.
3. The value of the paramenter k8 is given as 0.33 but the model uses a value of 0.033.
4. The value of the paramenter k41 is given as 0.002 but the model uses a value of 0.02.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/).(http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Kofahl" + }, + { + "name": "Edda Klipp" + } + ], + "issue": "10", + "journal": "Yeast (Chichester, England)", + "link": "http://identifiers.org/pubmed/15300679", + "month": "7", + "pages": "831-850", + "synopsis": "We present a mathematical model of the dynamics of the pheromone pathways in haploid yeast cells of mating type MATa after stimulation with pheromone alpha-factor. The model consists of a set of differential equations and describes the dynamics of signal transduction from the receptor via several steps, including a G protein and a scaffold MAP kinase cascade, up to changes in the gene expression after pheromone stimulation in terms of biochemical changes (complex formations, phosphorylations, etc.). The parameters entering the models have been taken from the literature or adapted to observed time courses or behaviour. Using this model we can follow the time course of the various complex formation processes and of the phosphorylation states of the proteins involved. Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone.", + "title": "Modelling the dynamics of the yeast pheromone pathway.", + "type": "PubMed ID", + "volume": "21", + "year": 2004 + }, + "publicationId": "BIOMD0000000032", + "submissionId": "MODEL6618819961", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000033": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Cornell University", + "email": "rng7@cornell.edu", + "external": false, + "name": "Ryan Gutenkunst" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Brown2004 - NGF and EGF signaling

This model is described in the article:

Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA.
Phys Biol 2004 Dec; 1(3-4): 184-195

Abstract:

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'


The figures in the paper show results from computationsperformed over an ensemble of all parameter sets that fit theavailable data. This file contains only the best fit parameters.The full ensemble of parameters is available athttp://www.lassp.cornell.edu/sethna/GeneDynamics/PC12DataFiles/(Also, the best-fit parameter set produces a curve for DN Rap1 thatis less \"peakish\" than the ensemble average.)

The conversion factors for EGF and NGF concentrations accountfor their molecular weights and the density of cells in the culturedish. These concentrations are saturating, so the exact values arenot critical.

Because the Erk data fit to measure only fold changes inactivity, there is no absolute scale for the y-axes. Thus thecurves from this file have different magnitudes than thosepublished.

To reproduce the figures from the paper:
2a) For EGF stimulation, set the initial concentration of EGFto 100 ng/ml * 100020 (molecule/cell)/(ng/ml) = 10002000.
For NGF stimulation, set the initial concentration of NGF to50 ng/ml * 4560 (molecule/cell)/(ng/ml) = 456000
5a) To simulate LY294002 addition, set kPI3KRas and kPI3K to0.
5b) To simulate a dominant negative Rap1, set kRap1ToBRaf to0.
To simulate a dominant negative Ras, set kRasToRaf1 andkPI3KRas to 0.

Almost all the data fit with this model by the authors arefrom Western blots. Given the uncertainties in antibodyeffectiveness and other factors, one can't a priori derive aconversion between the arbitrary units for a given set of data andmolecules per cell. So the authors used an adjustable \"scalefactor\" that converts between molecules per cell and Western blotunits.

For the EGF stimulation data in figure 2a) the scale factorconversion is 1.414e-05 (U/mg)/(molecule/cell). For the NGFstimulation data in figure 2a) it is 7.135e-06(U/mg)/(molecule/cell).

This model is hosted on BioModels Database and identified by: BIOMD0000000033.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Brown2004 - NGF and EGF signaling", + "publication": { + "accession": "16204838", + "affiliation": "LASSP, Department of Physics, Cornell University, Clark Hall, Ithaca, NY 14853, USA.", + "authors": [ + { + "institution": "LASSP, Department of Physics, Cornell University, Clark Hall, Ithaca, NY 14853, USA.", + "name": "K S Brown", + "orcid": "0000-0003-2959-2401" + }, + { + "name": "C C Hill" + }, + { + "name": "G A Calero" + }, + { + "name": "C R Myers" + }, + { + "name": "K H Lee", + "orcid": "0000-0003-0908-2981" + }, + { + "name": "J P Sethna" + }, + { + "name": "R A Cerione" + } + ], + "issue": "3-4", + "journal": "Physical biology", + "link": "http://identifiers.org/pubmed/16204838", + "month": "12", + "pages": "184-195", + "synopsis": "The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'", + "title": "The statistical mechanics of complex signaling networks: nerve growth factor signaling.", + "type": "PubMed ID", + "volume": "1", + "year": 2004 + }, + "publicationId": "BIOMD0000000033", + "submissionId": "MODEL6619514794", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000034": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

No inititial conditions are specified in the paper. Because there is a basal rate of transcription for each gene, it doesn't matter much. With the agreement of Paul Smolen, I put all the initial concentration at 0.001 nanomoles. N Le Nov\u00e8re.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Medical School, Houston, Texas 77225, USA.", + "name": "Paul Smolen" + }, + { + "name": "Paul E Hardin", + "orcid": "0000-0002-3370-9011" + }, + { + "name": "Brian S Lo" + }, + { + "name": "Douglas A Baxter" + }, + { + "name": "John H Byrne" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/15111397", + "month": "5", + "pages": "2786-2802", + "synopsis": "A model of Drosophila circadian rhythm generation was developed to represent feedback loops based on transcriptional regulation of per, Clk (dclock), Pdp-1, and vri (vrille). The model postulates that histone acetylation kinetics make transcriptional activation a nonlinear function of [CLK]. Such a nonlinearity is essential to simulate robust circadian oscillations of transcription in our model and in previous models. Simulations suggest that two positive feedback loops involving Clk are not essential for oscillations, because oscillations of [PER] were preserved when Clk, vri, or Pdp-1 expression was fixed. However, eliminating positive feedback by fixing vri expression altered the oscillation period. Eliminating the negative feedback loop in which PER represses per expression abolished oscillations. Simulations of per or Clk null mutations, of per overexpression, and of vri, Clk, or Pdp-1 heterozygous null mutations altered model behavior in ways similar to experimental data. The model simulated a photic phase-response curve resembling experimental curves, and oscillations entrained to simulated light-dark cycles. Temperature compensation of oscillation period could be simulated if temperature elevation slowed PER nuclear entry or PER phosphorylation. The model makes experimental predictions, some of which could be tested in transgenic Drosophila.", + "title": "Simulation of Drosophila circadian oscillations, mutations, and light responses by a model with VRI, PDP-1, and CLK.", + "type": "PubMed ID", + "volume": "86", + "year": 2004 + }, + "publicationId": "BIOMD0000000034", + "submissionId": "MODEL6619579403", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000035": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Minimal Model for Circadian Oscillations

Citation
Vilar JMG, Kueh HY, Barkai N, Leibler S,\t\t\t\t\t\t\t (2002)\t\t\t\t\t\t\t. Mechanisms of noise resistance in genetic oscillators,\t\t\t\t\t\t\t PNAS, 99(9):5988-5992. http://www.pnas.org/cgi/content/abstract/\t\t\t\t\t\t\t99/9/5988
Description
A minimal model of genomically based oscillation,\t\t\t\t\t\t\t based on two mutually interacting genes,\t\t\t\t\t\t\t an activator and a repressor. Postive feedback is provided by the activator protein,\t\t\t\t\t\t\t which binds to the promotors of both the activator and the repressor genes. Negative feedback is provided by the repressor protein which binds to the activator protein.
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Reaction
alphaA\u00a0=\u00a050 DA\u00a0->\u00a0DA\u00a0+\u00a0MA
alphaAp\u00a0=\u00a0500 DAp\u00a0->\u00a0DAp\u00a0+\u00a0MA
alphaR\u00a0=\u00a00.01 DR\u00a0->\u00a0DR\u00a0+\u00a0MR
alphaRp\u00a0=\u00a050 DRp\u00a0->\u00a0DRp\u00a0+\u00a0MR
betaA\u00a0=\u00a050 MA\u00a0->\u00a0A\u00a0+\u00a0MA
betaR\u00a0=\u00a05 MR\u00a0->\u00a0MR\u00a0+\u00a0R
gammaA\u00a0=\u00a01 A\u00a0+\u00a0DA\u00a0->\u00a0DAp
gammaC\u00a0=\u00a02 A\u00a0+\u00a0R\u00a0->\u00a0C
gammaR\u00a0=\u00a01 A\u00a0+\u00a0DR\u00a0->\u00a0DRp
deltaA\u00a0=\u00a01 A\u00a0->\u00a0EmptySet
deltaA\u00a0=\u00a01 C\u00a0->\u00a0R
deltaMA\u00a0=\u00a010 MA\u00a0->\u00a0EmptySet
deltaMR\u00a0=\u00a00.5 MR\u00a0->\u00a0EmptySet
deltaR\u00a0=\u00a00.2 R\u00a0->\u00a0EmptySet
thetaA\u00a0=\u00a050 DAp\u00a0->\u00a0A\u00a0+\u00a0DA
thetaR\u00a0=\u00a0100 DRp\u00a0->\u00a0A\u00a0+\u00a0DR
Variable IC\u00a0\u00a0 ODE
A 0 A'[t]\u00a0==\u00a0-(deltaA*A[t])\u00a0-\u00a0gammaA*A[t]*DA[t]\u00a0+\u00a0thetaA*DAp[\t\t\t\t\t\t\tt]\u00a0-\u00a0gammaR*A[t]*DR[t]\u00a0+\u00a0thetaR*DRp[t]\u00a0+\u00a0betaA*MA[t]\u00a0-\t\t\t\t\t\t\t\u00a0gammaC*A[t]*R[t]
C 0 C'[t]\u00a0==\u00a0-(deltaA*C[t])\u00a0+\u00a0gammaC*A[t]*R[t]
DA 1 DA'[t]\u00a0==\u00a0-(gammaA*A[t]*DA[t])\u00a0+\u00a0thetaA*DAp[t]
DAp 0 DAp'[t]\u00a0==\u00a0gammaA*A[t]*DA[t]\u00a0-\u00a0thetaA*DAp[t]
DR 1 DR'[t]\u00a0==\u00a0-(gammaR*A[t]*DR[t])\u00a0+\u00a0thetaR*DRp[t]
DRp 0 DRp'[t]\u00a0==\u00a0gammaR*A[t]*DR[t]\u00a0-\u00a0thetaR*DRp[t]
MA 0 MA'[t]\u00a0==\u00a0alphaA*DA[t]\u00a0+\u00a0alphaAp*DAp[t]\u00a0-\u00a0deltaMA*MA[t]
MR 0 MR'[t]\u00a0==\u00a0alphaR*DR[t]\u00a0+\u00a0alphaRp*DRp[t]\u00a0-\u00a0deltaMR*MR[t]
R 0 R'[t]\u00a0==\u00a0deltaA*C[t]\u00a0+\u00a0betaR*MR[t]\u00a0-\u00a0deltaR*R[t]\u00a0-\t\t\t\t\t\t\t\u00a0gammaC*A[t]*R[t]

Generated by Cellerator Version 1.0 update 2.1127 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), November 27, 2002 12:17:46, using (PowerMac,PowerPC,\t\t\t\tMac OS X,MacOSX,Darwin)

author=B.E.Shapiro

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Princeton University, Princeton, NJ 08544, USA.", + "authors": [ + { + "institution": "Integrative Biological Modeling Laboratory, Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America. vilar@cbio.mskcc.org", + "name": "Jos\u00e9 M G Vilar", + "orcid": "0000-0003-4037-0746" + }, + { + "name": "Hao Yuan Kueh" + }, + { + "name": "Naama Barkai" + }, + { + "name": "Stanislas Leibler" + } + ], + "issue": "9", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/11972055", + "month": "4", + "pages": "5988-5992", + "synopsis": "A wide range of organisms use circadian clocks to keep internal sense of daily time and regulate their behavior accordingly. Most of these clocks use intracellular genetic networks based on positive and negative regulatory elements. The integration of these \"circuits\" at the cellular level imposes strong constraints on their functioning and design. Here, we study a recently proposed model [Barkai, N. & Leibler, S. (2000) Nature (London), 403, 267-268] that incorporates just the essential elements found experimentally. We show that this type of oscillator is driven mainly by two elements: the concentration of a repressor protein and the dynamics of an activator protein forming an inactive complex with the repressor. Thus, the clock does not need to rely on mRNA dynamics to oscillate, which makes it especially resistant to fluctuations. Oscillations can be present even when the time average of the number of mRNA molecules goes below one. Under some conditions, this oscillator is not only resistant to but, paradoxically, also enhanced by the intrinsic biochemical noise.", + "title": "Mechanisms of noise-resistance in genetic oscillators.", + "type": "PubMed ID", + "volume": "99", + "year": 2002 + }, + "publicationId": "BIOMD0000000035", + "submissionId": "MODEL6619651140", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000036": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Under constant conditions of illumination and temperature, these internal biological rhythms persist with a period close to 1 day (\"circadian\"), but it is usually not exactly 24h. Recent discoveries have uncovered stunning similarities among the molecular circuitries of circadian clocks in mice, fruit flies, and bread molds. A consensus picture is coming into focus around two proteins (called PER and TIM in fruit flies), which dimerize and then inhibit transcription of their own genes. Although this picture seems to confirm a venerable model of circadian rhythms based on time-delayed negative feedback, we suggest that just as crucial to the circadian oscillator is a positive feedback loop based on stabilization of PER upon dimerization. These ideas can be expressed in simple mathematical form (phase plane portraits), and the model accounts naturally for several hallmarks of circadian rhythms, including temperature compensation and the per(L) mutant phenotype. In addition, the model suggests how an endogenous circadian oscillator could have evolved from a more primitive, light-activated switch.", + "title": "A simple model of circadian rhythms based on dimerization and proteolysis of PER and TIM.", + "type": "PubMed ID", + "volume": "77", + "year": 1999 + }, + "publicationId": "BIOMD0000000036", + "submissionId": "MODEL6619720081", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000037": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

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jrmaties.sun.ac.za", + "name": "J M Rohwer" + }, + { + "name": "N D Meadow" + }, + { + "name": "S Roseman" + }, + { + "institution": "Infrastructure for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "H V Westerhoff", + "orcid": "0000-0002-0443-6114" + }, + { + "name": "P W Postma" + } + ], + "issue": "45", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/10889194", + "month": "11", + "pages": "34909-34921", + "synopsis": "The kinetic parameters in vitro of the components of the phosphoenolpyruvate:glycose phosphotransferase system (PTS) in enteric bacteria were collected. To address the issue of whether the behavior in vivo of the PTS can be understood in terms of these enzyme kinetics, a detailed kinetic model was constructed. Each overall phosphotransfer reaction was separated into two elementary reactions, the first entailing association of the phosphoryl donor and acceptor into a complex and the second entailing dissociation of the complex into dephosphorylated donor and phosphorylated acceptor. Literature data on the K(m) values and association constants of PTS proteins for their substrates, as well as equilibrium and rate constants for the overall phosphotransfer reactions, were related to the rate constants of the elementary steps in a set of equations; the rate constants could be calculated by solving these equations simultaneously. No kinetic parameters were fitted. As calculated by the model, the kinetic parameter values in vitro could describe experimental results in vivo when varying each of the PTS protein concentrations individually while keeping the other protein concentrations constant. Using the same kinetic constants, but adjusting the protein concentrations in the model to those present in cell-free extracts, the model could reproduce experiments in vitro analyzing the dependence of the flux on the total PTS protein concentration. For modeling conditions in vivo it was crucial that the PTS protein concentrations be implemented at their high in vivo values. The model suggests a new interpretation of results hitherto not understood; in vivo, the major fraction of the PTS proteins may exist as complexes with other PTS proteins or boundary metabolites, whereas in vitro, the fraction of complexed proteins is much smaller.", + "title": "Understanding glucose transport by the bacterial phosphoenolpyruvate:glycose phosphotransferase system on the basis of kinetic measurements in vitro.", + "type": "PubMed ID", + "volume": "275", + "year": 2000 + }, + "publicationId": "BIOMD0000000038", + "submissionId": "MODEL6621395815", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000039": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

In order to reproduce the model, the volume of all compartment is set to 1, and the stoichiometry of CaER and CaM has been set to 0.25, corresponding to betaER/rhoER and betaM/rhoM described in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Marhl2000_CaOscillations", + "publication": { + "accession": "11004387", + "affiliation": "Department of Physics, Faculty of Education, University of Maribor, Koroska cesta 160, SI-2000, Maribor, Slovenia. marko.marhl@uni-mb.si", + "authors": [ + { + "institution": "Department of Physics, Faculty of Education, University of Maribor, Koroska cesta 160, SI-2000, Maribor, Slovenia. marko.marhl@uni-mb.si", + "name": "M Marhl" + }, + { + "name": "T Haberichter" + }, + { + "name": "M Brumen" + }, + { + "name": "R Heinrich" + } + ], + "issue": "2", + "journal": "Bio Systems", + "link": "http://identifiers.org/pubmed/11004387", + "month": "7", + "pages": "75-86", + "synopsis": "Intracellular calcium oscillations, which are oscillatory changes of cytosolic calcium concentration in response to agonist stimulation, are experimentally well observed in various living cells. Simple calcium oscillations represent the most common pattern and many mathematical models have been published to describe this type of oscillation. On the other hand, relatively few theoretical studies have been proposed to give an explanation of complex intracellular calcium oscillations, such as bursting and chaos. In this paper, we develop a new possible mechanism for complex calcium oscillations based on the interplay between three calcium stores in the cell: the endoplasmic reticulum (ER), mitochondria and cytosolic proteins. The majority ( approximately 80%) of calcium released from the ER is first very quickly sequestered by mitochondria. Afterwards, a much slower release of calcium from the mitochondria serves as the calcium supply for the intermediate calcium exchanges between the ER and the cytosolic proteins causing bursting calcium oscillations. Depending on the permeability of the ER channels and on the kinetic properties of calcium binding to the cytosolic proteins, different patterns of complex calcium oscillations appear. With our model, we are able to explain simple calcium oscillations, bursting and chaos. Chaos is also observed for calcium oscillations in the bursting mode.", + "title": "Complex calcium oscillations and the role of mitochondria and cytosolic proteins.", + "type": "PubMed ID", + "volume": "57", + "year": 2000 + }, + "publicationId": "BIOMD0000000039", + "submissionId": "MODEL6621933497", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000040": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Field-Noyes Model of BZ Reaction

Citation
R.J.Field and R.M.Noyes,J.Chem.Phys.60,1877 (1974)
Description
Field Noyes Version of Belousov-Zhabotinsky Reaction. BrO3 is held constant; HOBr is typically ignored,\t\t\t\t\t\t\t and can be replaced by an empty-set. The stoichiometry f is typically taken as 1/2 or 1.\t\t\t\t\t\t\t.

Initially Generated by Cellerator Version 1.0 update 2.1220 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), December 26, 2002 10:43:53, using (PowerMac,PowerPC,\t\t\t\tMac OS X,MacOSX,Darwin). author=B.E.Shapiro

Modified with SBMLeditor by Nicolas Le Nov\u00e8re, to fit the original article.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Kongas2007 - Creatine Kinase in energy metabolic signaling in muscle

This model is described in the article:

Olav Kongas and Johannes H. G. M. van Beek
Available from Nature Precedings

Abstract:

There has been much debate on the mechanism of regulation of mitochondrial ATP synthesis to balance ATP consumption during changing cardiac workloads. A key role of creatine kinase (CK) isoenzymes in this regulation of oxidative phosphorylation and in intracellular energy transport had been proposed, but has in the mean time been disputed for many years. It was hypothesized that high-energy phosphorylgroups are obligatorily transferred via CK; this is termed the phosphocreatine shuttle. The other important role ascribed to the CK system is its ability to buffer ADP concentration in cytosol near sites of ATP hydrolysis.

Almost all of the experiments to determine the role of CK had been done in the steady state, but recently the dynamic response of oxidative phosphorylation to quick changes in cytosolic ATP hydrolysis has been assessed at various levels of inhibition of CK. Steady state models of CK function in energy transfer existed but were unable to explain the dynamic response with CK inhibited.

The aim of this study was to explain the mode of functioning of the CK system in heart, and in particular the role of different CK isoenzymes in the dynamic response to workload steps. For this purpose we used a mathematical model of cardiac muscle cell energy metabolism containing the kinetics of the key processes of energy production, consumption and transfer pathways. The model underscores that CK plays indeed a dual role in the cardiac cells. The buffering role of CK system is due to the activity of myofibrillar CK (MMCK) while the energy transfer role depends on the activity of mitochondrial CK (MiCK). We propose that this may lead to the differences in regulation mechanisms and energy transfer modes in species with relatively low MiCK activity such as rabbit in comparison with species with high MiCK activity such as rat.

The model needed modification to explain the new type of experimental data on the dynamic response of the mitochondria. We submit that building a Virtual Muscle Cell is not possible without continuous experimental tests to improve the model. In close interaction with experiments we are developing a model for muscle energy metabolism and transport mediated by the creatine kinase isoforms which now already can explain many different types of experiments.

The model has been designed according to the spirit of the paper. The list of rate in the appendix has been corrected as follow:

  1. d[ATP]/dt = (-Vhyd -Vmmck +Jatp) / Vcyt
  2. d[ADP]/dt = ( Vhyd +Vmmck +Jadp) / Vcyt
  3. d[PCr]/dt = ( Vmmck +Jpcr ) / Vcyt
  4. d[Cr]/dt = (-Vmmck +Jpcr ) / Vcyt
  5. d[Pi]/dt = ( Vhyd + Jpi ) / Vcyt
  6. d[ATPi]/dt = (+Vsyn -Vmick -Jatp) / Vims
  7. d[ADPi]/dt = (-Vsyn +Vmick -Jadp) / Vims
  8. d[PCri]/dt = ( Vmick -Jpcr ) / Vims
  9. d[Cri]/dt = (-Vmick -Jpcr ) / Vims
  10. d[Pii]/dt = (-Vsyn -Jpi ) / Vims

This model is hosted on BioModels Database and identified by: BIOMD0000000041 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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A key role of creatine kinase (CK) isoenzymes in this regulation of oxidative phosphorylation and in intracellular energy transport had been proposed, but has in the mean time been disputed for many years. It was hypothesized that high-energy phosphoryl groups are obligatorily transferred via CK; this is termed the phosphocreatine shuttle. The other important role ascribed to the CK system is its ability to buffer ADP concentration in cytosol near sites of ATP hydrolysis.\r\n\r\nAlmost all of the experiments to determine the role of CK had been done in the steady state, but recently the dynamic response of oxidative phosphorylation to quick changes in cytosolic ATP hydrolysis has been assessed at various levels of inhibition of CK. 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"resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Nielsen1998_Glycolysis", + "publication": { + "accession": "17029704", + "affiliation": "Department of Chemistry and CATS, H.C. \u00d8rsted Institute, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark.", + "authors": [ + { + "institution": "Department of Chemistry and CATS, H.C. \u00d8rsted Institute, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark.", + "name": "K Nielsen" + }, + { + "name": "P G S\u00f8rensen" + }, + { + "name": "F Hynne" + }, + { + "name": "H G Busse" + } + ], + "issue": "1-2", + "journal": "Biophysical chemistry", + "link": "http://identifiers.org/pubmed/17029704", + "month": "5", + "pages": "49-62", + "synopsis": "We report sustained oscillations in glycolysis conducted in an open system (a continuous-flow, stirred tank reactor; CSTR) with inflow of yeast extract as well as glucose. Depending on the operating conditions, we observe simple or complex periodic oscillations or chaos. We report the response of the system to instantaneous additions of small amounts of several substrates as functions of the amount added and the phase of the addition. We simulate oscillations and perturbations by a kinetic model based on the mechanism of glycolysis in a CSTR. We find that the response to particular perturbations forms an efficient tool for elucidating the mechanism of biochemical oscillations.", + "title": "Sustained oscillations in glycolysis: an experimental and theoretical study of chaotic and complex periodic behavior and of quenching of simple oscillations.", + "type": "PubMed ID", + "volume": "72", + "year": 1998 + }, + "publicationId": "BIOMD0000000042", + "submissionId": "MODEL6622455387", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000043": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Borghans1997 - Calcium Oscillation - Model 1

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis (see below). This model corresponds to the first hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6622689184 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.", + "title": "Complex intracellular calcium oscillations. A theoretical exploration of possible mechanisms.", + "type": "PubMed ID", + "volume": "66", + "year": 1997 + }, + "publicationId": "BIOMD0000000043", + "submissionId": "MODEL6622689184", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000044": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Borghans1997 - Calcium Oscillation - Model 2

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis (see below). This model corresponds to the second hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6622948601 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Borghans1997 - Calcium Oscillation - Model 3

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis. This model corresponds to the third hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6623009547 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Notes of the BioModels curators:

The current model reproduce the figure 7, panel B of the paper. Note that there is a typo in the figure. The ordinates represent the concentration of peroxyde, as stated in the legend, and not of oxygen. The model has been tested in COPASI (http://www.copasi.org/, build 13).

Notes of the original version of the SBML file:

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"publication": { + "accession": "12823550", + "affiliation": "European Media Laboratory, Heidelberg, Germany. lfo@bmb.sdu.dk", + "authors": [ + { + "name": "L F Olsen", + "orcid": "0000-0001-7391-2340" + }, + { + "name": "Marcus J B Hauser", + "orcid": "0000-0003-2105-3446" + }, + { + "name": "Ursula Kummer" + } + ], + "issue": "13", + "journal": "European journal of biochemistry", + "link": "http://identifiers.org/pubmed/12823550", + "month": "7", + "pages": "2796-2804", + "synopsis": "The peroxidase-oxidase reaction is known to involve reactive oxygen species as intermediates. These intermediates inactivate many types of biomolecules, including peroxidase itself. Previously, we have shown that oscillatory dynamics in the peroxidase-oxidase reaction seem to protect the enzyme from inactivation. It was suggested that this is due to a lower average concentration of reactive oxygen species in the oscillatory state compared to the steady state. Here, we studied the peroxidase-oxidase reaction with either 4-hydroxybenzoic acid or melatonin as cofactors. We show that the protective effect of oscillatory dynamics is present in both cases. We also found that the enzyme degradation depends on the concentration of the cofactor and on the pH of the reaction mixture. We simulated the oscillatory behaviour, including the oscillation/steady state bistability observed experimentally, using a detailed reaction scheme. The computational results confirm the hypothesis that protection is due to lower average concentrations of superoxide radical during oscillations. They also show that the shape of the oscillations changes with increasing cofactor concentration resulting in a further decrease in the average concentration of radicals. We therefore hypothesize that the protective effect of oscillatory dynamics is a general effect in this system.", + "title": "Mechanism of protection of peroxidase activity by oscillatory dynamics.", + "type": "PubMed ID", + "volume": "270", + "year": 2003 + }, + "publicationId": "BIOMD0000000046", + "submissionId": "MODEL6623265463", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000047": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model should reproduce the figure 1C of the article (successfully reproduced in MathSBML). If your software does not support the variable \"time\", you can replace the assignmentRule:
n = n0 * [ exp(-kbN*time) + kappa * (1 - exp(-kbN*time))]
by
n = n0 * kappa


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Stockholm, Sweden.", + "name": "Camilla Oxhamre" + }, + { + "name": "Agneta Richter-Dahlfors", + "orcid": "0000-0002-5479-7591" + }, + { + "name": "Vladimir P Zhdanov" + }, + { + "name": "Bengt Kasemo" + } + ], + "issue": "4", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/15596518", + "month": "4", + "pages": "2976-2981", + "synopsis": "The toxin alpha-hemolysin expressed by uropathogenic Escherichia coli bacteria was recently shown as the first pathophysiologically relevant protein to induce oscillations of the intracellular Ca(2+) concentration in target cells. 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With these ingredients, the predicted response of cells exposed to the toxin is in good agreement with the results of experiments.", + "title": "A minimal generic model of bacteria-induced intracellular Ca2+ oscillations in epithelial cells.", + "type": "PubMed ID", + "volume": "88", + "year": 2005 + }, + "publicationId": "BIOMD0000000047", + "submissionId": "MODEL6623415355", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000048": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "luli@ebi.ac.uk", + "external": false, + "name": "Lu Li" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kholodenko1999 - EGFR signaling

This model has been generated by the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based \t modelling using JWS Online , Bioinformatics, 20:2143-2144

This model is described in the article:

Kholodenko BN, Demin OV, Moehren G, Hoek JB
J. Biol. Chem. 1999 Oct; 274(42): 30169-30181

Abstract:

During the past decade, our knowledge of molecular mechanisms involved in growth factor signaling has proliferated almost explosively. However, the kinetics and control of information transfer through signaling networks remain poorly understood. This paper combines experimental kinetic analysis and computational modeling of the short term pattern of cellular responses to epidermal growth factor (EGF) in isolated hepatocytes. The experimental data show transient tyrosine phosphorylation of the EGF receptor (EGFR) and transient or sustained response patterns in multiple signaling proteins targeted by EGFR. Transient responses exhibit pronounced maxima, reached within 15-30 s of EGF stimulation and followed by a decline to relatively low (quasi-steady-state) levels. In contrast to earlier suggestions, we demonstrate that the experimentally observed transients can be accounted for without requiring receptor-mediated activation of specific tyrosine phosphatases, following EGF stimulation. The kinetic model predicts how the cellular response is controlled by the relative levels and activity states of signaling proteins and under what conditions activation patterns are transient or sustained. EGFR signaling patterns appear to be robust with respect to variations in many elemental rate constants within the range of experimentally measured values. On the other hand, we specify which changes in the kinetic scheme, rate constants, and total amounts of molecular factors involved are incompatible with the experimentally observed kinetics of signal transfer. Quantitation of signaling network responses to growth factors allows us to assess how cells process information controlling their growth and differentiation.

The model correctly reproduces all the figures from the paper. The curation has been done using SBMLodeSolver.

This model is hosted on BioModels Database and identified by: BIOMD0000000048 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Philadelphia, Pennsylvania 19107, USA. Boris.Kholodenko@mail.tju.edu", + "authors": [ + { + "name": "B N Kholodenko", + "orcid": "0000-0002-9483-4975" + }, + { + "name": "O V Demin" + }, + { + "name": "G Moehren" + }, + { + "name": "J B Hoek", + "orcid": "0000-0001-7127-4218" + } + ], + "issue": "42", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/10514507", + "month": "10", + "pages": "30169-30181", + "synopsis": "During the past decade, our knowledge of molecular mechanisms involved in growth factor signaling has proliferated almost explosively. However, the kinetics and control of information transfer through signaling networks remain poorly understood. This paper combines experimental kinetic analysis and computational modeling of the short term pattern of cellular responses to epidermal growth factor (EGF) in isolated hepatocytes. 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On the other hand, we specify which changes in the kinetic scheme, rate constants, and total amounts of molecular factors involved are incompatible with the experimentally observed kinetics of signal transfer. Quantitation of signaling network responses to growth factors allows us to assess how cells process information controlling their growth and differentiation.", + "title": "Quantification of short term signaling by the epidermal growth factor receptor.", + "type": "PubMed ID", + "volume": "274", + "year": 1999 + }, + "publicationId": "BIOMD0000000048", + "submissionId": "MODEL6624193277", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000049": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Tokyo University", + "email": "skuroda@is.s.u-tokyo.ac.jp", + "external": false, + "name": "Shinya Kuroda" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Prediction and validation of the distinct dynamics of transient and sustained ERK activation.
Sasagawa S, Ozaki Y, Fujita K, Kuroda S Nat. Cell Biol.[2005 Apr; Volume: 7 (Issue: 4 )]: 365-73 15793571,
Abstract:
To elucidate the hidden dynamics of extracellular-signal-regulated kinase (ERK) signalling networks, we developed a simulation model of ERK signalling networks by constraining in silico dynamics based on in vivo dynamics in PC12 cells. We predicted and validated that transient ERK activation depends on rapid increases of epidermal growth factor and nerve growth factor (NGF) but not on their final concentrations, whereas sustained ERK activation depends on the final concentration of NGF but not on the temporal rate of increase. These ERK dynamics depend on Ras and Rap1 dynamics, the inactivation processes of which are growth-factor-dependent and -independent, respectively. Therefore, the Ras and Rap1 systems capture the temporal rate and concentration of growth factors, and encode these distinct physical properties into transient and sustained ERK activation, respectively.

Dynamics of active Ras, active Rap1 and phosphorylated ERK were correctly reproduced with CellDesigner 3.0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modelling of Amadori N-(1-deoxy-D-fructos-1-yl)-glycine degradation pathways. Part II--kinetic analysis.
Martins SI, Van Boekel MA. Carbohydr Res2003 Jul;338(16):1665-78. 12873422,
Abstract:
A kinetic model for N-(1-deoxy-Image -fructos-1-yl)-glycine (DFG) thermal decomposition was proposed. Two temperatures (100 and 120 \u00b0C) and two pHs (5.5 and 6.8) were studied. The measured responses were DFG, 3-deoxyosone, 1-deoxyosone, methylglyoxal, acetic acid, formic acid, glucose, fructose, mannose and melanoidins. For each system the model parameters, the rate constants, were estimated by non-linear regression, via multiresponse modelling. The determinant criterion was used as the statistical fit criterion. Model discrimination was performed by both chemical insight and statistical tests (Posterior Probability and Akaike criterion). Kinetic analysis showed that at lower pH DFG 1,2-enolization is favoured whereas with increasing pH 2,3-enolization becomes a more relevant degradation pathway. The lower amount observed of 1-DG is related with its high reactivity. It was shown that acetic acid, a main degradation product from DFG, was mainly formed through 1-DG degradation. Also from the estimated parameters 3-DG was found to be the main precursor in carbohydrate fragments formation, responsible for colour formation. Some indication was given that as the reaction proceeded other compounds besides DFG become reactants themselves with the formation among others of methylglyoxal. The multiresponse kinetic analysis was shown to be both helpful in deriving relevant kinetic parameters as well as in obtaining insight into the reaction mechanism.

Model was intially tested in Jarnac.

The model was recently updated on 9th July 2010. The reference publication has reported two models M1 and M2, where the parameter values are given for conditions A) 100oC, pH5.5, B) 120oC, pH5.5, C) 100oC, pH6.8 and D) 120oC, pH6.8.

This model corresponds to the model M2 with condition 100oC, pH6.8

The model reproduces Figure 6 of the reference publication. The curation figure was recently added

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Box 8129, 6700 EV, Wageningen, The Netherlands.", + "authors": [ + { + "institution": "Department of Agrotechnology and Food Sciences, Product Design and Quality Management Group, Wageningen University, P.O. Box 8129, 6700 EV, Wageningen, The Netherlands.", + "name": "Sara I F S Martins" + }, + { + "name": "Martinus A J S Van Boekel" + } + ], + "issue": "16", + "journal": "Carbohydrate research", + "link": "http://identifiers.org/pubmed/12873422", + "month": "7", + "pages": "1665-1678", + "synopsis": "A kinetic model for N-(1-deoxy-D-fructos-1-yl)-glycine (DFG) thermal decomposition was proposed. Two temperatures (100 and 120 degrees C) and two pHs (5.5 and 6.8) were studied. The measured responses were DFG, 3-deoxyosone, 1-deoxyosone, methylglyoxal, acetic acid, formic acid, glucose, fructose, mannose and melanoidins. For each system the model parameters, the rate constants, were estimated by non-linear regression, via multiresponse modelling. 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The multiresponse kinetic analysis was shown to be both helpful in deriving relevant kinetic parameters as well as in obtaining insight into the reaction mechanism.", + "title": "Kinetic modelling of Amadori N-(1-deoxy-D-fructos-1-yl)-glycine degradation pathways. Part II--kinetic analysis.", + "type": "PubMed ID", + "volume": "338", + "year": 2003 + }, + "publicationId": "BIOMD0000000050", + "submissionId": "MODEL7981660691", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000051": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Figures 4,5 and 6 of the publication. The analytical functions for cometabolites Catp, Camp, Cnadph, and Cnadp slightly differ from the equations given in the paper. These changes were made in consultation with Dr. Christophe Chassagnole and are essential for reproducing the figures. The dependency of the rate of change of extracellular glucose concentration on the ratio of biomass concentration to specific weight of biomass (Cx*rPTS/Rhox) is taken into account by appropriately adjusting the stoichiometries of the species involved in the phosphotransferase system (rPTS). The rmax values for the various reactions are obtained from experiments and are not provided in the paper. However, these were personally communicated to the JWS repository. The model has been successfully tested on MathSBML.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Brands2002 - Monosaccharide-casein systems

A kinetic model of the Maillard reaction occurring in heated monosaccharide-casein system.

This model is described in the article:

Brands CM, van Boekel MA
Journal of Agricultural and Food Chemistry. 2002, 50(23):6725-6739

Abstract:

In the present study, a kinetic model of the Maillard reaction occurring in heated monosaccharide-casein systems was proposed. Its parameters, the reaction rate constants, were estimated via multiresponse modeling. The determinant criterion was used as the statistical fit criterion instead of the familiar least squares to avoid statistical problems. The kinetic model was extensively tested by varying the reaction conditions. Different sugars (glucose, fructose, galactose, and tagatose) were studied regarding their effect on the reaction kinetics. This study has shown the power of multiresponse modeling for the unraveling of complicated reaction routes as occur in the Maillard reaction. The iterative process of proposing a model, confronting it with experiments, and criticizing the model was passed through four times to arrive at a model that was largely consistent with all results obtained. A striking difference was found between aldose and ketose sugars as suggested by the modeling results: not the ketoses themselves but only their reaction products were found to be reactive in the Maillard reaction.

This model is hosted on BioModels Database and identifiedby: MODEL8177704759 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Box 8129, 6700 EV Wageningen, The Netherlands.", + "authors": [ + { + "institution": "Department of Agrotechnology and Food Sciences, Product Design and Quality Management Group, Wageningen University, P.O. Box 8129, 6700 EV Wageningen, The Netherlands.", + "name": "Carline M J Brands" + }, + { + "name": "Martinus A J S van Boekel" + } + ], + "issue": "23", + "journal": "Journal of agricultural and food chemistry", + "link": "http://identifiers.org/pubmed/12405768", + "month": "11", + "pages": "6725-6739", + "synopsis": "In the present study, a kinetic model of the Maillard reaction occurring in heated monosaccharide-casein systems was proposed. Its parameters, the reaction rate constants, were estimated via multiresponse modeling. The determinant criterion was used as the statistical fit criterion instead of the familiar least squares to avoid statistical problems. The kinetic model was extensively tested by varying the reaction conditions. Different sugars (glucose, fructose, galactose, and tagatose) were studied regarding their effect on the reaction kinetics. This study has shown the power of multiresponse modeling for the unraveling of complicated reaction routes as occur in the Maillard reaction. The iterative process of proposing a model, confronting it with experiments, and criticizing the model was passed through four times to arrive at a model that was largely consistent with all results obtained. A striking difference was found between aldose and ketose sugars as suggested by the modeling results: not the ketoses themselves but only their reaction products were found to be reactive in the Maillard reaction.", + "title": "Kinetic modeling of reactions in heated monosaccharide-casein systems.", + "type": "PubMed ID", + "volume": "50", + "year": 2002 + }, + "publicationId": "BIOMD0000000052", + "submissionId": "MODEL8177704759", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000053": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model should reproduce the figure 2F of the article.

The equation 7 has been split into equations 7a-7c, in order to take into account the different flux rates of Lysine and CML formation from Schiff.

The model was tested in Jarnac (SBML L2 V1) and Copasi (SBML L2 V3).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Ferreira2003_CML_generation2", + "publication": { + "accession": "12911334", + "affiliation": "Departamento de Qu\u00edmica e Bioqu\u00edmica, Faculdade de Ci\u00eancias da Universidade de Lisboa, Bloco C8, Campo Grande, 1749-016 Lisboa, Portugal. aeferreira@fc.ul.pt", + "authors": [ + { + "institution": "Departamento de Qu\u00edmica e Bioqu\u00edmica, Faculdade de Ci\u00eancias da Universidade de Lisboa, Bloco C8, Campo Grande, 1749-016 Lisboa, Portugal. aeferreira@fc.ul.pt", + "name": "Ant\u00f3nio E N Ferreira", + "orcid": "0000-0002-9625-8115" + }, + { + "name": "Ana M J Ponces Freire", + "orcid": "0000-0003-2280-8193" + }, + { + "name": "Eberhard O Voit" + } + ], + "issue": "Pt 1", + "journal": "The Biochemical journal", + "link": "http://identifiers.org/pubmed/12911334", + "month": "11", + "pages": "109-121", + "synopsis": "The Maillard reaction between reducing sugars and amino groups of biomolecules generates complex structures known as AGEs (advanced glycation endproducts). These have been linked to protein modifications found during aging, diabetes and various amyloidoses. To investigate the contribution of alternative routes to the formation of AGEs, we developed a mathematical model that describes the generation of CML [ N(epsilon)-(carboxymethyl)lysine] in the Maillard reaction between glucose and collagen. Parameter values were obtained by fitting published data from kinetic experiments of Amadori compound decomposition and glycoxidation of collagen by glucose. These raw parameter values were subsequently fine-tuned with adjustment factors that were deduced from dynamic experiments taking into account the glucose and phosphate buffer concentrations. The fine-tuned model was used to assess the relative contributions of the reaction between glyoxal and lysine, the Namiki pathway, and Amadori compound degradation to the generation of CML. The model suggests that the glyoxal route dominates, except at low phosphate and high glucose concentrations. The contribution of Amadori oxidation is generally the least significant at low glucose concentrations. Simulations of the inhibition of CML generation by aminoguanidine show that this compound effectively blocks the glyoxal route at low glucose concentrations (5 mM). Model results are compared with literature estimates of the contributions to CML generation by the three pathways. The significance of the dominance of the glyoxal route is discussed in the context of possible natural defensive mechanisms and pharmacological interventions with the goal of inhibiting the Maillard reaction in vivo.", + "title": "A quantitative model of the generation of N(epsilon)-(carboxymethyl)lysine in the Maillard reaction between collagen and glucose.", + "type": "PubMed ID", + "volume": "376", + "year": 2003 + }, + "publicationId": "BIOMD0000000053", + "submissionId": "MODEL0733584307", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000054": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces ion and adenylate pool concentration corresponding to line 2 of Fig 3 of the publication. This model was tested successfully on Jarnac


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Locke2005 - Circadian Clock

SBML model of the interlocked feedback loop network

The model describes the circuit depicted in Fig. 4 and reproduces the simulations in Figure 5A and 5B. It provides initial conditions, parameter values and rules for the production rates of the following species: LHY mRNA (cLm), cytoplasmic LHY (cLc), nuclear LHY (cLn), TOC1 mRNA (cTm), cytoplasmic TOC1 (cTc), nuclear TOC1 (cTn),X mRNA (cXm), cytoplasmic X (cXc), nuclear X (cXn), Y mRNA (cYm), cytoplasmic Y (cYc), nuclear Y (cYn), nuclear P (cPn). This model was successfully tested on MathSBML and SBML ODE Solver.

Fig 5B is not in the right phase. However, the data is correct relative to the light/dark bars at the top of the figure.

This model is described in the article:

Locke JC, Southern MM, Kozma-Bogn\u00e1r L, Hibberd V, Brown PE, Turner MS, Millar AJ
Molecular Systems Biology [2005; 1: 2005.0013]

Abstract:

Circadian clocks involve feedback loops that generate rhythmic expression of key genes. Molecular genetic studies in the higher plant Arabidopsis thaliana have revealed a complex clock network. The first part of the network to be identified, a transcriptional feedback loop comprising TIMING OF CAB EXPRESSION 1 (TOC1), LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), fails to account for significant experimental data. We develop an extended model that is based upon a wider range of data and accurately predicts additional experimental results. The model comprises interlocking feedback loops comparable to those identified experimentally in other circadian systems. We propose that each loop receives input signals from light, and that each loop includes a hypothetical component that had not been explicitly identified. Analysis of the model predicted the properties of these components, including an acute light induction at dawn that is rapidly repressed by LHY and CCA1. We found this unexpected regulation in RNA levels of the evening-expressed gene GIGANTEA (GI), supporting our proposed network and making GI a strong candidate for this component.

This model is hosted on BioModels Database and identified by: BIOMD0000000055 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Molecular genetic studies in the higher plant Arabidopsis thaliana have revealed a complex clock network. The first part of the network to be identified, a transcriptional feedback loop comprising TIMING OF CAB EXPRESSION 1 (TOC1), LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), fails to account for significant experimental data. We develop an extended model that is based upon a wider range of data and accurately predicts additional experimental results. The model comprises interlocking feedback loops comparable to those identified experimentally in other circadian systems. We propose that each loop receives input signals from light, and that each loop includes a hypothetical component that had not been explicitly identified. Analysis of the model predicted the properties of these components, including an acute light induction at dawn that is rapidly repressed by LHY and CCA1. We found this unexpected regulation in RNA levels of the evening-expressed gene GIGANTEA (GI), supporting our proposed network and making GI a strong candidate for this component.", + "title": "Extension of a genetic network model by iterative experimentation and mathematical analysis.", + "type": "PubMed ID", + "volume": "1", + "year": 2005 + }, + "publicationId": "BIOMD0000000055", + "submissionId": "MODEL0737170531", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000056": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Chen2004 - Cell Cycle Regulation

This is a hypothetical model of cell cycle that describes the molecular mechanism for regulating DNA synthesis, bud emergence, mitosis, and cell division in budding yeast.

This model is described in the article:

Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ
Mol. Biol. Cell. [2004 Aug; Volume: 15 (Issue: 8 )] Page info: 3841-62

Abstract:

The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.

The model reproduces the time profiles of the different species in Figure 2 of the paper. The figure depicts the cycle of a daughter cell. Since the Mass Doubling Time (MDT) is 90 minutes, time t=90 from the model simulation will correspond to time t=0 in the paper. The model was successfully tested using MathSBML and SBML odeSolver.

To create a valid SBML file, a local parameter k=1 was added in the reaction 'Inactivation_274_CDC20'. Also, in order to annotate the protein and to have the interaction in the reaction graph to match figure 1 of the article, the reaction rate constants k_{mad2}, k_{bub2} and k_{lte1} are considered as species and renamed as MAD2, BUB2 and LTE1 in the model.

This model is hosted on BioModels Database and identified by: BIOMD0000000056 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Laurence.Calzone@curie.fr", + "name": "Laurence Calzone", + "orcid": "0000-0002-7835-1148" + }, + { + "name": "Attila Csikasz-Nagy", + "orcid": "0000-0002-2919-5601" + }, + { + "name": "Frederick R Cross" + }, + { + "institution": "Department of Biochemistry, University of Oxford, Oxford, United Kingdom.", + "name": "B\u00e9la Nov\u00e1k", + "orcid": "0000-0002-6961-1366" + }, + { + "name": "John J Tyson" + } + ], + "issue": "8", + "journal": "Molecular biology of the cell", + "link": "http://identifiers.org/pubmed/15169868", + "month": "8", + "pages": "3841-3862", + "synopsis": "The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.", + "title": "Integrative analysis of cell cycle control in budding yeast.", + "type": "PubMed ID", + "volume": "15", + "year": 2004 + }, + "publicationId": "BIOMD0000000056", + "submissionId": "MODEL6624073334", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000057": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model was successfully tested on Jarnac and MathSBML. The model reproduces the time profile of \"Open Probability\" of the receptor as shown in Figure 4 of the publication. The value of calcium ion concentration \"c\" in this model is 10 microM.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL9098147664" + }, + { + "accession": "BIOMD0000000057", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000057" + }, + { + "accession": "11842185", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/11842185" + }, + { + "accession": "10117", + "name": "Rattus rattus", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/10117" + }, + { + "accession": "map04020", + "name": "Calcium signaling pathway", + "qualifier": "bqbiol:is", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/map04020" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Sneyd2002_IP3_Receptor", + "publication": { + "accession": "11842185", + "affiliation": "Institute of Information and Mathematical Sciences, Massey University, Albany Campus, Private Bag 102-904, North Shore Mail Centre, Auckland, New Zealand. jsneyd@massey.ac.nz", + "authors": [ + { + "name": "James Sneyd", + "orcid": "0000-0001-7305-2862" + }, + { + "name": "Jean-Francois Dufour", + "orcid": "0000-0002-8062-1346" + } + ], + "issue": "4", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/11842185", + "month": "2", + "pages": "2398-2403", + "synopsis": "The dynamic properties of the inositol (1,4,5)-trisphosphate (IP(3)) receptor are crucial for the control of intracellular Ca(2+), including the generation of Ca(2+) oscillations and waves. However, many models of this receptor do not agree with recent experimental data on the dynamic responses of the receptor. We construct a model of the IP(3) receptor and fit the model to dynamic and steady-state experimental data from type-2 IP(3) receptors. Our results indicate that, (i) Ca(2+) binds to the receptor using saturating, not mass-action, kinetics; (ii) Ca(2+) decreases the rate of IP(3) binding while simultaneously increasing the steady-state sensitivity of the receptor to IP(3); (iii) the rate of Ca(2+)-induced receptor activation increases with Ca(2+) and is faster than Ca(2+)-induced receptor inactivation; and (iv) IP(3) receptors are sequentially activated and inactivated by Ca(2+) even when IP(3) is bound. Our results emphasize that measurement of steady-state properties alone is insufficient to characterize the functional properties of the receptor.", + "title": "A dynamic model of the type-2 inositol trisphosphate receptor.", + "type": "PubMed ID", + "volume": "99", + "year": 2002 + }, + "publicationId": "BIOMD0000000057", + "submissionId": "MODEL9098147664", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000058": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the same amplitude antiphase calcium oscillations of coupled cells depicted in Figure 5B of the publication. This model was successfully tested on Jarnac and MathSBML. The values of \"h1\" and \"h2\" are not given in the publication, but the antiphase oscillations are reproduced over a narrow range of values of h1, h2,c1,c2,D and p. The values of D and p are given, while the other values were plugged in, in order to simulate the time profiles shown in the Figure. The time t=0 in the figure may have been fixed after the system was allowed to settle, and hence does not correspond to the t=0 of the simulation.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Such oscillations are believed to underlie oscillatory intercellular calcium waves in some cell types, and thus it is important to understand how they occur and are modified by intercellular coupling. Using a previous model of intracellular calcium oscillations in pancreatic acinar cells, this article explores the effects of coupling two cells with a simple linear diffusion term. Depending on the concentration of a signal molecule, inositol (1,4,5)-trisphosphate, coupling two identical cells by diffusion can give rise to synchronized in-phase oscillations, as well as different-amplitude in-phase oscillations and same-amplitude antiphase oscillations. Coupling two nonidentical cells leads to more complex behaviors such as cascades of period doubling and multiply periodic solutions. This study is a first step towards understanding the role and significance of the diffusion of calcium through gap junctions in the coordination of oscillatory calcium waves in a variety of cell types. (c) 2001 American Institute of Physics.", + "title": "A bifurcation analysis of two coupled calcium oscillators.", + "type": "PubMed ID", + "volume": "11", + "year": 2001 + }, + "publicationId": "BIOMD0000000058", + "submissionId": "MODEL9107330008", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000059": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces block A of Fig 5 and also Fig 3 (without the inclusion of Tg action). The model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"name": "Leonid E Fridlyand" + }, + { + "name": "Natalia Tamarina" + }, + { + "name": "Louis H Philipson" + } + ], + "issue": "1", + "journal": "American journal of physiology. Endocrinology and metabolism", + "link": "http://identifiers.org/pubmed/12644446", + "month": "7", + "pages": "E138-54", + "synopsis": "We have developed a detailed mathematical model of ionic flux in beta-cells that includes the most essential channels and pumps in the plasma membrane. This model is coupled to equations describing Ca2+, inositol 1,4,5-trisphosphate (IP3), ATP, and Na+ homeostasis, including the uptake and release of Ca2+ by the endoplasmic reticulum (ER). In our model, metabolically derived ATP activates inward Ca2+ flux by regulation of ATP-sensitive K+ channels and depolarization of the plasma membrane. Results from the simulations support the hypothesis that intracellular Na+ and Ca2+ in the ER can be the main variables driving both fast (2-7 osc/min) and slow intracellular Ca2+ concentration oscillations (0.3-0.9 osc/min) and that the effect of IP3 on Ca2+ leak from the ER contributes to the pattern of slow calcium oscillations. Simulations also show that filling the ER Ca2+ stores leads to faster electrical bursting and Ca2+ oscillations. Specific Ca2+ oscillations in isolated beta-cell lines can also be simulated.", + "title": "Modeling of Ca2+ flux in pancreatic beta-cells: role of the plasma membrane and intracellular stores.", + "type": "PubMed ID", + "volume": "285", + "year": 2003 + }, + "publicationId": "BIOMD0000000059", + "submissionId": "MODEL0928773489", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000060": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profile of Open probability of the ryanodine receptor as shown in Fig 2A and 2B of the paper. The model was successfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"institution": "Institute of Theoretical Dynamics, University of California, Davis 95616, USA. jekeizer@ucdavis.edu", + "name": "J Keizer" + }, + { + "name": "L Levine" + } + ], + "issue": "6", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/8968617", + "month": "12", + "pages": "3477-3487", + "synopsis": "A simplified mechanism that mimics \"adaptation\" of the ryanodine receptor (RyR) has been developed and its significance for Ca2+(-)induced Ca2+ release and Ca2+ oscillations investigated. For parameters that reproduce experimental data for the RyR from cardiac cells, adaptation of the RyR in combination with sarco/endoplasmic reticulum Ca2+ ATPase Ca2+ pumps in the internal stores can give rise to either low [Cai2+] steady states or Ca2+ oscillations coexisting with unphysiologically high [Cai2+] steady states. In this closed-cell-type model rapid, adaptation-dependent Ca2+ oscillations occur only in limited ranges of parameters. In the presence of Ca2+ influx and efflux from outside the cell (open-cell model) Ca2+ oscillations occur for a wide range of physiological parameter values and have a period that is determined by the rate of Ca2+ refilling of the stores. Although the rate of adaptation of the RyR has a role in determining the shape and the period of the Ca2+ spike, it is not essential for their existence. This is in marked contrast with what is observed for the inositol 1,4,5-trisphosphate receptor for which the biphasic activation and inhibition of its activity by Ca2+ are sufficient to produce oscillations. Results for this model are compared with those based on Ca2+(-)induced Ca2+ release alone in the bullfrog sympathetic neuron. This kinetic model should be suitable for analyzing phenomena associated with \"Ca2+ sparks,\" including their merger into Ca2+ waves in cardiac myocytes.", + "title": "Ryanodine receptor adaptation and Ca2+(-)induced Ca2+ release-dependent Ca2+ oscillations.", + "type": "PubMed ID", + "volume": "71", + "year": 1996 + }, + "publicationId": "BIOMD0000000060", + "submissionId": "MODEL0332756009", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000061": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 6 of the paper. The stoichiometry and rate of reactions involving uptake of metabolites from extracellular medium have been changed corresponding to Yvol (ratio of extracellular volume to cytosolic volume) mentioned in the publication. The extracellular and cytosolic compartments have been set to 1. Concentration of extracellular glucose, GlcX, is set to 6.7 according to the equation for cellular glucose uptake rate in Table 7 of the paper. The model was successfully tested on MathSBML and Jarnac

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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CATS, H.C. \u00d8rsted Institute, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark. fh@kiku.dk", + "authors": [ + { + "institution": "Department of Chemistry and CATS, H.C. \u00d8rsted Institute, University of Copenhagen, Universitetsparken 5, DK-2100 Copenhagen, Denmark. fh@kiku.dk", + "name": "F Hynne" + }, + { + "name": "S Dan\u00f8" + }, + { + "name": "P G S\u00f8rensen" + } + ], + "issue": "1-2", + "journal": "Biophysical chemistry", + "link": "http://identifiers.org/pubmed/11744196", + "month": "12", + "pages": "121-163", + "synopsis": "We present a powerful, general method of fitting a model of a biochemical pathway to experimental substrate concentrations and dynamical properties measured at a stationary state, when the mechanism is largely known but kinetic parameters are lacking. Rate constants and maximum velocities are calculated from the experimental data by simple algebra without integration of kinetic equations. Using this direct approach, we fit a comprehensive model of glycolysis and glycolytic oscillations in intact yeast cells to data measured on a suspension of living cells of Saccharomyces cerevisiae near a Hopf bifurcation, and to a large set of stationary concentrations and other data estimated from comparable batch experiments. The resulting model agrees with almost all experimentally known stationary concentrations and metabolic fluxes, with the frequency of oscillation and with the majority of other experimentally known kinetic and dynamical variables. The functional forms of the rate equations have not been optimized.", + "title": "Full-scale model of glycolysis in Saccharomyces cerevisiae.", + "type": "PubMed ID", + "volume": "94", + "year": 2001 + }, + "publicationId": "BIOMD0000000061", + "submissionId": "MODEL6624167348", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000062": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

SBML level 2 code originaly generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

BioModels Curation : The model reproduces Fig 3 of the publication. By substituting a value of 1.4 for Tex it is possible to reproduce Fig 3C and 3D(iii), Fig 3A and 3D(i), are obtained by setting Tex=0. Also, note that the tryptophan concentrations have been normalized by 82 micromolar in the figures; the normalized concetrations can be obtained via the parameters To/s/t_norm. The model was successfully tested on MathSBML and Copasi.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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+ { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724262751, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL6624111460", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL6624111460" + }, + { + "accession": "BIOMD0000000062", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000062" + }, + { + "accession": "12787031", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/12787031" + }, + { + "accession": "11171956", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/11171956" + }, + { + "accession": "GO:0000162", + "name": "tryptophan biosynthetic process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0000162" + }, + { + "accession": "562", + "name": "Escherichia coli", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/562" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Bhartiya2003_Tryptophan_operon", + "publication": { + "accession": "12787031", + "affiliation": "Department of Chemical Engineering and School of Biosciences and Bioengineering, Indian Institute of Technology, Bombay, Mumbai, India.", + "authors": [ + { + "institution": "Department of Chemical Engineering and School of Biosciences and Bioengineering, Indian Institute of Technology, Bombay, Mumbai, India.", + "name": "Sharad Bhartiya", + "orcid": "0000-0002-2856-695X" + }, + { + "name": "Subodh Rawool" + }, + { + "name": "K V Venkatesh" + } + ], + "issue": "12", + "journal": "European journal of biochemistry", + "link": "http://identifiers.org/pubmed/12787031", + "month": "6", + "pages": "2644-2651", + "synopsis": "A mathematical model has been developed to study the effect of external tryptophan on the trp operon. The model accounts for the effect of feedback repression by tryptophan through the Hill equation. We demonstrate that the trp operon maintains an intracellular steady-state concentration in a fivefold range irrespective of extracellular conditions. Dynamic behavior of the trp operon corresponding to varying levels of extracellular tryptophan illustrates the adaptive nature of regulation. Depending on the external tryptophan level in the medium, the transient response ranges from a rapid and underdamped to a sluggish and highly overdamped response. To test model fidelity, simulation results are compared with experimental data available in the literature. We further demonstrate the significance of the biological structure of the operon on the overall performance. Our analysis suggests that the tryptophan operon has evolved to a truly optimal design.", + "title": "Dynamic model of Escherichia coli tryptophan operon shows an optimal structural design.", + "type": "PubMed ID", + "volume": "270", + "year": 2003 + }, + "publicationId": "BIOMD0000000062", + "submissionId": "MODEL6624111460", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000063": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Fermentation pathway kinetics and metabolic flux control in suspended and immobilized Saccharomyces cerevisiae
Jorge L. Galazzo and James E. Bailey Enzyme and Microbial TechnologyVolume 12, Issue 3, 1990, Pages 162-172.
DOI:10.1016/0141-0229(90)90033-M
Abstract:
Measurements of rates of glucose uptake and of glycerol and ethanol formation combined with knowledge of the metabolic pathways involved in S. cerevisiae were employed to obtain in vivo rates of reaction catalysed by pathway enzymes for suspended and alginate-entrapped cells at pH 4.5 and 5.5. Intracellular concentrations of substrates and effectors for most key pathway enzymes were estimated from in vivo phosphorus-31 nuclear magnetic resonance measurements. These data show the validity in vivo of kinetic models previously proposed for phosphofructokinase and pyruvate kinase based on in vitro studies. Kinetic representations of hexokinase, glycogen synthetase, and glyceraldehyde 3-phosphate dehydrogenase, which incorporate major regulatory properties of these enzymes, are all consistent with the in vivo data. This detailed model of pathway kinetics and these data on intracellular metabolite concentrations allow evaluation of flux-control coefficients for all key enzymes involved in glucose catabolism under the four different cell environments examined. This analysis indicates that alginate entrapment increases the glucose uptake rate and shifts the step most influencing ethanol production from glucose uptake to phosphofructokinase. The rate of ATP utilization in these nongrowing cells strongly limits ethanol production at pH 5.5 but is relatively insignificant at pH 4.5.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

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Biomodels Curation: The model reproduces Fig 2 of the paper. However, it appears that the figures are swapped, hence the plot for V/Vmax vs Glucose actually represnts V/Vmax vs ATP and the vice versa is true for the other figure. The rate of hexokinase reaction that is obtained upon simulation of the model is 17.24 mM/min, therefore V/Vmax has a value of 17.24/68.5=0.25. For steady state values of Glucose and ATP (0.038 and 1.213 mM respectively), the V/Vmax values correctly correspond to 0.25, if we were to assume that the figures are swapped.

BioModels Curation updated on 25th November 2010: Figure 3 of the reference publication has been reproduced and added as a curation figure for the model.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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California Institute of Technology, Pasadena, CA, USA", + "authors": [ + { + "name": "Jorge L. Galazzo and James E. Bailey" + } + ], + "journal": "Enzyme and Microbial Technology 1990; 12(3): 162-172", + "link": "http://identifiers.org/doi/10.1016/0141-0229(90)90033-M", + "synopsis": "Measurements of rates of glucose uptake and of glycerol and ethanol formation combined with knowledge of the metabolic pathways involved in S. cerevisiae were employed to obtain in vivo rates of reaction catalysed by pathway enzymes for suspended and alginate-entrapped cells at pH 4.5 and 5.5. Intracellular concentrations of substrates and effectors for most key pathway enzymes were estimated from in vivo phosphorus-31 nuclear magnetic resonance measurements. These data show the validity in vivo of kinetic models previously proposed for phosphofructokinase and pyruvate kinase based on in vitro studies. Kinetic representations of hexokinase, glycogen synthetase, and glyceraldehyde 3-phosphate dehydrogenase, which incorporate major regulatory properties of these enzymes, are all consistent with the in vivo data. This detailed model of pathway kinetics and these data on intracellular metabolite concentrations allow evaluation of flux-control coefficients for all key enzymes involved in glucose catabolism under the four different cell environments examined. This analysis indicates that alginate entrapment increases the glucose uptake rate and shifts the step most influencing ethanol production from glucose uptake to phosphofructokinase. The rate of ATP utilization in these nongrowing cells strongly limits ethanol production at pH 5.5 but is relatively insignificant at pH 4.5.", + "title": "Fermentation pathway kinetics and metabolic flux control in suspended and immobilized Saccharomyces cerevisiae", + "type": "DOI", + "year": 1990 + }, + "publicationId": "BIOMD0000000063", + "submissionId": "MODEL6624154196", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000064": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry.
Teusink,B et al.: Eur J Biochem 2000 Sep;267(17):5313-29.
The model reproduces the steady-state fluxes and metabolite concentrations of the branched model as given in Table 4 of the paper. It is derived from the model on JWS online, but has the ATP consumption in the succinate branch with the same stoichiometrie as in the publication. The model was successfully tested on copasi v.4.4(build 26).
For Vmax values, please note that there is a conversion factor of approx. 270 to convert from U/mg-protein as shown in Table 1 of the paper to mmol/(min*L_cytosol). The equilibrium constant for the ADH reaction in the paper is given for the reverse reaction (Keq = 1.45*10 4 ). The value used in this model is for the forward reaction: 1/Keq = 6.9*10 -5 .
Vmax parameters values used (in [mM/min] except VmGLT):
VmGLT 97.264 mmol/min
VmGLK 226.45
VmPGI 339.667
VmPFK 182.903
VmALD 322.258
VmGAPDH_f 1184.52
VmGAPDH_r 6549.68
VmPGK 1306.45
VmPGM 2525.81
VmENO 365.806
VmPYK 1088.71
VmPDC 174.194
VmG3PDH 70.15
The result of the G6P steady state concentration (marked in red) differs slightly from the one given in table 4. of the publication
Results for steady state:
orig. article this model
Fluxes[mM/min] \u00a0
Glucose\u00a0 88\u00a0 88\u00a0
Ethanol\u00a0 129\u00a0 129\u00a0
Glycogen\u00a0 6\u00a0 6\u00a0
Trehalose\u00a0 4.8\u00a0 4.8\u00a0 (G6P flux through trehalose branch)
Glycerol\u00a0 18.2\u00a0 18.2\u00a0
Succinate\u00a0 3.6\u00a0 3.6\u00a0
Conc.[mM] \u00a0
G6P\u00a0 1.07\u00a0 1.03\u00a0
F6P\u00a0 0.11\u00a0 0.11\u00a0
F1,6P\u00a0 0.6\u00a0 0.6\u00a0
DHAP\u00a0 0.74\u00a0 0.74\u00a0
3PGA\u00a0 0.36\u00a0 0.36\u00a0
2PGA\u00a0 0.04\u00a0 0.04\u00a0
PEP\u00a0 0.07\u00a0 0.07\u00a0
PYR\u00a0 8.52\u00a0 8.52\u00a0
AcAld\u00a0 0.17\u00a0 0.17\u00a0
ATP\u00a0 2.51\u00a0 2.51\u00a0
ADP\u00a0 1.29\u00a0 1.29\u00a0
AMP\u00a0 0.3\u00a0 0.3\u00a0
NAD\u00a0 1.55\u00a0 1.55\u00a0
NADH\u00a0 0.04\u00a0 0.04\u00a0
Authors of the publication also mentioned a few misprints in the original article:
in the kinetic law for ADH :

  1. the species a should denote NAD and b Ethanol
  2. the last term in the equation should read bpq /( K ib K iq K p )
in the kinetic law for PFK :
  1. R = 1 + \u03bb 1 + \u03bb 2 + g r \u03bb 1 \u03bb 2
  2. equation L should read: L = L0*(..) 2 *(..) 2 *(..) 2 not L = L0*(..) 2 *(..) 2 *(..)
To make the model easier to curate, the species ATP , ADP and AMP were added. These are calculated via assignment rules from the active phosphate species, P , and the sum of all AXP , SUM_P .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Slater Institute, BioCentrum Amsterdam, University of Amsterdam, the Netherlands.", + "authors": [ + { + "institution": "Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU Amsterdam, Amsterdam, The Netherlands.", + "name": "B Teusink", + "orcid": "0000-0003-3929-0423" + }, + { + "name": "J Passarge" + }, + { + "name": "C A Reijenga" + }, + { + "name": "E Esgalhado" + }, + { + "name": "C C van der Weijden", + "orcid": "0000-0001-8663-7579" + }, + { + "name": "M Schepper" + }, + { + "name": "M C Walsh" + }, + { + "institution": "Microbial Physiology, BioCentrum Amsterdam, Vrije Universiteit, De Boelelaan 1087, NL-1081 HV Amsterdam, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12, NL-1018 TV Amsterdam, The Netherlands.", + "name": "B M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "name": "K van Dam" + }, + { + "institution": "Infrastructure for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "H V Westerhoff", + "orcid": "0000-0002-0443-6114" + }, + { + "name": "Snoep JL", + "orcid": "0000-0002-0405-8854" + } + ], + "issue": "17", + "journal": "European journal of biochemistry", + "link": "http://identifiers.org/pubmed/10951190", + "month": "9", + "pages": "5313-5329", + "synopsis": "This paper examines whether the in vivo behavior of yeast glycolysis can be understood in terms of the in vitro kinetic properties of the constituent enzymes. In nongrowing, anaerobic, compressed Saccharomyces cerevisiae the values of the kinetic parameters of most glycolytic enzymes were determined. For the other enzymes appropriate literature values were collected. By inserting these values into a kinetic model for glycolysis, fluxes and metabolites were calculated. Under the same conditions fluxes and metabolite levels were measured. In our first model, branch reactions were ignored. This model failed to reach the stable steady state that was observed in the experimental flux measurements. Introduction of branches towards trehalose, glycogen, glycerol and succinate did allow such a steady state. The predictions of this branched model were compared with the empirical behavior. Half of the enzymes matched their predicted flux in vivo within a factor of 2. For the other enzymes it was calculated what deviation between in vivo and in vitro kinetic characteristics could explain the discrepancy between in vitro rate and in vivo flux.", + "title": "Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry.", + "type": "PubMed ID", + "volume": "267", + "year": 2000 + }, + "publicationId": "BIOMD0000000064", + "submissionId": "MODEL6623915522", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000065": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Feedback regulation in the lactose operon: a mathematical modeling study and comparison with experimental data.
Yildirim N, Mackey MC Biophys. J. 2003 12719218 ,
Abstract:
A mathematical model for the regulation of induction in the lac operon in Escherichia coli is presented. This model takes into account the dynamics of the permease facilitating the internalization of external lactose; internal lactose; beta-galactosidase, which is involved in the conversion of lactose to allolactose, glucose and galactose; the allolactose interactions with the lac repressor; and mRNA. The final model consists of five nonlinear differential delay equations with delays due to the transcription and translation process. We have paid particular attention to the estimation of the parameters in the model. We have tested our model against two sets of beta-galactosidase activity versus time data, as well as a set of data on beta-galactosidase activity during periodic phosphate feeding. In all three cases we find excellent agreement between the data and the model predictions. Analytical and numerical studies also indicate that for physiologically realistic values of the external lactose and the bacterial growth rate, a regime exists where there may be bistable steady-state behavior, and that this corresponds to a cusp bifurcation in the model dynamics.

The model reproduces the time profile of beta-galactosidase activity as shown in Fig 3 of the paper. The delay functions for transcription (M) and translation (B and P) have been implemented by introducing intermediates ( I1, I2 and I3) in the reaction scheme which then give their respective products (I1-> M, I2 ->B and I3 ->P) after an appropriate length of time. The steady state values, attained upon simulation of model equations, for Allolactose (A), mRNA (M), beta-galactosidase (B), Lactose (L), and Permease (P) match with those predicted by the paper. The model was successfully tested on Jarnac, MathSBML and COPASI

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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}, + { + "name": "Michael C Mackey" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/12719218", + "month": "5", + "pages": "2841-2851", + "synopsis": "A mathematical model for the regulation of induction in the lac operon in Escherichia coli is presented. This model takes into account the dynamics of the permease facilitating the internalization of external lactose; internal lactose; beta-galactosidase, which is involved in the conversion of lactose to allolactose, glucose and galactose; the allolactose interactions with the lac repressor; and mRNA. The final model consists of five nonlinear differential delay equations with delays due to the transcription and translation process. We have paid particular attention to the estimation of the parameters in the model. We have tested our model against two sets of beta-galactosidase activity versus time data, as well as a set of data on beta-galactosidase activity during periodic phosphate feeding. In all three cases we find excellent agreement between the data and the model predictions. Analytical and numerical studies also indicate that for physiologically realistic values of the external lactose and the bacterial growth rate, a regime exists where there may be bistable steady-state behavior, and that this corresponds to a cusp bifurcation in the model dynamics.", + "title": "Feedback regulation in the lactose operon: a mathematical modeling study and comparison with experimental data.", + "type": "PubMed ID", + "volume": "84", + "year": 2003 + }, + "publicationId": "BIOMD0000000065", + "submissionId": "MODEL6624248569", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000066": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation: The model reproduces Fig 2f of the paper. The Vmax values for different reactions are obtained by multiplying the specific activites given in Table 3 of the paper with the protein concentration and an assay correction factor that was provided by the authors. The protein concentration is 202 mg/litre. The specific activities that need to be taken into consideration are those given for \"variable threonine\" in Table 3. The following are the assay correction factors provided by the authors: vak1=1.49; vak3=1.12; vasd=1.14; vhsd=1.42; vts=1.15; vhk=1.13. The model was successfully tested on MathSBML and Jarnac

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EMI 9929, University Victor Segalen Bordeaux 2, 146 rue L\u00e9o Saignat, 33076 Bordeaux, France.", + "authors": [ + { + "institution": "Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany.", + "name": "C Chassagnole", + "orcid": "0000-0002-3764-9407" + }, + { + "name": "D A Fell", + "orcid": "0000-0001-6669-2247" + }, + { + "name": "B Ra\u00efs" + }, + { + "name": "B Kudla" + }, + { + "name": "J P Mazat" + } + ], + "issue": "Pt 2", + "journal": "The Biochemical journal", + "link": "http://identifiers.org/pubmed/11368770", + "month": "6", + "pages": "433-444", + "synopsis": "A computer simulation of the threonine-synthesis pathway in Escherichia coli Tir-8 has been developed based on our previous measurements of the kinetics of the pathway enzymes under near-physiological conditions. The model successfully simulates the main features of the time courses of threonine synthesis previously observed in a cell-free extract without alteration of the experimentally determined parameters, although improved quantitative fits can be obtained with small parameter adjustments. At the concentrations of enzymes, precursors and products present in cells, the model predicts a threonine-synthesis flux close to that required to support cell growth. Furthermore, the first two enzymes operate close to equilibrium, providing an example of a near-equilibrium feedback-inhibited enzyme. The predicted flux control coefficients of the pathway enzymes under physiological conditions show that the control of flux is shared between the first three enzymes: aspartate kinase, aspartate semialdehyde dehydrogenase and homoserine dehydrogenase, with no single activity dominating the control. The response of the model to the external metabolites shows that the sharing of control between the three enzymes holds across a wide range of conditions, but that the pathway flux is sensitive to the aspartate concentration. When the model was embedded in a larger model to simulate the variable demands for threonine at different growth rates, it showed the accumulation of free threonine that is typical of the Tir-8 strain at low growth rates. At low growth rates, the control of threonine flux remains largely with the pathway enzymes. As an example of the predictive power of the model, we studied the consequences of over-expressing different enzymes in the pathway.", + "title": "Control of the threonine-synthesis pathway in Escherichia coli: a theoretical and experimental approach.", + "type": "PubMed ID", + "volume": "356", + "year": 2001 + }, + "publicationId": "BIOMD0000000066", + "submissionId": "MODEL6624131052", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000067": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

A Synthetic Gene-Metabolic Oscillator

Reference: Fung et al; Nature (2005) 435:118-122
Name of kinetic law Reaction
Glycolytic flux, V_gly: nil -> AcCoA;
Flux to TCA cycle/ETOH, V_TCA: AcCoA -> TCA/EtOH;
HOAc ex/import,reversible, V_out: HOAc -> HOAc_E
V_Pta: AcCoA + Pi -> AcP + CoA
reversible, V_Ack: AcP + ADP -> OAc + ATP
V_Acs: OAC + ATP -> AcCoA +PPi
Acetic acid-base equillibrium, reversible, V_Ace: OAC + H -> HOAc
Expression of LacI, R_LacI: nil -> LacI
Expression of Acs, R_Acs: nil -> Acs
Expression of Pta, R_Pta: nil -> Pta
LacI degradation, R_dLacI: LacI -> nil
Acs degradation, R_dAcs: Acs -> nil
Pta degradation, R_dPta: Pta -> nil

For this model the differential equation for V_Ace was changed from:
C*(AcP*H-K_eq*OAC) with C = 100 in the supplemental material
to C*(OAc*H-K_eq*HOAc) with C = 100, as in Bulter et. al; PNAS(2004),101,2299-2304 , and a value for K_eq of 5*10^-4 after communication with the authors.

translated to SBML by:
Lukas Endler(luen at tbi.univie.ac.at), Christoph Flamm (xtof at tbi.univie.ac.at)

Biomodels Curation The model reproduces 3a of the paper for glycolytic flux Vgly = 0.5. The authors have agreed that the values on Y-axis are marked wrong and hence there is a discrepancy between model simulation results and the figure. Also, note that the values of concentration and time are in dimensionless units. The model was successfully tested on MathSBML and Jarnac.

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California-Los Angeles, Los Angeles, California 90095, USA.", + "authors": [ + { + "institution": "Department of Chemical Engineering, University of California-Los Angeles, Los Angeles, California 90095, USA.", + "name": "Eileen Fung", + "orcid": "0009-0004-5853-4280" + }, + { + "name": "Wilson W Wong" + }, + { + "name": "Jason K Suen" + }, + { + "name": "Thomas Bulter", + "orcid": "0000-0002-7968-5844" + }, + { + "name": "Sun-gu Lee" + }, + { + "name": "James C Liao", + "orcid": "0000-0002-4580-7276" + } + ], + "issue": "7038", + "journal": "Nature", + "link": "http://identifiers.org/pubmed/15875027", + "month": "5", + "pages": "118-122", + "synopsis": "Autonomous oscillations found in gene expression and metabolic, cardiac and neuronal systems have attracted significant attention both because of their obvious biological roles and their intriguing dynamics. In addition, de novo designed oscillators have been demonstrated, using components that are not part of the natural oscillators. Such oscillators are useful in testing the design principles and in exploring potential applications not limited by natural cellular behaviour. To achieve transcriptional and metabolic integration characteristic of natural oscillators, here we designed and constructed a synthetic circuit in Escherichia coli K12, using glycolytic flux to generate oscillation through the signalling metabolite acetyl phosphate. If two metabolite pools are interconverted by two enzymes that are placed under the transcriptional control of acetyl phosphate, the system oscillates when the glycolytic rate exceeds a critical value. We used bifurcation analysis to identify the boundaries of oscillation, and verified these experimentally. This work demonstrates the possibility of using metabolic flux as a control factor in system-wide oscillation, as well as the predictability of a de novo gene-metabolic circuit designed using nonlinear dynamic analysis.", + "title": "A synthetic gene-metabolic oscillator.", + "type": "PubMed ID", + "volume": "435", + "year": 2005 + }, + "publicationId": "BIOMD0000000067", + "submissionId": "MODEL6624231052", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000068": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A kinetic model of the branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana.
Curien G, Ravanel S, Dumas R Eur. J. Biochem. 2003 Dec; Volume: 270 (Issue: 23 )]:4615-27 14622248 ,
Abstract:
This work proposes a model of the metabolic branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana which involves kinetic competition for phosphohomoserine between the allosteric enzyme threonine synthase and the two-substrate enzyme cystathionine gamma-synthase. Threonine synthase is activated by S-adenosylmethionine and inhibited by AMP. Cystathionine gamma-synthase condenses phosphohomoserine to cysteine via a ping-pong mechanism. Reactions are irreversible and inhibited by inorganic phosphate. The modelling procedure included an examination of the kinetic links, the determination of the operating conditions in chloroplasts and the establishment of a computer model using the enzyme rate equations. To test the model, the branch-point was reconstituted with purified enzymes. The computer model showed a partial agreement with the in vitro results. The model was subsequently improved and was then found consistent with flux partition in vitro and in vivo. Under near physiological conditions, S-adenosylmethionine, but not AMP, modulates the partition of a steady-state flux of phosphohomoserine. The computer model indicates a high sensitivity of cystathionine flux to enzyme and S-adenosylmethionine concentrations. Cystathionine flux is sensitive to modulation of threonine flux whereas the reverse is not true. The cystathionine gamma-synthase kinetic mechanism favours a low sensitivity of the fluxes to cysteine. Though sensitivity to inorganic phosphate is low, its concentration conditions the dynamics of the system. Threonine synthase and cystathionine gamma-synthase display similar kinetic efficiencies in the metabolic context considered and are first-order for the phosphohomoserine substrate. Under these conditions outflows are coordinated.


SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

Biomodels Curation The model simulates the flux for TS and CGS under conditions given in Table 2 and reproduces the dotted lines given in Table 3 of the paper. There is a typo in the equation for the apparent specificity constant for Phser, Kts (equation13). This was changed after communication with the authors to be: Kts = 5.9E-4+6.2E-2*pow(AdoMet,2.9)/(pow(32,2.9)+pow(AdoMet,2.9)). The model was successfully tested on Jarnac and Copasi. Due to a suggestion from Pedro Mendez the parameter AdoMet, TS and CGS where made constant species.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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two-substrate enzyme cystathionine gamma-synthase. 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Cystathionine flux is sensitive to modulation of threonine flux whereas the reverse is not true. The cystathionine gamma-synthase kinetic mechanism favours a low sensitivity of the fluxes to cysteine. Though sensitivity to inorganic phosphate is low, its concentration conditions the dynamics of the system. Threonine synthase and cystathionine gamma-synthase display similar kinetic efficiencies in the metabolic context considered and are first-order for the phosphohomoserine substrate. Under these conditions outflows are coordinated.", + "title": "A kinetic model of the branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana.", + "type": "PubMed ID", + "volume": "270", + "year": 2003 + }, + "publicationId": "BIOMD0000000068", + "submissionId": "MODEL6624146302", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000069": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Ulster, Northern Ireland", + "email": "fuss-h@ulster.ac.uk", + "external": false, + "name": "Hendrik Fu\u00df" + } + ] + }, + "curationStatus": "CURATED", + "description": "

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"uri": "http://identifiers.org/biomodels.db/MODEL7146478294" + }, + { + "accession": "BIOMD0000000069", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000069" + }, + { + "accession": "16873466", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/16873466" + }, + { + "accession": "REACT_152", + "name": "Cell Cycle, Mitotic", + "qualifier": "bqbiol:isVersionOf", + "resource": "Reactome", + "uri": "http://identifiers.org/reactome/REACT_152" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Fuss2006_MitoticActivation", + "publication": { + "accession": "16873466", + "affiliation": "University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA, Northern Ireland. fuss-h@ulster.ac.uk", + "authors": [ + { + "institution": "University of Ulster, School of Biomedical Sciences, Cromore Road, Coleraine, BT52 1SA, Northern Ireland. fuss-h@ulster.ac.uk", + "name": "Hendrik Fuss" + }, + { + "name": "Werner Dubitzky" + }, + { + "name": "Stephen Downes" + }, + { + "name": "Mary Jo Kurth" + } + ], + "issue": "14", + "journal": "Bioinformatics (Oxford, England)", + "link": "http://identifiers.org/pubmed/16873466", + "month": "7", + "pages": "e158-65", + "synopsis": "

Motivation

The protein tyrosine kinase Src is involved in a multitude of biochemical pathways and cellular functions. A complex network of interactions with other kinases and phosphatases obscures its precise mode of operation.

Results

We have constructed a semi-quantitative computational dynamic systems model of the activation of Src at mitosis based on protein interactions described in the literature. Through numerical simulation and bifurcation analysis we show that Src regulation involves a bistable switch, a pattern increasingly recognised as essential to biochemical signalling. The switch is operated by the tyrosine kinase CSK, which itself is involved in a negative feedback loop with Src. Negative feedback generates an excitable system, which produces transient activation of Src. One of the system parameters, which is linked to the cyclin dependent kinase cdc2, controls excitability via a second bistable switch. This topology allows for differentiated responses to a multitude of signals. The model offers explanations for the existence of the positive and negative feedback loops involving protein tyrosine phosphatase alpha (PTPalpha) and translocation of CSK and predicts a specific relationship between Src phosphorylation and activity.", + "title": "Bistable switching and excitable behaviour in the activation of Src at mitosis.", + "type": "PubMed ID", + "volume": "22", + "year": 2006 + }, + "publicationId": "BIOMD0000000069", + "submissionId": "MODEL7146478294", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000070": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

.

.

.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

.

.

.

.

.

.

Biomodels CurationThe model simulates the flux values as given for \"kinetic model\" in Table 1 of the paper. The model was successfully tested on Jarnac.

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functions are ultimately linked to metabolic fluxes brought about by thousands of chemical reactions and transport processes. The synthesis of the underlying enzymes and membrane transporters causes the cell a certain 'effort' of energy and external resources. Considering that those cells should have had a selection advantage during natural evolution that enabled them to fulfil vital functions (such as growth, defence against toxic compounds, repair of DNA alterations, etc.) with minimal effort, one may postulate the principle of flux minimization, as follows: given the available external substrates and given a set of functionally important 'target' fluxes required to accomplish a specific pattern of cellular functions, the stationary metabolic fluxes have to become a minimum. To convert this principle into a mathematical method enabling the prediction of stationary metabolic fluxes, the total flux in the network is measured by a weighted linear combination of all individual fluxes whereby the thermodynamic equilibrium constants are used as weighting factors, i.e. the more the thermodynamic equilibrium lies on the right-hand side of the reaction, the larger the weighting factor for the backward reaction. A linear programming technique is applied to minimize the total flux at fixed values of the target fluxes and under the constraint of flux balance (= steady-state conditions) with respect to all metabolites. The theoretical concept is applied to two metabolic schemes: the energy and redox metabolism of erythrocytes, and the central metabolism of Methylobacterium extorquens AM1. The flux rates predicted by the flux-minimization method exhibit significant correlations with flux rates obtained by either kinetic modelling or direct experimental determination. Larger deviations occur for segments of the network composed of redundant branches where the flux-minimization method always attributes the total flux to the thermodynamically most favourable branch. Nevertheless, compared with existing methods of structural modelling, the principle of flux minimization appears to be a promising theoretical approach to assess stationary flux rates in metabolic systems in cases where a detailed kinetic model is not yet available.", + "title": "The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks.", + "type": "PubMed ID", + "volume": "271", + "year": 2004 + }, + "publicationId": "BIOMD0000000070", + "submissionId": "MODEL6624180840", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000071": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

.

.

.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

.

.

Biomodels Curation: The paper refers to the model equations present in Bakker et al's \" Glycolysis in bloodstream from Trypanosoma brucei can be understood in terms of the kinetics of glycolytic enzymes\" (Pubmed ID: 9013556), also, the authors claim that some of the modifications in these equations were made based on the experimental results from the paper \"Contribution of glucose transport in the control of glycolytic flux in Trypanosoma brucei\" (Pubmed ID: 10468568). The model reproduces the various flux values in Fig 3 for 100% TPI activity. It also matches with the values provided in Table 2 of the paper. The model was successfully tested with Copasi and SBML ODE Solver.
The volumes are set to the values containing 1 mg of total protein per microlitre total cell volume. To change the protein concentration use Vt , the total cell volume in micro litre per mg protein.
To change the TPI activity use the global parameter TPIact .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"http://identifiers.org/reactome/REACT_1383" + }, + { + "accession": "GO:0006096", + "name": "glycolytic process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0006096" + }, + { + "accession": "MODEL6624099213", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL6624099213" + }, + { + "accession": "BIOMD0000000071", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000071" + }, + { + "accession": "11415442", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/11415442" + }, + { + "accession": "MODEL1101100000", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1101100000" + }, + { + "accession": "10468568", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/10468568" + }, + { + "accession": "5691", + "name": "Trypanosoma brucei", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/5691" + }, + { + "accession": "tbr00010", + "name": "Glycolysis / Gluconeogenesis - Trypanosoma brucei", + "qualifier": "bqbiol:is", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/tbr00010" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Bakker2001_Glycolysis", + "publication": { + "accession": "11415442", + "affiliation": "Zentrum f\u00fcr Molekularbiologie der Universit\u00e4t Heidelberg (ZMBH), Im Neuenheimer Feld 282, D-69120 Heidelberg, Germany.", + "authors": [ + { + "institution": "Zentrum f\u00fcr Molekularbiologie der Universit\u00e4t Heidelberg (ZMBH), Im Neuenheimer Feld 282, D-69120 Heidelberg, Germany.", + "name": "S Helfert" + }, + { + "name": "A M Est\u00e9vez", + "orcid": "0000-0002-8272-5405" + }, + { + "name": "B Bakker" + }, + { + "name": "Paul A M Michels", + "orcid": "0000-0003-3726-6104" + }, + { + "name": "C Clayton", + "orcid": "0000-0002-6384-0731" + } + ], + "issue": "Pt 1", + "journal": "The Biochemical journal", + "link": "http://identifiers.org/pubmed/11415442", + "month": "7", + "pages": "117-125", + "synopsis": "Kinetoplastid protozoa compartmentalize the first seven enzymes of glycolysis and two enzymes of glycerol metabolism in a microbody, the glycosome. While in its mammalian host, Trypanosoma brucei depends entirely on glucose for ATP generation. Under aerobic conditions, most of the glucose is metabolized to pyruvate. Aerobic metabolism depends on the activities of glycosomal triosephosphate isomerase and a mitochondrial glycerophosphate oxidase, and on glycerophosphate<-->dihydroxyacetone phosphate exchange across the glycosomal membrane. Using a combination of genetics and computer modelling, we show that triosephosphate isomerase is probably essential for bloodstream trypanosome survival, but not for the insect-dwelling procyclics, which preferentially use amino acids as an energy source. When the enzyme level decreased to about 15% of that of the wild-type, the growth rate was halved. Below this level, a lethal rise in dihydroxyacetone phosphate was predicted. Expression of cytosolic triosephosphate isomerase inhibited cell growth. Attempts to knockout the trypanosome alternative oxidase genes (which are needed for glycerophosphate oxidase activity) were unsuccessful, but when we lowered the level of the corresponding mRNA by expressing a homologous double-stranded RNA, oxygen consumption was reduced fourfold and the rate of trypanosome growth was halved.", + "title": "Roles of triosephosphate isomerase and aerobic metabolism in Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "357", + "year": 2001 + }, + "publicationId": "BIOMD0000000071", + "submissionId": "MODEL6624099213", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000072": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The paper describes both wild-type and mutant cells of G protein cycle by using different values of G protein deactivation. We chosed the wild-type, k=0.11 s-1.

The unit of the concentration for the proteins are numbers of molecules per cell.

Figure5(A) was reproduced with COPASI 4.0 (Build 18) and SBML_odeSolver. Figure5(B) was reproduced with COPASI 4.0 (Build 18).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Kitano Symbiotic Systems Project, Japan Science and Technology Corporation, Shibuya, Tokyo, Japan.", + "authors": [ + { + "institution": "Systems Biology Group, Exploratory Research for Advanced Technology Kitano Symbiotic Systems Project, Japan Science and Technology Corporation, Shibuya, Tokyo, Japan.", + "name": "Tau-Mu Yi" + }, + { + "name": "Hiroaki Kitano" + }, + { + "name": "Melvin I Simon" + } + ], + "issue": "19", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/12960402", + "month": "9", + "pages": "10764-10769", + "synopsis": "The yeast mating response is one of the best understood heterotrimeric G protein signaling pathways. Yet, most descriptions of this system have been qualitative. We have quantitatively characterized the heterotrimeric G protein cycle in yeast based on direct in vivo measurements. We used fluorescence resonance energy transfer to monitor the association state of cyan fluorescent protein (CFP)-Galpha and Gbetagamma-yellow fluorescent protein (YFP), and we found that receptor-mediated G protein activation produced a loss of fluorescence resonance energy transfer. Quantitative time course and dose-response data were obtained for both wild-type and mutant cells possessing an altered pheromone response. These results paint a quantitative portrait of how regulators such as Sst2p and the C-terminal tail of alpha-factor receptor modulate the kinetics and sensitivity of G protein signaling. We have explored critical features of the dynamics including the rapid rise and subsequent decline of active G proteins during the early response, and the relationship between the G protein activation dose-response curve and the downstream dose-response curves for cell-cycle arrest and transcriptional induction. Fitting the data to a mathematical model produced estimates of the in vivo rates of heterotrimeric G protein activation and deactivation in yeast.", + "title": "A quantitative characterization of the yeast heterotrimeric G protein cycle.", + "type": "PubMed ID", + "volume": "100", + "year": 2003 + }, + "publicationId": "BIOMD0000000072", + "submissionId": "MODEL9468910329", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000073": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper Toward a detailed computational model for the mammalian circadian clock . In this model only interlocked negative and positive regulation of Per, Cry, Bmal gene are involved. Some initial values were not provided, therefore they were chosen to fit the curve from the paper.

Figure2A re-produced by Copasi 4.0.19 and roadRunner online.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "authors": [ + { + "institution": "Unit\u00e9 de Chronobiologie Th\u00e9orique, Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "name": "Jean-Christophe Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "Albert Goldbeter" + } + ], + "issue": "12", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/12775757", + "month": "6", + "pages": "7051-7056", + "synopsis": "We present a computational model for the mammalian circadian clock based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, Clock, and Rev-Erb alpha genes. In agreement with experimental observations, the model can give rise to sustained circadian oscillations in continuous darkness, characterized by an antiphase relationship between Per/Cry/Rev-Erbalpha and Bmal1 mRNAs. Sustained oscillations correspond to the rhythms autonomously generated by suprachiasmatic nuclei. For other parameter values, damped oscillations can also be obtained in the model. These oscillations, which transform into sustained oscillations when coupled to a periodic signal, correspond to rhythms produced by peripheral tissues. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark cycles. Simulations show that the phase of the oscillations can then vary by several hours with relatively minor changes in parameter values. Such a lability of the phase could account for physiological disorders related to circadian rhythms in humans, such as advanced or delayed sleep phase syndrome, whereas the lack of entrainment by light-dark cycles can be related to the non-24h sleep-wake syndrome. The model uncovers the possible existence of multiple sources of oscillatory behavior. Thus, in conditions where the indirect negative autoregulation of Per and Cry expression is inoperative, the model indicates the possibility that sustained oscillations might still arise from the negative autoregulation of Bmal1 expression.", + "title": "Toward a detailed computational model for the mammalian circadian clock.", + "type": "PubMed ID", + "volume": "100", + "year": 2003 + }, + "publicationId": "BIOMD0000000073", + "submissionId": "MODEL1109528188", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000074": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is model in continous darkness (DD) described in the article Toward a detailed computational model for the mammalian circadian clock

This model features the full interlocked negative and positive regulation of Per,Cry,Bmal and REV-ERBalpha. The model exhibits robust oscillations quite independent of the initial conditions for teh parameters given. Each species is assigned zero as initial value, and the graph started at time=120h.

Simulation results could be reproduced using Copasi 4.0.19(development) and roadRunner online.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Th\u00e9orique, Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "authors": [ + { + "institution": "Unit\u00e9 de Chronobiologie Th\u00e9orique, Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "name": "Jean-Christophe Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "Albert Goldbeter" + } + ], + "issue": "12", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/12775757", + "month": "6", + "pages": "7051-7056", + "synopsis": "We present a computational model for the mammalian circadian clock based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, Clock, and Rev-Erb alpha genes. In agreement with experimental observations, the model can give rise to sustained circadian oscillations in continuous darkness, characterized by an antiphase relationship between Per/Cry/Rev-Erbalpha and Bmal1 mRNAs. 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Thus, in conditions where the indirect negative autoregulation of Per and Cry expression is inoperative, the model indicates the possibility that sustained oscillations might still arise from the negative autoregulation of Bmal1 expression.", + "title": "Toward a detailed computational model for the mammalian circadian clock.", + "type": "PubMed ID", + "volume": "100", + "year": 2003 + }, + "publicationId": "BIOMD0000000074", + "submissionId": "MODEL1272626224", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000075": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Xu2003 - Phosphoinositide turnover

The model reproduces the percentage change of PIP_PM, PIP2_PM and IP3_Cyt as depicted in Figure 1 of the paper. The model also contains the equations for the analysis of PH-GFP experiments, however the initial value of PH_GFP has been set to zero to more accurately reproduce Figure 1. The units of cytosolic species are given in molecules/um^3. In order to convert them to uM, divide the concentration by 602. For the analysis of PH_GFP experiments, one should plug in the values of PH_GFP, IP3_PHGFP and PIP2_PHGFP from Table AI in the appendix. The model was successfully tested on MathSBML.

This model has been generated by VCell

This model is described in the article:

Xu C, Watras J, Loew LM.
J. Cell Biol. 2003 May; 161(4): 779-791

Abstract:

We studied the bradykinin-induced changes in phosphoinositide composition of N1E-115 neuroblastoma cells using a combination of biochemistry, microscope imaging, and mathematical modeling. Phosphatidylinositol-4,5-bisphosphate (PIP2) decreased over the first 30 s, and then recovered over the following 2-3 min. However, the rate and amount of inositol-1,4,5-trisphosphate (InsP3) production were much greater than the rate or amount of PIP2 decline. A mathematical model of phosphoinositide turnover based on this data predicted that PIP2 synthesis is also stimulated by bradykinin, causing an early transient increase in its concentration. This was subsequently confirmed experimentally. Then, we used single-cell microscopy to further examine phosphoinositide turnover by following the translocation of the pleckstrin homology domain of PLCdelta1 fused to green fluorescent protein (PH-GFP). The observed time course could be simulated by incorporating binding of PIP2 and InsP3 to PH-GFP into the model that had been used to analyze the biochemistry. Furthermore, this analysis could help to resolve a controversy over whether the translocation of PH-GFP from membrane to cytosol is due to a decrease in PIP2 on the membrane or an increase in InsP3 in cytosol; by computationally clamping the concentrations of each of these compounds, the model shows how both contribute to the dynamics of probe translocation.

This model is hosted on BioModels Database and identified by: BIOMD0000000075 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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changes in phosphoinositide composition of N1E-115 neuroblastoma cells using a combination of biochemistry, microscope imaging, and mathematical modeling. Phosphatidylinositol-4,5-bisphosphate (PIP2) decreased over the first 30 s, and then recovered over the following 2-3 min. However, the rate and amount of inositol-1,4,5-trisphosphate (InsP3) production were much greater than the rate or amount of PIP2 decline. A mathematical model of phosphoinositide turnover based on this data predicted that PIP2 synthesis is also stimulated by bradykinin, causing an early transient increase in its concentration. This was subsequently confirmed experimentally. Then, we used single-cell microscopy to further examine phosphoinositide turnover by following the translocation of the pleckstrin homology domain of PLCdelta1 fused to green fluorescent protein (PH-GFP). The observed time course could be simulated by incorporating binding of PIP2 and InsP3 to PH-GFP into the model that had been used to analyze the biochemistry. Furthermore, this analysis could help to resolve a controversy over whether the translocation of PH-GFP from membrane to cytosol is due to a decrease in PIP2 on the membrane or an increase in InsP3 in cytosol; by computationally clamping the concentrations of each of these compounds, the model shows how both contribute to the dynamics of probe translocation.", + "title": "Kinetic analysis of receptor-activated phosphoinositide turnover.", + "type": "PubMed ID", + "volume": "161", + "year": 2003 + }, + "publicationId": "BIOMD0000000075", + "submissionId": "MODEL3095606944", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000076": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation The model reproduces the flux value of \"Gpp p\" (rate of Glycerol synthesis) as depicted in Fig 3 of the paper. The model reproduces the flux for early exponential phase , however it can be used to reproduce the values for other phases by plugging in appropriate values for maximal rates as given in Table 1 and metabolite concentrations as given in Table 2 of the paper. The model was succesfully reproduced using Jarnac.

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"revisions": [ + { + "comment": "Original import of cronwright", + "submitted": 1163116040, + "submitter": "Nicolas Le Nov\u00e8re", + "version": 1 + }, + { + "comment": "Current version of Cronwright2002_Glycerol_Synthesis", + "submitted": 1337163527, + "submitter": "Nicolas Le Nov\u00e8re", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724263136, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "4932", + "name": "Saccharomyces cerevisiae", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/4932" + }, + { + "accession": "MODEL6624139162", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL6624139162" + }, + { + "accession": "BIOMD0000000076", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": 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"http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Cronwright2002_Glycerol_Synthesis", + "publication": { + "accession": "12200299", + "affiliation": "Department of Microbiology, Stellenbosch University, Matieland 7602, South Africa. garth.cronwright@tmb.lth.se", + "authors": [ + { + "institution": "Department of Microbiology, Stellenbosch University, Matieland 7602, South Africa. garth.cronwright@tmb.lth.se", + "name": "Garth R Cronwright" + }, + { + "name": "Johann M Rohwer", + "orcid": "0000-0001-6288-8904" + }, + { + "name": "Bernard A Prior" + } + ], + "issue": "9", + "journal": "Applied and environmental microbiology", + "link": "http://identifiers.org/pubmed/12200299", + "month": "9", + "pages": "4448-4456", + "synopsis": "Glycerol, a major by-product of ethanol fermentation by Saccharomyces cerevisiae, is of significant importance to the wine, beer, and ethanol production industries. To gain a clearer understanding of and to quantify the extent to which parameters of the pathway affect glycerol flux in S. cerevisiae, a kinetic model of the glycerol synthesis pathway has been constructed. Kinetic parameters were collected from published values. Maximal enzyme activities and intracellular effector concentrations were determined experimentally. The model was validated by comparing experimental results on the rate of glycerol production to the rate calculated by the model. Values calculated by the model agreed well with those measured in independent experiments. The model also mimics the changes in the rate of glycerol synthesis at different phases of growth. Metabolic control analysis values calculated by the model indicate that the NAD(+)-dependent glycerol 3-phosphate dehydrogenase-catalyzed reaction has a flux control coefficient (C(J)v1) of approximately 0.85 and exercises the majority of the control of flux through the pathway. Response coefficients of parameter metabolites indicate that flux through the pathway is most responsive to dihydroxyacetone phosphate concentration (R(J)DHAP= 0.48 to 0.69), followed by ATP concentration (R(J)ATP = -0.21 to -0.50). Interestingly, the pathway responds weakly to NADH concentration (R(J)NADH = 0.03 to 0.08). The model indicates that the best strategy to increase flux through the pathway is not to increase enzyme activity, substrate concentration, or coenzyme concentration alone but to increase all of these parameters in conjunction with each other.", + "title": "Metabolic control analysis of glycerol synthesis in Saccharomyces cerevisiae.", + "type": "PubMed ID", + "volume": "68", + "year": 2002 + }, + "publicationId": "BIOMD0000000076", + "submissionId": "MODEL6624139162", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000077": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

A mathematical model quantifying GnRH-induced LH secretion from gonadotropes by Blum et al (2000)

This paper includes three stages, and the model does not include the third stage. Also an event is included which remove the hormone GnRH at time=5min. Figure 1 and Figure 2 of the paper are reproduced, using SBML odeSolver. We choose to encode the model with the concentration of GnRH equal to 1.0nM.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Endocrinology and metabolism", + "link": "http://identifiers.org/pubmed/10662710", + "month": "2", + "pages": "E263-72", + "synopsis": "A mathematical model is developed to investigate the rate of release of luteinizing hormone (LH) from pituitary gonadotropes in response to short pulses of gonadotropin-releasing hormone (GnRH). The model includes binding of the hormone to its receptor, dimerization, interaction with a G protein, production of inositol 1,4, 5-trisphosphate, release of Ca(2+) from the endoplasmic reticulum, entrance of Ca(2+) into the cytosol via voltage-gated membrane channels, pumping of Ca(2+) out of the cytosol via membrane and endoplasmic reticulum pumps, and release of LH. Cytosolic Ca(2+) dynamics are simplified (i.e., oscillations are not included in the model), and it is assumed that there is only one pool of releasable LH. Despite these and other simplifications, the model explains the qualitative features of LH release in response to GnRH pulses of various durations and different concentrations in the presence and absence of external Ca(2+).", + "title": "A mathematical model quantifying GnRH-induced LH secretion from gonadotropes.", + "type": "PubMed ID", + "volume": "278", + "year": 2000 + }, + "publicationId": "BIOMD0000000077", + "submissionId": "MODEL3092061764", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000078": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is described in the paper Toward a detailed computational model for the mammalian circadian clock . In this model only interlocked negative and positive regulation of Per, Cry, Bmal gene are involved. Some initial values were not provided, therefore they were chosen to fit the curves from the paper.

Figure2C is re-produced by odeSolver.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "authors": [ + { + "institution": "Unit\u00e9 de Chronobiologie Th\u00e9orique, Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "name": "Jean-Christophe Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "Albert Goldbeter" + } + ], + "issue": "12", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/12775757", + "month": "6", + "pages": "7051-7056", + "synopsis": "We present a computational model for the mammalian circadian clock based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, Clock, and Rev-Erb alpha genes. In agreement with experimental observations, the model can give rise to sustained circadian oscillations in continuous darkness, characterized by an antiphase relationship between Per/Cry/Rev-Erbalpha and Bmal1 mRNAs. Sustained oscillations correspond to the rhythms autonomously generated by suprachiasmatic nuclei. For other parameter values, damped oscillations can also be obtained in the model. These oscillations, which transform into sustained oscillations when coupled to a periodic signal, correspond to rhythms produced by peripheral tissues. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark cycles. Simulations show that the phase of the oscillations can then vary by several hours with relatively minor changes in parameter values. Such a lability of the phase could account for physiological disorders related to circadian rhythms in humans, such as advanced or delayed sleep phase syndrome, whereas the lack of entrainment by light-dark cycles can be related to the non-24h sleep-wake syndrome. The model uncovers the possible existence of multiple sources of oscillatory behavior. Thus, in conditions where the indirect negative autoregulation of Per and Cry expression is inoperative, the model indicates the possibility that sustained oscillations might still arise from the negative autoregulation of Bmal1 expression.", + "title": "Toward a detailed computational model for the mammalian circadian clock.", + "type": "PubMed ID", + "volume": "100", + "year": 2003 + }, + "publicationId": "BIOMD0000000078", + "submissionId": "MODEL4108701438", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000079": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper of A model for the dynamics of human weight cycling by A. Goldbeter 2006.The figure3 (A) and (B) have been reproduced by Copasi 4.0.19(development) and SBMLodeSolver.The writer of the paper did not specify any units for the metabolites, so the creator of the model did not define the units as well.Both Q and R are normalized to vary between 0 and 1.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A simple mathematical model for weight cycling is presented. The model is based on a feedback of psychological nature by which a subject decides to reduce dietary intake once a threshold weight is exceeded. The analysis of the model indicates that sustained oscillations in body weight occur in a parameter range bounded by critical values. Only outside this range can body weight reach a stable steady state. The model provides a theoretical framework that captures key facets of weight cycling and suggests ways to control the phenomenon. The view that weight cycling represents self-sustained oscillations has indeed specific implications. In dynamical terms, to bring weight cycling to an end, parameter values should change in such a way as to induce the transition of body weight from sustained oscillations around an unstable steady state to a stable steady state. Maintaining weight under a critical value should prevent weight cycling and allow body weight to stabilize below the oscillatory range.", + "title": "A model for the dynamics of human weight cycling.", + "type": "PubMed ID", + "volume": "31", + "year": 2006 + }, + "publicationId": "BIOMD0000000079", + "submissionId": "MODEL4644593552", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000080": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model reproduces figure 5 and figure 4(B)of the paper, with Kinh represented by [G-GTP]. We arbitrarily chosed to set the initial concentration of D to 31 micorMolar based on legend of figure 4. [R] was not given anywhere in the paper and was chosen to calibrate the sigmoid response to an increased [GTP]. THe figure 5 in the model was successfully simulated on COPASI 4.0 ,the figure 4(B) was sucessfully simulated on both COPASI and SBML_odeSolver.

There are two curves for Kinh in the absence and presence of NaCl in the figure obtained from simulations of the model using parameters of set C and set D.Here in the model the initial value given is from set D.The parameters in set C :k7=0.5, k10=1.0,k5=0.1,the others are the same with set D.

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In order to identify the rate-limiting steps in adenylate cyclase regulation, we measured the kinetics of stimulation and inhibition of the enzyme on a subsecond to second time scale using a novel rapid-mix quench technique. Even using our rapid-mix quench method, activation by PGE1 and forskolin was instantaneous (cAMP accumulation was linear between 0.5 and 30 s). In contrast, we found a lag period of 1.2-10 s for epinephrine-mediated inhibition. The length of the lag depended on the concentration of GTP and monovalent cations present. In the absence of NaCl, the rate constant for the onset of inhibition (kinh) increased only slightly with GTP concentration saturating at a value of 0.16 s-1 (t1/2 4.3 s) at 1 microM GTP. In the presence of 100 mM NaCl, kinh was strongly dependent on GTP concentration, reaching a maximum value of 0.57 s-1 (t1/2 1.2 s) at 100 microM GTP. Thus, activation of both Gi and Gs in intact platelet membranes is much faster (t1/2 less than 5 s) than previously reported for reconstituted systems. Also, the strong dependence of the rate of adenylate cyclase inhibition on GTP concentration implies that the rate-limiting step in inhibition is distal to GTP binding. The effect of NaCl to increase the maximal rate of inhibition is specific for sodium since KCl has no effect on kinh.(ABSTRACT TRUNCATED AT 250 WORDS)", + "title": "Rapid kinetics of alpha 2-adrenergic inhibition of adenylate cyclase. Evidence for a distal rate-limiting step.", + "type": "PubMed ID", + "volume": "28", + "year": 1989 + }, + "publicationId": "BIOMD0000000080", + "submissionId": "MODEL1173806337", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000081": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces FIG 11A and FIG 11B of the paper. However, please note that FIG 11B is a plot of normalised amounts versus time. The \"stoichiometry\" field has been used to convert fluxes from membrane species to volume species. The value of 0.0009967 is a product of (Surface to Volume_M*(1/Avagadro's number)*1E21. 0.6 is the surface to volume ratio of the plasma membrane, 1E21 is required for a unit surface to volume ratio and the Avagadro's number is present in the denominator to convert molecules to moles. The model was successfully tested using MathSBML and SBML ODESolver.
All the kinetic laws have the unit items per second , which requires the one reaction taking place in the cytoplasm - IP3Phosphatase - to include an explicit conversion factor both in the kinetic law and the stoichiometry of IP3_C . The kinetic law is multiplied and the stoichiometry divided by the number of molecules per micro-mole. This conversion factor is only required for correct units and can be replaced by 1, if it should lead to numerical problems.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Biophysics, University of Washington School of Medicine, G-424 Health Sciences Building, Box 357290, Seattle, WA 98195-7290, USA.", + "authors": [ + { + "institution": "Department of Physiology and Biophysics, University of Washington School of Medicine, G-424 Health Sciences Building, Box 357290, Seattle, WA 98195-7290, USA.", + "name": "Byung-Chang Suh" + }, + { + "name": "Lisa F Horowitz" + }, + { + "name": "Wiebke Hirdes" + }, + { + "name": "Ken Mackie", + "orcid": "0000-0001-8501-6199" + }, + { + "name": "Bertil Hille" + } + ], + "issue": "6", + "journal": "The Journal of general physiology", + "link": "http://identifiers.org/pubmed/15173220", + "month": "6", + "pages": "663-683", + "synopsis": "Receptor-mediated modulation of KCNQ channels regulates neuronal excitability. This study concerns the kinetics and mechanism of M1 muscarinic receptor-mediated regulation of the cloned neuronal M channel, KCNQ2/KCNQ3 (Kv7.2/Kv7.3). Receptors, channels, various mutated G-protein subunits, and an optical probe for phosphatidylinositol 4,5-bisphosphate (PIP2) were coexpressed by transfection in tsA-201 cells, and the cells were studied by whole-cell patch clamp and by confocal microscopy. Constitutively active forms of Galphaq and Galpha11, but not Galpha13, caused a loss of the plasma membrane PIP2 and a total tonic inhibition of the KCNQ current. There were no further changes upon addition of the muscarinic agonist oxotremorine-M (oxo-M). Expression of the regulator of G-protein signaling, RGS2, blocked PIP2 hydrolysis and current suppression by muscarinic stimulation, confirming that the Gq family of G-proteins is necessary. Dialysis with the competitive inhibitor GDPbetaS (1 mM) lengthened the time constant of inhibition sixfold, decreased the suppression of current, and decreased agonist sensitivity. Removal of intracellular Mg2+ slowed both the development and the recovery from muscarinic suppression. When combined with GDPbetaS, low intracellular Mg2+ nearly eliminated muscarinic inhibition. With nonhydrolyzable GTP analogs, current suppression developed spontaneously and muscarinic inhibition was enhanced. Such spontaneous suppression was antagonized by GDPbetaS or GTP or by expression of RGS2. These observations were successfully described by a kinetic model representing biochemical steps of the signaling cascade using published rate constants where available. The model supports the following sequence of events for this Gq-coupled signaling: A classical G-protein cycle, including competition for nucleotide-free G-protein by all nucleotide forms and an activation step requiring Mg2+, followed by G-protein-stimulated phospholipase C and hydrolysis of PIP2, and finally PIP2 dissociation from binding sites for inositol lipid on the channels so that KCNQ current was suppressed. Further experiments will be needed to refine some untested assumptions.", + "title": "Regulation of KCNQ2/KCNQ3 current by G protein cycling: the kinetics of receptor-mediated signaling by Gq.", + "type": "PubMed ID", + "volume": "123", + "year": 2004 + }, + "publicationId": "BIOMD0000000081", + "submissionId": "MODEL4662889298", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000082": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model was created according to the paper Inhibition of Adenylate Cyclase Is Mediated by the High Affinity Conformation of the alpha2-Adrenergic Receptor published in 1988.

The figure4 (steady state curve) in the paper has been simulated having the same plot with Copasi 4.0.19 (development) and roadRunner(online).Because the initial concentration of R and D were not given in the paper ,so we gave it 1e-9 Mol/L and 1e-8 Mol/L respectively.

Pay attention that the simulations of steady state concentration of species in arbitrary units are shown for figure4 and figure6 in the paper.

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agonist binding to receptors that interact with guanine nucleotide regulatory proteins has remained controversial. Preincubation of human platelet membranes with the full alpha 2-agonist UK 14,304 in the absence of GTP increases the potency of the agonist to inhibit adenylate cyclase in a pre-steady state (15-sec) assay. The EC50 after preincubation (6 +/- 1 nM) is within a factor of 2 of the high affinity Kd for [3H]UK 14,304 binding determined under identical conditions (2.7 +/- 0.1 nM). In contrast, in the usual steady state measurements (15 min) or in pre-steady state measurements without agonist preincubation, the EC50 values (74 +/- 1 and 207 +/- 8 nM, respectively) are near the low affinity Kd for [3H]UK 14,304 binding. Reduction of the GTP concentration in steady state adenylate cyclase assays also decreases the EC50 for UK 14,304 from 40 +/- 5 nM at 10 microM GTP to 14 +/- 5 nM with no added GTP. Both sets of experimental observations are accommodated by a complete kinetic model of inhibition in which the high affinity ternary complex of drug, receptor, and G protein leads to the response. Explicit rate parameters are included for agonist binding, receptor-G protein interactions, GTP binding, and hydrolysis. Despite the functional role of the high affinity state of the alpha 2-receptor in this model, the steady state EC50 for agonist-mediated inhibition correlates best with the Kd of low affinity agonist binding in the presence of high levels of GTP. Under conditions in which formation of the high affinity ternary complex is favored, the EC50 for responses approaches the high affinity Kd.", + "title": "Inhibition of adenylate cyclase is mediated by the high affinity conformation of the alpha 2-adrenergic receptor.", + "type": "PubMed ID", + "volume": "34", + "year": 1988 + }, + "publicationId": "BIOMD0000000082", + "submissionId": "MODEL1166069080", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000083": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ashleythomasxavier@gmail.com", + "external": false, + "name": "Ashley Xavier", + "orcid": "0000-0001-5372-3024" + }, + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is model is according to the paperToward a detailed computational model for the mammalian circadian clock

In this model interlocked negative and positive regulation of Per,Cry,Bmal,REV-ERBalpha genes are all involved.The model is actually robust so the initial conditions are unimportant.We gave every entity zero as initial value,and start the graph at time=132h.

The simulation results in figure 8B can be reproduced by roadRunner online and Copasi. We use a ceiling function to simulate the day-light cycle.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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P. 231, B-1050 Brussels, Belgium.", + "authors": [ + { + "institution": "Unit\u00e9 de Chronobiologie Th\u00e9orique, Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "name": "Jean-Christophe Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "Albert Goldbeter" + } + ], + "issue": "12", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/12775757", + "month": "6", + "pages": "7051-7056", + "synopsis": "We present a computational model for the mammalian circadian clock based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, Clock, and Rev-Erb alpha genes. In agreement with experimental observations, the model can give rise to sustained circadian oscillations in continuous darkness, characterized by an antiphase relationship between Per/Cry/Rev-Erbalpha and Bmal1 mRNAs. Sustained oscillations correspond to the rhythms autonomously generated by suprachiasmatic nuclei. For other parameter values, damped oscillations can also be obtained in the model. These oscillations, which transform into sustained oscillations when coupled to a periodic signal, correspond to rhythms produced by peripheral tissues. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark cycles. Simulations show that the phase of the oscillations can then vary by several hours with relatively minor changes in parameter values. Such a lability of the phase could account for physiological disorders related to circadian rhythms in humans, such as advanced or delayed sleep phase syndrome, whereas the lack of entrainment by light-dark cycles can be related to the non-24h sleep-wake syndrome. The model uncovers the possible existence of multiple sources of oscillatory behavior. Thus, in conditions where the indirect negative autoregulation of Per and Cry expression is inoperative, the model indicates the possibility that sustained oscillations might still arise from the negative autoregulation of Bmal1 expression.", + "title": "Toward a detailed computational model for the mammalian circadian clock.", + "type": "PubMed ID", + "volume": "100", + "year": 2003 + }, + "publicationId": "BIOMD0000000083", + "submissionId": "MODEL4710748444", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000084": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

Biomodels Curation The model reproduces the time series depicted in Fig 2 of the paper. Also, by varying the values of Vmax for the second kinase (k5) the time series of X3P as shown in Fig3 can be reproduced. The model was successfully tested on MathSBML and Jarnac.

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for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "Hans V Westerhoff", + "orcid": "0000-0002-0443-6114" + } + ], + "issue": "1", + "journal": "The FEBS journal", + "link": "http://identifiers.org/pubmed/15634347", + "month": "1", + "pages": "244-258", + "synopsis": "General and simple principles are identified that govern signal transduction. The effects of kinase and phosphatase inhibition on a MAP kinase pathway are first examined in silico. Quantitative measures for the control of signal amplitude, duration and integral strength are introduced. We then identify and prove new principles, such that total control on signal amplitude and on final signal strength must amount to zero, and total control on signal duration and on integral signal intensity must equal -1. Collectively, kinases control amplitudes more than duration, whereas phosphatases tend to control both. We illustrate and validate these principles experimentally: (a) a kinase inhibitor affects the amplitude of EGF-induced ERK phosphorylation much more than its duration and (b) a phosphatase inhibitor influences both signal duration and signal amplitude, in particular long after EGF administration. Implications for the cellular decision between growth and differentiation are discussed.", + "title": "Principles behind the multifarious control of signal transduction. ERK phosphorylation and kinase/phosphatase control.", + "type": "PubMed ID", + "volume": "272", + "year": 2005 + }, + "publicationId": "BIOMD0000000084", + "submissionId": "MODEL6623610941", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000085": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper Reduced-order modeling of biochemical networks: application to the GTPase-cycle signalling module by Maurya et al 2006.The figure 4c is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z at various concentrations of R and GAP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ "name": "S J Bornheimer" + }, + { + "name": "V Venkatasubramanian" + }, + { + "name": "S Subramaniam" + } + ], + "issue": "4", + "journal": "Systems biology", + "link": "http://identifiers.org/pubmed/16986265", + "month": "12", + "pages": "229-242", + "synopsis": "Biochemical systems embed complex networks and hence development and analysis of their detailed models pose a challenge for computation. Coarse-grained biochemical models, called reduced-order models (ROMs), consisting of essential biochemical mechanisms are more useful for computational analysis and for studying important features of a biochemical network. The authors present a novel method to model-reduction by identifying potentially important parameters using multidimensional sensitivity analysis. A ROM is generated for the GTPase-cycle module of m1 muscarinic acetylcholine receptor, Gq, and regulator of G-protein signalling 4 (a GTPase-activating protein or GAP) starting from a detailed model of 48 reactions. The resulting ROM has only 17 reactions. The ROM suggested that complexes of G-protein coupled receptor (GPCR) and GAP--which were proposed in the detailed model as a hypothesis--are required to fit the experimental data. Models previously published in the literature are also simulated and compared with the ROM. Through this comparison, a minimal ROM, that also requires complexes of GPCR and GAP, with just 15 parameters is generated. The proposed reduced-order modelling methodology is scalable to larger networks and provides a general framework for the reduction of models of biochemical systems.", + "title": "Reduced-order modelling of biochemical networks: application to the GTPase-cycle signalling module.", + "type": "PubMed ID", + "volume": "152", + "year": 2005 + }, + "publicationId": "BIOMD0000000085", + "submissionId": "MODEL5317679037", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000086": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction by Bornheimer et al 2004.The figure 3 is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z and v at various concentrations of R and GAP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Gilman Drive, La Jolla, CA 92093, USA.", + "name": "Scott J Bornheimer" + }, + { + "name": "Mano R Maurya" + }, + { + "name": "Marilyn Gist Farquhar" + }, + { + "name": "Shankar Subramaniam" + } + ], + "issue": "45", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/15520372", + "month": "11", + "pages": "15899-15904", + "synopsis": "Heterotrimeric G protein signaling is regulated by signaling modules composed of heterotrimeric G proteins, active G protein-coupled receptors (Rs), which activate G proteins, and GTPase-activating proteins (GAPs), which deactivate G proteins. We term these modules GTPase-cycle modules. The local concentrations of these proteins are spatially regulated between plasma membrane microdomains and between the plasma membrane and cytosol, but no data or models are available that quantitatively explain the effect of such regulation on signaling. We present a computational model of the GTPase-cycle module that predicts that the interplay of local G protein, R, and GAP concentrations gives rise to 16 distinct signaling regimes and numerous intermediate signaling phenomena. The regimes suggest alternative modes of the GTPase-cycle module that occur based on defined local concentrations of the component proteins. In one mode, signaling occurs while G protein and receptor are unclustered and GAP eliminates signaling; in another, G protein and receptor are clustered and GAP can rapidly modulate signaling but does not eliminate it. Experimental data from multiple GTPase-cycle modules is interpreted in light of these predictions. The latter mode explains previously paradoxical data in which GAP does not alter maximal current amplitude of G protein-activated ion channels, but hastens signaling. The predictions indicate how variations in local concentrations of the component proteins create GTPase-cycle modules with distinctive phenotypes. They provide a quantitative framework for investigating how regulation of local concentrations of components of the GTPase-cycle module affects signaling.", + "title": "Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction.", + "type": "PubMed ID", + "volume": "101", + "year": 2004 + }, + "publicationId": "BIOMD0000000086", + "submissionId": "MODEL4822989369", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000087": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "C J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "D A Lydall" + }, + { + "name": "R J Boys" + }, + { + "name": "C S Gillespie", + "orcid": "0000-0003-1787-0275" + }, + { + "name": "Daryl P Shanley", + "orcid": "0000-0003-3096-6386" + }, + { + "name": "Darren J Wilkinson", + "orcid": "0000-0003-0736-802X" + }, + { + "name": "T B L Kirkwood" + } + ], + "issue": "12", + "journal": "Journal of the Royal Society, Interface", + "link": "http://identifiers.org/pubmed/17015293", + "month": "2", + "pages": "73-90", + "synopsis": "One of the DNA damage-response mechanisms in budding yeast is temporary cell-cycle arrest while DNA repair takes place. The DNA damage response requires the coordinated interaction between DNA repair and checkpoint pathways. Telomeres of budding yeast are capped by the Cdc13 complex. In the temperature-sensitive cdc13-1 strain, telomeres are unprotected over a specific temperature range leading to activation of the DNA damage response and subsequently cell-cycle arrest. Inactivation of cdc13-1 results in the generation of long regions of single-stranded DNA (ssDNA) and is affected by the activity of various checkpoint proteins and nucleases. This paper describes a mathematical model of how uncapped telomeres in budding yeast initiate the checkpoint pathway leading to cell-cycle arrest. The model was encoded in the Systems Biology Markup Language (SBML) and simulated using the stochastic simulation system Biology of Ageing e-Science Integration and Simulation (BASIS). Each simulation follows the time course of one mother cell keeping track of the number of cell divisions, the level of activity of each of the checkpoint proteins, the activity of nucleases and the amount of ssDNA generated. The model can be used to carry out a variety of in silico experiments in which different genes are knocked out and the results of simulation are compared to experimental data. Possible extensions to the model are also discussed.", + "title": "Modelling the checkpoint response to telomere uncapping in budding yeast.", + "type": "PubMed ID", + "volume": "4", + "year": 2007 + }, + "publicationId": "BIOMD0000000087", + "submissionId": "MODEL8679489165", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000088": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department of Biophysics and Biochemistry, Graduate School of Science, University of Tokyo", + "email": "oza@bi.s.u-tokyo.ac.jp", + "external": false, + "name": "Yu-ichi Ozaki" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 2B, D, F, and 2H. The dynamics correspond to a stimulus of 1 U/ml of thrombin which is equal to 0.01 uM. Phosphorylated MLC is the sum of pMLC (s359) and ppMLC (s360). A slight discrepancy in peak values of species between the figure in the paper and simulation result might be due to different initial conditions in the two sets. The model was successfully tested on MathSBML. It is possible to simulate the model on other software that do not support \"Events\" at this time by removing the \"listOfEvents\" and substituting a value of 0.01 for thrombin (s2). This does not change the model very much. With the latter format, the model was also successfully tested on Copasi.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Science and Technology, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Akio Maeda" + }, + { + "name": "Yu-ichi Ozaki" + }, + { + "name": "Sudhir Sivakumaran", + "orcid": "0000-0002-8586-0469" + }, + { + "name": "Tetsuro Akiyama" + }, + { + "name": "Hidetoshi Urakubo", + "orcid": "0000-0002-1816-0040" + }, + { + "name": "Ayako Usami" + }, + { + "name": "Miharu Sato" + }, + { + "name": "Kozo Kaibuchi" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo 113-0033, Japan. Electronic address: skuroda@bs.s.u-tokyo.ac.jp.", + "name": "Shinya Kuroda", + "orcid": "0000-0001-5059-8299" + } + ], + "issue": "9", + "journal": "Genes to cells : devoted to molecular & cellular mechanisms", + "link": "http://identifiers.org/pubmed/16923126", + "month": "9", + "pages": "1071-1083", + "synopsis": "Sustained contraction of cells depends on sustained Rho-associated kinase (Rho-kinase) activation. We developed a computational model of the Rho-kinase pathway to understand the systems characteristics. Thrombin-dependent in vivo transient responses of Rho activation and Ca2+ increase could be reproduced in silico. Low and high thrombin stimulation induced transient and sustained phosphorylation, respectively, of myosin light chain (MLC) and myosin phosphatase targeting subunit 1 (MYPT1) in vivo. The transient phosphorylation of MLC and MYPT1 could be reproduced in silico, but their sustained phosphorylation could not. This discrepancy between in vivo and in silico in the sustained responses downstream of Rho-kinase indicates that a missing pathway(s) may be responsible for the sustained Rho-kinase activation. We found, experimentally, that the sustained phosphorylation of MLC and MYPT1 exhibit all-or-none responses. Bromoenol lactone, a specific inhibitor of Ca2+ -independent phospholipase A2 (iPLA2), inhibited sustained phosphorylation of MLC and MYPT1, which indicates that sustained Rho-kinase activation requires iPLA2 activity. Thus, the systems analysis of the Rho-kinase pathway identified a novel iPLA2-dependent mechanism of the sustained Rho-kinase activation, which exhibits an all-or-none response.", + "title": "Ca2+ -independent phospholipase A2-dependent sustained Rho-kinase activation exhibits all-or-none response.", + "type": "PubMed ID", + "volume": "11", + "year": 2006 + }, + "publicationId": "BIOMD0000000088", + "submissionId": "MODEL7944007619", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000089": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Nature Publishing Group", + "email": "msbforum@embo.org", + "external": false, + "name": "Molecular Systems Biology" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana.
Locke JC, Kozma-Bogn\u00e1r L, Gould PD, Feh\u00e9r B, Kevei E, Nagy F, Turner MS, Hall A, Millar AJ Mol. Syst. Biol.2006;Volume:2;Page:59 17102804,
Abstract:
Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse.


The model describes a three loops model of the Arabidopsis circadian clock. It provides initial conditions, parameter values and reactions for the production rates of the following species: LHY mRNA (cLm), cytoplasmic LHY (cLc), nuclear LHY (cLn), TOC1 mRNA (cTm), cytoplasmic TOC1 (cTc), nuclear TOC1 (cTn), X mRNA (cXm), cytoplasmic X (cXc), nuclear X (cXn), Y mRNA (cYm), cytoplasmic Y (cYc), nuclear Y (cYn), nuclear P (cPn), APRR7/9 mRNA, cytoplasmic APRR7/9, and nuclear APRR7/9.

The paper describes the behaviour of the model in constant light (LL) and day-night cycle (LD). However, the current model only contains the LL cycle. Some parameter values should be changed from the wild-type (WT) ones in order to simulate the effect of mutations. These changes are listed in the notes of relevant parameters.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"0000-0002-8289-193X" + }, + { + "name": "Peter D Gould", + "orcid": "0000-0002-0709-1190" + }, + { + "name": "Bal\u00e1zs Feh\u00e9r" + }, + { + "name": "Eva Kevei", + "orcid": "0000-0002-0560-9208" + }, + { + "institution": "Institute of Molecular Plant Sciences, The University of Edinburgh, Kings Buildings, Mayfield Road, Edinburgh EH9 3JH, UK.Institute of Plant Biology, Biological Research Center, Temesvari krt. 62, H-6726, Szeged, Hungary.", + "name": "Ferenc Nagy", + "orcid": "0000-0002-6157-9269" + }, + { + "name": "Matthew S Turner" + }, + { + "name": "Anthony Hall", + "orcid": "0000-0002-1806-020X" + }, + { + "name": "Andrew J Millar", + "orcid": "0000-0003-1756-3654" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/17102804", + "month": "0", + "pages": "59", + "synopsis": "Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse.", + "title": "Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000089", + "submissionId": "MODEL4025803561", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000090": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Vienna", + "email": "raim@tbi.univie.ac.at", + "external": false, + "name": "Rainer Machne" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model by Jana Wolf et al. 2001 is the first mechanistic model of respiratory oscillations in Saccharomyces cerevisae. It is based on the assumption that feedback inhibition of cysteine on the sulfate transporters leads to oscillations in this pathway and causes oscillations in respiratory activity via inhibition of cytochrome c oxidase by hydrogen disulfide. The model is qualitative/semi-quantitative and reproduces the respiratory oscillation pattern quite well. It is based on very coarse-grained representations of the mitochondrial tricarboxylic acid cycle and the mitochondrial electron transport chain (oxidative phosphorylation). The sulfate assimilatory pathways also contains some significant simplifcations.

The model corresponds to Fig. 2B of the paper, with a slight phase shift of the oscillations. No initial conditions were given in the paper, and thus they were chosen arbitrarily in a range that lies within the basin of attraction of the limit cycle oscillations. Species IDs correspond to IDs used by the authors, while SBML names are more common abbreviations.

Caveats:
1) Equilibrated transport:
The model assumes fast equilibration between mitochondria and cytoplasm for the metabolites NADH, NAD+, H2S and Acetyl-CoA.
2) Cytosolic mass conservation ATP/ADP:
The model uses mass conservation for cytosolic adenosine nucleotides with is however not encoded in the stoichiometry, but is implied by the lumped reaction v4. This reaction combines the enzymatic reactions of phosphoadenylyl-sulfate reductase (thioredoxin) (yeast protein Met16p, EC 1.8.4.8) and sulfite reductase (NADPH) (subunits Met5p and Met10p, EC 1.8.1.2). EC 1.8.4.8 also has adenosine-3',5'-bismonophosphate (PAP, not to confuse with ID pap in this model, standing for PAPS) as a product. PAP is the substrate for enzyme 3'(2'),5'-bisphosphate nucleotidase (Met22p, EC:3.1.3.7) which would revover AMP (and Pi). Then AMP can be assumed to be equilibrated with ATP and ADP via adenylate kinase, as often used in metabolic models. This AMP production is implied in the mass conservation for cytosolic adenosine phosphates. Accounting for these reactions explicitly does not change the dynamics of the model significantly. An according version can be obtained from the SBML creator (Rainer Machne, mailto:raim@tbi.univie.ac.at).
3) Redox balance:
The enzyme sulfite reductase (NADPH) (subunits Met5p and Met10p, EC 1.8.1.2, part of reaction v4) actually uses NADPH, and the authors assume equilibration of NADH and NADPH. But actually S. cerevisiae specifically is missing the according enzyme transhydrogenase (EC 1.6.1.1 or EC 1.6.1.2). EC 1.8.4.8 also oxidizes thioredoxin and would actually require an additional NADPH for thioredoxin recovery (reduction). This would slightly affect the redox balance of the model.
4) Energy balance:
Reaction v7 lumps NAD-dependent alcohol dehydrogenase (EC 1.1.1.1), aldehyde dehydrogenase (NAD+) (EC 1.2.1.3) and acetyl-CoA synthase (EC 6.2.1.1). The latter reaction would actually consume ATP as a co-factor, producing AMP+PPi, and this is not included in the model. This would slightly bias the model's energy balance.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Proctor2005 - Actions of chaperones and theirrole in ageing

This model is described in the article:

Proctor CJ, Soti C, Boys RJ, Gillespie CS, Shanley DP, Wilkinson DJ, Kirkwood TB.
Mech. Ageing Dev. 2005 Jan; 126(1): 119-131

Abstract:

Many molecular chaperones are also known as heat shock proteins because they are synthesised in increased amounts after brief exposure of cells to elevated temperatures. They have many cellular functions and are involved in the folding of nascent proteins, the re-folding of denatured proteins, the prevention of protein aggregation, and assisting the targeting of proteins for degradation by the proteasome and lysosomes. They also have a role in apoptosis and are involved in modulating signals for immune and inflammatory responses. Stress-induced transcription of heat shock proteins requires the activation of heat shock factor (HSF). Under normal conditions, HSF is bound to heat shock proteins resulting in feedback repression. During stress, cellular proteins undergo denaturation and sequester heat shock proteins bound to HSF, which is then able to become transcriptionally active. The induction of heat shock proteins is impaired with age and there is also a decline in chaperone function. Aberrant/damaged proteins accumulate with age and are implicated in several important age-related conditions (e.g. Alzheimer's disease, Parkinson's disease, and cataract). Therefore, the balance between damaged proteins and available free chaperones may be greatly disturbed during ageing. We have developed a mathematical model to describe the heat shock system. The aim of the model is two-fold: to explore the heat shock system and its implications in ageing; and to demonstrate how to build a model of a biological system using our simulation system (biology of ageing e-science integration and simulation (BASIS)).

This model is hosted on BioModels Database and identified by: BIOMD0000000091.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Biogerontology Research, School of Clinical and Medical Sciences-Gerontology, University of Newcastle, Newcastle upon Tyne NE4 6BE, UK. c.j.proctor@ncl.ac.uk", + "authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "Csaba Soti", + "orcid": "0000-0002-4057-7678" + }, + { + "name": "Richard J Boys" + }, + { + "name": "Colin S Gillespie", + "orcid": "0000-0003-1787-0275" + }, + { + "name": "Daryl P Shanley", + "orcid": "0000-0003-3096-6386" + }, + { + "name": "Darren J Wilkinson", + "orcid": "0000-0003-0736-802X" + }, + { + "name": "Thomas B L Kirkwood" + } + ], + "issue": "1", + "journal": "Mechanisms of ageing and development", + "link": "http://identifiers.org/pubmed/15610770", + "month": "1", + "pages": "119-131", + "synopsis": "Many molecular chaperones are also known as heat shock proteins because they are synthesised in increased amounts after brief exposure of cells to elevated temperatures. They have many cellular functions and are involved in the folding of nascent proteins, the re-folding of denatured proteins, the prevention of protein aggregation, and assisting the targeting of proteins for degradation by the proteasome and lysosomes. They also have a role in apoptosis and are involved in modulating signals for immune and inflammatory responses. Stress-induced transcription of heat shock proteins requires the activation of heat shock factor (HSF). Under normal conditions, HSF is bound to heat shock proteins resulting in feedback repression. During stress, cellular proteins undergo denaturation and sequester heat shock proteins bound to HSF, which is then able to become transcriptionally active. The induction of heat shock proteins is impaired with age and there is also a decline in chaperone function. Aberrant/damaged proteins accumulate with age and are implicated in several important age-related conditions (e.g. Alzheimer's disease, Parkinson's disease, and cataract). Therefore, the balance between damaged proteins and available free chaperones may be greatly disturbed during ageing. We have developed a mathematical model to describe the heat shock system. The aim of the model is two-fold: to explore the heat shock system and its implications in ageing; and to demonstrate how to build a model of a biological system using our simulation system (biology of ageing e-science integration and simulation (BASIS)).", + "title": "Modelling the actions of chaperones and their role in ageing.", + "type": "PubMed ID", + "volume": "126", + "year": 2005 + }, + "publicationId": "BIOMD0000000091", + "submissionId": "MODEL2223638385", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000092": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

.

.

.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

.

.

.

.

.

.

The model reproduces Fig 1A of the paper. The model was successfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

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"http://identifiers.org/biomodels.db/MODEL6623597435" + }, + { + "accession": "BIOMD0000000092", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000092" + }, + { + "accession": "15634334", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/15634334" + }, + { + "accession": "7558", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/7558" + }, + { + "accession": "GO:0031638", + "name": "zymogen activation", + "qualifier": "bqbiol:is", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0031638" + }, + { + "accession": "131567", + "name": "cellular organisms", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/131567" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Fuentes2005_ZymogenActivation", + "publication": { + "accession": "15634334", + "affiliation": "Grupo de Modelizaci\u00f3n en Bioqu\u00edmica, Departamento de Qu\u00edmica-F\u00edsica, Escuela Polit\u00e9cnica Superior de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain.", + "authors": [ + { + "institution": "Grupo de Modelizaci\u00f3n en Bioqu\u00edmica, Departamento de Qu\u00edmica-F\u00edsica, Escuela Polit\u00e9cnica Superior de Albacete, Universidad de Castilla-La Mancha, Albacete, Spain.", + "name": "Matilde Esther Fuentes" + }, + { + "name": "Ram\u00f3n Var\u00f3n" + }, + { + "name": "Manuela Garc\u00eda-Moreno", + "orcid": "0000-0003-4150-1656" + }, + { + "name": "Edelmira Valero", + "orcid": "0000-0001-8636-4574" + } + ], + "issue": "1", + "journal": "The FEBS journal", + "link": "http://identifiers.org/pubmed/15634334", + "month": "1", + "pages": "85-96", + "synopsis": "A mathematical description was made of an autocatalytic zymogen activation mechanism involving both intra- and intermolecular routes. The reversible formation of an active intermediary enzyme-zymogen complex was included in the intermolecular activation route, thus allowing a Michaelis-Menten constant to be defined for the activation of the zymogen towards the active enzyme. Time-concentration equations describing the evolution of the species involved in the system were obtained. In addition, we have derived the corresponding kinetic equations for particular cases of the general model studied. Experimental design and kinetic data analysis procedures to evaluate the kinetic parameters, based on the derived kinetic equations, are suggested. The validity of the results obtained were checked by using simulated progress curves of the species involved. The model is generally good enough to be applied to the experimental kinetic study of the activation of different zymogens of physiological interest. The system is illustrated by following the transformation kinetics of pepsinogen into pepsin.", + "title": "Kinetics of intra- and intermolecular zymogen activation with formation of an enzyme-zymogen complex.", + "type": "PubMed ID", + "volume": "272", + "year": 2005 + }, + "publicationId": "BIOMD0000000092", + "submissionId": "MODEL6623597435", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000093": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": "

NCBS Curation Comments This model shows the control mechanism of Jak-Stat pathway, here SOCS1 (Suppressor of cytokine signaling-I) was identified as the negative regulator of Jak and STAT signal transduction pathway. Note: There are a few ambiguities in the paper like initial concentration of IFN and some reactions were missing in the paper that were employed for obtaining the results. The graphs are almost similar to the graphs as shown in the paper but still some ambiguities regarding the concentration are there. Thanks to Dr Satoshi Yamada for clarifying some of those ambiguities and providing the values used in simulations.

Biomodels Curation Comments The model reproduces Fig 2 (A,C,E,G,I,K,M) of the paper. The set of equations present in the paper are inadequate to reproduce the figures mentioned . The model appears to have been fine tuned after correspondence between the curators at NCBS and the authors. There is however a slight discrepancy between the simulation results and the plots in the paper. The model was tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

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NCBS Curation Comments: This model shows the control mechanism of Jak-Stat pathway, here SOCS1 (Suppressor of cytokine signaling-I) was identified as the negative regulator of Jak and STAT signal transduction pathway. This is the knockout version of Jak-Stat pathway in this model the SOCS1 has been knocked out i.e it formation is not shown. The graphs are almost similar to the graphs as shown in the paper but STAT1n graph has some ambiguities. Thanks to Dr Satoshi Yamada for clarifying some of those ambiguities and providing the values used in simulations.

Biomodels Curation Comments: The model reproduces the figures 2 (B,D,F,H,J,L,N) corresponding to JAK/STAT activation in SOCS1 knock out cells. The model was successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

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The model reproduces the circadian charecteristics as given in Table 1 for the PRR7-PRR9-Y model. The model makes use of the event section to introduce light at 30 hours. The Zeitgeber (ZT) times for species shown in Table 1 can be reproduced by looking at the time it takes for species to reach peak values after the introduction of light. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the time profile of cYm and cTm under light-dark cycles as depicted in Fig 4 and Fig 5 respectively. 12 hour light-dark cycles are accomplished using a simple algorithm in the event section. The model was successfully tested using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the time profile of TOC1 and Y mRNA for a 8:16 cycle as depicted in Fig7A and 7B. A simple algorithm in the event section accomplishes the 8 hour light and 16 hour dark cycle. The model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A Kay" + }, + { + "name": "Francis J Doyle" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/17102803", + "month": "0", + "pages": "58", + "synopsis": "In plants, as in animals, the core mechanism to retain rhythmic gene expression relies on the interaction of multiple feedback loops. In recent years, molecular genetic techniques have revealed a complex network of clock components in Arabidopsis. To gain insight into the dynamics of these interactions, new components need to be integrated into the mathematical model of the plant clock. Our approach accelerates the iterative process of model identification, to incorporate new components, and to systematically test different proposed structural hypotheses. Recent studies indicate that the pseudo-response regulators PRR7 and PRR9 play a key role in the core clock of Arabidopsis. We incorporate PRR7 and PRR9 into an existing model involving the transcription factors TIMING OF CAB (TOC1), LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED (CCA1). We propose candidate models based on experimental hypotheses and identify the computational models with the application of an optimization routine. Validation is accomplished through systematic analysis of various mutant phenotypes. We introduce and apply sensitivity analysis as a novel tool for analyzing and distinguishing the characteristics of proposed architectures, which also allows for further validation of the hypothesized structures.", + "title": "A novel computational model of the circadian clock in Arabidopsis that incorporates PRR7 and PRR9.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000097", + "submissionId": "MODEL4025650992", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000098": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profile of cytosolic and intracellular calcium as depicted in the upper panel of Fig 2 in the paper. The model was successfully tested on MathSBML and Jarnac.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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+ "name": "A Goldbeter" + }, + { + "name": "G Dupont" + }, + { + "name": "M J Berridge" + } + ], + "issue": "4", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/2304911", + "month": "2", + "pages": "1461-1465", + "synopsis": "In a variety of cells, hormonal or neurotransmitter signals elicit a train of intracellular Ca2+ spikes. The analysis of a minimal model based on Ca2(+)-induced Ca2+ release from intracellular stores shows how sustained oscillations of cytosolic Ca2+ may develop as a result of a rise in inositol 1,4,5-trisphosphate (InsP3) triggered by external stimulation. This rise elicits the release of a certain amount of Ca2+ from an InsP3-sensitive intracellular store. The subsequent rise in cytosolic Ca2+ in turn triggers the release of Ca2+ from a second store insensitive to InsP3. In contrast to the model proposed by Meyer and Stryer [Meyer, T. & Stryer, L. (1988) Proc. Natl. Acad. Sci. USA 85, 5051-5055], the present model, which contains only two variables, predicts the occurrence of periodic Ca2+ spikes in the absence of InsP3 oscillations. Such results indicate that repetitive Ca2+ spikes evoked by external stimuli do not necessarily require the concomitant, periodic variation of InsP3. The model is closely related to that proposed by Kuba and Takeshita [Kuba, K. & Takeshita, S. (1981) J. Theor. Biol. 93, 1009-1031] for Ca2+ oscillations in sympathetic neurones, based on Ca2(+)-induced Ca2+ release. We extend their results by showing the minimal conditions in which the latter process gives rise to periodic behavior and take into account the role of the rise in InsP3 caused by external stimulation. The analysis further shows how signal-induced Ca2+ oscillations might be effectively encoded in terms of their frequency through the phosphorylation of a cellular substrate by a protein kinase activated by cytosolic Ca2+.", + "title": "Minimal model for signal-induced Ca2+ oscillations and for their frequency encoding through protein phosphorylation.", + "type": "PubMed ID", + "volume": "87", + "year": 1990 + }, + "publicationId": "BIOMD0000000098", + "submissionId": "MODEL4519937652", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000099": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is model according to the paper \"A Molecular Network That Produces Spontaneous Oscillations in Excitalbe Cells of Dictyostelium. Figure 3 has been reproduced by Copasi 4.0.20(development) \". However four of the parameters have been changed , see details in notes.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"name": "SBML", + "version": "L2V1" + }, + "history": { + "revisions": [ + { + "comment": "Original import of Laub1998_SpontaneousOscillations", + "submitted": 1174521151, + "submitter": "Enuo He", + "version": 1 + }, + { + "comment": "Current version of Laub1998_SpontaneousOscillations", + "submitted": 1424867701, + "submitter": "Enuo He", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724263789, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL4515710981", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL4515710981" + }, + { + "accession": "BIOMD0000000099", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000099" + }, + { + "accession": "9843585", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": 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Diego, La Jolla, California 92093, USA.", + "authors": [ + { + "institution": "Center for Molecular Genetics, Department of Biology, University of California, San Diego, La Jolla, California 92093, USA.", + "name": "M T Laub", + "orcid": "0000-0002-8288-7607" + }, + { + "name": "W F Loomis" + } + ], + "issue": "12", + "journal": "Molecular biology of the cell", + "link": "http://identifiers.org/pubmed/9843585", + "month": "12", + "pages": "3521-3532", + "synopsis": "A network of interacting proteins has been found that can account for the spontaneous oscillations in adenylyl cyclase activity that are observed in homogenous populations of Dictyostelium cells 4 h after the initiation of development. Previous biochemical assays have shown that when extracellular adenosine 3',5'-cyclic monophosphate (cAMP) binds to the surface receptor CAR1, adenylyl cyclase and the MAP kinase ERK2 are transiently activated. A rise in the internal concentration of cAMP activates protein kinase A such that it inhibits ERK2 and leads to a loss-of-ligand binding by CAR1. ERK2 phosphorylates the cAMP phosphodiesterase REG A that reduces the internal concentration of cAMP. A secreted phosphodiesterase reduces external cAMP concentrations between pulses. Numerical solutions to a series of nonlinear differential equations describing these activities faithfully account for the observed periodic changes in cAMP. The activity of each of the components is necessary for the network to generate oscillatory behavior; however, the model is robust in that 25-fold changes in the kinetic constants linking the activities have only minor effects on the predicted frequency. Moreover, constant high levels of external cAMP lead to attenuation, whereas a brief pulse of cAMP can advance or delay the phase such that interacting cells become entrained.", + "title": "A molecular network that produces spontaneous oscillations in excitable cells of Dictyostelium.", + "type": "PubMed ID", + "volume": "9", + "year": 1998 + }, + "publicationId": "BIOMD0000000099", + "submissionId": "MODEL4515710981", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000100": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the temporal evolution of Glycogen phosphorylase for a vale of Vm5=30 as depicted in Fig 1a of the paper. The model makes use of calcium oscillations from the Borghans model to stimulate the activation of glycogen phosphorylase. Hence, this is a simple extension of the Borghans model. The model was succesfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces Fig 5A of the paper. The ligand concentration is increased from 3E-5 to 0.01 at time t=2500 to ensure that the system reaches steady state. Hence, the time t=0 of the paper corresponds to t=2500 in the model. The peak value of the active ligand receptor complex is off by a value of 1.25, the authors have stated that this discrepancy is due to the fact that the figure in the paper corresponds to a slightly different parameter set. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces active Caspase-3 time profile corresponding to the total Apaf-1 value of 20 nM as depicted in Fig 2-A . The model was successfully tested on MathSBML.

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Bl\u00fcthgen", + "orcid": "0000-0002-0171-7447" + }, + { + "name": "Hanspeter Herzel" + } + ], + "issue": "9", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/16978046", + "month": "9", + "pages": "e120", + "synopsis": "The intrinsic, or mitochondrial, pathway of caspase activation is essential for apoptosis induction by various stimuli including cytotoxic stress. 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Finally, known regulators of the pathway are shown to efficiently shift the apoptotic threshold stimulus, suggesting that the bistable caspase cascade computes multiple inputs into an all-or-none caspase output. As cellular inhibitory proteins (e.g., IAPs) frequently inhibit consecutive intermediates in cellular signaling cascades (e.g., Casp3 and Casp9), the feedback mechanism described in this paper is likely to be a widespread principle on how cells achieve ultrasensitivity, bistability, and irreversibility.", + "title": "Mathematical modeling identifies inhibitors of apoptosis as mediators of positive feedback and bistability.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000102", + "submissionId": "MODEL8567576821", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000103": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model represents the non-competitive binding of XIAP to Casapase-3 and Caspase-9. In other words, XIAP mediated feedback is abolished in this model. The authors state that this leads to bistable-reversible behaviour as depicted in Fig 4C. The wild-type model displays a bistable-irreversible profile. This shows that irreversibility requires XIAP mediated feedback. The model was tested on MathSBML. However, please note that the paper does not contain any figure that corresponds to simulation of the Non-Competitive model.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Klipp2002_MetabolicOptimization_linearPathway(n=2)

The model describes time dependent gene expression as a means to enable cells to adapt metabolic activity optimally based on environmental conditions. It uses a simple unbranched pathway and a constraint of fixed total enzyme. It calculates enzyme profiles at different times which optimise a performance function, and compares them to experimental data. The initial model is cell-type agnostic, while the experimeental data is from yeast.

This model is described in the article:

Klipp E, Heinrich R, Holzh\u00fctter HG.
Eur. J. Biochem. 2002 Nov; 269(22): 5406-5413

Abstract:

A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time- dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.

This model is from the paper Prediction of temporal gene expression metabolic optimization by re-distribution of enzyme activities. The model describes optimal enzyme profiles and metabolite time courses for a simple linear metabolic pathway (n=2). Figure 1 was reproduced using roadRunner. The values of k1 and k2 were not explicitly stated in the publication, but calculations were performed for equal catalytic efficiencies of the enzymes (ki=k), hence the curator assigned k1=k2=1. Also enzyme concentrations are given in units of Etot; times are given in units of 1/(k*Etot) in the papaer, for simplicity , we use defalut units of the SBML to present the concentration and time.

This model is hosted on BioModels Database and identifiedby: MODEL4931762955 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time-dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.", + "title": "Prediction of temporal gene expression. Metabolic opimization by re-distribution of enzyme activities.", + "type": "PubMed ID", + "volume": "269", + "year": 2002 + }, + "publicationId": "BIOMD0000000104", + "submissionId": "MODEL4931762955", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000105": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system

This is a stochastic model of the ubiquitin-proteasome system for a generic pool of native proteins (NatP), which have a half-life of about 10 hours under normal conditions. It is assumed that these proteins are only degraded after they have lost their native structure due to a damage event. This is represented in the model by the misfolding reaction which depends on the level of reactive oxygen species (ROS) in the cell. Misfolded proteins (MisP) are first bound by an E3 ubiquitin ligase. Ubiquitin (Ub) is activated by E1 (ubiquitin-activating enzyme) and then passed to E2 (ubiquitin-conjugating enzyme). The E2 enzyme then passes the ubiquitin molecule to the E3/MisP complex with the net effect that the misfolded protein is monoubiquitinated and both E2 and E3 are released. Further ubiquitin molecules are added in a step-wise manner. When the chain of ubiquitin molecules is of length 4 or more, the polyubiquitinated misfolded protein may bind to the proteasome. The model also includes de-ubiquitinating enzymes (DUB) which cleave ubiquitin molecules from the chain in a step-wise manner. They work on chains attached to misfolded proteins both unbound and bound to the proteasomes. Misfolded proteins bound to the proteasome may be degraded releasing ubiquitin. Misfolded proteins including ubiquitinated proteins may also aggregate. Aggregates (AggP) may be sequestered (Seq_AggP) which takes them out of harm's way or they may bind to the proteasome (AggP_Proteasome). Proteasomes bound by aggregates are no longer available for protein degradation.

Figure 2 and Figure 3 has been simulated using Gillespie2.

This model is described in the article:

Proctor CJ, Tsirigotis M, Gray DA.
BMC Syst Biol 2007; 1: 17

Abstract:

BACKGROUND: The ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation. RESULTS: The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics. CONCLUSION: We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process.

This model is hosted on BioModels Database and identified by: BIOMD0000000105.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Had to change the qualifier 'isDerivedFrom' to anything else since this qualifier results in a bug in COPASI's annotations.", + "submitted": 1525429925, + "submitter": "administrator", + "version": 3 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724263950, + "submitter": "Lucian Smith", + "version": 4 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL6540028317", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL6540028317" + }, + { + "accession": "BIOMD0000000105", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000105" + }, + { + "accession": "17408507", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/17408507" + }, + { + "accession": "BIOMD0000000091", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000091" + }, + { + "accession": "DOID:1289", + "name": "neurodegenerative disease", + "qualifier": "bqbiol:hasProperty", + "resource": "Human Disease Ontology", + "uri": "http://identifiers.org/doid/DOID:1289" + }, + { + "accession": "GO:0043161", + "name": "proteasome-mediated ubiquitin-dependent protein catabolic process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0043161" + }, + { + "accession": "2759", + "name": "Eukaryota", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/2759" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system", + "publication": { + "accession": "17408507", + "affiliation": "School of Clinical and Medical Sciences-Gerontology, Newcastle University, UK. c.j.proctor@ncl.ac.uk", + "authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "Maria Tsirigotis" + }, + { + "name": "Douglas A Gray", + "orcid": "0000-0002-8634-4984" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/17408507", + "month": "3", + "pages": "17", + "synopsis": "

Background

The ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation.

Results

The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics.

Conclusion

We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process.", + "title": "An in silico model of the ubiquitin-proteasome system that incorporates normal homeostasis and age-related decline.", + "type": "PubMed ID", + "volume": "1", + "year": 2007 + }, + "publicationId": "BIOMD0000000105", + "submissionId": "MODEL6540028317", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000106": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Beijing National Laboratory for Molecular Sciences", + "email": "hlai@pku.edu.cn", + "external": false, + "name": "Kun Yang" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper Dynamic Simulation on the Arachidonic Acid Metabolic Network . Figure 2A has been reproduced by SBML ode solver on line. In the original model, all the reactions are presented as ODE directly. So curator rewrite each reaction according to the semantics of the paper. In this paper, the authors used quict complex kinetics law to describe the catalysis in the network, curators did not necessarily know all the complete meanings of the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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}, + { + "accession": "GO:0019369", + "name": "arachidonic acid metabolic process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0019369" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Yang2007_ArachidonicAcid", + "publication": { + "accession": "17381237", + "affiliation": "Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.", + "authors": [ + { + "institution": "Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.", + "name": "Kun Yang", + "orcid": "0000-0002-1060-8082" + }, + { + "name": "Wenzhe Ma" + }, + { + "name": "Huanhuan Liang" + }, + { + "name": "Qi Ouyang" + }, + { + "name": "Tang C", + "orcid": "0000-0003-1474-3705" + }, + { + "name": "Luhua Lai" + } + ], + "issue": "3", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/17381237", + "month": "3", + "pages": "e55", + "synopsis": "Drug molecules not only interact with specific targets, but also alter the state and function of the associated biological network. How to design drugs and evaluate their functions at the systems level becomes a key issue in highly efficient and low-side-effect drug design. The arachidonic acid metabolic network is the network that produces inflammatory mediators, in which several enzymes, including cyclooxygenase-2 (COX-2), have been used as targets for anti-inflammatory drugs. However, neither the century-old nonsteriodal anti-inflammatory drugs nor the recently revocatory Vioxx have provided completely successful anti-inflammatory treatment. To gain more insights into the anti-inflammatory drug design, the authors have studied the dynamic properties of arachidonic acid (AA) metabolic network in human polymorphous leukocytes. Metabolic flux, exogenous AA effects, and drug efficacy have been analyzed using ordinary differential equations. The flux balance in the AA network was found to be important for efficient and safe drug design. When only the 5-lipoxygenase (5-LOX) inhibitor was used, the flux of the COX-2 pathway was increased significantly, showing that a single functional inhibitor cannot effectively control the production of inflammatory mediators. When both COX-2 and 5-LOX were blocked, the production of inflammatory mediators could be completely shut off. The authors have also investigated the differences between a dual-functional COX-2 and 5-LOX inhibitor and a mixture of these two types of inhibitors. Their work provides an example for the integration of systems biology and drug discovery.", + "title": "Dynamic simulations on the arachidonic acid metabolic network.", + "type": "PubMed ID", + "volume": "3", + "year": 2007 + }, + "publicationId": "BIOMD0000000106", + "submissionId": "MODEL8610058649", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000107": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Novak1993 - Cell cycle M-phase control
The model reproduces Figure 9 of the paper. Please note that active MPF and cyclin concentrations in the paper are given relative to total cdc2 concentration (100nM). Active MPF (dimer_p) is the cyclin-cdc2 complex that is phosphorylated at Thr161. The earlier versions of the model was successfully tested on MathSBML and Jarnac, and the current version was checked in Copasi.

This model is described in the article:

Novak B, Tyson JJ.
J. Cell. Sci. 1993 Dec; 106 ( Pt 4): 1153-1168

Abstract:

To contribute to a deeper understanding of M-phase control in eukaryotic cells, we have constructed a model based on the biochemistry of M-phase promoting factor (MPF) in Xenopus oocyte extracts, where there is evidence for two positive feedback loops (MPF stimulates its own production by activating Cdc25 and inhibiting Wee1) and a negative feedback loop (MPF stimulates its own destruction by indirectly activating the ubiquitin pathway that degrades its cyclin subunit). To uncover the full dynamical possibilities of the control system, we translate the regulatory network into a set of differential equations and study these equations by graphical techniques and computer simulation. The positive feedback loops in the model account for thresholds and time lags in cyclin-induced and MPF-induced activation of MPF, and the model can be fitted quantitatively to these experimental observations. The negative feedback loop is consistent with observed time lags in MPF-induced cyclin degradation. Furthermore, our model indicates that there are two possible mechanisms for autonomous oscillations. One is driven by the positive feedback loops, resulting in phosphorylation and abrupt dephosphorylation of the Cdc2 subunit at an inhibitory tyrosine residue. These oscillations are typical of oocyte extracts. The other type is driven by the negative feedback loop, involving rapid cyclin turnover and negligible phosphorylation of the tyrosine residue of Cdc2. The early mitotic cycles of intact embryos exhibit such characteristics. In addition, by assuming that unreplicated DNA interferes with M-phase initiation by activating the phosphatases that oppose MPF in the positive feedback loops, we can simulate the effect of addition of sperm nuclei to oocyte extracts, and the lengthening of cycle times at the mid-blastula transition of intact embryos.

This model is hosted on BioModels Database and identified by: BIOMD0000000107.

To cite BioModels Database, please use: BioModels: ten-year anniversary .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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degrades its cyclin subunit). To uncover the full dynamical possibilities of the control system, we translate the regulatory network into a set of differential equations and study these equations by graphical techniques and computer simulation. The positive feedback loops in the model account for thresholds and time lags in cyclin-induced and MPF-induced activation of MPF, and the model can be fitted quantitatively to these experimental observations. The negative feedback loop is consistent with observed time lags in MPF-induced cyclin degradation. Furthermore, our model indicates that there are two possible mechanisms for autonomous oscillations. One is driven by the positive feedback loops, resulting in phosphorylation and abrupt dephosphorylation of the Cdc2 subunit at an inhibitory tyrosine residue. These oscillations are typical of oocyte extracts. The other type is driven by the negative feedback loop, involving rapid cyclin turnover and negligible phosphorylation of the tyrosine residue of Cdc2. The early mitotic cycles of intact embryos exhibit such characteristics. In addition, by assuming that unreplicated DNA interferes with M-phase initiation by activating the phosphatases that oppose MPF in the positive feedback loops, we can simulate the effect of addition of sperm nuclei to oocyte extracts, and the lengthening of cycle times at the mid-blastula transition of intact embryos.", + "title": "Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos.", + "type": "PubMed ID", + "volume": "106 ( Pt 4)", + "year": 1993 + }, + "publicationId": "BIOMD0000000107", + "submissionId": "MODEL5876465734", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000108": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper from Axel Kowald Alternative pathways as mechanism for the negative effects associated with overexpression of superoxide dismutase.

Reactions from 1 to 17 are listed in the paper, note that for clarity species whose concentrations are assumed to be constant (e.g.water, oxygen,protons, metal ions) are omitted from the diagram. In the paper, v16 is a fast reaction, but we do not use fast reaction in the model.

Figure2 has been reproduced by both SBMLodeSolver and Copasi4.0.20(development) . Figure 3 has been obtained with Copasi4.0.20(development) using parameter scan.

The steady-state of [LOO*] a little bit lower than showed on the paper, I guess it may be the simulation method used in the paper use fast reaction and also the reaction (5) listed on Page 831 on the paper is slightly different from equation (2) on Page 832. The rest of them are the quite the same.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The enzyme plays an important role in diseases like trisomy 21 and also in theories of the mechanisms of aging. But instead of being beneficial, intensified oxidative stress is associated with the increased expression of SOD and also studies on bacteria and transgenic animals show that high levels of SOD actually lead to increased lipid peroxidation and hypersensitivity to oxidative stress. Using mathematical models we investigate the question how overexpression of SOD can lead to increased oxidative stress, although it is an antioxidant enzyme. We consider the following possibilities that have been proposed in the literature: (i) Reaction of H(2)O(2) with CuZnSOD leading to hydroxyl radical formation. (ii) Superoxide radicals might reduce membrane damage by acting as radical chain breaker. (iii) While detoxifying superoxide radicals SOD cycles between a reduced and oxidized state. At low superoxide levels the intermediates might interact with other redox partners and increase the superoxide reductase (SOR) activity of SOD. This short-circuiting of the SOD cycle could lead to an increased hydrogen peroxide production. We find that only one of the proposed mechanisms is under certain circumstances able to explain the increased oxidative stress caused by SOD. But furthermore we identified an additional mechanism that is of more general nature and might be a common basis for the experimental findings. We call it the alternative pathway mechanism.", + "title": "Alternative pathways as mechanism for the negative effects associated with overexpression of superoxide dismutase.", + "type": "PubMed ID", + "volume": "238", + "year": 2006 + }, + "publicationId": "BIOMD0000000108", + "submissionId": "MODEL5128825728", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000109": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper A systems biology dynamical model of mammalian G1 cell cycle progression. Supplementary Figure 2A has been reproduced by the MathSBML and CellDesigner. All the data of this model are from the set 2 of Supplementary talbe2.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ { + "name": "Naohisa Yoshioka" + }, + { + "name": "Anjali Dhiman" + }, + { + "name": "Robert Miller" + }, + { + "name": "Rina Gendelman" + }, + { + "name": "Sergej V Aksenov", + "orcid": "0000-0001-6390-9850" + }, + { + "name": "Iya G Khalil" + }, + { + "name": "Steven F Dowdy" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/17299420", + "month": "0", + "pages": "84", + "synopsis": "The current dogma of G(1) cell-cycle progression relies on growth factor-induced increase of cyclin D:Cdk4/6 complex activity to partially inactivate pRb by phosphorylation and to sequester p27(Kip1)-triggering activation of cyclin E:Cdk2 complexes that further inactivate pRb. pRb oscillates between an active, hypophosphorylated form associated with E2F transcription factors in early G(1) phase and an inactive, hyperphosphorylated form in late G(1), S and G(2)/M phases. However, under constant growth factor stimulation, cells show constitutively active cyclin D:Cdk4/6 throughout the cell cycle and thereby exclude cyclin D:Cdk4/6 inactivation of pRb. To address this paradox, we developed a mathematical model of G(1) progression using physiological expression and activity profiles from synchronized cells exposed to constant growth factors and included a metabolically responsive, activating modifier of cyclin E:Cdk2. Our mathematical model accurately simulates G(1) progression, recapitulates observations from targeted gene deletion studies and serves as a foundation for development of therapeutics targeting G(1) cell-cycle progression.", + "title": "A systems biology dynamical model of mammalian G1 cell cycle progression.", + "type": "PubMed ID", + "volume": "3", + "year": 2007 + }, + "publicationId": "BIOMD0000000109", + "submissionId": "MODEL3734058719", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000110": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Dynamics of the cell cycle: checkpoints, sizers, and timers.
\t Qu Z, MacLellan WR, Weiss JN Biophys. J.2003 Dec; 85(6): 3600-11 14645053,
Abstract:
We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings.


Figure6B has been reproduced by both SBMLodeSolver online and MathSBML. We do not include the synthesis of cyclins is proportional to cell size (Equation 2 in Page3604 of the paper) in this model. The author of the paper keep all the variables and parameters dimensionless. But in the model, we choose to use default units of SBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Weiss" + } + ], + "issue": "6", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/14645053", + "month": "12", + "pages": "3600-3611", + "synopsis": "We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings.", + "title": "Dynamics of the cell cycle: checkpoints, sizers, and timers.", + "type": "PubMed ID", + "volume": "85", + "year": 2003 + }, + "publicationId": "BIOMD0000000110", + "submissionId": "MODEL6931308526", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000111": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time evolution of several species as depicted in Fig 4 of the paper. Events have been used to reset cell mass when the value of M-phase promoting factor (MPF) decreases through 0.1. The model was successfully tested on Cell Designer.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Tyson" + } + ], + "issue": "1", + "journal": "Chaos (Woodbury, N.Y.)", + "link": "http://identifiers.org/pubmed/12779461", + "month": "3", + "pages": "277-286", + "synopsis": "Much is known about the genes and proteins controlling the cell cycle of fission yeast. Can these molecular components be spun together into a consistent mechanism that accounts for the observed behavior of growth and division in fission yeast cells? To answer this question, we propose a mechanism for the control system, convert it into a set of 14 differential and algebraic equations, study these equations by numerical simulation and bifurcation theory, and compare our results to the physiology of wild-type and mutant cells. In wild-type cells, progress through the cell cycle (G1-->S-->G2-->M) is related to cyclic progression around a hysteresis loop, driven by cell growth and chromosome alignment on the metaphase plate. However, the control system operates much differently in double-mutant cells, wee1(-) cdc25Delta, which are defective in progress through the latter half of the cell cycle (G2 and M phases). These cells exhibit \"quantized\" cycles (interdivision times clustering around 90, 160, and 230 min). We show that these quantized cycles are associated with a supercritical Hopf bifurcation in the mechanism, when the wee1 and cdc25 genes are disabled. (c) 2001 American Institute of Physics.", + "title": "Mathematical model of the cell division cycle of fission yeast.", + "type": "PubMed ID", + "volume": "11", + "year": 2001 + }, + "publicationId": "BIOMD0000000111", + "submissionId": "MODEL6488296959", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000112": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Hertfordshire", + "email": "k.wegner@herts.ac.uk", + "external": false, + "name": "Katja Wegner" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the temporal evolution of four variables depicted in Fig 2a. The solution is generated for median parameter values as given in Table 3. Result shown was generated by MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Boulder, CO 80309, USA.", + "name": "D C Clarke", + "orcid": "0000-0002-1520-5426" + }, + { + "name": "M D Betterton", + "orcid": "0000-0002-5430-5518" + }, + { + "name": "Xuedong Liu", + "orcid": "0000-0001-7209-4964" + } + ], + "issue": "6", + "journal": "Systems biology", + "link": "http://identifiers.org/pubmed/17186703", + "month": "11", + "pages": "412-424", + "synopsis": "Transforming growth factor-beta (TGFbeta) signalling is an important regulator of cellular growth and differentiation. The principal intracellular mediators of TGFbeta signalling are the Smad proteins, which upon TGFbeta stimulation accumulate in the nucleus and regulate the transcription of target genes. To investigate the mechanisms of Smad nuclear accumulation, we developed a simple mathematical model of canonical Smad signalling. The model was built using both published data and our experimentally determined cellular Smad concentrations (isoforms 2, 3 and 4). We found in mink lung epithelial cells that Smad2 (8.5-12 x 10(4) molecules cell(-1)) was present in similar amounts to Smad4 (9.3-12 x 10(4) molecules cell(-1)), whereas both were in excess of Smad3 (1.1-2.0 x 10(4) molecules cell(-1)). Variation of the model parameters and statistical analysis showed that Smad nuclear accumulation is most sensitive to parameters affecting the rates of R-Smad phosphorylation and dephosphorylation and Smad complex formation/ dissociation in the nucleus. Deleting Smad4 from the model revealed that rate-limiting phospho-R-Smad dephosphorylation could be an important mechanism for Smad nuclear accumulation. Furthermore, we observed that binding factors constitutively localised to the nucleus do not efficiently mediate Smad nuclear accumulation, if dephosphorylation is rapid. We therefore conclude that an imbalance in the rates of R-Smad phosphorylation and dephosphorylation is likely an important mechanism of Smad nuclear accumulation during TGFbeta signalling.", + "title": "Systems theory of Smad signalling.", + "type": "PubMed ID", + "volume": "153", + "year": 2006 + }, + "publicationId": "BIOMD0000000112", + "submissionId": "MODEL4024559990", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000113": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Model reproduces Fig 4 of the paper. For fraction of phosphorylated protein, W_star, the model reproduces panel b in the same figure. Model successfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Among these responses, we focus on those involving protein phosphorylation. We examine, by numerical simulations of a theoretical model, the situation where a protein is phosphorylated by a Ca(2+)-activated kinase and dephosphorylated by a phosphatase. This reversible phosphorylation system is coupled to a mechanism generating cytosolic Ca2+ oscillations; for definiteness, this oscillatory mechanism is based on the process of Ca(2+)-induced Ca2+ release. The analysis shows that the average fraction of phosphorylated protein increases with the frequency of repetitive Ca2+ spikes; the latter frequency generally rises with the extent of external stimulation. Protein phosphorylation therefore provides a mechanism for the encoding of the external stimulation in terms of the frequency of signal-induced Ca2+ oscillations. Such a frequency encoding requires precise kinetic conditions on the Michaelis-Menten constants of the kinase and phosphatase, their maximal rates, and the degree of cooperativity in kinase activation by Ca2+. In particular, the most efficient encoding of Ca2+ oscillations based on protein phosphorylation occurs in conditions of zero-order ultrasensitivity, when the kinase and phosphatase are saturated by their protein substrate. The kinetic analysis uncovers a wide variety of temporal patterns of phosphorylation that could be driven by signal-induced Ca2+ oscillations.", + "title": "Protein phosphorylation driven by intracellular calcium oscillations: a kinetic analysis.", + "type": "PubMed ID", + "volume": "42", + "year": 1992 + }, + "publicationId": "BIOMD0000000113", + "submissionId": "MODEL7007141507", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000114": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model encoded according to the paper Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simple One Pool Model. The values of parameters a and alpha are varioused inorder to simulate results in different situations. For Figure 3A, a=3.5,alpha=1.2 ; Figure 3B, a=3,alpha=5 ; Figure 3C a= 0.95, alpha=1.5; Figure3D, a=1, alpha=5. Keep in mind that the value for the xy axies are arbitrary value. Figures3 in the paper are reproduced by COPASI 4.0.20(development) , and SBMLodeSolver online.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"CRBM-sponsored manual and automated updates.", + "submitted": 1724264203, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "GO:0019722", + "name": "calcium-mediated signaling", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0019722" + }, + { + "accession": "10114", + "name": "Rattus", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/10114" + }, + { + "accession": "MODEL8907365389", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL8907365389" + }, + { + "accession": "BIOMD0000000114", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000114" + }, + { + "accession": "1904060", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": 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Bern, Switzerland.", + "authors": [ + { + "institution": "Pharmakologisches Institut, Universit\u00e4t Bern, Switzerland.", + "name": "R Somogyi" + }, + { + "name": "J W Stucki" + } + ], + "issue": "17", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/1904060", + "month": "6", + "pages": "11068-11077", + "synopsis": "Hormone-induced oscillations of the free intracellular calcium concentration are thought to be relevant for frequency encoding of hormone signals. In liver cells, such Ca2+ oscillations occur in response to stimulation by hormones acting via phosphoinositide breakdown. This observation may be explained by cooperative, positive feedback of Ca2+ on its own release from one inositol 1,4,5-trisphosphate-sensitive pool, obviating oscillations of inositol 1,4,5-trisphosphate. The kinetic rate laws of the associated model have a mathematical structure reminiscent of the Brusselator, a hypothetical chemical model involving a rather improbable trimolecular reaction step, thus giving a realistic biological interpretation to this hallmark of dissipative structures. We propose that calmodulin is involved in mediating this cooperativity and positive feedback, as suggested by the presented experiments. For one, hormone-induced calcium oscillations can be inhibited by the (nonphenothiazine) calmodulin antagonists calmidazolium or CGS 9343 B. Alternatively, in cells overstimulated by hormone, as characterized by a non-oscillatory elevated Ca2+ concentration, these antagonists could again restore sustained calcium oscillations. The experimental observations, including modulation of the oscillations by extracellular calcium, were in qualitative agreement with the predictions of our mathematical model.", + "title": "Hormone-induced calcium oscillations in liver cells can be explained by a simple one pool model.", + "type": "PubMed ID", + "volume": "266", + "year": 1991 + }, + "publicationId": "BIOMD0000000114", + "submissionId": "MODEL8907365389", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000115": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Another model from Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simply One Pool Model. Anatomy of a single Ca2+ spike. Figure4A has been simulated by COPASI4.0.20(development). However, the simulated figure is slightly different from the paper, single spike of Ca2+ is around \"6\" time arbitrary units instead \"9\" time arbitrary units displayed in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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He", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724264227, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "GO:0019722", + "name": "calcium-mediated signaling", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0019722" + }, + { + "accession": "10114", + "name": "Rattus", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/10114" + }, + { + "accession": "1904060", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/1904060" + }, + { + "accession": "GO:0051924", + "name": "regulation of calcium ion transport", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0051924" + }, + { + "accession": "GO:0009755", + "name": "hormone-mediated signaling pathway", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0009755" + }, + { + "accession": "MODEL8910960676", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL8910960676" + }, + { + "accession": "BIOMD0000000115", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000115" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Somogyi1990_CaOscillations_SingleCaSpike", + "publication": { + "accession": "1904060", + "affiliation": "Pharmakologisches Institut, Universit\u00e4t Bern, Switzerland.", + "authors": [ + { + "institution": "Pharmakologisches Institut, Universit\u00e4t Bern, Switzerland.", + "name": "R Somogyi" + }, + { + "name": "J W Stucki" + } + ], + "issue": "17", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/1904060", + "month": "6", + "pages": "11068-11077", + "synopsis": "Hormone-induced oscillations of the free intracellular calcium concentration are thought to be relevant for frequency encoding of hormone signals. In liver cells, such Ca2+ oscillations occur in response to stimulation by hormones acting via phosphoinositide breakdown. This observation may be explained by cooperative, positive feedback of Ca2+ on its own release from one inositol 1,4,5-trisphosphate-sensitive pool, obviating oscillations of inositol 1,4,5-trisphosphate. The kinetic rate laws of the associated model have a mathematical structure reminiscent of the Brusselator, a hypothetical chemical model involving a rather improbable trimolecular reaction step, thus giving a realistic biological interpretation to this hallmark of dissipative structures. We propose that calmodulin is involved in mediating this cooperativity and positive feedback, as suggested by the presented experiments. For one, hormone-induced calcium oscillations can be inhibited by the (nonphenothiazine) calmodulin antagonists calmidazolium or CGS 9343 B. Alternatively, in cells overstimulated by hormone, as characterized by a non-oscillatory elevated Ca2+ concentration, these antagonists could again restore sustained calcium oscillations. The experimental observations, including modulation of the oscillations by extracellular calcium, were in qualitative agreement with the predictions of our mathematical model.", + "title": "Hormone-induced calcium oscillations in liver cells can be explained by a simple one pool model.", + "type": "PubMed ID", + "volume": "266", + "year": 1991 + }, + "publicationId": "BIOMD0000000115", + "submissionId": "MODEL8910960676", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000116": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max Planck Institute For Molecular Genetics", + "email": "christian.waltermann@molgen.mpg.de", + "external": false, + "name": "Christian Waltermann" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model encoded according to the paper Cross-talk and decision making in MAP kinase pathways. Supplementary Figure 2 has been reproduced by COPASI4.0.20 (development) using parameter scan method. You probably need to uncheck \"always use initial conditions\" in copasi when you simulate for the second run in order to get the figure. S1 scale from 0 to 12. Keep in mind that the y axis is the fractions of excited X3 and Y3, meaning that X3P and Y3P are normalized by total concentration X3T and Y3T.

The results from modeling the pathway in Supplementary Figure1a, including both activation and inhibition. According to the paper, the value of ka and kd should in the orange region (ka belongs [0,1], kd belongs [1,10]) so assigned ka=0, kd=1.

The author made the simplifying assumption that the interactions between the pathways are symmetric. Thus the k12xy=k12yx=ka, k33xy=k33yx=kd.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The signal transduction pathways involved in sensing these stimuli often share the same or homologous proteins. Despite potential cross-wiring, cells show specificity of response. We show, through modeling, that the physiological response of such pathways exposed to simultaneous and temporally ordered inputs can demonstrate system-level mechanisms by which pathways achieve specificity. We apply these results to the hyperosmolar and pheromone mitogen-activated protein (MAP) kinase pathways in the yeast Saccharomyces cerevisiae. These two pathways specifically sense osmolar and pheromone signals, despite sharing a MAPKKK, Ste11, and having homologous MAPKs (Fus3 and Hog1). We show that in a single cell, the pathways are bistable over a range of inputs, and the cell responds to only one stimulus even when exposed to both. Our results imply that these pathways achieve specificity by filtering out spurious cross-talk through mutual inhibition. The variability between cells allows for heterogeneity of the decisions.", + "title": "Cross-talk and decision making in MAP kinase pathways.", + "type": "PubMed ID", + "volume": "39", + "year": 2007 + }, + "publicationId": "BIOMD0000000116", + "submissionId": "MODEL7321452458", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000117": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper Signal-induced Ca2+ oscillations: Properties of a model based on Ca2+-induced Ca2+ release. Figure4B in the paper has been reproduced by RoadRunner and MathSBML. Damped Ca2+ oscillations elicited by a transient pulse of InsP3 applied intracellularly to a resting, non-oscillatory cell.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model takes into account the existence of two pools of intracellular Ca2+, namely, one sensitive to inositol 1,4,5 trisphosphate (InsP3) whose synthesis is elicited by the stimulus, and one insensitive to InsP3. The discharge of the latter pool into the cytosol is activated by cytosolic Ca2+. Oscillations in cytosolic Ca2+ arise in this model either spontaneously or in an appropriate range of external stimulation; these oscillations do not require the concomitant, periodic variation of InsP3. The following properties of the model are reviewed and compared with experimental observations: (a) Control of the frequency of Ca2+ oscillations by the external stimulus or extracellular Ca2+; (b) correlation of latency with period of Ca2+ oscillations obtained at different levels of stimulation; (c) effect of a transient increase in InsP3; (d) phase shift and transient suppression of Ca2+ oscillations by Ca2+ pulses, and (e) propagation of Ca2+ waves. It is shown that on all these counts the model provides a simple, unified explanation for a number of experimental observations in a variety of cell types. The model based on Ca(2+)-induced Ca2+ release can be extended to incorporate variations in the level of InsP3 as well as desensitization of the InsP3 receptor; besides accounting for the phenomena described by the minimal model, the extended model might also account for the occurrence of complex Ca2+ oscillations.", + "title": "Signal-induced Ca2+ oscillations: properties of a model based on Ca(2+)-induced Ca2+ release.", + "type": "PubMed ID", + "volume": "12", + "year": 1991 + }, + "publicationId": "BIOMD0000000117", + "submissionId": "MODEL0466937756", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000118": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Model is according to the paper Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cell: Combined Experimental. Figure6Da has been reproduced by MathSBML. The original model from ModelDB. http://senselab.med.yale.edu/modeldb/


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Burstiness is low in neurons exposed to a normal extracellular Ca(2+) concentration ([Ca(2+)](o)), but is markedly enhanced by lowering [Ca(2+)](o), although not by blocking Ca(2+) and Ca(2+)-activated K(+) currents. We show, using intracellular recordings, that burstiness in low [Ca(2+)](o) persists even after truncating the apical dendrites, suggesting that bursts are generated by an interplay of membrane currents at or near the soma. To study the mechanisms of bursting, we have constructed a conductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model, reduced [Ca(2+)](o) is simulated by negatively shifting the activation curve of the persistent Na(+) current (I(NaP)) as indicated by recent experimental results. The neuron model accounts, with different parameter sets, for the diversity of firing patterns observed experimentally in both zero and normal [Ca(2+)](o). 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We suggest that bursting in CA1 pyramidal cells can be explained by a single compartment \"square bursting\" mechanism with one slow variable, the activation of I(M).", + "title": "Contribution of persistent Na+ current and M-type K+ current to somatic bursting in CA1 pyramidal cells: combined experimental and modeling study.", + "type": "PubMed ID", + "volume": "96", + "year": 2006 + }, + "publicationId": "BIOMD0000000118", + "submissionId": "MODEL1243247625", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000119": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Model is according to the paper Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cell: Combined Experimental. This is the second model from this paper for the non-zero [Ca2+] initial value, parameters and the kinetics quations from Table2 in the paper. Figure9Aa has been reproduced by MathSBML. The original model from ModelDB. http://senselab.med.yale.edu/modeldb/


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In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Israel. golomb@bgu.ac.il", + "name": "David Golomb", + "orcid": "0000-0001-6575-3685" + }, + { + "name": "Cuiyong Yue" + }, + { + "name": "Yoel Yaari" + } + ], + "issue": "4", + "journal": "Journal of neurophysiology", + "link": "http://identifiers.org/pubmed/16807352", + "month": "10", + "pages": "1912-1926", + "synopsis": "The intrinsic firing modes of adult CA1 pyramidal cells vary along a continuum of \"burstiness\" from regular firing to rhythmic bursting, depending on the ionic composition of the extracellular milieu. Burstiness is low in neurons exposed to a normal extracellular Ca(2+) concentration ([Ca(2+)](o)), but is markedly enhanced by lowering [Ca(2+)](o), although not by blocking Ca(2+) and Ca(2+)-activated K(+) currents. We show, using intracellular recordings, that burstiness in low [Ca(2+)](o) persists even after truncating the apical dendrites, suggesting that bursts are generated by an interplay of membrane currents at or near the soma. To study the mechanisms of bursting, we have constructed a conductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model, reduced [Ca(2+)](o) is simulated by negatively shifting the activation curve of the persistent Na(+) current (I(NaP)) as indicated by recent experimental results. The neuron model accounts, with different parameter sets, for the diversity of firing patterns observed experimentally in both zero and normal [Ca(2+)](o). 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We suggest that bursting in CA1 pyramidal cells can be explained by a single compartment \"square bursting\" mechanism with one slow variable, the activation of I(M).", + "title": "Contribution of persistent Na+ current and M-type K+ current to somatic bursting in CA1 pyramidal cells: combined experimental and modeling study.", + "type": "PubMed ID", + "volume": "96", + "year": 2006 + }, + "publicationId": "BIOMD0000000119", + "submissionId": "MODEL1580005336", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000120": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 3a of the paper. Please note that the authors mention that they used a value of 2 for n, n being the power in the positive feedback function for kinase autocatalysis, however the model here has n=1.95 because this results in a simulation that is identical to Fig 3a. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"15255048", + "affiliation": "Department of Immunology, Division of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 ONN, UK.", + "authors": [ + { + "institution": "Department of Immunology, Division of Medicine, Imperial College London, Hammersmith Hospital, Du Cane Road, London W12 ONN, UK.", + "name": "Cliburn Chan" + }, + { + "name": "Jaroslav Stark" + }, + { + "name": "Andrew J T George", + "orcid": "0000-0002-2866-0241" + } + ], + "issue": "1542", + "journal": "Proceedings. Biological sciences", + "link": "http://identifiers.org/pubmed/15255048", + "month": "5", + "pages": "931-939", + "synopsis": "The specificity and sensitivity of T-cell recognition is vital to the immune response. Ligand engagement with the T-cell receptor (TCR) results in the activation of a complex sequence of signalling events, both on the cell membrane and intracellularly. Feedback is an integral part of these signalling pathways, yet is often ignored in standard accounts of T-cell signalling. Here we show, using a mathematical model, that these feedback loops can explain the ability of the TCR to discriminate between ligands with high specificity and sensitivity, as well as provide a mechanism for sustained signalling. The model also explains the recent counter-intuitive observation that endogenous 'null' ligands can significantly enhance T-cell signalling. Finally, the model may provide an archetype for receptor switching based on kinase-phosphatase switches, and thus be of interest to the wider signalling community.", + "title": "Feedback control of T-cell receptor activation.", + "type": "PubMed ID", + "volume": "271", + "year": 2004 + }, + "publicationId": "BIOMD0000000120", + "submissionId": "MODEL2514697386", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000121": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is according to the paper Cellular consequences of HEGR mutations in the long QT syndrome: precursors to sudden cardiac death. The author used Markovian model of cardiac Ikr in the paper. Figure4B in the paper has been reproduced using CellDesigner3.5.1. The cell is depolarized to the indicated test potential for 250ms (from 50ms to 300ms) from a holding potential of -40mV and then repolarized to -40mV. Change the value for vtest from -30,-20,-10,0,10,20,30,40 for each simulation in order to produce the different cureve in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"qualifier": "bqbiol:isHomologTo", + "resource": "OMIM", + "uri": "http://identifiers.org/mim/152427" + }, + { + "accession": "10141", + "name": "Cavia porcellus", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/10141" + }, + { + "accession": "GO:0005251", + "name": "delayed rectifier potassium channel activity", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0005251" + }, + { + "accession": "GO:0003015", + "name": "heart process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0003015" + }, + { + "accession": "GO:0043266", + "name": "regulation of potassium ion transport", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0043266" + }, + { + "accession": "DOID:0050434", + "name": "Andersen-Tawil syndrome", + "qualifier": "bqbiol:hasVersion", + "resource": "Human Disease Ontology", + "uri": "http://identifiers.org/doid/DOID:0050434" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Clancy2001_Kchannel", + "publication": { + "accession": "11334834", + "affiliation": "Cardiac Bioelectricity Research and Training Center, Department of Physiology and Biophysics, 509 Wickenden Building, Case Western Reserve University, Cleveland, Ohio 44106-7207, USA.", + "authors": [ + { + "institution": "Cardiac Bioelectricity Research and Training Center, Department of Physiology and Biophysics, 509 Wickenden Building, Case Western Reserve University, Cleveland, Ohio 44106-7207, USA.", + "name": "C E Clancy" + }, + { + "name": "Y Rudy" + } + ], + "issue": "2", + "journal": "Cardiovascular research", + "link": "http://identifiers.org/pubmed/11334834", + "month": "5", + "pages": "301-313", + "synopsis": "

Background

A variety of mutations in HERG, the major subunit of the rapidly activating component of the cardiac delayed rectifier I(Kr), have been found to underlie the congenital Long-QT syndrome, LQT2. LQT2 may give rise to severe arrhythmogenic phenotypes leading to sudden cardiac death.

Objective

We attempt to elucidate the mechanisms by which heterogeneous LQT2 genotypes can lead to prolongation of the action potential duration (APD) and consequently the QT interval on the ECG.

Methods

We develop Markovian models of wild-type (WT) and mutant I(Kr) channels and incorporate these models into a comprehensive model of the cardiac ventricular cell.

Results

Using this virtual transgenic cell model, we describe the effects of HERG mutations on the cardiac ventricular action potential (AP) and provide insight into the mechanism by which each defect results in a net loss of repolarizing current and prolongation of APD.

Conclusions

This study demonstrates which mutations can prolong APD sufficiently to generate early afterdepolarizations (EADs), which may trigger life-threatening arrhythmias. The severity of the phenotype is shown to depend on the specific kinetic changes and how they affect I(Kr) during the time course of the action potential. Clarifying how defects in HERG can lead to impaired cellular electrophysiology can improve our understanding of the link between channel structure and cellular function.", + "title": "Cellular consequences of HERG mutations in the long QT syndrome: precursors to sudden cardiac death.", + "type": "PubMed ID", + "volume": "50", + "year": 2001 + }, + "publicationId": "BIOMD0000000121", + "submissionId": "MODEL2898180293", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000122": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the calcium oscillation dependent activation-deactivation kinetics of nuclear factor of activated T cells (NFAT) as depicted in Fig 4a of the paper. A simple algorithm in the events section takes care of the calcium oscillation. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Fisher2006_Ca_Oscillation_dpdnt_NFAT_dynamics", + "publication": { + "accession": "17031595", + "affiliation": "Eugene McDermott Center for Human Growth and Development, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.", + "authors": [ + { + "institution": "Eugene McDermott Center for Human Growth and Development, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.", + "name": "Wayne G Fisher" + }, + { + "name": "Pei-Chi Yang" + }, + { + "name": "Ram K Medikonduri" + }, + { + "name": "M Saleet Jafri", + "orcid": "0000-0003-3174-1007" + } + ], + "issue": "11", + "journal": "Annals of biomedical engineering", + "link": "http://identifiers.org/pubmed/17031595", + "month": "11", + "pages": "1712-1728", + "synopsis": "Mathematical models for the regulation of the Ca(2+)-dependent transcription factors NFAT and NFkappaB that are involved in the activation of the immune and inflammatory responses in T lymphocytes have been developed. These pathways are important targets for drugs, which act as powerful immunosuppressants by suppressing activation of NFAT and NFkappaB in T cells. The models simulate activation and deactivation over physiological concentrations of Ca(2+), diacyl glycerol (DAG), and PKCtheta using single and periodic step increases. The model suggests the following: (1) the activation NFAT does not occur at low frequencies as NFAT requires calcineurin activated by Ca(2+) to remain dephosphorylated and in the nucleus; (2) NFkappaB is activated at lower Ca(2+) oscillation frequencies than NFAT as IkappaB is degraded in response to elevations in Ca(2+) allowing free NFkappaB to translocate into the nucleus; and (3) the degradation of IkappaB is essential for efficient translocation of NFkappaB to the nucleus. Through sensitivity analysis, the model also suggests that the largest controlling factor for NFAT activation is the dissociation/reassociation rate of the NFAT:calcineurin complex and the translocation rate of the complex into the nucleus and for NFkappaB is the degradation/resynthesis rate of IkappaB and the import rate of IkappaB into the nucleus.", + "title": "NFAT and NFkappaB activation in T lymphocytes: a model of differential activation of gene expression.", + "type": "PubMed ID", + "volume": "34", + "year": 2006 + }, + "publicationId": "BIOMD0000000122", + "submissionId": "MODEL2977476330", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000123": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the kinetics of the nuclear factor of activated cells (NFAT) as depicted in Figure 3a of the paper. Model was successfully tested on Jarnac and MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Jafri", + "orcid": "0000-0003-3174-1007" + } + ], + "issue": "11", + "journal": "Annals of biomedical engineering", + "link": "http://identifiers.org/pubmed/17031595", + "month": "11", + "pages": "1712-1728", + "synopsis": "Mathematical models for the regulation of the Ca(2+)-dependent transcription factors NFAT and NFkappaB that are involved in the activation of the immune and inflammatory responses in T lymphocytes have been developed. These pathways are important targets for drugs, which act as powerful immunosuppressants by suppressing activation of NFAT and NFkappaB in T cells. The models simulate activation and deactivation over physiological concentrations of Ca(2+), diacyl glycerol (DAG), and PKCtheta using single and periodic step increases. The model suggests the following: (1) the activation NFAT does not occur at low frequencies as NFAT requires calcineurin activated by Ca(2+) to remain dephosphorylated and in the nucleus; (2) NFkappaB is activated at lower Ca(2+) oscillation frequencies than NFAT as IkappaB is degraded in response to elevations in Ca(2+) allowing free NFkappaB to translocate into the nucleus; and (3) the degradation of IkappaB is essential for efficient translocation of NFkappaB to the nucleus. Through sensitivity analysis, the model also suggests that the largest controlling factor for NFAT activation is the dissociation/reassociation rate of the NFAT:calcineurin complex and the translocation rate of the complex into the nucleus and for NFkappaB is the degradation/resynthesis rate of IkappaB and the import rate of IkappaB into the nucleus.", + "title": "NFAT and NFkappaB activation in T lymphocytes: a model of differential activation of gene expression.", + "type": "PubMed ID", + "volume": "34", + "year": 2006 + }, + "publicationId": "BIOMD0000000123", + "submissionId": "MODEL2977131407", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000124": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model is described in the paper by Wu and Chang (2006). Diethyl pyrocarbonate, a histidine-modifying agent, directly stimulates activity of ATP-sensitive potassium channels in pituitary GH3 cells. Biochem Pharmacol. 71(5): 615-23.

The unit of time is ms, and the simulation time is 80 s, that is 8e4 ms. Therfore, you probably need to increase the maximum steps for your simulator.

The figure 7 has been reproduced by MathSBML. Application of DEPC as indicated at horizontal bar was mimicked by an increase of maximal conductance of Katp-channels from 500 to 530 ps at t=30 s.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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inward rectifier potassium channel activity", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0015272" + }, + { + "accession": "GO:0016570", + "name": "histone modification", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0016570" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Wu2006_K+Channel", + "publication": { + "accession": "16375866", + "affiliation": "Department of Physiology, National Cheng Kung University Medical College, No 1, University Road, Tainan 70101, Taiwan. snwu@mail.ncku.edu.tw", + "authors": [ + { + "institution": "Department of Physiology, National Cheng Kung University Medical College, No 1, University Road, Tainan 70101, Taiwan. snwu@mail.ncku.edu.tw", + "name": "Sheng-Nan Wu", + "orcid": "0000-0002-5208-3253" + }, + { + "name": "Han-Dong Chang" + } + ], + "issue": "5", + "journal": "Biochemical pharmacology", + "link": "http://identifiers.org/pubmed/16375866", + "month": "2", + "pages": "615-623", + "synopsis": "The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. Diethyl pyrocarbonate (DEPC), a histidine-specific alkylating reagent, is known to modify the histidine residues of the structure of proteins. The objective of this study was to determine whether DEPC modifies K(ATP)-channel activity in pituitary GH(3) cells. Steady-state fluctuation analyses of macroscopic K(+) current at -120 mV produced power spectra that could be fitted with a single Lorentzian curve in these cells. The time constants in the presence of DEPC were increased. Consistent with fluctuation analyses, the mean open time of K(ATP)-channels was significantly increased during exposure to DEPC. However, DEPC produced no change in single-channel conductance, despite the ability of this compound to enhance K(ATP)-channel activity in a concentration-dependent manner with an EC(50) value of 16 microM. DEPC-stimulated K(ATP)-channel activity was attenuated by pretreatment with glibenclamide. In current-clamp configuration, DEPC decreased the firing of action potentials in GH(3) cells. A further application of glibenclamide reversed DEPC-induced inhibition of spontaneous action potentials. Intracellullar Ca(2+) measurements revealed the ability of DEPC to decrease Ca(2+) oscillations in GH(3) cells. Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells.", + "title": "Diethyl pyrocarbonate, a histidine-modifying agent, directly stimulates activity of ATP-sensitive potassium channels in pituitary GH(3) cells.", + "type": "PubMed ID", + "volume": "71", + "year": 2006 + }, + "publicationId": "BIOMD0000000124", + "submissionId": "MODEL2981976537", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000125": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model according to the paper A Theoretical Framework for Specificity in Cell Signalling The model is \"basic architecture\" of Figure2A. Figure2B, Figure2C have been reproduced by MathSBML. The reproduced figures are slightly different from the original ones in the paper, the peak of [x2] is higher than 1 and is not decreasing dramatically when [x0]=0. And I think maybe the author shift the or scale the curves.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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However, inclusion of either of two related insulating mechanisms--compartmentalization or the action of a scaffold protein--allows both properties to be achieved, provided deactivation rates are fast compared to exchange rates.", + "title": "A theoretical framework for specificity in cell signaling.", + "type": "PubMed ID", + "volume": "1", + "year": 2005 + }, + "publicationId": "BIOMD0000000125", + "submissionId": "MODEL2451230486", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000126": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model is according to the paper Na+ Channel Mutation That Causes Both Brugada and Long-QT Syndrome Phenotypes: A Simulation Study of Mechanism Original model comes from ModelDB with accession number: 62661. This is the wide type model. All the values and reactions obtained from Data Supplement6: Appendix of the paper. Figure3 has been reproduced by MathSBML. The stimulus v=-30mV during the time from 5ms to 20 ms displayed in the event. The meaning for the keyword, C: Close states; O: Open states; IF: Fast inactivation states; IC: Closed-Inactivation states; IM: Intermediat Inactivation states.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

Complex physiological interactions determine the functional consequences of gene abnormalities and make mechanistic interpretation of phenotypes extremely difficult. A recent example is a single mutation in the C terminus of the cardiac Na(+) channel, 1795insD. The mutation causes two distinct clinical syndromes, long QT (LQT) and Brugada, leading to life-threatening cardiac arrhythmias. Coexistence of these syndromes is seemingly paradoxical; LQT is associated with enhanced Na(+) channel function, and Brugada with reduced function.

Methods and results

Using a computational approach, we demonstrate that the 1795insD mutation exerts variable effects depending on the myocardial substrate. We develop Markov models of the wild-type and 1795insD cardiac Na(+) channels. By incorporating the models into a virtual transgenic cell, we elucidate the mechanism by which 1795insD differentially disrupts cellular electrical behavior in epicardial and midmyocardial cell types. We provide a cellular mechanistic basis for the ECG abnormalities observed in patients carrying the 1795insD gene mutation.

Conclusions

We demonstrate that the 1795insD mutation can cause both LQT and Brugada syndromes through interaction with the heterogeneous myocardium in a rate-dependent manner. The results highlight the complexity and multiplicity of genotype-phenotype relationships, and the usefulness of computational approaches in establishing a mechanistic link between genetic defects and functional abnormalities.", + "title": "Na(+) channel mutation that causes both Brugada and long-QT syndrome phenotypes: a simulation study of mechanism.", + "type": "PubMed ID", + "volume": "105", + "year": 2002 + }, + "publicationId": "BIOMD0000000126", + "submissionId": "MODEL4716238623", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000127": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model is according to the paper Simple Model of Spiking Neurons In this paper, a simple spiking model is presented that is as biologically plausible as the Hodgkin-Huxley model, yet as computationally efficient as the integrate-and-fire model. Known types of neurons correspond to different values of the parameters a,b,c,d in the model. Figure2RS,IB,CH,FS,LTS have been simulated by MathSBML.

RS: a=0.02, b=0.2, c=-65, d=8.

IB: a=0.02,b=0.2,c=-55,d=4

CH: a=0.02,b=0.2,c=-50,d=2

FS:a=0.1b=0.2c=-65,d=2

LTS:a=0.02,b=0.25,c=-65,d=2


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.", + "title": "Simple model of spiking neurons.", + "type": "PubMed ID", + "volume": "14", + "year": 2003 + }, + "publicationId": "BIOMD0000000127", + "submissionId": "MODEL4880479792", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000128": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model is according to the paper Endothelin Action on Pituitary Lactotrophs: One Receptor, Many GTP-Binding Proteins Figure 1 has been simulated by MathSBML. The figure for the [Ca2+]i and [Ca2+]ER have been normalized in the paper.Original model comes from http://www.math.fsu.edu/~bertram/software/pituitary

The units for parameters and species are varied from one to another, so I omit the unit definition here . Conductances in pS; currents in fA; Ca concentrations in uM; time in ms


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(S) inhibition-induced spiking has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=-0.02; b=-1; c=-60; d=8; V=-63.8; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich, EM Neural Networks, IEEE Transactions on2004:15(5):1063-1070 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(L) integrator has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.02; b=-0.1; c=-55; d=6; V=-60; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(N) rebound burst has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.03; b=0.25; c=-52; d=0;V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(M) rebound spike has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.03; b=0.25; c=-60; d=4; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(K) resonator has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.1; b=0.26; c=-60; d=-1; V=-62; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(I) spike latency has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. In this model a=0.02; b=0.2; c=-65; d=6; V=-70; u=b*V=0.2*(-70);

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(J) subthreshold oscillations has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.05; b=0.26; c=-60; d=0; V=-62; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(O) threshold variability has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.03; b=0.25; c=-60; d=4; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Model reproduces the various plots in Figure 6 and 7 of the paper. It was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Endocrinology and metabolism", + "link": "http://identifiers.org/pubmed/12376338", + "month": "11", + "pages": "E1084-101", + "synopsis": "We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-zeta is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.", + "title": "A mathematical model of metabolic insulin signaling pathways.", + "type": "PubMed ID", + "volume": "283", + "year": 2002 + }, + "publicationId": "BIOMD0000000137", + "submissionId": "MODEL6526728774", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000138": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model is encoded according to the paper Low dose of dopamine may stimulate prolactin secretion by increasing fast potassium currents Figure5 has been reproduced by MathSBML. One need to change the value of ga in order to get the three correct results.

the xppaut file of the model is avaiable on the following address offered by the author , http://www.math.fsu.edu/%7Ebertram/software/pituitary/JCNS_07.ode


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"http://identifiers.org/pubmed/17058022", + "month": "4", + "pages": "211-222", + "synopsis": "Dopamine (DA) released from the hypothalamus tonically inhibits pituitary lactotrophs. DA (at micromolar concentration) opens potassium channels, hyperpolarizing the lactotrophs and thus preventing the calcium influx that triggers prolactin hormone release. Surprisingly, at concentrations approximately 1000 lower, DA can stimulate prolactin secretion. Here, we investigated whether an increase in a K+ current could mediate this stimulatory effect. We considered the fast K+ currents flowing through large-conductance BK channels and through A-type channels. We developed a minimal lactotroph model to investigate the effects of these two currents. Both IBK and IA could transform the electrical pattern of activity from spiking to bursting, but through distinct mechanisms. IBK always increased the intracellular Ca2+ concentration, while IA could either increase or decrease it. Thus, the stimulatory effects of DA could be mediated by a fast K+ conductance which converts tonically spiking cells to bursters. In addition, the study illustrates that", + "title": "Low dose of dopamine may stimulate prolactin secretion by increasing fast potassium currents.", + "type": "PubMed ID", + "volume": "22", + "year": 2007 + }, + "publicationId": "BIOMD0000000138", + "submissionId": "MODEL6090663405", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000139": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model corresponds to the knock out model of beta-/-, epsilon -/- and reproduces the upper panel in Fig 2C. In order to reproduce the other knock out models the transcription rate of the species that are not present must be set to zero and the rate of the one that is present must be set as seven times its corresponding value for the wild type model. This is done so as to compensate for the loss of other isoforms. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Based on simplifying reductions of the IkappaB-NF-kappaB signaling module in knockout cell lines, we present a computational model that describes the temporal control of NF-kappaB activation by the coordinated degradation and synthesis of IkappaB proteins. The model demonstrates that IkappaBalpha is responsible for strong negative feedback that allows for a fast turn-off of the NF-kappaB response, whereas IkappaBbeta and - epsilon function to reduce the system's oscillatory potential and stabilize NF-kappaB responses during longer stimulations. Bimodal signal-processing characteristics with respect to stimulus duration are revealed by the model and are shown to generate specificity in gene expression.", + "title": "The IkappaB-NF-kappaB signaling module: temporal control and selective gene activation.", + "type": "PubMed ID", + "volume": "298", + "year": 2002 + }, + "publicationId": "BIOMD0000000139", + "submissionId": "MODEL7157773062", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000140": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model corresponds to the IkB-NFkB signaling in wild type cells and reproduces the dynamics of the species as depicted in Figure 2 F of the paper. The authors mention that the simulation is carried out in three phases, where the steady state values of the species in one phase are fed to the succeding phase. This model captures the simulation dynamics of two phases and makes use of the event section to introduce the stimulus and thereby transition to the next phase. Accordingly, a few terms have been introduced that make this transition possible, this in no way compromises the original model. Also, the simulation plots are not an exact reproduction of the figures in the paper, they do however match the simulation results that the authors shared with us. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(G) Class 1 excitable has been reproduced by MathSBML. The ODE and the parameters values are originally taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.02; b=-0.1; c=-55; d=6; V=-60; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(H) Class 2 excitable has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.2; b=0.26; c=-65; d=0; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Olsen2003_neutrophil_oscillatory_metabolism

This model is described in the article:

Olsen LF, Kummer U, Kindzelskii AL, Petty HR.
Biophys. J. 2003 Jan; 84(1): 69-81

Abstract:

We present a two-compartment model to explain the oscillatory behavior observed experimentally in activated neutrophils. Our model is based mainly on the peroxidase-oxidase reaction catalyzed by myeloperoxidase with melatonin as a cofactor and NADPH oxidase, a major protein in the phagosome membrane of the leukocyte. The model predicts that after activation of a neutrophil, an increase in the activity of the hexose monophosphate shunt and the delivery of myeloperoxidase into the phagosome results in oscillations in oxygen and NAD(P)H concentration. The period of oscillation changes from >200 s to 10-30 s. The model is consistent with previously reported oscillations in cell metabolism and oxidant production. Key features and predictions of the model were confirmed experimentally. The requirement of the hexose monophosphate pathway for 10 s oscillations was verified using 6-aminonicotinamide and dexamethasone, which are inhibitors of glucose-6-phosphate dehydrogenase. The role of the NADPH oxidase in promoting oscillations was confirmed by dose-response studies of the effect of diphenylene iodonium, an inhibitor of the NADPH oxidase. Moreover, the model predicted an increase in the amplitude of NADPH oscillations in the presence of melatonin, which was confirmed experimentally. Successful computer modeling of complex chemical dynamics within cells and their chemical perturbation will enhance our ability to identify new antiinflammatory compounds.

This model is hosted on BioModels Database and identified by: BIOMD0000000143.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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observed experimentally in activated neutrophils. Our model is based mainly on the peroxidase-oxidase reaction catalyzed by myeloperoxidase with melatonin as a cofactor and NADPH oxidase, a major protein in the phagosome membrane of the leukocyte. The model predicts that after activation of a neutrophil, an increase in the activity of the hexose monophosphate shunt and the delivery of myeloperoxidase into the phagosome results in oscillations in oxygen and NAD(P)H concentration. The period of oscillation changes from >200 s to 10-30 s. The model is consistent with previously reported oscillations in cell metabolism and oxidant production. Key features and predictions of the model were confirmed experimentally. The requirement of the hexose monophosphate pathway for 10 s oscillations was verified using 6-aminonicotinamide and dexamethasone, which are inhibitors of glucose-6-phosphate dehydrogenase. The role of the NADPH oxidase in promoting oscillations was confirmed by dose-response studies of the effect of diphenylene iodonium, an inhibitor of the NADPH oxidase. Moreover, the model predicted an increase in the amplitude of NADPH oscillations in the presence of melatonin, which was confirmed experimentally. Successful computer modeling of complex chemical dynamics within cells and their chemical perturbation will enhance our ability to identify new antiinflammatory compounds.", + "title": "A model of the oscillatory metabolism of activated neutrophils.", + "type": "PubMed ID", + "volume": "84", + "year": 2003 + }, + "publicationId": "BIOMD0000000143", + "submissionId": "MODEL5564211615", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000144": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institut Curie", + "email": "laurence.calzone@curie.fr", + "external": false, + "name": "Laurence Calzone" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the Dynamical model of nuclear division cycles during early embryogenesis of Drosophila, without StringT regulation. so ksstg=kdstg=0. Figure1B has been simulated by MathSBML. Curator changed model from only one compartment into two compartments according to the paper. Detail explaination of the models are in the supplement information of the paper.The author didn't specify which compartment Xm, Stgm, Xp are located, we assume that they locate in cytoplasm.

Some of the parameter values for the equations are dimensionless parameters.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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In this paper, we propose a dynamical model for the molecular events underlying these early nuclear division cycles in Drosophila. The model distinguishes nuclear and cytoplasmic compartments of the embryo and permits exploration of a variety of rules for protein transport between the compartments. Numerical simulations reproduce the main features of wild-type mitotic cycles: patterns of protein accumulation and degradation, lengthening of later cycles, and arrest in interphase 14. The model is consistent with mutations that introduce subtle changes in the number of mitotic cycles before interphase arrest. Bifurcation analysis of the differential equations reveals the dependence of mitotic oscillations on cycle number, and how this dependence is altered by mutations. 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Wang2007 - ATP induced intracellular Calicum Oscillation

The model simulate the ATP-induced intracellular Ca2+ oscillations and the quantitative effect of ATP concentration on the oscillation characteristics such as the duration, peak concentration of intracellular Ca2+ and average interval.

This model is described in the article:

Wang J, Huang X, Huang W.
J. Theor. Biol. 2007 Apr; 245(3): 510-519

Abstract:

A quantitative kinetic model is proposed to simulate the ATP-induced intracellular Ca(2+) oscillations. The quantitative effect of ATP concentration upon the oscillations was successfully simulated. Our simulation results support previous experimental explanations that the Ca(2+) oscillations are mainly due to interaction of Ca(2+) release from the endoplasmic reticulum (ER) and the ATP-dependent Ca(2+) pump back into the ER, and the oscillations are prolonged by extracellular Ca(2+) entry that maintains the constant Ca(2+) supplies to its intracellular stores. The model is also able to simulate the sudden disappearance phenomenon of the Ca(2+) oscillations observed in some cell types by taking into account of the biphasic characteristic of the Ca(2+) release from the endoplasmic reticulum (ER). Moreover, the model simulation results for the Ca(2+) oscillations characteristics such as duration, peak [Ca(2+)](cyt), and average interval, etc., lead to prediction of some possible factors responsible for the variations of Ca(2+) oscillations in different types of cells.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000145.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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ATP induced intracellular Calcium Oscillation", + "publication": { + "accession": "17188305", + "affiliation": "Environmental Science Division, School of Earth and Space Science, University of Science and Technology of China, Hefei, Anhui Province 230026, China.", + "authors": [ + { + "institution": "Environmental Science Division, School of Earth and Space Science, University of Science and Technology of China, Hefei, Anhui Province 230026, China.", + "name": "Jinhui Wang" + }, + { + "name": "Xudong Huang" + }, + { + "name": "Weidong Huang" + } + ], + "issue": "3", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/17188305", + "month": "4", + "pages": "510-519", + "synopsis": "A quantitative kinetic model is proposed to simulate the ATP-induced intracellular Ca(2+) oscillations. The quantitative effect of ATP concentration upon the oscillations was successfully simulated. Our simulation results support previous experimental explanations that the Ca(2+) oscillations are mainly due to interaction of Ca(2+) release from the endoplasmic reticulum (ER) and the ATP-dependent Ca(2+) pump back into the ER, and the oscillations are prolonged by extracellular Ca(2+) entry that maintains the constant Ca(2+) supplies to its intracellular stores. The model is also able to simulate the sudden disappearance phenomenon of the Ca(2+) oscillations observed in some cell types by taking into account of the biphasic characteristic of the Ca(2+) release from the endoplasmic reticulum (ER). Moreover, the model simulation results for the Ca(2+) oscillations characteristics such as duration, peak [Ca(2+)](cyt), and average interval, etc., lead to prediction of some possible factors responsible for the variations of Ca(2+) oscillations in different types of cells.", + "title": "A quantitative kinetic model for ATP-induced intracellular Ca2+ oscillations.", + "type": "PubMed ID", + "volume": "245", + "year": 2007 + }, + "publicationId": "BIOMD0000000145", + "submissionId": "MODEL8342350003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000146": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Oxford", + "email": "enuo.he@wolfson.ox.ac.uk", + "external": false, + "name": "Enuo He" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Figure4 and Figure5 can be simulated by Copasi. Figure4 can be simulated in MathSBML as well. There are some typos in the paper:K29=234, is it should k_29; Table2, reaction17, is there are \"slash\" missing in between the rate equation; reaction 33,\"Akt-PI-PP\" in the last term of denominator instead of \"AktPI-P\" . For plotting figure4, we create another extra parameter *_percent, and use assignment rule calculate percentage of each species.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Genomic Sciences Center, 1-7-22 Suehirocho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. marikoh@gsc.riken.go.jp", + "authors": [ + { + "institution": "RIKEN Genomic Sciences Center, 1-7-22 Suehirocho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. marikoh@gsc.riken.go.jp", + "name": "Mariko Hatakeyama" + }, + { + "name": "Shuhei Kimura" + }, + { + "name": "Takashi Naka" + }, + { + "name": "Takuji Kawasaki" + }, + { + "name": "Noriko Yumoto" + }, + { + "name": "Mio Ichikawa" + }, + { + "name": "Jae-Hoon Kim" + }, + { + "name": "Kazuki Saito", + "orcid": "0000-0003-4663-1134" + }, + { + "name": "Mihoro Saeki" + }, + { + "name": "Mikako Shirouzu" + }, + { + "name": "Shigeyuki Yokoyama", + "orcid": "0000-0003-3133-7338" + }, + { + "name": "Akihiko Konagaya" + } + ], + "issue": "Pt 2", + "journal": "The Biochemical journal", + "link": "http://identifiers.org/pubmed/12691603", + "month": "7", + "pages": "451-463", + "synopsis": "ErbB tyrosine kinase receptors mediate mitogenic signal cascade by binding a variety of ligands and recruiting the different cassettes of adaptor proteins. In the present study, we examined heregulin (HRG)-induced signal transduction of ErbB4 receptor and found that the phosphatidylinositol 3'-kinase (PI3K)-Akt pathway negatively regulated the extracellular signal-regulated kinase (ERK) cascade by phosphorylating Raf-1 on Ser(259). As the time-course kinetics of Akt and ERK activities seemed to be transient and complex, we constructed a mathematical simulation model for HRG-induced ErbB4 receptor signalling to explain the dynamics of the regulation mechanism in this signal transduction cascade. The model reflected well the experimental results observed in HRG-induced ErbB4 cells and in other modes of growth hormone-induced cell signalling that involve Raf-Akt cross-talk. The model suggested that HRG signalling is regulated by protein phosphatase 2A as well as Raf-Akt cross-talk, and protein phosphatase 2A modulates the kinase activity in both the PI3K-Akt and MAPK (mitogen-activated protein kinase) pathways.", + "title": "A computational model on the modulation of mitogen-activated protein kinase (MAPK) and Akt pathways in heregulin-induced ErbB signalling.", + "type": "PubMed ID", + "volume": "373", + "year": 2003 + }, + "publicationId": "BIOMD0000000146", + "submissionId": "MODEL8256371999", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000147": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Nature Publishing Group", + "email": "msbforum@embo.org", + "external": false, + "name": "Molecular Systems Biology" + } + ] + }, + "curationStatus": "CURATED", + "description": "

O'Dea, E.L., Barken, D., Peralta, R.Q., Tran K.T., Werner, S.L., Kearns, J.D., Levchenko, A., Hoffmann, A. A homeostatic model of IkB metabolism to control constitutive activity. Molecular Systems Biology, 3:111, pp. 1-7. 2007

Questions concerning the paper should be addressed to the corresponding author. Alexander Hoffmann (ahoffmann@ucsd.edu)


The original model was written and simulated within MathWorks MatLab 2006a using the ode15s (stiff/NDF) solver. It is highly recommended that those wanting to model this system use the MatLab version which we will freely provide upon request. As always, simulation results vary according to the numerical solver used.

Translation to SBML Level 2.1 was performed via reconstruction of the model within MathWorks SimBiology Desktop (version 2.1) followed by an Export to SBML. Please address questions about this SBML model to Jeff Kearns (jkearns@ucsd.edu).

BioModels DB curation: The model reproduces the values of diffferent species depicted in Fig 3A and 3B (wt) of the paper corresponding to Model1.1. To depict the the total IkB alpha, beta epsilon species, three additional parameters and their corresponding assignment rules have been introduced in this model by the creator. Model succesfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Jolla, CA 92037, USA.", + "name": "Ellen L O'Dea" + }, + { + "name": "Derren Barken" + }, + { + "name": "Raechel Q Peralta" + }, + { + "name": "Kim T Tran" + }, + { + "name": "Shannon L Werner" + }, + { + "name": "Jeffrey D Kearns" + }, + { + "name": "Andre Levchenko" + }, + { + "name": "Alexander Hoffmann" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/17486138", + "month": "0", + "pages": "111", + "synopsis": "Cellular signal transduction pathways are usually studied following administration of an external stimulus. However, disease-associated aberrant activity of the pathway is often due to misregulation of the equilibrium state. The transcription factor NF-kappaB is typically described as being held inactive in the cytoplasm by binding its inhibitor, IkappaB, until an external stimulus triggers IkappaB degradation through an IkappaB kinase-dependent degradation pathway. Combining genetic, biochemical, and computational tools, we investigate steady-state regulation of the NF-kappaB signaling module and its impact on stimulus responsiveness. We present newly measured in vivo degradation rate constants for NF-kappaB-bound and -unbound IkappaB proteins that are critical for accurate computational predictions of steady-state IkappaB protein levels and basal NF-kappaB activity. Simulations reveal a homeostatic NF-kappaB signaling module in which differential degradation rates of free and bound pools of IkappaB represent a novel cross-regulation mechanism that imparts functional robustness to the signaling module.", + "title": "A homeostatic model of IkappaB metabolism to control constitutive NF-kappaB activity.", + "type": "PubMed ID", + "volume": "3", + "year": 2007 + }, + "publicationId": "BIOMD0000000147", + "submissionId": "MODEL2784772048", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000148": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.
Komarova SV, Smith RJ, Dixon SJ, Sims SM, Wahl LM Bone2003 Aug;33(2):206-15 14499354,
Abstract:
Bone remodeling occurs asynchronously at multiple sites in the adult skeleton and involves resorption by osteoclasts, followed by formation of new bone by osteoblasts. Disruptions in bone remodeling contribute to the pathogenesis of disorderssuch as osteoporosis, osteoarthritis, and Paget's disease. Interactions among cells of osteoblast and osteoclast lineages are critical in the regulation of bone remodeling. We constructed a mathematical model of autocrine and paracrine interactions among osteoblasts and osteoclasts that allowed us to calculate cell population dynamics and changes in bone mass at a discrete site of bone remodeling. Themodel predicted different modes of dynamic behavior: a single remodeling cycle in response to an external stimulus, a series of internally regulated cycles of bone remodeling, or unstable behavior similar to pathological bone remodeling in Paget's disease. Parametric analysis demonstrated that the mode of dynamic behaviorin the system depends strongly on the regulation of osteoclasts by autocrine factors, such as transforming growth factor beta. Moreover, simulations demonstratedthat nonlinear dynamics of the system may explain the differing effects of immunosuppressants on bone remodeling in vitro and in vivo. In conclusion, the mathematical model revealed that interactions among osteoblasts and osteoclasts result in complex, nonlinear system behavior, which cannot be deduced from studies of each cell type alone. The model will be useful in future studies assessing the impact of cytokines, growth factors, and potential therapies on the overall process ofremodeling in normal bone and in pathological conditions such as osteoporosis and Paget's disease.

The model reproduces Fig 2A and Fig 2B of the paper. Note that the Y-axis scale is not right, the osteoblast steadystate is approximatley 212 and not 0 as depicted in the figure. Also, there is atypo in the equation for x2_bar which has been corrected here. Model successfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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The model will be useful in future studies assessing the impact of cytokines, growth factors, and potential therapies on the overall process of remodeling in normal bone and in pathological conditions such as osteoporosis and Paget's disease.", + "title": "Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.", + "type": "PubMed ID", + "volume": "33", + "year": 2003 + }, + "publicationId": "BIOMD0000000148", + "submissionId": "MODEL6029826395", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000149": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kim2007 - Crosstalk between Wnt and ERK pathways

Experimental studies have shown that both Wnt and the MAPK pathways are involved in the pathogenesis of various kinds of cancers (eg. colorectal cancer). The crosstalk between the two pathways have also been identified. Here, Kim et al., (2007) have integrated the experimental evidences on crosstalk mechanisms between the two pathways into a pathway model, and have identified the existence of a hidden positive feedback loop and suggest that this positive feedback loop might participate in the pathogenesis of colorectal cancer.

This model is described in the article:

Kim D, Rath O, Kolch W, Cho KH.
Oncogene 2007 Jul; 26(31): 4571-4579

Abstract:

The Wnt and the extracellular signal regulated-kinase (ERK) pathways are both involved in the pathogenesis of various kinds of cancers. Recently, the existence of crosstalk between Wnt and ERK pathways was reported. Gathering all reported results, we have discovered a positive feedback loop embedded in the crosstalk between the Wnt and ERK pathways. We have developed a plausible model that represents the role of this hidden positive feedback loop in the Wnt/ERK pathway crosstalk based on the integration of experimental reports and employing established basic mathematical models of each pathway. Our analysis shows that the positive feedback loop can generate bistability in both the Wnt and ERK signaling pathways, and this prediction was further validated by experiments. In particular, using the commonly accepted assumption that mutations in signaling proteins contribute to cancerogenesis, we have found two conditions through which mutations could evoke an irreversible response leading to a sustained activation of both pathways. One condition is enhanced production of beta-catenin, the other is a reduction of the velocity of MAP kinase phosphatase(s). This enables that high activities of Wnt and ERK pathways are maintained even without a persistent extracellular signal. Thus, our study adds a novel aspect to the molecular mechanisms of carcinogenesis by showing that mutational changes in individual proteins can cause fundamental functional changes well beyond the pathway they function in by a positive feedback loop embedded in crosstalk. Thus, crosstalk between signaling pathways provides a vehicle through which mutations of individual components can affect properties of the system at a larger scale.

Figure 6 of the reference publication has been reproduced. The model as such reproduces the plots corresponding to the normal conditions. To obtain simulations under 1) beta-cataenin mutation; set V12=0.846 (two-fold of the beta-catenin synthetic rate than the normal system. i.e. 2*0.426), 2) PP2A mutation; set Vmax4=Vmax5=33.75 (three-fourths of the PP2A activity that the normal system. i.e. (3/4)*45). The simulation was performed using Copasi 4.10 (Build 55).

This model is hosted on BioModels Database and identifiedby: BIOMD0000000149 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Recently, the existence of crosstalk between Wnt and ERK pathways was reported. Gathering all reported results, we have discovered a positive feedback loop embedded in the crosstalk between the Wnt and ERK pathways. We have developed a plausible model that represents the role of this hidden positive feedback loop in the Wnt/ERK pathway crosstalk based on the integration of experimental reports and employing established basic mathematical models of each pathway. Our analysis shows that the positive feedback loop can generate bistability in both the Wnt and ERK signaling pathways, and this prediction was further validated by experiments. In particular, using the commonly accepted assumption that mutations in signaling proteins contribute to cancerogenesis, we have found two conditions through which mutations could evoke an irreversible response leading to a sustained activation of both pathways. 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Thus, crosstalk between signaling pathways provides a vehicle through which mutations of individual components can affect properties of the system at a larger scale.", + "title": "A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways.", + "type": "PubMed ID", + "volume": "26", + "year": 2007 + }, + "publicationId": "BIOMD0000000149", + "submissionId": "MODEL4159212701", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000150": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Notes from the original DOCQS curator:
In this version of the CDK2/Cyclin A complex activation there is discrepancy in the first curve which plots the binding reaction of CDK2 and Cyclin A expressed in E. coli. With the published rate constants the simulation does not match the published graph (Fig.1B) in Morris MC. et al. J Biol Chem. 277(26):23847-53 .

Notes from BioModels DB curator:
Although the parameters are those reported in the table I for CDK2/Cyclin A, the total fluorescence follows exactly the curve reported in the paper for CDK2/Cyclin H in figure 1B. Either the plot legend or the table is wrong.

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Molecular Biology, La Jolla, California 92037, USA.", + "authors": [ + { + "institution": "The Scripps Research Institute, Molecular Biology, La Jolla, California 92037, USA.", + "name": "May C Morris", + "orcid": "0000-0001-8106-9728" + }, + { + "name": "Claire Gondeau" + }, + { + "name": "John A Tainer", + "orcid": "0000-0003-1659-2429" + }, + { + "name": "Gilles Divita" + } + ], + "issue": "26", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/11959850", + "month": "6", + "pages": "23847-23853", + "synopsis": "Eukaryotic cell cycle progression is controlled by the ordered action of cyclin-dependent kinases, activation of which occurs through the binding of the cyclin to the Cdk followed by phosphorylation of a conserved threonine in the T-loop of the Cdk by Cdk-activating kinase (CAK). Despite our understanding of the structural changes, which occur upon Cdk/cyclin formation and activation, little is known about the dynamics of the molecular events involved. We have characterized the mechanism of Cdk2/cyclin A complex formation and activation at the molecular and dynamic level by rapid kinetics and demonstrate here that it is a two-step process. The first step involves the rapid association between the PSTAIRE helix of Cdk2 and helices 3 and 5 of the cyclin to yield an intermediate complex in which the threonine in the T-loop is not accessible for phosphorylation. Additional contacts between the C-lobe of the Cdk and the N-terminal helix of the cyclin then induce the isomerization of the Cdk into a fully mature form by promoting the exposure of the T-loop for phosphorylation by CAK and the formation of the substrate binding site. This conformational change is selective for the cyclin partner.", + "title": "Kinetic mechanism of activation of the Cdk2/cyclin A complex. Key role of the C-lobe of the Cdk.", + "type": "PubMed ID", + "volume": "277", + "year": 2002 + }, + "publicationId": "BIOMD0000000150", + "submissionId": "MODEL4821491663", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000151": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 2 , Fig3A and Fig 3B of the paper. The ODE for x1(gp180) and x3 (gp 130) is wrong and the authors have communicated to the curator that the species ought to have a constant value. There are a few other differences from the paper and these were made in consultation with the authors. Model was successfully tested on MathSBML.


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"850-862", + "synopsis": "Cytokines like interleukin-6 (IL-6) play an important role in triggering the acute phase response of the body to injury or inflammation. Signaling by IL-6 involves two pathways: Janus-associated kinases (JAK) and signal transducers and activators of transcription (STAT 3) are activated in the first pathway while the second pathway involves the activation of mitogen-activated protein kinases (MAPK). While it is recognized that both pathways play a major role in IL-6 signal transduction, a majority of studies have focused on signaling through either one of the pathways. However, simultaneous signaling through both JAK/STAT and MAPK pathways is still poorly understood. In this work, a mathematical model has been developed that integrates signaling through both the JAK/STAT and the MAPK pathway. The presented model is used to analyze the effect of three molecules that are involved in the regulation of IL-6 signaling-SHP-2 (domain containing tyrosine phosphatase 2), SOCS3 (suppressor of cytokine signaling 3), and a STAT3 nuclear phosphatase (PP2)-on the dynamics of IL-6 signal transduction in hepatocytes. The obtained results suggest that interactions between SHP-2 and SOCS3 influence signaling through the JAK/STAT and the MAPK pathways. It is shown that SHP-2 and SOCS3 do not just regulate the pathway that they are known to be associated with, (SHP-2 with MAPK and SOCS3 with JAK/STAT), but also have a strong effect on the other pathway. Several simulations with SOCS3, SHP-2, and PP2 knockout cells, that is, where the signaling pathway is unable to produce these proteins, have been performed to characterize the effect of these regulatory proteins on IL-6 signal transduction in hepatocytes.", + "title": "Modeling regulatory mechanisms in IL-6 signal transduction in hepatocytes.", + "type": "PubMed ID", + "volume": "95", + "year": 2006 + }, + "publicationId": "BIOMD0000000151", + "submissionId": "MODEL9750203466", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000152": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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neuromodulator signals in the striatum plays a central role in the functions and dysfunctions of the basal ganglia. DARPP-32 is a key actor of this integration in the GABAergic medium-size spiny neurons, in particular in response to dopamine and glutamate. When phosphorylated by cAMP-dependent protein kinase (PKA), DARPP-32 inhibits protein phosphatase-1 (PP1), whereas when phosphorylated by cyclin-dependent kinase 5 (CDK5) it inhibits PKA. DARPP-32 is also regulated by casein kinases and by several protein phosphatases. These complex and intricate regulations make simple predictions of DARPP-32 dynamic behaviour virtually impossible. We used detailed quantitative modelling of the regulation of DARPP-32 phosphorylation to improve our understanding of its function. The models included all the combinations of the three best-characterized phosphorylation sites of DARPP-32, their regulation by kinases and phosphatases, and the regulation of those enzymes by cAMP and Ca(2+) signals. Dynamic simulations allowed us to observe the temporal relationships between cAMP and Ca(2+) signals. We confirmed that the proposed regulation of protein phosphatase-2A (PP2A) by calcium can account for the observed decrease of Threonine 75 phosphorylation upon glutamate receptor activation. DARPP-32 is not simply a switch between PP1-inhibiting and PKA-inhibiting states. Sensitivity analysis showed that CDK5 activity is a major regulator of the response, as previously suggested. Conversely, the strength of the regulation of PP2A by PKA or by calcium had little effect on the PP1-inhibiting function of DARPP-32 in these conditions. The simulations showed that DARPP-32 is not only a robust signal integrator, but that its response also depends on the delay between cAMP and calcium signals affecting the response to the latter. This integration did not depend on the concentration of DARPP-32, while the absolute effect on PP1 varied linearly. In silico mutants showed that Ser137 phosphorylation affects the influence of the delay between dopamine and glutamate, and that constitutive phosphorylation in Ser137 transforms DARPP-32 in a quasi-irreversible switch. This work is a first attempt to better understand the complex interactions between cAMP and Ca(2+) regulation of DARPP-32. Progressive inclusion of additional components should lead to a realistic model of signalling networks underlying the function of striatal neurons.", + "title": "DARPP-32 is a robust integrator of dopamine and glutamate signals.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000152", + "submissionId": "MODEL3492630792", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000153": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"orcid": "0000-0001-5104-9507" + }, + { + "name": "Jean-Antoine Girault", + "orcid": "0000-0002-7900-1705" + }, + { + "name": "Nicolas Le Nov\u00e8re" + } + ], + "issue": "12", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/17194217", + "month": "12", + "pages": "e176", + "synopsis": "Integration of neurotransmitter and neuromodulator signals in the striatum plays a central role in the functions and dysfunctions of the basal ganglia. DARPP-32 is a key actor of this integration in the GABAergic medium-size spiny neurons, in particular in response to dopamine and glutamate. When phosphorylated by cAMP-dependent protein kinase (PKA), DARPP-32 inhibits protein phosphatase-1 (PP1), whereas when phosphorylated by cyclin-dependent kinase 5 (CDK5) it inhibits PKA. DARPP-32 is also regulated by casein kinases and by several protein phosphatases. These complex and intricate regulations make simple predictions of DARPP-32 dynamic behaviour virtually impossible. We used detailed quantitative modelling of the regulation of DARPP-32 phosphorylation to improve our understanding of its function. The models included all the combinations of the three best-characterized phosphorylation sites of DARPP-32, their regulation by kinases and phosphatases, and the regulation of those enzymes by cAMP and Ca(2+) signals. Dynamic simulations allowed us to observe the temporal relationships between cAMP and Ca(2+) signals. We confirmed that the proposed regulation of protein phosphatase-2A (PP2A) by calcium can account for the observed decrease of Threonine 75 phosphorylation upon glutamate receptor activation. DARPP-32 is not simply a switch between PP1-inhibiting and PKA-inhibiting states. Sensitivity analysis showed that CDK5 activity is a major regulator of the response, as previously suggested. Conversely, the strength of the regulation of PP2A by PKA or by calcium had little effect on the PP1-inhibiting function of DARPP-32 in these conditions. The simulations showed that DARPP-32 is not only a robust signal integrator, but that its response also depends on the delay between cAMP and calcium signals affecting the response to the latter. This integration did not depend on the concentration of DARPP-32, while the absolute effect on PP1 varied linearly. In silico mutants showed that Ser137 phosphorylation affects the influence of the delay between dopamine and glutamate, and that constitutive phosphorylation in Ser137 transforms DARPP-32 in a quasi-irreversible switch. This work is a first attempt to better understand the complex interactions between cAMP and Ca(2+) regulation of DARPP-32. Progressive inclusion of additional components should lead to a realistic model of signalling networks underlying the function of striatal neurons.", + "title": "DARPP-32 is a robust integrator of dopamine and glutamate signals.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000153", + "submissionId": "MODEL3492674214", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000154": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 6B of the paper for model 3. The model was reproduced using XPP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces Fig 6B of the paper for model 6. The model was reproduced using XPP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 5. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 4. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 2. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"Naama Geva-Zatorsky", + "orcid": "0000-0002-7303-854X" + }, + { + "name": "Nitzan Rosenfeld" + }, + { + "name": "Shalev Itzkovitz", + "orcid": "0000-0003-0685-2522" + }, + { + "name": "Ron Milo", + "orcid": "0000-0003-1641-2299" + }, + { + "name": "Alex Sigal", + "orcid": "0000-0001-8571-2004" + }, + { + "name": "Erez Dekel" + }, + { + "name": "Talia Yarnitzky" + }, + { + "name": "Yuvalal Liron" + }, + { + "name": "Paz Polak" + }, + { + "name": "Galit Lahav" + }, + { + "name": "Uri Alon" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/16773083", + "month": "0", + "pages": "2006.0033", + "synopsis": "Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time.", + "title": "Oscillations and variability in the p53 system.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000158", + "submissionId": "MODEL0076306022", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000159": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profile of p53 and Mdm2 as depicted in Fig 6B of the plot for model 1. Results obtained on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"2006.0033", + "synopsis": "Understanding the dynamics and variability of protein circuitry requires accurate measurements in living cells as well as theoretical models. To address this, we employed one of the best-studied protein circuits in human cells, the negative feedback loop between the tumor suppressor p53 and the oncogene Mdm2. We measured the dynamics of fluorescently tagged p53 and Mdm2 over several days in individual living cells. We found that isogenic cells in the same environment behaved in highly variable ways following DNA-damaging gamma irradiation: some cells showed undamped oscillations for at least 3 days (more than 10 peaks). The amplitude of the oscillations was much more variable than the period. Sister cells continued to oscillate in a correlated way after cell division, but lost correlation after about 11 h on average. Other cells showed low-frequency fluctuations that did not resemble oscillations. We also analyzed different families of mathematical models of the system, including a novel checkpoint mechanism. The models point to the possible source of the variability in the oscillations: low-frequency noise in protein production rates, rather than noise in other parameters such as degradation rates. This study provides a view of the extensive variability of the behavior of a protein circuit in living human cells, both from cell to cell and in the same cell over time.", + "title": "Oscillations and variability in the p53 system.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000159", + "submissionId": "MODEL0076281110", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000160": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the oscillations for mRNA and protein species as depicted in Fig 3 of the plot. The model differs slightly from that given in the paper and this was made after a communication from the authors. The values of parameters tcvriclkp, tcdvpmt and dccpt are slightly different. Also, although it is not given in the paper, rate laws for reactions re20, re28, re35, re42, re43 and re45 are multiplied by a specie. Model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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University, Canterbury, New Zealand.", + "authors": [ + { + "institution": "Centre for Advanced Computational Solutions (C-fACS), Lincoln University, Canterbury, New Zealand.", + "name": "Z Xie", + "orcid": "0000-0002-5589-4836" + }, + { + "institution": "Centre for Advanced Computational Solutions, Lincoln University, New Zealand. Electronic address: don.kulasiri@lincoln.ac.nz.", + "name": "Kulasiri D", + "orcid": "0000-0001-8744-1578" + } + ], + "issue": "2", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/17157878", + "month": "3", + "pages": "290-304", + "synopsis": "Circadian rhythms of gene activity, metabolism, physiology and behaviour are observed in all the eukaryotes and some prokaryotes. In this study, we present a model to represent the transcriptional regulatory network essential for the circadian rhythmicity in Drosophila. The model incorporates the transcriptional feedback loops revealed so far in the network of the circadian clock (PER/TIM and VRI/PDP1 loops). Conventional Hill functions are not assumed to describe the regulation of genes, instead of the explicit reactions of binding and unbinding processes of transcription factors to promoters are modelled. The model simulates sustained circadian oscillations in mRNA and protein concentrations in constant darkness in agreement with experimental observations. It also simulates entrainment by light-dark cycles, disappearance of the rhythmicity in constant light and the shape of phase response curves resembling that of the experimental results. The model is robust over a wide range of parameter variations. In addition, the simulated E-box mutation, per(S) and per(L) mutants are similar to that observed in the experiments. The deficiency between the simulated mRNA levels and experimental observations in per(01), tim(01) and clk(Jrk) mutants suggests some difference on the part of the model from reality.", + "title": "Modelling of circadian rhythms in Drosophila incorporating the interlocked PER/TIM and VRI/PDP1 feedback loops.", + "type": "PubMed ID", + "volume": "245", + "year": 2007 + }, + "publicationId": "BIOMD0000000160", + "submissionId": "MODEL1029395046", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000161": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profiles of Golgi Ras-GTP and plasma membrane Ras-GTP, subjected to a palmitoylation rate of 0.00015849 second inverse. This is depicted in Fig 5a and 5b for various palmitolylation rates, however the value used in this model is not present in the figure in the paper but corresponds to Fig S2 of the supplement. Model successfully reproduced using MathSBML. Please note that the units of volumetric species in this model are molecules/micrometer cubed, to convert this to microMolar as given in the paper, multiply the simulation result by 1/602.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Trafficking between these subcellular locations is a regulated dynamic process. The effects of trafficking and the molecular mechanisms underlying compartment-specific Ras activation were studied using numerical simulations of an ordinary differential equation-based multi-compartment model. The simulations show that interplay between two distinct mechanisms, a palmitoylation cycle that controls Ras trafficking and a phospholipase C-epsilon (PLC-epsilon) driven feedback loop, can convert a transient calcium signal into prolonged Ras activation at the Golgi. Detailed analysis of the network identified PLC-epsilon as a key determinant of \"compartment switching\". Modulation of PLC-epsilon activity switches the location of activated Ras between the plasma membrane and Golgi through a new mechanism termed \"kinetic scaffolding\". These simulations indicate that multiple biochemical mechanisms, when appropriately coupled, can give rise to an intracellular compartment-specific sustained Ras activation in response to stimulation of growth factor receptors at the plasma membrane.", + "title": "Compartment-specific feedback loop and regulated trafficking can result in sustained activation of Ras at the Golgi.", + "type": "PubMed ID", + "volume": "92", + "year": 2007 + }, + "publicationId": "BIOMD0000000161", + "submissionId": "MODEL1633592197", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000162": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profiles of Calcium in the spine and dendrites as depicted in Fig 8 and Fig 9 of the paper for CF activation.

The model was reproduced using MathSBML.

Please note that the units of volume species is molecules/micrometer cubed as against the units of microMolar given in the paper. To convert the units to microMolar multiply the species concentration by the conversion factor 1/602.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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that precede long-term depression of synaptic activity in cerebellar Purkinje cells are performed using the Virtual Cell biological modeling framework. It is found that the unusually high density and low sensitivity of inositol-1,4,5-trisphosphate receptors (IP3R) are critical to the ability of the cell to generate and localize a calcium spike in a single dendritic spine. The results also demonstrate the model's capability to simulate the supralinear calcium spike observed experimentally during coincident activation of the parallel and climbing fibers. The sensitivity of the calcium spikes to certain biological and geometrical effects is investigated as well as the mechanisms that underlie the cell's ability to generate the supralinear spike. The sensitivity of calcium release rates from the IP3R to calcium concentrations, as well as IP3 concentrations, allows the calcium spike to form. The diffusion barrier caused by the small radius of the spine neck is shown to be important, as a threshold radius is observed above which a spike cannot be formed. Additionally, the calcium buffer capacity and diffusion rates from the spine are found to be important parameters in shaping the calcium spike.", + "title": "Modeling and analysis of calcium signaling events leading to long-term depression in cerebellar Purkinje cells.", + "type": "PubMed ID", + "volume": "89", + "year": 2005 + }, + "publicationId": "BIOMD0000000162", + "submissionId": "MODEL1629310283", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000163": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max Planck Institute for Molecular Genetics", + "email": "zhike_zi@molgen.mpg.de", + "external": false, + "name": "Zhike Zi" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profiles of Total Smad2 in the nucleus as well as the cytoplasm as depicted in 2D and also the other time profiles as depicted in Fig 2. Two parameters that are not present in the paper are introduced here for illustration purposes and they are Total Smad2n and Total Smad2c. The term kr_EE*LRC_EE has not been included in the ODE's for T1R_surf, T2R_surf and TGFbeta in the paper but is included in this model. MathSBML was used to reproduce the simulation result.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"SBML", + "version": "L2V1" + }, + "history": { + "revisions": [ + { + "comment": "Original import of Zi_PLoS ONE_TGF_beta Signaling Pathway", + "submitted": 1203897951, + "submitter": "Zhike Zi", + "version": 1 + }, + { + "comment": "Current version of Zi2007_TGFbeta_signaling", + "submitted": 1341506746, + "submitter": "Zhike Zi", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724265497, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "131567", + "name": "cellular organisms", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/131567" + }, + { + "accession": "MODEL3388742457", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL3388742457" + }, + { + "accession": "BIOMD0000000163", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000163" + }, + { + "accession": "17895977", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/17895977" + }, + { + "accession": "GO:0017015", + "name": "regulation of transforming growth factor beta receptor signaling pathway", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0017015" + }, + { + "accession": "hsa04350", + "name": "TGF-beta signaling pathway - Homo sapiens (human)", + "qualifier": "bqbiol:isPartOf", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/hsa04350" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Zi2007_TGFbeta_signaling", + "publication": { + "accession": "17895977", + "affiliation": "Computational Systems Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany.", + "authors": [ + { + "institution": "BIOSS Centre for Biological Signalling Studies and Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany. zhike.zi@bioss.uni-freiburg.de", + "name": "Zhike Zi", + "orcid": "0000-0002-7601-915X" + }, + { + "name": "Edda Klipp" + } + ], + "issue": "9", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/17895977", + "month": "9", + "pages": "e936", + "synopsis": "

Background

Investigation of dynamics and regulation of the TGF-beta signaling pathway is central to the understanding of complex cellular processes such as growth, apoptosis, and differentiation. In this study, we aim at using systems biology approach to provide dynamic analysis on this pathway.

Methodology/principal findings

We proposed a constraint-based modeling method to build a comprehensive mathematical model for the Smad dependent TGF-beta signaling pathway by fitting the experimental data and incorporating the qualitative constraints from the experimental analysis. The performance of the model generated by constraint-based modeling method is significantly improved compared to the model obtained by only fitting the quantitative data. The model agrees well with the experimental analysis of TGF-beta pathway, such as the time course of nuclear phosphorylated Smad, the subcellular location of Smad and signal response of Smad phosphorylation to different doses of TGF-beta.

Conclusions/significance

The simulation results indicate that the signal response to TGF-beta is regulated by the balance between clathrin dependent endocytosis and non-clathrin mediated endocytosis. This model is useful to be built upon as new precise experimental data are emerging. The constraint-based modeling method can also be applied to quantitative modeling of other signaling pathways.", + "title": "Constraint-based modeling and kinetic analysis of the Smad dependent TGF-beta signaling pathway.", + "type": "PubMed ID", + "volume": "2", + "year": 2007 + }, + "publicationId": "BIOMD0000000163", + "submissionId": "MODEL3388742457", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000164": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the compartmental model for Ran transport as depicted in Fig 3 of the paper. Model reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces Fig 2B of the paper. Model successfully tested on MathSBML


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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These findings indicate that combining live-cell FRET imaging and mechanistic computational models can provide quantitative understanding of spatiotemporal signaling.", + "title": "Systems analysis of PKA-mediated phosphorylation gradients in live cardiac myocytes.", + "type": "PubMed ID", + "volume": "103", + "year": 2006 + }, + "publicationId": "BIOMD0000000165", + "submissionId": "MODEL8003201958", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000166": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A theoretical study on activation of transcription factor modulated by intracellular Ca2+ oscillations.
Zhu CL, Zheng Y, Jia Y Biophys. Chem.[2007 Aug:129(1):49-55 17560007,
Abstract:
This work presents both deterministic and stochastic models of genetic expression modulated by intracellular calcium (Ca2+) oscillations, based on macroscopic differential equations and chemical Langevin equations, respectively. In deterministic case, the oscillations of intracellular Ca2+ decrease the effective Ca2+ threshold for the activation of transcriptional activator (TF-A). The average activation of TF-A increases with the increase of the average amplitude of intracellular Ca2+ oscillations, but decreases with the increase of the period of intracellular Ca2+ oscillations, which are qualitatively consistent with the experimental results on the gene expression in lymphocytes. In stochastic case, it is found that a large internal fluctuation of the biochemical reaction can enhance gene expression efficiency specifically at a low level of external stimulations or at a small rate of TF-A dimer phosphorylation activated by Ca2+, which reduces the threshold of the average intracellular Ca2+ concentration for gene expression.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M.(2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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+ { + "institution": "Department of Physics, Jianghan University, Wuhan 430056, China.", + "name": "Chun-lian Zhu" + }, + { + "name": "Yuan Zheng" + }, + { + "name": "Ya Jia", + "orcid": "0000-0002-2818-9074" + } + ], + "issue": "1", + "journal": "Biophysical chemistry", + "link": "http://identifiers.org/pubmed/17560007", + "month": "8", + "pages": "49-55", + "synopsis": "This work presents both deterministic and stochastic models of genetic expression modulated by intracellular calcium (Ca2+) oscillations, based on macroscopic differential equations and chemical Langevin equations, respectively. In deterministic case, the oscillations of intracellular Ca2+ decrease the effective Ca2+ threshold for the activation of transcriptional activator (TF-A). The average activation of TF-A increases with the increase of the average amplitude of intracellular Ca2+ oscillations, but decreases with the increase of the period of intracellular Ca2+ oscillations, which are qualitatively consistent with the experimental results on the gene expression in lymphocytes. In stochastic case, it is found that a large internal fluctuation of the biochemical reaction can enhance gene expression efficiency specifically at a low level of external stimulations or at a small rate of TF-A dimer phosphorylation activated by Ca2+, which reduces the threshold of the average intracellular Ca2+ concentration for gene expression.", + "title": "A theoretical study on activation of transcription factor modulated by intracellular Ca2+ oscillations.", + "type": "PubMed ID", + "volume": "129", + "year": 2007 + }, + "publicationId": "BIOMD0000000166", + "submissionId": "MODEL8262954331", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000167": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 2B of the paper. Model successfully reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Biology and Center for Cell Analysis and Modeling, University of Connecticut Health Center, Farmington 06030, USA. les@volt.uchc.edu", + "name": "V Mayya", + "orcid": "0000-0001-5668-9124" + }, + { + "name": "L M Loew", + "orcid": "0000-0002-1851-4646" + } + ], + "issue": "1", + "journal": "Systems biology", + "link": "http://identifiers.org/pubmed/17091582", + "month": "3", + "pages": "43-52", + "synopsis": "Signal transducer and actuator of transcription (STATs) are a family of transcription factors activated by various cytokines, growth factors and hormones. They are important mediators of immune responses and growth and differentiation of various cell types. The STAT signalling system represents a defined functional module with a pattern of signalling that is conserved from flies to mammals. In order to probe and gain insights into the signalling properties of the STAT module by computational means, we developed a simple non-linear ordinary differential equations model within the 'Virtual Cell' framework. Our results demonstrate that the STAT module can operate as a 'biphasic amplitude filter' with an ability to amplify input signals within a specific intermediate range. We show that dimerisation of phosphorylated STAT is crucial for signal amplification and the amplitude filtering function. We also demonstrate that maximal amplification at intermediate levels of STAT activation is a moderately robust property of STAT module. We propose that these observations can be extrapolated to the analogous SMAD signalling module.", + "title": "STAT module can function as a biphasic amplitude filter.", + "type": "PubMed ID", + "volume": "2", + "year": 2005 + }, + "publicationId": "BIOMD0000000167", + "submissionId": "MODEL6095659304", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000168": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profiles of the different species depicted in Fig 3a of the paper. Model successfully reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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O. Box 237, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA. mandri@odin.mdacc.tmc.edu", + "authors": [ + { + "institution": "Department of Biomathematics, P. O. Box 237, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, USA. mandri@odin.mdacc.tmc.edu", + "name": "M N Obeyesekere" + }, + { + "name": "S O Zimmerman" + }, + { + "name": "E S Tecarro" + }, + { + "name": "G Auchmuty" + } + ], + "issue": "5", + "journal": "Bulletin of mathematical biology", + "link": "http://identifiers.org/pubmed/17886749", + "month": "9", + "pages": "917-934", + "synopsis": "A modified version of a previously developed mathematical model [Obeyesekere et al., Cell Prolif. (1997)] of the G1-phase of the cell cycle is presented. This model describes the regulation of the G1-phase that includes the interactions of the nuclear proteins, RB, cyclin E, cyclin D, cdk2, cdk4 and E2F. The effects of the growth factors on cyclin D synthesis under saturated or unsaturated growth factor conditions are investigated based on this model. The solutions to this model (a system of nonlinear ordinary differential equations) are discussed with respect to existing experiments. Predictions based on mathematical analysis of this model are presented. In particular, results are presented on the existence of two stable solutions, i.e., bistability within the G1-phase. It is shown that this bistability exists under unsaturated growth factor concentration levels. This phenomenon is very noticeable if the efficiency of the signal transduction, initiated by the growth factors leading to cyclin D synthesis, is low. The biological significance of this result as well as possible experimental designs to test these predictions are presented.", + "title": "A model of cell cycle behavior dominated by kinetics of a pathway stimulated by growth factors.", + "type": "PubMed ID", + "volume": "61", + "year": 1999 + }, + "publicationId": "BIOMD0000000168", + "submissionId": "MODEL3896909694", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000169": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the time profiles of p27, E2F and aE/cdk2 as depicted in Figure 5 c of the paper. Model was simulated on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the time profile of the species as depicted in Fig 3A of the paper. Model successfully tested on MathSBML and Jarnac.

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Wolf", + "orcid": "0000-0003-3254-5868" + }, + { + "name": "Hanspeter Herzel" + }, + { + "name": "Achim Kramer", + "orcid": "0000-0001-9671-6078" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/15347590", + "month": "11", + "pages": "3023-3034", + "synopsis": "The suprachiasmatic nucleus governs daily variations of physiology and behavior in mammals. Within single neurons, interlocked transcriptional/translational feedback loops generate circadian rhythms on the molecular level. We present a mathematical model that reflects the essential features of the mammalian circadian oscillator to characterize the differential roles of negative and positive feedback loops. The oscillations that are obtained have a 24-h period and are robust toward parameter variations even when the positive feedback is replaced by a constantly expressed activator. This demonstrates the crucial role of the negative feedback for rhythm generation. Moreover, it explains the rhythmic phenotype of Rev-erbalpha-/- mutant mice, where a positive feedback is missing. The interplay of negative and positive feedback reveals a complex dynamics. In particular, the model explains the unexpected rescue of circadian oscillations in Per2Brdm1/Cry2-/- double-mutant mice (Per2Brdm1 single-mutant mice are arrhythmic). Here, a decrease of positive feedback strength associated with mutating the Per2 gene is compensated by the Cry2-/- mutation that simultaneously decreases the negative feedback strength. Finally, this model leads us to a testable prediction of a molecular and behavioral phenotype: circadian oscillations should be rescued when arrhythmic Per2Brdm1 mutant mice are crossed with Rev- erbalpha -/- mutant mice.", + "title": "Modeling feedback loops of the Mammalian circadian oscillator.", + "type": "PubMed ID", + "volume": "87", + "year": 2004 + }, + "publicationId": "BIOMD0000000170", + "submissionId": "MODEL8378813456", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000171": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Leloup and Goldbeter, 1998

This model was created after the article by Leloup and Goldbeter, J Biol Rhythms 1998, Vol:13(1),pp70-87, pubmedID: 9486845
A Model for Circadian Rhythms in Drosophila Incorporating the Formation of a Complex between the PER and TIM Proteins
The parameters and initial concentrations are taken to reproduce figs. 4 D,E,F in the publication.
For a simulation without light dependent degradation of TIM_pp, change the the parameter v_dT_fac to 1.
The light/dark phases length can be set using the parameter l_d .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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P. 231, B-1050 Brussels, Belgium.", + "name": "Jean-Christophe Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "A Goldbeter" + } + ], + "issue": "1", + "journal": "Journal of biological rhythms", + "link": "http://identifiers.org/pubmed/9486845", + "month": "2", + "pages": "70-87", + "synopsis": "The authors present a model for circadian oscillations of the Period (PER) and Timeless (TIM) proteins in Drosophila. The model for the circadian clock is based on multiple phosphorylation of PER and TIM and on the negative feedback exerted by a nuclear PER-TIM complex on the transcription of the per and tim genes. Periodic behavior occurs in a large domain of parameter space in the form of limit cycle oscillations. These sustained oscillations occur in conditions corresponding to continuous darkness or to entrainment by light-dark cycles and are in good agreement with experimental observations on the temporal variations of PER and TIM and of per and tim mRNAs. Birhythmicity (coexistence of two periodic regimes) and aperiodic oscillations (chaos) occur in a restricted range of parameter values. The results are compared to the predictions of a model based on the sole regulation by PER. Both the formation of a complex between PER and TIM and protein phosphorylation are found to favor oscillatory behavior. Determining how the period depends on several key parameters allows us to test possible molecular explanations proposed for the altered period in the per(l) and per(s) mutants. The extended model further allows the construction of phase-response curves based on the light-induced triggering of TIM degradation. These curves, established as a function of both the duration and magnitude of the effect of a light pulse, match the phase-response curves obtained experimentally in the wild type and per(s) mutant of Drosophila.", + "title": "A model for circadian rhythms in Drosophila incorporating the formation of a complex between the PER and TIM proteins.", + "type": "PubMed ID", + "volume": "13", + "year": 1998 + }, + "publicationId": "BIOMD0000000171", + "submissionId": "MODEL0243843132", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000172": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

from:
Schemes of fluc control in a model of Saccharomyces cerevisiae glycolysis

Pritchard, L and Kell, DB Eur. J. Biochem. 269(2002), 3894-3904.
It represents a modified version of Teusink et al. Eur. J. Biochem. 267(2000), 5313-5329.
The model is a translation from the GEPASI file encoded by Leighton Pritchard.
This version uses the Vmaxes found by the best fit (R1) of Table 1 of the \tPritchard and Kell paper and simulates a decrease of external glucose concentration from 100 to 2 mM.
To reproduce the values in table 2 of the publication, set GLCo to 50 mM and compute the steady state.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"12180966", + "affiliation": "Institute of Biological Sciences, University of Wales, Aberystwyth, UK.", + "authors": [ + { + "institution": "Institute of Biological Sciences, University of Wales, Aberystwyth, UK.", + "name": "Leighton Pritchard", + "orcid": "0000-0002-8392-2822" + }, + { + "name": "Douglas B Kell", + "orcid": "0000-0001-5838-7963" + } + ], + "issue": "16", + "journal": "European journal of biochemistry", + "link": "http://identifiers.org/pubmed/12180966", + "month": "8", + "pages": "3894-3904", + "synopsis": "We used parameter scanning to emulate changes to the limiting rate for steps in a fitted model of glucose-derepressed yeast glycolysis. Three flux-control regimes were observed, two of which were under the dominant control of hexose transport, in accordance with various experimental studies and other model predictions. A third control regime in which phosphofructokinase exerted dominant glycolytic flux control was also found, but it appeared to be physiologically unreachable by this model, and all realistically obtainable flux control regimes featured hexose transport as a step involving high flux control.", + "title": "Schemes of flux control in a model of Saccharomyces cerevisiae glycolysis.", + "type": "PubMed ID", + "volume": "269", + "year": 2002 + }, + "publicationId": "BIOMD0000000172", + "submissionId": "MODEL8293171637", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000173": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Developmental Signalling Lab, Cancer Research UK London Research Institute", + "email": "Bernhard.Schmierer@ymail.com", + "external": false, + "name": "Bernhard Schmierer" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This sbml file describes the RECI model from:
\t \"Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system\" by Bernhard Schmierer, Alexander L. Tournier, Paul A. Bates and Caroline S. Hill, Proc Natl Acad Sci U S A. 2008 May 6;105(18):6608-13.
\t All parameter and species names are as in Figure S3 of the original publication. The original model was done in copasi.
SB-431542 addition to a concentration of 10000 nM is set at 2700 sec. The initial concentration of SB, the time point of addition and the final concentration can be set by altering the parameters SB_0, t_SB and SB_end.
This model file has been used to reproduce Figures 2D and 5A from the research paper using SBMLodesolver. To get the results for the figures, sum the corresponding concentrations:
fig 2D: nuclear EGFP-Smad2 = G_n + pG_n + G2_n + G4_n + 2* GG_n
fig 5A (either n or c for nucleus or cytosol):
monomeric Smad2 = S2_n/c + G_n/c
monomeric P-Smad2 = pS2_n/c + pG_n/c
Smad2/Smad4 complexes = S24_n/c + G4_n/c
Smad2/Smad2 complexes = S22_n/c + G2_n/c + GG_n/c

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Membrane identity and GTPase cascades regulated by toggle and cut-out switches
\t Perla Del Conte-Zerial, Lutz Brusch, Jochen C Rink, Claudio Collinet, Yannis Kalaidzidis, Marino Zerial, and Andreas Deutsch: Molecular Systems Biology4:206 15 July 2008, doi:10.1038/msb.2008.45

This is the cut-out switch model for the Rab5 - Rab7 transition, also referred to as model 2 in the original publication.
This model is not completely described in all details in the publication. Thanks go to Barbara Szomolay and Lutz Brusch for finding and clarifying this. According to Dr. Brusch this model represents the mechanism identified by the qualitative analysis in the article in the scenario deemed most useful by the authors. For the time-course simulations it was necessary to add a time dependency to one of the parameters, which is only verbally described in the article.
As argued in the publication the switch between early and late endosomes can be triggered by a parameter change. While with fixed parameter values each switch just converges to one steady state from its initial conditions and stays there, endosomes should switch between two different states. These changes would in reality of course depend on many different factors, such as cargo composition and amount in the specific endosome, its location and some additional cellular control mechanisms and encompass many different parameters. To keep the model simple the authors chose to add a time dependency to only one reaction - ke in the activation of RAB5 is multiplied with a term monotonously increasing over time from 0 to 1. They also hard coded a time dependence in this term, 100 minutes, to make the switch occur after several hundred minutes. As long as this modulating term remains monotonic all resulting time courses should look similar, with the switching behavior depending on the initial conditions and whether the term is increasing or decreasing. Monotonic increase is a reasonable assumption for the described mechanism of cargo accumulation.
Not explicitly described in the article:activation of Rab5 (time) : r*ke*time/(100+time) /(1+e(kg-R)*kf) instead of r*ke/(1+e(kg-R)*kf)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Performance Computing, University of Technology Dresden, Dresden, Germany.", + "authors": [ + { + "institution": "Center for Information Services and High Performance Computing, University of Technology Dresden, Dresden, Germany.", + "name": "Perla Del Conte-Zerial" + }, + { + "institution": "Center for Information Services and High Performance Computing (ZIH), TUD Dresden University of Technology", + "name": "Lutz Brusch", + "orcid": "0000-0003-0137-5106" + }, + { + "name": "Jochen C Rink" + }, + { + "name": "Claudio Collinet", + "orcid": "0000-0002-8532-2601" + }, + { + "name": "Yannis Kalaidzidis", + "orcid": "0000-0002-6137-1193" + }, + { + "name": "Marino Zerial" + }, + { + "name": "Andreas Deutsch" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/18628746", + "month": "0", + "pages": "206", + "synopsis": "Key cellular functions and developmental processes rely on cascades of GTPases. GTPases of the Rab family provide a molecular ID code to the generation, maintenance and transport of intracellular compartments. Here, we addressed the molecular design principles of endocytosis by focusing on the conversion of early endosomes into late endosomes, which entails replacement of Rab5 by Rab7. We modelled this process as a cascade of functional modules of interacting Rab GTPases. We demonstrate that intermodule interactions share similarities with the toggle switch described for the cell cycle. However, Rab5-to-Rab7 conversion is rather based on a newly characterized 'cut-out switch' analogous to an electrical safety-breaker. Both designs require cooperativity of auto-activation loops when coupled to a large pool of cytoplasmic proteins. Live cell imaging and endosome tracking provide experimental support to the cut-out switch in cargo progression and conversion of endosome identity along the degradative pathway. We propose that, by reconciling module performance with progression of activity, the cut-out switch design could underlie the integration of modules in regulatory cascades from a broad range of biological processes.", + "title": "Membrane identity and GTPase cascades regulated by toggle and cut-out switches.", + "type": "PubMed ID", + "volume": "4", + "year": 2008 + }, + "publicationId": "BIOMD0000000174", + "submissionId": "MODEL5937037510", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000175": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Nature Publishing Group", + "email": "msbforum@embo.org", + "external": false, + "name": "Molecular Systems Biology" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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}, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724265827, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "9606", + "name": "Homo sapiens", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/9606" + }, + { + "accession": "MODEL5563731079", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL5563731079" + }, + { + "accession": "BIOMD0000000175", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000175" + }, + { + "accession": "18004277", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/18004277" + }, + { + "accession": "REACT_12058.1", + "name": "Signalling to ERKs", + "qualifier": "bqbiol:hasVersion", + "resource": "Reactome", + "uri": 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DE, USA.", + "authors": [ + { + "name": "Marc R Birtwistle", + "orcid": "0000-0002-0341-0705" + }, + { + "name": "Mariko Hatakeyama" + }, + { + "name": "Noriko Yumoto" + }, + { + "name": "Babatunde A Ogunnaike", + "orcid": "0000-0002-8246-070X" + }, + { + "name": "Jan B Hoek", + "orcid": "0000-0001-7127-4218" + }, + { + "name": "Boris N Kholodenko", + "orcid": "0000-0002-9483-4975" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/18004277", + "month": "0", + "pages": "144", + "synopsis": "Deregulation of ErbB signaling plays a key role in the progression of multiple human cancers. To help understand ErbB signaling quantitatively, in this work we combine traditional experiments with computational modeling, building a model that describes how stimulation of all four ErbB receptors with epidermal growth factor (EGF) and heregulin (HRG) leads to activation of two critical downstream proteins, extracellular-signal-regulated kinase (ERK) and Akt. Model analysis and experimental validation show that (i) ErbB2 overexpression, which occurs in approximately 25% of all breast cancers, transforms transient EGF-induced signaling into sustained signaling, (ii) HRG-induced ERK activity is much more robust to the ERK cascade inhibitor U0126 than EGF-induced ERK activity, and (iii) phosphoinositol-3 kinase is a major regulator of post-peak but not pre-peak EGF-induced ERK activity. Sensitivity analysis leads to the hypothesis that ERK activation is robust to parameter perturbation at high ligand doses, while Akt activation is not.", + "title": "Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses.", + "type": "PubMed ID", + "volume": "3", + "year": 2007 + }, + "publicationId": "BIOMD0000000175", + "submissionId": "MODEL5563731079", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000176": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Smurfit Institute of Genetics, University of Dublin, Trinity College, Dublin 2, Ireland", + "email": "conantg@tcd.ie", + "external": false, + "name": "Gavin Conant" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Increased glycolytic flux as an outcome of whole-genome duplication in yeast.
Conant GC, Wolfe KH Mol. Syst. Biol. [2007 ; Volume: 3 (Issue: )]: 129 17667951 ,
Abstract:
After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.



The original model submitted by the authors was slightly altered and now comprises the models originally submitted as MODEL2426780967, MODEL2427021978, MODEL2427095802. It reproduces figures 2A,3A and 3B from the publication.

This model uses the glycolysis model from Pritchard and Kell (2002) with an additional parameter, WGD_E , to adjust for the differing enzyme conzentrations before the whole genome duplication (WGD) and parameters fV_xxx that adjust the Vmax of the different reactions (xxx eg. HXT or PYK).
Figure 3A from the article can be reproduced by changing the value of the parameters fV_xxx to 0.9 indiviually, with xxx signifying the different enzymes (HXT, HXK ...)
Figure 3B from the publication can be reproduced by setting the parameter WGD_E to 0.75 and individually setting the parameters fV_xxx to 1.333.
To reproduce figure 2A from the article change the parameter WGD_E in the range between 0.65 and 1.0.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Increased glycolytic flux as an outcome of whole-genome duplication in yeast.
Conant GC, Wolfe KH Mol. Syst. Biol. [2007 ; Volume: 3 (Issue: )]: 129 17667951 ,
Abstract:
After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.



This model reproduces fig. 2C from the corrigendum to the publication
The parameter Vmax_PDH was corrected by a factor 60 from 6.32 mM/min in the publication to 379.2 mM/min in accordance with the authors.
see the corrigendum at msb or its pubmed entry (pmid:18594520)

This model comprises the glycolysis model from Pritchard and Kell (2002) with an extension for the metabolisation of pyruvate in the mitochondria by pyruvate dehydrogenase and an additional parameter, WGD_E , to adjust for the differing enzyme concentrations before the whole genome duplication (WGD).
To switch off transport of pyruvate to the mitochondria, set the parameter t_m = 0.
Figure 2C from the article can be reproduced by manually changing the value of parameter WGD_E in the range between 0.65 and 1.0 and calculating the ratios of ratio of PDC/PDH fluxes in the altered model to the one of the model with WGD_E = 1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Lebeda2008 - BoTN Paralysis (4 stepmodel)
The onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction is described in this model. This model is the extended model of\u00a0 BIOMD0000000267 , which itself is the reduced form of the model developed by Simpson 1980; PMID\u00a0 6243359

This model is described in the article:

Lebeda FJ, Adler M, Erickson K, Chushak Y.
J Pharmacokinet Pharmacodyn 2008 Jun; 35(3): 251-267

Abstract:

Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Frank.Lebeda@amedd.army.mil", + "authors": [ + { + "institution": "Integrated Toxicology Division, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702-5011, USA. Frank.Lebeda@amedd.army.mil", + "name": "Frank J Lebeda" + }, + { + "name": "Michael Adler" + }, + { + "name": "Keith Erickson" + }, + { + "name": "Yaroslav Chushak" + } + ], + "issue": "3", + "journal": "Journal of pharmacokinetics and pharmacodynamics", + "link": "http://identifiers.org/pubmed/18551355", + "month": "6", + "pages": "251-267", + "synopsis": "Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.", + "title": "Onset dynamics of type A botulinum neurotoxin-induced paralysis.", + "type": "PubMed ID", + "volume": "35", + "year": 2008 + }, + "publicationId": "BIOMD0000000178", + "submissionId": "MODEL8472829123", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000179": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Interlinked mutual inhibitory positive feedbacks induce robust cellular memory effects.
Kim TH, Jung SH, Cho KH FEBS Lett.2007 Oct; 581(25) 17892872,
Abstract:
Mutual inhibitory positive feedback (MIPF), or double-negative feedback, is a key regulatory motif of cellular memory with the capability of maintaining switched states for transient stimuli. Such MIPFs are found in various biological systems where they are interlinked in many cases despite a single MIPF can still realize such a memory effect. An intriguing question then arises about the advantage of interlinking MIPFs instead of exploiting an isolated single MIPF to realize the memory effect. We have investigated the advantages of interlinked MIPF systems through mathematical modeling and computer simulations. Our results revealed that interlinking MIPFs expands the parameter range of achieving the memory effect, or the memory region, thereby making the system more robust to parameter perturbations. Moreover, the minimal duration and amplitude of an external stimulus required for off-to-on state transition are increased and, as a result, external noises can more effectively be filtered out. Hence, interlinked MIPF systems can realize more robust cellular memories with respect to both parameter perturbations and external noises. Our study suggests that interlinked MIPF systems might be an evolutionary consequence acquired for a more reliable memory effect by enhancing robustness against noisy cellular environments.

Note: The model reproduces the simulation result for an asymmetric model as depicted in Fig 3G of the paper. Model successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Interlinked mutual inhibitory positive feedbacks induce robust cellular memory effects.
Kim TH, Jung SH, Cho KH FEBS Lett.2007 Oct; 581(25) 17892872,
Abstract:
Mutual inhibitory positive feedback (MIPF), or double-negative feedback, is a key regulatory motif of cellular memory with the capability of maintaining switched states for transient stimuli. Such MIPFs are found in various biological systems where they are interlinked in many cases despite a single MIPF can still realize such a memory effect. An intriguing question then arises about the advantage of interlinking MIPFs instead of exploiting an isolated single MIPF to realize the memory effect. We have investigated the advantages of interlinked MIPF systems through mathematical modeling and computer simulations. Our results revealed that interlinking MIPFs expands the parameter range of achieving the memory effect, or the memory region, thereby making the system more robust to parameter perturbations. Moreover, the minimal duration and amplitude of an external stimulus required for off-to-on state transition are increased and, as a result, external noises can more effectively be filtered out. Hence, interlinked MIPF systems can realize more robust cellular memories with respect to both parameter perturbations and external noises. Our study suggests that interlinked MIPF systems might be an evolutionary consequence acquired for a more reliable memory effect by enhancing robustness against noisy cellular environments.

Note: The model reproduces the simulation result for the symmetric model as depicted in Fig 3H of the paper. Model successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the time profile of species depicted in Figure 12a and 12 b. The authors communicated to the curator that there is a typo in the paper, the values of kd1 and kd2 are reversed. Model successfully reproduced using MathSBML.

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Neves2008 - Role of cell shape and size in controlling intracellular signalling

The role of cell shape and size in the flow of spatial information from the cell surface receptor to downstream components within the cell has been studied on the \u03b2-adrenergic receptor to MAPK-signalling network.

This model is described in the article:

Neves SR, Tsokas P, Sarkar A, Grace EA, Rangamani P, Taubenfeld SM, Alberini CM, Schaff JC, Blitzer RD, Moraru II, Iyengar R
Cell. 2008, 133(4):666-680

Abstract:

The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells.

This model is hosted on BioModels Database and identifiedby: MODEL8609366518 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Levy Place, Box 1215, New York, NY 10029, USA.", + "name": "Susana R Neves" + }, + { + "name": "Panayiotis Tsokas", + "orcid": "0000-0002-1679-8980" + }, + { + "name": "Anamika Sarkar" + }, + { + "name": "Elizabeth A Grace" + }, + { + "name": "Padmini Rangamani" + }, + { + "name": "Stephen M Taubenfeld" + }, + { + "name": "Cristina M Alberini" + }, + { + "name": "James C Schaff" + }, + { + "name": "Robert D Blitzer" + }, + { + "name": "Ion I Moraru", + "orcid": "0000-0002-3746-9676" + }, + { + "name": "Ravi Iyengar" + } + ], + "issue": "4", + "journal": "Cell", + "link": "http://identifiers.org/pubmed/18485874", + "month": "5", + "pages": "666-680", + "synopsis": "The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells.", + "title": "Cell shape and negative links in regulatory motifs together control spatial information flow in signaling networks.", + "type": "PubMed ID", + "volume": "133", + "year": 2008 + }, + "publicationId": "BIOMD0000000182", + "submissionId": "MODEL8609366518", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000183": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mstefan@ebi.ac.uk", + "external": false, + "name": "Melanie Stefan" + } + ] + }, + "curationStatus": "CURATED", + "description": " \t
\t Stefan2008 - calmodulin allostery\t
\t
\t

An allosteric model for calmodulin activation, in which binding to calcium facilitates the transition between a low-affinity [tense (T)] and a high-affinity [relaxed (R)] state.

\t
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This model is described in the article:

\t \t
Stefan MI, Edelstein SJ, Le Nov\u00e8re N
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Proc. Natl. Acad. Sci. U.S.A. 2008 Aug; 105(31): 10768-10773
\t

Abstract:

\t
\t

Calmodulin plays a vital role in mediating bidirectional synaptic plasticity by activating either calcium/calmodulin-dependent protein kinase II (CaMKII) or protein phosphatase 2B (PP2B) at different calcium concentrations. We propose an allosteric model for calmodulin activation, in which binding to calcium facilitates the transition between a low-affinity [tense (T)] and a high-affinity [relaxed (R)] state. The four calcium-binding sites are assumed to be nonidentical. The model is consistent with previously reported experimental data for calcium binding to calmodulin. It also accounts for known properties of calmodulin that have been difficult to model so far, including the activity of nonsaturated forms of calmodulin (we predict the existence of open conformations in the absence of calcium), an increase in calcium affinity once calmodulin is bound to a target, and the differential activation of CaMKII and PP2B depending on calcium concentration.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000183.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The model reproduces the time profile of cytoplasmic Calcium as depicted in Fig 3 of the paper. Model successfully reproduced using Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Lavrentovich2008_Ca_Oscillations", + "publication": { + "accession": "18275973", + "affiliation": "Department of Chemistry, Kenyon College, Gambier, OH 43022, USA.", + "authors": [ + { + "institution": "Department of Chemistry, Kenyon College, Gambier, OH 43022, USA.", + "name": "Maxim Lavrentovich", + "orcid": "0000-0002-5125-7979" + }, + { + "name": "Sheryl Hemkin" + } + ], + "issue": "4", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/18275973", + "month": "4", + "pages": "553-560", + "synopsis": "Astrocytes exhibit oscillations and waves of Ca2+ ions within their cytosol and it appears that this behavior helps facilitate the astrocyte's interaction with its environment, including its neighboring neurons. Often changes in the oscillatory behavior are initiated by an external stimulus such as glutamate, recently however, it has been observed that oscillations are also initiated spontaneously. We propose here a mathematical model of how spontaneous Ca2+ oscillations arise in astrocytes. This model uses the calcium-induced calcium release and inositol cross-coupling mechanisms coupled with a receptor-independent method for producing inositol (1,4,5)-trisphosphate as the heart of the model. By computationally mimicking experimental constraints we have found that this model provides results that are qualitatively similar to experiment.", + "title": "A mathematical model of spontaneous calcium(II) oscillations in astrocytes.", + "type": "PubMed ID", + "volume": "251", + "year": 2008 + }, + "publicationId": "BIOMD0000000184", + "submissionId": "MODEL9223106020", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000185": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 2A of the paper. Model successfully reproduced using Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724266095, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "40674", + "name": "Mammalia", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/40674" + }, + { + "accession": "MODEL9223733527", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL9223733527" + }, + { + "accession": "BIOMD0000000185", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000185" + }, + { + "accession": "18312618", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/18312618" + }, + { + "accession": "GO:0032922", + "name": "circadian regulation of gene expression", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0032922" + }, + { + "accession": "GO:0048512", + "name": "circadian behavior", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0048512" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Locke2008_Circadian_Clock", + "publication": { + "accession": "18312618", + "affiliation": "Institute for Theoretical Biology, Humboldt-University Berlin, 10115 Berlin, Germany. jlocke@caltech.edu.", + "authors": [ + { + "name": "James C W Locke", + "orcid": "0000-0003-0670-1943" + }, + { + "name": "P\u00e5l O Westermark" + }, + { + "name": "Achim Kramer", + "orcid": "0000-0001-9671-6078" + }, + { + "name": "Hanspeter Herzel" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/18312618", + "month": "2", + "pages": "22", + "synopsis": "

Background

Virtually all living organisms have evolved a circadian (~24 hour) clock that controls physiological and behavioural processes with exquisite precision throughout the day/night cycle. The suprachiasmatic nucleus (SCN), which generates these ~24 h rhythms in mammals, consists of several thousand neurons. Each neuron contains a gene-regulatory network generating molecular oscillations, and the individual neuron oscillations are synchronised by intercellular coupling, presumably via neurotransmitters. Although this basic mechanism is currently accepted and has been recapitulated in mathematical models, several fundamental questions about the design principles of the SCN remain little understood. For example, a remarkable property of the SCN is that the phase of the SCN rhythm resets rapidly after a 'jet lag' type experiment, i.e. when the light/dark (LD) cycle is abruptly advanced or delayed by several hours.

Results

Here, we describe an extensive parameter optimization of a previously constructed simplified model of the SCN in order to further understand its design principles. By examining the top 50 solutions from the parameter optimization, we show that the neurotransmitters' role in generating the molecular circadian rhythms is extremely important. In addition, we show that when a neurotransmitter drives the rhythm of a system of coupled damped oscillators, it exhibits very robust synchronization and is much more easily entrained to light/dark cycles. We were also able to recreate in our simulations the fast rhythm resetting seen after a 'jet lag' type experiment.

Conclusion

Our work shows that a careful exploration of parameter space for even an extremely simplified model of the mammalian clock can reveal unexpected behaviours and non-trivial predictions. Our results suggest that the neurotransmitter feedback loop plays a crucial role in the robustness and phase resetting properties of the mammalian clock, even at the single neuron level.", + "title": "Global parameter search reveals design principles of the mammalian circadian clock.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000185", + "submissionId": "MODEL9223733527", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000186": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center, Heidelberg", + "email": "b.ibrahim@dkfz.de", + "external": false, + "name": "Bashar Ibrahim" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Dissociation variant

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism. This model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. MCC:APC dissociation is described by two alternatives models, namely the \"Dissociation\" and the \"Convey\" model variants. Both these model are available in BioModels Database. This model corresponds to the \"Dissociation\" variant.

This model is described in the article:

Ibrahim B, Diekmann S, Schmitt E, Dittrich P
PLoS One. 2008 Feb 6;3(2):e1555.

Abstract:

BACKGROUND: The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

PRINCIPLE FINDINGS: We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

CONCLUSION: Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.

This model describes the controlled dissociation variant of the mitotic spindle assembly checkpoint. If the tool you use has problems with events, you can uncomment the assignment rules for u and u_prime and comment out the list of events.

In accordance with the authors due to typos in the original publication some initial conditions and parameters were slightly changed in the model:
article model
[O-Mad2] 1.5e-7 M 1.3e-7 M
[BubR1:Bub3] 1.30e-7 M 1.27e-7 M
k -4 0.01 M -1 s -1 0.02 M -1 s -1
k -5 0.1 M -1 s -1 0.2 M -1 s -1

This model is hosted on BioModels Database and identified by: MODEL6655615431 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dittrich" + } + ], + "issue": "2", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/18253502", + "month": "2", + "pages": "e1555", + "synopsis": "

Background

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

Principle findings

We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

Conclusion

Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.", + "title": "In-silico modeling of the mitotic spindle assembly checkpoint.", + "type": "PubMed ID", + "volume": "3", + "year": 2008 + }, + "publicationId": "BIOMD0000000186", + "submissionId": "MODEL6655615431", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000187": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center, Heidelberg", + "email": "b.ibrahim@dkfz.de", + "external": false, + "name": "Bashar Ibrahim" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Convey variant

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism. This model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. MCC:APC dissociation is described by two alternatives models, namely the \"Dissociation\" and the \"Convey\" model variants. Both these model are available in BioModels Database. This model corresponds to the \"Convey\" variant.

This model is described in the article:

Ibrahim B, Diekmann S, Schmitt E, Dittrich P
PLoS One. 2008 Feb 6;3(2):e1555.

Abstract:

BACKGROUND: The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

PRINCIPLE FINDINGS: We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

CONCLUSION: Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.

This model describes the controlled dissociation variant of the mitotic spindle assembly checkpoint. If the tool you use has problems with events, you can uncomment the assignment rules for u and u_prime and comment out the list of events.

In accordance with the authors due to typos in the original publication some initial conditions and parameters were slightly changed in the model:
article model
[O-Mad2] 1.5e-7 M 1.3e-7 M
[BubR1:Bub3] 1.30e-7 M 1.27e-7 M
k -4 0.01 M -1 s -1 0.02 M -1 s -1
k -5 0.1 M -1 s -1 0.2 M -1 s -1

This model is hosted on BioModels Database and identified by: MODEL6655578762 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Spindle Assembly Checkpoint - Convey variant", + "submitted": 1401807010, + "submitter": "Bashar Ibrahim", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724266147, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "9606", + "name": "Homo sapiens", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/9606" + }, + { + "accession": "18253502", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/18253502" + }, + { + "accession": "15694304", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/15694304" + }, + { + "accession": "REACT_2137.2", + "name": "Mitotic Spindle Checkpoint", + "qualifier": "bqbiol:isVersionOf", + "resource": "Reactome", + "uri": "http://identifiers.org/reactome/REACT_2137.2" + }, + { + "accession": "GO:0007094", + "name": "mitotic spindle assembly checkpoint", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0007094" + }, + { + "accession": "hsa04110", + "name": "Cell cycle - Homo sapiens (human)", + "qualifier": "bqbiol:isPartOf", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/hsa04110" + }, + { + "accession": "MODEL6655578762", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL6655578762" + }, + { + "accession": "BIOMD0000000187", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000187" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Convey variant", + "publication": { + "accession": "18253502", + "affiliation": "Bio System Analysis Group, Institute of Computer Science, Friedrich-Schiller-University Jena, Jena, Germany.", + "authors": [ + { + "institution": "Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally 32093, Kuwait.Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally 32093, Kuwait.", + "name": "Bashar Ibrahim", + "orcid": "0000-0001-7773-0122" + }, + { + "name": "Stephan Diekmann" + }, + { + "name": "Eberhard Schmitt" + }, + { + "name": "Peter Dittrich" + } + ], + "issue": "2", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/18253502", + "month": "2", + "pages": "e1555", + "synopsis": "

Background

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

Principle findings

We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

Conclusion

Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.", + "title": "In-silico modeling of the mitotic spindle assembly checkpoint.", + "type": "PubMed ID", + "volume": "3", + "year": 2008 + }, + "publicationId": "BIOMD0000000187", + "submissionId": "MODEL6655578762", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000188": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM

This model is described in the article:

Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75

Abstract:

BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000188.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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+ "submitted": 1460130198, + "submitter": "Carole Proctor", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724266174, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "9606", + "name": "Homo sapiens", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/9606" + }, + { + "accession": "MODEL5836973167", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL5836973167" + }, + { + "accession": "BIOMD0000000188", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000188" + }, + { + "accession": "18706112", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/18706112" + }, + { + "accession": "REACT_85", + "name": "p53-Dependent G1/S DNA damage checkpoint", + "qualifier": "bqbiol:isPartOf", + "resource": "Reactome", + "uri": "http://identifiers.org/reactome/REACT_85" + }, + { + "accession": "hsa04115", + "name": "p53 signaling pathway - Homo sapiens (human)", + "qualifier": "bqbiol:isPartOf", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/hsa04115" + }, + { + "accession": "REACT_309", + "name": "Stabilization of p53", + "qualifier": "bqbiol:isVersionOf", + "resource": "Reactome", + "uri": "http://identifiers.org/reactome/REACT_309" + }, + { + "accession": "GO:0043516", + "name": "regulation of DNA damage response, signal transduction by p53 class mediator", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0043516" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + 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"pages": "75", + "synopsis": "

Background

In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.

Results

The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.

Conclusion

The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.", + "title": "Explaining oscillations and variability in the p53-Mdm2 system.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000188", + "submissionId": "MODEL5836973167", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000189": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARF

This model is described in the article:

Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75

Abstract:

BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000188.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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p14ARF", + "submitted": 1460390808, + "submitter": "Carole Proctor", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724266200, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "9606", + "name": "Homo sapiens", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/9606" + }, + { + "accession": "MODEL8142536273", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL8142536273" + }, + { + "accession": "BIOMD0000000189", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000189" + }, + { + "accession": "18706112", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/18706112" + }, + { + "accession": "GO:0030330", + "name": "DNA damage response, signal transduction by p53 class mediator", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0030330" + }, + { + "accession": "REACT_85.1", + "name": "p53-Dependent G1/S DNA damage checkpoint", + "qualifier": "bqbiol:isPartOf", + "resource": "Reactome", + "uri": "http://identifiers.org/reactome/REACT_85.1" + }, + { + "accession": "hsa04115", + "name": "p53 signaling pathway - Homo sapiens (human)", + "qualifier": "bqbiol:isPartOf", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/hsa04115" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Proctor2008 - p53/Mdm2 circuit - p53 stablisation by p14ARF", + "publication": { + "accession": "18706112", + "affiliation": "Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK. c.j.proctor@ncl.ac.uk", + "authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "Douglas A Gray", + "orcid": "0000-0002-8634-4984" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/18706112", + "month": "8", + "pages": "75", + "synopsis": "

Background

In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing.

Results

The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells.

Conclusion

The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.", + "title": "Explaining oscillations and variability in the p53-Mdm2 system.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000189", + "submissionId": "MODEL8142536273", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000190": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of M?laga. CIBERER (Enfermedades Raras)", + "email": "armando@uma.es", + "external": false, + "name": "Armando Reyes-Palomares" + } + ] + }, + "curationStatus": "CURATED", + "description": "

SBML creators: Armando Reyes-Palomares * , Carlos Rodr\u00edguez-Caso +, Raul Monta\u00f1ez * , Marta Cascante $, Francisca S\u00e1nchez-Jim\u00e9nez * , Miguel A. Medina *

* ProCel Group, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga and CIBER de Enfermedades Raras (CIBER-ER). + Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB). $ Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona.

http://asp.uma.es

Metabolic modeling of polyamine metabolism in mammals.
Rodr\u00edguez-Caso,C et al.: J Biol Chem 2006 : 281:21799-812.
The model reproduces the dynamical behavior of the polyamine metabolism in mammals. In this model there are some additions and corrections to the publication. All perturbations and analysis have produced results very close to the published experiments. The model was successfully tested on CoPaSi v.4.4 (build 26).

Parameters not included in the publication:

1. Parameters for SSAT kinetic constants:

KmAcCoA = 1.5 \u00b5M

KmCoA = 40 \u00b5M

2. Parameters for equation MAT (table 1):

Vmax_MAT = 0.45 \u00b5M/min

Km_MAT = 41 \u00b5M

Ki_MET_MAT = 50 \u00b5M

3. Erratum.: The corrected ODE for time-dependent variable Antz is:

KsANTZ*(1-1/(1+Keq*0.01*([D]+[S])))-KdANTZ*[Antz]

According to these modifications the new steady-state analysis results are:

Metabolites:

[P]= 104.681 \u00b5M

[D]= 76.7492 \u00b5M

[S]= 58.0135 \u00b5M

[SAM]= 52.327 \u00b5M

[A]= 0.0101962 \u00b5M

[aS]= 0.0245375 \u00b5M

[aD]= 0.832236 \u00b5M

Time-dependent global parameters:

[Antz] = 0.574038 \u00b5M

Vmaxodc = 1.28315 \u00b5M/min

Vmaxssat = 0.673814 \u00b5M/min

Vmaxsamdc = 0.36829 \u00b5M/min


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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SBML creators: Armando Reyes-Palomares * , Raul Monta\u00f1ez *, Carlos Rodriguez-Caso +, Francisca Sanchez-Jimenez * , Miguel A. Medina *

* ProCel Group, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga and CIBER de Enfermedades Raras (CIBER-ER). + Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB).

http://asp.uma.es

In silico analysis of arginine catabolism as a source of nitric oxide or polyamines in endothelial cells.
Monta\u00f1ez, R et al.: Amino Acids. 2008 Feb;34(2):223-9.
The model reproduces the dynamical behavior of the arginine catabolism and transport in relation to the nitric oxide production. In this model there are some additions and corrections to the publication. All perturbations and analysis have produced results very close to the published experiments. The model was successfully tested on CoPaSi v.4.4 (build 26).

Erratum: parameters values modificated respect to the publication to reach the steady-state:

Kmodc=90 \u00b5M (60 \u00b5M in the paper)

Kiornhat (is equivalent to the parameter Kmefflhat Eq ) = 360 \u00b5M (380 \u00b5M in the paper)


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ "authors": [ + { + "name": "R Monta\u00f1ez", + "orcid": "0000-0003-1667-2218" + }, + { + "institution": "Departamento de Biolog\u00eda Molecular y Bioqu\u00edmica, Facultad de Ciencias, Universidad de M\u00e1laga, M\u00e1laga E-29071, Spain.", + "name": "C Rodr\u00edguez-Caso", + "orcid": "0000-0002-0423-6681" + }, + { + "name": "F S\u00e1nchez-Jim\u00e9nez", + "orcid": "0000-0002-4958-6060" + }, + { + "name": "M A Medina", + "orcid": "0000-0001-7275-6462" + } + ], + "issue": "2", + "journal": "Amino acids", + "link": "http://identifiers.org/pubmed/17520329", + "month": "2", + "pages": "223-229", + "synopsis": "We use a modeling and simulation approach to carry out an in silico analysis of the metabolic pathways involving arginine as a precursor of nitric oxide or polyamines in aorta endothelial cells. Our model predicts conditions of physiological steady state, as well as the response of the system to changes in the control parameter, external arginine concentration. Metabolic flux control analysis allowed us to predict the values of flux control coefficients for all the transporters and enzymes included in the model. This analysis fulfills the flux control coefficient summation theorem and shows that both the low affinity transporter and arginase share the control of the fluxes through these metabolic pathways.", + "title": "In silico analysis of arginine catabolism as a source of nitric oxide or polyamines in endothelial cells.", + "type": "PubMed ID", + "volume": "34", + "year": 2008 + }, + "publicationId": "BIOMD0000000191", + "submissionId": "MODEL7416883636", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000192": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": " \t This model represents a concentration gradient of RanGTP across the nuclear envelope. This gradient is generated by distribution of regulators of RanGTPase. We have taken a log linear plot of graphs generated by GENESIS and compared with the experimental graphs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2008 The BioModels Team.
For more information see the terms of use.

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"name": "G\u00f6rlich2003_RanGTP_gradient", + "publication": { + "accession": "12606574", + "affiliation": "ZMBH, INF 282, 69120 Heidelberg, Germany. dg@zmbh.uni-heidelberg.de", + "authors": [ + { + "institution": "ZMBH, INF 282, 69120 Heidelberg, Germany. dg@zmbh.uni-heidelberg.de", + "name": "Dirk G\u00f6rlich", + "orcid": "0000-0002-4343-5210" + }, + { + "name": "Michael J Seewald", + "orcid": "0000-0001-6967-8993" + }, + { + "name": "Katharina Ribbeck" + } + ], + "issue": "5", + "journal": "The EMBO journal", + "link": "http://identifiers.org/pubmed/12606574", + "month": "3", + "pages": "1088-1100", + "synopsis": "Here, we analyse the RanGTPase system and its coupling to receptor-mediated nuclear transport. 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This indicates that RanGTP gradients can provide positional information for mitotic spindle and NE assembly in early embryonic cells, but hardly any in small somatic cells.", + "title": "Characterization of Ran-driven cargo transport and the RanGTPase system by kinetic measurements and computer simulation.", + "type": "PubMed ID", + "volume": "22", + "year": 2003 + }, + "publicationId": "BIOMD0000000192", + "submissionId": "MODEL4969417017", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000193": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center, Heidelberg", + "email": "b.ibrahim@dkfz.de", + "external": false, + "name": "Bashar Ibrahim" + } + ] + }, + "curationStatus": "CURATED", + "description": " BioSystems (2007), doi:10.1016/j.biosystems.2008.06.007

In-silico study of kinetochore control, amplification, and inhibition effects in MCC assembly


Bashar Ibrahim, Eberhard Schmitt, Peter Dittrich, Stephan Diekmann
This is the kinetochore dependent MCC model (KDM) from the article. For the kinetochore independent MCC model (KIM) replace u*k4f in R4 by k4f and u*k5f in R5 by k5f .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"affiliation": "Bio Systems Analysis Group, Institute of Computer Science, Friedrich-Schiller-University Jena, Germany. ibrahim@minet.uni-jena.de", + "authors": [ + { + "institution": "Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally 32093, Kuwait.Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally 32093, Kuwait.", + "name": "Bashar Ibrahim", + "orcid": "0000-0001-7773-0122" + }, + { + "name": "Eberhard Schmitt" + }, + { + "name": "Peter Dittrich" + }, + { + "name": "Stephan Diekmann" + } + ], + "issue": "1", + "journal": "Bio Systems", + "link": "http://identifiers.org/pubmed/18675311", + "month": "1", + "pages": "35-50", + "synopsis": "Eukaryotic cells rely on a surveillance mechanism, the \"Spindle Assembly Checkpoint\"SACM in order to ensure accurate chromosome segregation by preventing anaphase initiation until all chromosomes are correctly attached to the mitotic spindle. In different organisms, a mitotic checkpoint complex (MCC) composed of Mad2, Bub3, BubR1/Mad3, and Cdc20 inhibits the anaphase promoting complex (APC/C) to initiate promotion into anaphase. The mechanism of MCC formation and its regulation by the kinetochore are unclear. Here, we constructed dynamical models of MCC formation involving different kinetochore control mechanisms including amplification as well as inhibition effects, and analysed their quantitative properties. In particular, in this system, fast and stable metaphase to anaphase transition can only be triggered when the kinetochore controls the Bub3:BubR1-related reactions; signal amplification and inhibition play a subordinate role. Furthermore, when introducing experimentally determined parameter values into the models analysed here, we found that effective MCC formation is not combined with complete Cdc20 sequestering. Instead, the MCC might bind and completely block the APC/C. The SACM might function by an MCC:APC/C complex rearrangement.", + "title": "In silico study of kinetochore control, amplification, and inhibition effects in MCC assembly.", + "type": "PubMed ID", + "volume": "95", + "year": 2009 + }, + "publicationId": "BIOMD0000000193", + "submissionId": "MODEL1667758030", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000194": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center, Heidelberg", + "email": "b.ibrahim@dkfz.de", + "external": false, + "name": "Bashar Ibrahim" + } + ] + }, + "curationStatus": "CURATED", + "description": " Biophysical Chemistry 134 (2008) 93-100

Mad2 binding is not sufficient for complete Cdc20 sequestering in mitotic transition control (an in silico study)


Bashar Ibrahim, Peter Dittrich, Stephan Diekmann, Eberhard Schmitt

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Systems Analysis Group, Institute of Computer Science, Friedrich-Schiller-University Jena, Ernst-Abbe-Platz 1-4, D-07743 Jena, Germany.", + "authors": [ + { + "institution": "Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally 32093, Kuwait.Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally 32093, Kuwait.", + "name": "Bashar Ibrahim", + "orcid": "0000-0001-7773-0122" + }, + { + "name": "Peter Dittrich" + }, + { + "name": "Stephan Diekmann" + }, + { + "name": "Eberhard Schmitt" + } + ], + "issue": "1-2", + "journal": "Biophysical chemistry", + "link": "http://identifiers.org/pubmed/18295960", + "month": "4", + "pages": "93-100", + "synopsis": "For successful mitosis, metaphase has to be arrested until all centromeres are properly attached. The onset of anaphase, which is initiated by activating the APC, is controlled by the spindle assembly checkpoint (M)SAC. Mad2, which is a constitutive member of the (M)SAC, is supposed to inhibit the activity of the APC by sequestering away its co-activator Cdc20. Mad1 recruits Mad2 to unattached kinetochores and is compulsory for the establishment of the Mad2 and Cdc20 complexes. Recently, based on results from in vivo and in vitro studies, two biochemical models were proposed: the Template and the Exchange model. Here, we derive a mathematical description to compare the dynamical behaviour of the two models. Our simulation analysis supports the Template model. Using experimentally determined values for the model parameters, the Cdc20 concentration is reduced down to only about half. Thus, although the Template model displays good metaphase-to-anaphase switching behaviour, it is not able to completely describe (M)SAC regulation. This situation is neither improved by amplification nor by p31(comet) inhibition. We speculate that either additional reaction partners are required for total inhibition of Cdc20 or an extended mechanism has to be introduced for (M)SAC regulation.", + "title": "Mad2 binding is not sufficient for complete Cdc20 sequestering in mitotic transition control (an in silico study).", + "type": "PubMed ID", + "volume": "134", + "year": 2008 + }, + "publicationId": "BIOMD0000000194", + "submissionId": "MODEL1667525941", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000195": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model describes the budding yeast cell cycle model used in fig 8 a in
Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions.
Tyson JJ and Novak B., J Theor Biol 2001 May;210(2):249-63.
It consitsts of the equations (2)-(8), with mu=0.005 min -1 . It was taken from Cell Cycle DB ( file ) and only slightly altered.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Tyson2001_Cell_Cycle_Regulation", + "publication": { + "accession": "11371178", + "affiliation": "Department of Biology, Virginian Polytechnic Institute and State University, Blacksburg, VA 24061, USA. tyson@vt.edu", + "authors": [ + { + "name": "J J Tyson" + }, + { + "name": "B Novak" + } + ], + "issue": "2", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/11371178", + "month": "5", + "pages": "249-263", + "synopsis": "In recent years, molecular biologists have uncovered a wealth of information about the proteins controlling cell growth and division in eukaryotes. The regulatory system is so complex that it defies understanding by verbal arguments alone. Quantitative tools are necessary to probe reliably into the details of cell cycle control. To this end, we convert hypothetical molecular mechanisms into sets of nonlinear ordinary differential equations and use standard analytical and numerical methods to study their solutions. First, we present a simple model of the antagonistic interactions between cyclin-dependent kinases and the anaphase promoting complex, which shows how progress through the cell cycle can be thought of as irreversible transitions (Start and Finish) between two stable states (G1 and S-G2-M) of the regulatory system. Then we add new pieces to the \"puzzle\" until we obtain reasonable models of the control systems in yeast cells, frog eggs, and cultured mammalian cells.", + "title": "Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions.", + "type": "PubMed ID", + "volume": "210", + "year": 2001 + }, + "publicationId": "BIOMD0000000195", + "submissionId": "MODEL3897475441", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000196": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

In this model the values of \"free CDK\" (Id: x2), \"cdc25_P\" (x4) \"Wee1_P\" (Id: y5) and \"APC\" (Id: y6) are assigned using the parameters describing the total concentrations totcdk (Id: c)), totcdc5, totwee1 and totAPC. So if you want to change the levels of these proteins, you need to change the values ofthese parameters.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"month": "8", + "pages": "617-627", + "synopsis": "We propose a seven variable model with time delay in one of the variables for the cell cycle in higher eukaryotes. The model consists of four important phosphorylation-dephosphorylation (P-D) cycles that govern the cell cycle, namely Pre-MPF-MPF, Cdc25P-Cdc25, Wee1P-Wee1 and APCP-APC. Other variables are cyclin, free cyclin dependent kinase (Cdk) and mass. The mass acts as a G2/M checkpoint and the checkpoint is represented by a saddle node loop bifurcation. The key feature of the model is that a time lag has been introduced in the activation of anaphase promoting complex (APC) by maturation promoting factor (MPF). This is effected by treating MPF as a time-delayed variable in the activation step of APC. The time lag acts as a spindle checkpoint. Absence of time delay induces a bistability in our model. Time delay also brings about variability in G1 phase timings. The model also reproduces the mutant phenotype experiments on wee1 cells. Stochasticity has been introduced in the model to simulate the dependence of the cycle time on cell birth length. Mutant phenotypes in the stochastic model reproduce the experimental observations better than the deterministic model.", + "title": "A simple time delay model for eukaryotic cell cycle.", + "type": "PubMed ID", + "volume": "241", + "year": 2006 + }, + "publicationId": "BIOMD0000000196", + "submissionId": "MODEL1502077979", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000197": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department for brain research, Anatomical Institute I, Hansastr. 9a, D-79104 Freiburg, Germany", + "email": "bartholo@uni-freiburg.de", + "external": false, + "name": "Kilian Bartholom\u00e9" + } + ] + }, + "curationStatus": "CURATED", + "description": "

SBML model exported from PottersWheel on 2007-09-19 15:35:47.

The values for parameters and the inital concentrations of this model where directly provided by the main author:
Parameter values
parameter value unit
p1 0.0025 1/min
p2 0.0784 1/min
p3 0.0013 1/min
p4 0.0827 1/min
p5 0.0091 1/min
p6 0.000064 1/(nmole*min)
p7 0.0397 1/min
p8 1000 nmole
p9 0.0098 1/(nmole*min)
p10 1.6 1/min
p11 1000 nmole
p12 0.0003 ml/min
The basal chamber volume was taken as 1 ml, the apical as 1.5. As starting values x1 was set to 88 nmole, all other species to 0.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The same systems biology approach may be applied to other cellular systems and to different substances.", + "title": "Data-based mathematical modeling of vectorial transport across double-transfected polarized cells.", + "type": "PubMed ID", + "volume": "35", + "year": 2007 + }, + "publicationId": "BIOMD0000000197", + "submissionId": "MODEL0212154960", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000198": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Stone1996 - activation of soluble guanylatecyclase by nitric oxide
This features the two step binding ofNO to soluble Guanylyl Cyclase as proposed by StoneJR, Marletta MA. Biochemistry (1996) 35(4):1093-9 . There is afast step binding scheme and a slow step binding scheme. Thedifference lies in the binding of a NO to a non-heme site on sGC,which may not necessarily be the same site of binding during theinitial binding. The rates have been directly used models.

This model is described in the article:

Stone JR, Marletta MA.
Biochemistry 1996 Jan; 35(4): 1093-1099

Abstract:

The soluble form of guanylate cyclase (sGC) is the only definitive receptor for the signaling agent nitric oxide (.NO). The enzyme is a heterodimer of homologous subunits in which each subunit binds 1 equiv of 5-coordinate high-spin heme. .NO increases the Vmax of sGC up to 400-fold and has previously been shown to bind to the heme to form a 5-coordinate complex. Using stopped-flow spectrophotometry, it is demonstrated that the binding of .NO to the heme of sGC is a complex process. .NO first binds to the heme to form a 6-coordinate nitrosyl complex, which then converts to a 5-coordinate nitrosyl complex through one of two ways. For 28 +/- 4% of the heme, the 6-coordinate nitrosyl complex rapidly (approximately 20 s-1) converts to the 5-coordinate complex. For the remaining 72 +/- 4% of the heme, the conversion of the 6-coordinate nitrosyl complex to a 5-coordinate nitrosyl complex is slow (0.1-1.0 s-1) and is dependent upon the interaction of .NO with an unidentified non-heme site on the protein. The heme (200 nM) was completely converted to the 5-coordinate state with as little as 500 nM .NO, and the equilibrium dissociation constant of .NO for activating the enzyme was determined to be < or = 250 nM. Gel-filtration analysis indicates that the binding of .NO to the heme has no effect on the native molecular mass of the protein. Correlation of electronic absorption spectra with activity measurements indicates that the 5-coordinate nitrosyl form of the enzyme is activated relative to the resting 5-coordinate ferrous form of the enzyme.

This model is hosted on BioModels Database and identified by: BIOMD0000000198.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The enzyme is a heterodimer of homologous subunits in which each subunit binds 1 equiv of 5-coordinate high-spin heme. .NO increases the Vmax of sGC up to 400-fold and has previously been shown to bind to the heme to form a 5-coordinate complex. Using stopped-flow spectrophotometry, it is demonstrated that the binding of .NO to the heme of sGC is a complex process. .NO first binds to the heme to form a 6-coordinate nitrosyl complex, which then converts to a 5-coordinate nitrosyl complex through one of two ways. For 28 +/- 4% of the heme, the 6-coordinate nitrosyl complex rapidly (approximately 20 s-1) converts to the 5-coordinate complex. For the remaining 72 +/- 4% of the heme, the conversion of the 6-coordinate nitrosyl complex to a 5-coordinate nitrosyl complex is slow (0.1-1.0 s-1) and is dependent upon the interaction of .NO with an unidentified non-heme site on the protein. 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Correlation of electronic absorption spectra with activity measurements indicates that the 5-coordinate nitrosyl form of the enzyme is activated relative to the resting 5-coordinate ferrous form of the enzyme.", + "title": "Spectral and kinetic studies on the activation of soluble guanylate cyclase by nitric oxide.", + "type": "PubMed ID", + "volume": "35", + "year": 1996 + }, + "publicationId": "BIOMD0000000198", + "submissionId": "MODEL4779732381", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000199": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": " \t This is a model of neuronal Nitric Oxide Synthase expressed in Escherichia coli based on Santolini J. et al. J Biol Chem. (2001) 276(2):1233-43.
Differing from the article, oxygen explicitly included in the reaction 2, 5 and 10 (numbers as in scheme 1 in the article). In the article the assumed oxygen concentration of 140 uM was included in the pseudo first order rate constant.
Fig 2E in the article shows different time courses for citrulline and NO than the ones produced by this model. Dr. Santolini, one of the authors of the article, wrote that the legends in fig. 2E might be mixed up and should rather denote NO and NO3 instead of citrulline and NO.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This down-regulates activity and increases enzyme K(m,O(2)). To understand this process, we developed a 10-step kinetic model in which the ferric heme-NO enzyme forms as the immediate product of catalysis, and then partitions between NO dissociation versus reduction to a ferrous heme-NO complex. Rate constants used for the model were derived from recent literature or were determined here. Computer simulations of the model precisely described both pre-steady and steady-state features of nNOS catalysis, including NADPH consumption and NO production, buildup of a heme-NO complex, changes between pre-steady and steady-state rates, and the change in enzyme K(m,O(2)) in the presence or absence of NO synthesis. The model also correctly simulated the catalytic features of nNOS mutants W409F and W409Y, which are hyperactive and display less heme-NO complex formation in the steady state. Model simulations showed how the rate of heme reduction influences several features of nNOS catalysis, including populations of NO-bound versus NO-free enzyme in the steady state and the rate of NO synthesis. The simulation predicts that there is an optimum rate of heme reduction that is close to the measured rate in nNOS. Ratio between NADPH consumption and NO synthesis is also predicted to increase with faster heme reduction. Our kinetic model is an accurate and versatile tool for understanding catalytic behavior and will provide new perspectives on NOS regulation.", + "title": "A kinetic simulation model that describes catalysis and regulation in nitric-oxide synthase.", + "type": "PubMed ID", + "volume": "276", + "year": 2001 + }, + "publicationId": "BIOMD0000000199", + "submissionId": "MODEL4734733125", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000200": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Mutants in which one or the other of the proteins of this complex are absent, inactive, or expressed at elevated amounts show altered chemotactic behavior and the phenotypes are difficult to interpret for some overexpression mutants. We have examined the possibility that these unexpected phenotypes might arise from the binding steps that lead to active complex formation. A limited genetic algorithm was used to search for sets of binding reactions and associated binding constants expected to give mutant phenotypes in accord with experimental data. Different sets of binding equilibria and different assumptions about the activity of particular receptor complexes were tried. Computer analysis demonstrated that it is possible to obtain sets of binding equilibria consistent with the observed phenotypes and provided a simple explanation for these phenotypes in terms of the distribution of active and inactive complexes formed under various conditions. Optimization methods of this kind offer a unique way to analyze reactions taking place inside living cells based on behavioral data.", + "title": "Computer analysis of the binding reactions leading to a transmembrane receptor-linked multiprotein complex involved in bacterial chemotaxis.", + "type": "PubMed ID", + "volume": "6", + "year": 1995 + }, + "publicationId": "BIOMD0000000200", + "submissionId": "MODEL6409760567", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000201": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is a model of the coupled Natch, Wnt and FGF modules as described in:
A. Goldbeter and O. Pourqui\u00e9 , Modeling the segmentation clock as a network of coupled oscillations in the Notch, Wnt and FGF signaling pathways. J Theor Biol. 2008 Jun 7;252(3):574-85, pubmed ID: 18308339
To uncouple the modules remove the reaction MAx_trans_Xa and set vsFK=vsF .
The SBML version of the model was converted from the CellML version by Catherine Lloyd for the CellML repository .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"affiliation": "Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C.P. 231, B-1050 Brussels, Belgium. agoldbet@ulb.ac.be", + "authors": [ + { + "institution": "Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C.P. 231, B-1050 Brussels, Belgium. agoldbet@ulb.ac.be", + "name": "Albert Goldbeter" + }, + { + "name": "Olivier Pourqui\u00e9", + "orcid": "0000-0001-5189-1227" + } + ], + "issue": "3", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/18308339", + "month": "6", + "pages": "574-585", + "synopsis": "The formation of somites in the course of vertebrate segmentation is governed by an oscillator known as the segmentation clock, which is characterized by a period ranging from 30 min to a few hours depending on the organism. This oscillator permits the synchronized activation of segmentation genes in successive cohorts of cells in the presomitic mesoderm in response to a periodic signal emitted by the segmentation clock, thereby defining the future segments. Recent microarray experiments [Dequeant, M.L., Glynn, E., Gaudenz, K., Wahl, M., Chen, J., Mushegian, A., Pourquie, O., 2006. A complex oscillating network of signaling genes underlies the mouse segmentation clock. Science 314, 1595-1598] indicate that the Notch, Wnt and Fibroblast Growth Factor (FGF) signaling pathways are involved in the mechanism of the segmentation clock. By means of computational modeling, we investigate the conditions in which sustained oscillations occur in these three signaling pathways. First we show that negative feedback mediated by the Lunatic Fringe protein on intracellular Notch activation can give rise to periodic behavior in the Notch pathway. We then show that negative feedback exerted by Axin2 on the degradation of beta-catenin through formation of the Axin2 destruction complex can produce oscillations in the Wnt pathway. Likewise, negative feedback on FGF signaling mediated by the phosphatase product of the gene MKP3/Dusp6 can produce oscillatory gene expression in the FGF pathway. Coupling the Wnt, Notch and FGF oscillators through common intermediates can lead to synchronized oscillations in the three signaling pathways or to complex periodic behavior, depending on the relative periods of oscillations in the three pathways. The phase relationships between cycling genes in the three pathways depend on the nature of the coupling between the pathways and on their relative autonomous periods. The model provides a framework for analyzing the dynamics of the segmentation clock in terms of a network of oscillating modules involving the Wnt, Notch and FGF signaling pathways.", + "title": "Modeling the segmentation clock as a network of coupled oscillations in the Notch, Wnt and FGF signaling pathways.", + "type": "PubMed ID", + "volume": "252", + "year": 2008 + }, + "publicationId": "BIOMD0000000201", + "submissionId": "MODEL6415057828", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000202": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces the plots in Figures 1 and 2. Note that the units of the time scale \"A\" are not right in the paper, it was corrected by the curator. Model successfully tested on MathSBML.

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Chickarmane2006 - Stem cell switch reversible

Kinetic modeling approach of the transcriptional dynamics of the embryonic stem cell switch.

This model is described in the article:

Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C
PLoS Computational Biology. 2006; 2(9):e123

Abstract:

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.

This model is hosted on BioModels Database and identified by: MODEL7957907314 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Stem cell switch reversible", + "publication": { + "accession": "16978048", + "affiliation": "Keck Graduate Institute, Claremont, California, United States of America.", + "authors": [ + { + "institution": "Keck Graduate Institute, Claremont, California, United States of America.", + "name": "Vijay Chickarmane" + }, + { + "name": "Carl Troein" + }, + { + "name": "Ulrike A Nuber" + }, + { + "name": "Herbert M Sauro", + "orcid": "0000-0002-3659-6817" + }, + { + "name": "Carsten Peterson" + } + ], + "issue": "9", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/16978048", + "month": "9", + "pages": "e123", + "synopsis": "Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. 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We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.", + "title": "Transcriptional dynamics of the embryonic stem cell switch.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000203", + "submissionId": "MODEL7957907314", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000204": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Chickarmane2006 - Stem cell switch irreversible

Kinetic modeling approach of the transcriptional dynamics of the embryonic stem cell switch.

This model is described in the article:

Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C
PLoS Computational Biology. 2006; 2(9):e123

Abstract:

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.

This model is hosted on BioModels Database and identified by: MODEL7957942740 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Stem cell switch irreversible", + "publication": { + "accession": "16978048", + "affiliation": "Keck Graduate Institute, Claremont, California, United States of America.", + "authors": [ + { + "institution": "Keck Graduate Institute, Claremont, California, United States of America.", + "name": "Vijay Chickarmane" + }, + { + "name": "Carl Troein" + }, + { + "name": "Ulrike A Nuber" + }, + { + "name": "Herbert M Sauro", + "orcid": "0000-0002-3659-6817" + }, + { + "name": "Carsten Peterson" + } + ], + "issue": "9", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/16978048", + "month": "9", + "pages": "e123", + "synopsis": "Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.", + "title": "Transcriptional dynamics of the embryonic stem cell switch.", + "type": "PubMed ID", + "volume": "2", + "year": 2006 + }, + "publicationId": "BIOMD0000000204", + "submissionId": "MODEL7957942740", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000205": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Model reproduces the various plots in the publication for \"Control\" concentrations. Model successfully tested on MathSBML.

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Mutations that impaired c-Cbl-EGFR association delay EGFR endocytosis and produce higher mitogenic signals in lung cancer. ROCK, an effector of small GTPase RhoA was shown to negatively regulate EGFR endocytosis via endophilin A1. A mathematical model was developed to study how RhoA and ROCK regulate EGFR endocytosis. Our study suggested that over-expressing RhoA as well as ROCK prolonged ERK activation partly by reducing EGFR endocytosis. 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Model reproduces the dynamics of ATP and NADH as depicted in Fig 4 of the paper. Model successfully tested on Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "H V Westerhoff", + "orcid": "0000-0002-0443-6114" + } + ], + "issue": "3", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/10692304", + "month": "3", + "pages": "1145-1153", + "synopsis": "Under certain well-defined conditions, a population of yeast cells exhibits glycolytic oscillations that synchronize through intercellular acetaldehyde. This implies that the dynamic phenomenon of the oscillation propagates within and between cells. We here develop a method to establish by which route dynamics propagate through a biological reaction network. Application of the method to yeast demonstrates how the oscillations and the synchronization signal can be transduced. That transduction is not so much through the backbone of glycolysis, as via the Gibbs energy and redox coenzyme couples (ATP/ADP, and NADH/NAD), and via both intra- and intercellular acetaldehyde.", + "title": "Transduction of intracellular and intercellular dynamics in yeast glycolytic oscillations.", + "type": "PubMed ID", + "volume": "78", + "year": 2000 + }, + "publicationId": "BIOMD0000000206", + "submissionId": "MODEL3352181362", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000207": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 3 of the paper. Model successfully reproduced using MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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the entry into the M (mitosis) and S (DNA replication) phases, respectively. The ordered progression between these phases is achieved thanks to the existence of checkpoint mechanisms based on mutual inhibition of these processes. Here we study a simple theoretical model for oscillations in cdk1 and cdk2 activity, involving mutual inhibition of the two oscillators. Each minimal oscillator is described by a three-variable cascade involving a cdk, together with the associated cyclin and cyclin-degrading enzyme. The dynamics of this skeleton model of coupled oscillators is determined as a function of the strength of their mutual inhibition. The most common mode of dynamic behavior, obtained under conditions of strong mutual inhibition, is that of alternating oscillations in cdk1 and cdk2, which correspond to the physiological situation of the ordered recurrence of the M and S phases. In addition, for weaker inhibition we obtain evidence for a variety of dynamic phenomena such as complex periodic oscillations, chaos, and the coexistence between multiple periodic or chaotic attractors. We discuss the conditions of occurrence of these various modes of oscillatory behavior, as well as their possible physiological significance.", + "title": "Alternating oscillations and chaos in a model of two coupled biochemical oscillators driving successive phases of the cell cycle.", + "type": "PubMed ID", + "volume": "879", + "year": 1999 + }, + "publicationId": "BIOMD0000000207", + "submissionId": "MODEL1111382868", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000208": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The model reproduces Fig 3 of the paper corresponding to the transition to S phase. Units have not been defined for this model because the paper mentions the use of arbitrary units for the various species and parameters. Model reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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De\u012dneko" + }, + { + "name": "A E Kel'" + }, + { + "name": "O V Kel'-Margulis" + }, + { + "name": "E Wingender", + "orcid": "0000-0002-7729-8453" + }, + { + "name": "V A Ratner" + } + ], + "issue": "9", + "journal": "Genetika", + "link": "http://identifiers.org/pubmed/14582399", + "month": "9", + "pages": "1285-1292", + "synopsis": "The study of the molecular mechanisms determining cellular programs of proliferation, differentiation, and apoptosis is currently attracting much attention. Recent studies have demonstrated that the system of cell-cycle control based on the transcriptional regulation of the expression of specific genes is responsible for the transition between programs. These groups of functionally connected genes from so-called gene networks characterized by numerous feedbacks and a complex behavioral dynamics. Computer simulation methods have been applied to studying the dynamics of gene networks regulating the cell cycle of vertebrates. The data on the regulation of the key genes obtained from the CYCLE-TRRD database have been used as a basis to construct gene networks of different degrees of complexity controlling the G1/S transition, one of the most important stages of the cell cycle. The behavior dynamics of the model constructed has been analyzed. Two qualitatively different functional modes of the system has been obtained. It has also been shown that the transition between these modes depends on the duration of the proliferation signal. It has also been demonstrated that the additional feedback from factor E2F to genes c-fos and c-jun, which was predicted earlier based on the computer analysis of promoters, plays an important role in the transition of the cell to the S phase.", + "title": "[Modeling dynamics of gene net, regulating the cell cycle in mammalian cells].", + "type": "PubMed ID", + "volume": "39", + "year": 2003 + }, + "publicationId": "BIOMD0000000208", + "submissionId": "MODEL1475866846", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000209": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Chickarmane2008 - Stem cell lineage determination

In this work, a dynamical model of lineage determination based upon a minimal circuit, as discussed in PMID: 17215298 , which contains the Oct4/Sox2/Nanog core as well its interaction with a few other key genes is discussed.

This model is described in the article:

Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478

Abstract:

BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/

PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.

This model is hosted on BioModels Database and identifiedby: MODEL8390025091 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Stem cell lineage determination", + "publication": { + "accession": "18941526", + "affiliation": "Division of Biology, California Institute of Technology, Pasadena, California, United States of America.", + "authors": [ + { + "institution": "Division of Biology, California Institute of Technology, Pasadena, California, United States of America.", + "name": "Vijay Chickarmane" + }, + { + "name": "Carsten Peterson" + } + ], + "issue": "10", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/18941526", + "month": "0", + "pages": "e3478", + "synopsis": "

Background

Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4.

Methodology/principal findings

We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

Conclusions

The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.", + "title": "A computational model for understanding stem cell, trophectoderm and endoderm lineage determination.", + "type": "PubMed ID", + "volume": "3", + "year": 2008 + }, + "publicationId": "BIOMD0000000209", + "submissionId": "MODEL8390025091", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000210": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Chickarmane2008 - Stem cell lineage - NANOG GATA-6 switch

In this work, a dynamical model of lineage determination based upon a minimal circuit, as discussed in PMID: 17215298 , which contains the Oct4/Sox2/Nanog core as well its interaction with a few other key genes is discussed.

This model is described in the article:

Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478

Abstract:

BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/

PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.

This model is hosted on BioModels Database and identifiedby: MODEL8389825246 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Background

Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4.

Methodology/principal findings

We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

Conclusions

The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.", + "title": "A computational model for understanding stem cell, trophectoderm and endoderm lineage determination.", + "type": "PubMed ID", + "volume": "3", + "year": 2008 + }, + "publicationId": "BIOMD0000000210", + "submissionId": "MODEL8389825246", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000211": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Humboldt University Berlin", + "email": "schulzma@mi.fu-berlin.de", + "external": false, + "name": "Marvin Schulz" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Experimental and in silico analyses of glycolytic flux control in bloodstream form Trypanosoma brucei.
Albert MA, Haanstra JR, Hannaert V, Van Roy J, Opperdoes FR, Bakker BM, Michels PA. J Biol Chem2005 Aug 5;280(31):28306-15. 15955817,
Abstract:
A mathematical model of glycolysis in bloodstream form Trypanosoma brucei was developed previously on the basis of all available enzyme kinetic data (Bakker, B. M., Michels, P. A. M., Opperdoes, F. R., and Westerhoff, H. V. (1997) J. Biol. Chem. 272, 3207-3215). The model predicted correctly the fluxes and cellular metabolite concentrations as measured in non-growing trypanosomes and the major contribution to the flux control exerted by the plasma membrane glucose transporter. Surprisingly, a large overcapacity was predicted for hexokinase (HXK), phosphofructokinase (PFK), and pyruvate kinase (PYK). Here, we present our further analysis of the control of glycolytic flux in bloodstream form T. brucei. First, the model was optimized and extended with recent information about the kinetics of enzymes and their activities as measured in lysates of in vitro cultured growing trypanosomes. Second, the concentrations of five glycolytic enzymes (HXK, PFK, phosphoglycerate mutase, enolase, and PYK) in trypanosomes were changed by RNA interference. The effects of the knockdown of these enzymes on the growth, activities, and levels of various enzymes and glycolytic flux were studied and compared with model predictions. Data thus obtained support the conclusion from the in silico analysis that HXK, PFK, and PYK are in excess, albeit less than predicted. Interestingly, depletion of PFK and enolase had an effect on the activity (but not, or to a lesser extent, expression) of some other glycolytic enzymes. Enzymes located both in the glycosomes (the peroxisome-like organelles harboring the first seven enzymes of the glycolytic pathway of trypanosomes) and in the cytosol were affected. These data suggest the existence of novel regulatory mechanisms operating in trypanosome glycolysis.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ "name": "Barbara M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "name": "Paul A M Michels", + "orcid": "0000-0003-3726-6104" + } + ], + "issue": "31", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/15955817", + "month": "8", + "pages": "28306-28315", + "synopsis": "A mathematical model of glycolysis in bloodstream form Trypanosoma brucei was developed previously on the basis of all available enzyme kinetic data (Bakker, B. M., Michels, P. A. M., Opperdoes, F. R., and Westerhoff, H. V. (1997) J. Biol. Chem. 272, 3207-3215). The model predicted correctly the fluxes and cellular metabolite concentrations as measured in non-growing trypanosomes and the major contribution to the flux control exerted by the plasma membrane glucose transporter. Surprisingly, a large overcapacity was predicted for hexokinase (HXK), phosphofructokinase (PFK), and pyruvate kinase (PYK). Here, we present our further analysis of the control of glycolytic flux in bloodstream form T. brucei. First, the model was optimized and extended with recent information about the kinetics of enzymes and their activities as measured in lysates of in vitro cultured growing trypanosomes. Second, the concentrations of five glycolytic enzymes (HXK, PFK, phosphoglycerate mutase, enolase, and PYK) in trypanosomes were changed by RNA interference. The effects of the knockdown of these enzymes on the growth, activities, and levels of various enzymes and glycolytic flux were studied and compared with model predictions. Data thus obtained support the conclusion from the in silico analysis that HXK, PFK, and PYK are in excess, albeit less than predicted. Interestingly, depletion of PFK and enolase had an effect on the activity (but not, or to a lesser extent, expression) of some other glycolytic enzymes. Enzymes located both in the glycosomes (the peroxisome-like organelles harboring the first seven enzymes of the glycolytic pathway of trypanosomes) and in the cytosol were affected. These data suggest the existence of novel regulatory mechanisms operating in trypanosome glycolysis.", + "title": "Experimental and in silico analyses of glycolytic flux control in bloodstream form Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "280", + "year": 2005 + }, + "publicationId": "BIOMD0000000211", + "submissionId": "MODEL1511155915", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000212": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Centre National de la Recherchce Scientifique (CNRS)", + "email": "gcurien@cea.fr", + "external": false, + "name": "Gilles Curien" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model described in the article:
Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters.
Curien G, Bastien O, Robert-Genthon M, Cornish-Bowden A, C\u00e1rdenas ML, Dumas R. Mol Syst Biol. 2009;5:271. Epub 2009 May 19. PMID: 19455135 , doi: 10.1038/msb.2009.29
Abstract:
The aspartate-derived amino-acid pathway from plants is well suited for analysing the function of the allosteric network of interactions in branched pathways. For this purpose, a detailed kinetic model of the system in the plant model Arabidopsis was constructed on the basis of in vitro kinetic measurements. The data, assembled into a mathematical model, reproduce in vivo measurements and also provide non-intuitive predictions. A crucial result is the identification of allosteric interactions whose function is not to couple demand and supply but to maintain a high independence between fluxes in competing pathways. In addition, the model shows that enzyme isoforms are not functionally redundant, because they contribute unequally to the flux and its regulation. Another result is the identification of the threonine concentration as the most sensitive variable in the system, suggesting a regulatory role for threonine at a higher level of integration.

The limiting rates for the tRNA synthetase reactions, V_Lys_RS, V_Thr_RS and V_Ile_RS, are all assigned a joined value, Vmax_AA_RS, to facilitate reproduction of the results in the publication. To alter these rates seperately these assignments have to be changed or removed.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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+ "accession": "BIOMD0000000212", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000212" + }, + { + "accession": "19455135", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/19455135" + }, + { + "accession": "3702", + "name": "Arabidopsis thaliana", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/3702" + }, + { + "accession": "GO:0006531", + "name": "aspartate metabolic process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0006531" + }, + { + "accession": "ath00260", + "name": "Glycine, serine and threonine metabolism - Arabidopsis thaliana (thale cress)", + "qualifier": "bqbiol:isPartOf", + "resource": "KEGG Pathway", + "uri": "http://identifiers.org/kegg.pathway/ath00260" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential 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"month": "0", + "pages": "271", + "synopsis": "The aspartate-derived amino-acid pathway from plants is well suited for analysing the function of the allosteric network of interactions in branched pathways. For this purpose, a detailed kinetic model of the system in the plant model Arabidopsis was constructed on the basis of in vitro kinetic measurements. The data, assembled into a mathematical model, reproduce in vivo measurements and also provide non-intuitive predictions. A crucial result is the identification of allosteric interactions whose function is not to couple demand and supply but to maintain a high independence between fluxes in competing pathways. In addition, the model shows that enzyme isoforms are not functionally redundant, because they contribute unequally to the flux and its regulation. Another result is the identification of the threonine concentration as the most sensitive variable in the system, suggesting a regulatory role for threonine at a higher level of integration.", + "title": "Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters.", + "type": "PubMed ID", + "volume": "5", + "year": 2009 + }, + "publicationId": "BIOMD0000000212", + "submissionId": "MODEL3336584391", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000213": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Washington", + "email": "mgaldzic@u.washington.edu", + "external": false, + "name": "Michal Galdzicki" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is an SBML version of the folate cycle model model from:
A mathematical model of the folate cycle: new insights into folate homeostasis.
Nijhout HF, Reed MC, Budu P, Ulrich CM J. Biol. Chem.,2004, 279 (53),55008-16
pubmedID: 15496403
Abstract:
A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the \"methyl trap\"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased.
This model was encoded by Michal Galdzicki from a MatLab file send to him by Prof. Michael Reed. There some differences in this model compared to the one described in the article, possible due to typos in the publication:
1) reaction NE (THF + H2CO <=> 5,10-CH2-THF) in the article has H2C=O as areactant and is mentioned to display pseudo first order mass actionkinetics, while in the matlab file formic acid, also used in reaction FTS, is included in the rate law for the forward reaction.
2) the reaction MS is modeled after Reed et al. 2004, which is notexplicitly mentioned in the article, although Kd and the parametersfrom Reed et al. 2004 are given.
3) in the kinetic law of the SHTM reaction (THF + Ser <=>5,10-CH2-THF + Gly), there are separate values given for Km,Glyand Km,5,10-CH2-THF in the article. in the matlab file and the SBMLmodel Km,Ser and Km,THF are used instead of Km,Gly and Km,5,10-CH2-THFfor the backwards reaction.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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hfn@duke.edu", + "authors": [ + { + "institution": "Departments of Biology and Mathematics, Duke University, Durham, NC 27708, USA. hfn@duke.edu", + "name": "H Frederik Nijhout" + }, + { + "name": "Michael C Reed" + }, + { + "name": "Paula Budu" + }, + { + "name": "Cornelia M Ulrich" + } + ], + "issue": "53", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/15496403", + "month": "12", + "pages": "55008-55016", + "synopsis": "A mathematical model is developed for the folate cycle based on standard biochemical kinetics. 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In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased.", + "title": "A mathematical model of the folate cycle: new insights into folate homeostasis.", + "type": "PubMed ID", + "volume": "279", + "year": 2004 + }, + "publicationId": "BIOMD0000000213", + "submissionId": "MODEL4336555445", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000214": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model 2 described in the supplement of the article below. It is parameterized for the WT at 24\u00b0C. To reproduce figure 6 the results have to be rescaled to circadian time by multiplying time by 24/tau, with tau being the period of the free-running oscillator. For the wild-type parameter set tau is equal to 22.7149.
Article:
Isoform switching facilitates period control in the Neurospora crassa circadian clock.
Akman OE, Locke JC, Tang S, Carr\u00e9 I, Millar AJ, Rand DA. Mol Syst Biol. 2008;4:164. Epub 2008 Feb 12. PMID: 18277380, doi:10.1038/msb.2008.5
Abstract:
A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. 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This a model from the article:
Sequential polarization and imprinting of type 1 T helper lymphocytes by interferon-gamma and interleukin-12.
Schulz EG, Mariani L, Radbruch A, H\u00f6fer T. Immunity.2009;30(5):666-8. 19409816,
Abstract:
Differentiation of naive T lymphocytes into type I T helper (Th1) cells requires interferon-gamma and interleukin-12. It is puzzling that interferon-gamma induces the Th1 transcription factor T-bet, whereas interleukin-12 mediates Th1 cell lineage differentiation. We use mathematical modeling to analyze the expression kinetics of T-bet, interferon-gamma, and the IL-12 receptor beta2 chain (IL-12Rbeta2) during Th1 cell differentiation, in the presence or absence of interleukin-12 or interferon-gamma signaling. We show that interferon-gamma induced initial T-bet expression, whereas IL-12Rbeta2 was repressed by T cell receptor (TCR) signaling. The termination of TCR signaling permitted upregulation of IL-12Rbeta2 by T-bet and interleukin-12 signaling that maintained T-bet expression. This late expression of T-bet, accompanied by the upregulation of the transcription factors Runx3 and Hlx, was required to imprint the Th cell for interferon-gamma re-expression. Thus initial polarization and subsequent imprinting of Th1 cells are mediated by interlinked, sequentially acting positive feedback loops of TCR-interferon-gamma-Stat1-T-bet and interleukin-12-Stat4-T-bet signaling.


The original model was created by:
Edda G. Schulz
schulz@drfz.de
Theoretical Biophysics, Institute of Biology, Humboldt Universit\u00e4t, Invalidenstrasse 42, 10115 Berlin, Germany.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Minimum criteria for DNA damage-induced phase advances in circadian rhythms.
Hong CI, Z\u00e1mborszky J, Csik\u00e1sz-Nagy A. PLoS Comput Biol. 2009 May;5(5):e1000384. 19424508,
Abstract:
Robust oscillatory behaviors are common features of circadian and cell cycle rhythms. These cyclic processes, however, behave distinctively in terms of their periods and phases in response to external influences such as light, temperature, nutrients, etc. Nevertheless, several links have been found between these two oscillators. Cell division cycles gated by the circadian clock have been observed since the late 1950s. On the other hand, ionizing radiation (IR) treatments cause cells to undergo a DNA damage response, which leads to phase shifts (mostly advances) in circadian rhythms. Circadian gating of the cell cycle can be attributed to the cell cycle inhibitor kinase Wee1 (which is regulated by the heterodimeric circadian clock transcription factor, BMAL1/CLK), and possibly in conjunction with other cell cycle components that are known to be regulated by the circadian clock (i.e., c-Myc and cyclin D1). It has also been shown that DNA damage-induced activation of the cell cycle regulator, Chk2, leads to phosphorylation and destruction of a circadian clock component (i.e., PER1 in Mus or FRQ in Neurospora crassa). However, the molecular mechanism underlying how DNA damage causes predominantly phase advances in the circadian clock remains unknown. In order to address this question, we employ mathematical modeling to simulate different phase response curves (PRCs) from either dexamethasone (Dex) or IR treatment experiments. Dex is known to synchronize circadian rhythms in cell culture and may generate both phase advances and delays. We observe unique phase responses with minimum delays of the circadian clock upon DNA damage when two criteria are met: (1) existence of an autocatalytic positive feedback mechanism in addition to the time-delayed negative feedback loop in the clock system and (2) Chk2-dependent phosphorylation and degradation of PERs that are not bound to BMAL1/CLK.

The original xpp file of the model is available as a supplement of the article (Text S1).\t

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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These cyclic processes, however, behave distinctively in terms of their periods and phases in response to external influences such as light, temperature, nutrients, etc. Nevertheless, several links have been found between these two oscillators. Cell division cycles gated by the circadian clock have been observed since the late 1950s. On the other hand, ionizing radiation (IR) treatments cause cells to undergo a DNA damage response, which leads to phase shifts (mostly advances) in circadian rhythms. Circadian gating of the cell cycle can be attributed to the cell cycle inhibitor kinase Wee1 (which is regulated by the heterodimeric circadian clock transcription factor, BMAL1/CLK), and possibly in conjunction with other cell cycle components that are known to be regulated by the circadian clock (i.e., c-Myc and cyclin D1). 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We observe unique phase responses with minimum delays of the circadian clock upon DNA damage when two criteria are met: (1) existence of an autocatalytic positive feedback mechanism in addition to the time-delayed negative feedback loop in the clock system and (2) Chk2-dependent phosphorylation and degradation of PERs that are not bound to BMAL1/CLK.", + "title": "Minimum criteria for DNA damage-induced phase advances in circadian rhythms.", + "type": "PubMed ID", + "volume": "5", + "year": 2009 + }, + "publicationId": "BIOMD0000000216", + "submissionId": "MODEL7984093336", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000217": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica.
Bruggeman FJ, Boogerd FC, Westerhoff HV. FEBS J. 2005 Apr;272(8):1965-85. 15819889 ,
Abstract:
Ammonium assimilation in Escherichia coli is regulated through multiple mechanisms (metabolic, signal transduction leading to covalent modification, transcription, and translation), which (in-)directly affect the activities of its two ammonium-assimilating enzymes, i.e. glutamine synthetase (GS) and glutamate dehydrogenase (GDH). Much is known about the kinetic properties of the components of the regulatory network that these enzymes are part of, but the ways in which, and the extents to which the network leads to subtle and quasi-intelligent regulation are unappreciated. To determine whether our present knowledge of the interactions between and the kinetic properties of the components of this network is complete - to the extent that when integrated in a kinetic model it suffices to calculate observed physiological behaviour - we now construct a kinetic model of this network, based on all of the kinetic data on the components that is available in the literature. We use this model to analyse regulation of ammonium assimilation at various carbon statuses for cells that have adapted to low and high ammonium concentrations. We show how a sudden increase in ammonium availability brings about a rapid redirection of the ammonium assimilation flux from GS/glutamate synthase (GOGAT) to GDH. The extent of redistribution depends on the nitrogen and carbon status of the cell. We develop a method to quantify the relative importance of the various regulators in the network. We find the importance is shared among regulators. We confirm that the adenylylation state of GS is the major regulator but that a total of 40% of the regulation is mediated by ADP (22%), glutamate (10%), glutamine (7%) and ATP (1%). The total steady-state ammonium assimilation flux is remarkably robust against changes in the ammonium concentration, but the fluxes through GS and GDH are completely nonrobust. Gene expression of GOGAT above a threshold value makes expression of GS under ammonium-limited conditions, and of GDH under glucose-limited conditions, sufficient for ammonium assimilation.

This version of the model originates from JWS online . The original model can be retrieved here .

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Boogerd", + "orcid": "0000-0002-3329-7459" + }, + { + "institution": "Infrastructure for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "Hans V Westerhoff", + "orcid": "0000-0002-0443-6114" + } + ], + "issue": "8", + "journal": "The FEBS journal", + "link": "http://identifiers.org/pubmed/15819889", + "month": "4", + "pages": "1965-1985", + "synopsis": "Ammonium assimilation in Escherichia coli is regulated through multiple mechanisms (metabolic, signal transduction leading to covalent modification, transcription, and translation), which (in-)directly affect the activities of its two ammonium-assimilating enzymes, i.e. glutamine synthetase (GS) and glutamate dehydrogenase (GDH). Much is known about the kinetic properties of the components of the regulatory network that these enzymes are part of, but the ways in which, and the extents to which the network leads to subtle and quasi-intelligent regulation are unappreciated. 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Gene expression of GOGAT above a threshold value makes expression of GS under ammonium-limited conditions, and of GDH under glucose-limited conditions, sufficient for ammonium assimilation.", + "title": "The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica.", + "type": "PubMed ID", + "volume": "272", + "year": 2005 + }, + "publicationId": "BIOMD0000000217", + "submissionId": "MODEL3579243073", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000218": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Bioinformatics Centre, University of Pune, Ganeshkhind Road, Pune, Maharashtra, India, 411007", + "email": "indira@bioinfo.ernet.in", + "external": false, + "name": "Indira Ghosh" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one ofthe two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model.

Results

We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria.

Conclusion

We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.", + "title": "Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.", + "type": "PubMed ID", + "volume": "3", + "year": 2006 + }, + "publicationId": "BIOMD0000000218", + "submissionId": "MODEL8584468482", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000219": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Bioinformatics Centre, University of Pune, Ganeshkhind Road, Pune, Maharashtra, India, 411007", + "email": "indira@bioinfo.ernet.in", + "external": false, + "name": "Indira Ghosh" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model.

Results

We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria.

Conclusion

We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.", + "title": "Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.", + "type": "PubMed ID", + "volume": "3", + "year": 2006 + }, + "publicationId": "BIOMD0000000219", + "submissionId": "MODEL8584292730", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000220": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institut Curie", + "email": "laurence.calzone@curie.fr", + "external": false, + "name": "Laurence Calzone" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This the model used in the article:
Quantitative analysis of pathways controlling extrinsic apoptosis in single cells.
Albeck JG, Burke JM, Aldridge BB, Zhang M, Lauffenburger DA, Sorger PK. Mol Cell. 2008 Apr 11;30(1):11-25. PMID: 18406323 , doi: 10.1016/j.molcel.2008.02.012
Abstract:
Apoptosis in response to TRAIL or TNF requires the activation of initiator\tcaspases, which then activate the effector caspases that dismantle\tcells and cause death. However, little is known about the dynamics\tand regulatory logic linking initiators and effectors. Using a combination\tof live-cell reporters, flow cytometry, and immunoblotting, we find\tthat initiator caspases are active during the long and variable delay\tthat precedes mitochondrial outer membrane permeabilization (MOMP)\tand effector caspase activation. When combined with a mathematical\tmodel of core apoptosis pathways, experimental perturbation of regulatory\tlinks between initiator and effector caspases reveals that XIAP and\tproteasome-dependent degradation of effector caspases are important\tin restraining activity during the pre-MOMP delay. We identify conditions\tin which restraint is impaired, creating a physiologically indeterminate\tstate of partial cell death with the potential to generate genomic\tinstability. Together, these findings provide a quantitative picture\tof caspase regulatory networks and their failure modes.
The mitochondrial compartment is just added as a logical partition and its volume is not used in the mathematical formulas, to stick closer to the expressions used in the matlab files distributed with the original publication. There only the rate constants for bimolecular reactions are adapted by division by v , the ration of the volumes of the mitochondrial compartment and the total cell.
For BCL2 overexpression in figure 5, the initial BCL2 amount was increased by a factor 12 to 2.4*10 5 . For siRNA downregulation of XIAP its amount was multiplied by 0.13 to 1.3*10 4 .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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response to TRAIL or TNF requires the activation of initiator caspases, which then activate the effector caspases that dismantle cells and cause death. However, little is known about the dynamics and regulatory logic linking initiators and effectors. Using a combination of live-cell reporters, flow cytometry, and immunoblotting, we find that initiator caspases are active during the long and variable delay that precedes mitochondrial outer membrane permeabilization (MOMP) and effector caspase activation. When combined with a mathematical model of core apoptosis pathways, experimental perturbation of regulatory links between initiator and effector caspases reveals that XIAP and proteasome-dependent degradation of effector caspases are important in restraining activity during the pre-MOMP delay. We identify conditions in which restraint is impaired, creating a physiologically indeterminate state of partial cell death with the potential to generate genomic instability. Together, these findings provide a quantitative picture of caspase regulatory networks and their failure modes.", + "title": "Quantitative analysis of pathways controlling extrinsic apoptosis in single cells.", + "type": "PubMed ID", + "volume": "30", + "year": 2008 + }, + "publicationId": "BIOMD0000000220", + "submissionId": "MODEL6964793701", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000221": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Bioinformatics Centre, University of Pune, Ganeshkhind Road, Pune, Maharashtra, India, 411007", + "email": "indira@bioinfo.ernet.in", + "external": false, + "name": "Indira Ghosh" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one ofthe two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model.

Results

We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria.

Conclusion

We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.", + "title": "Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.", + "type": "PubMed ID", + "volume": "3", + "year": 2006 + }, + "publicationId": "BIOMD0000000221", + "submissionId": "MODEL8584137422", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000222": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Bioinformatics Centre, University of Pune, Ganeshkhind Road, Pune, Maharashtra, India, 411007", + "email": "indira@bioinfo.ernet.in", + "external": false, + "name": "Indira Ghosh" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drugtargets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amountof active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model.

Results

We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria.

Conclusion

We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.", + "title": "Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.", + "type": "PubMed ID", + "volume": "3", + "year": 2006 + }, + "publicationId": "BIOMD0000000222", + "submissionId": "MODEL8583955822", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000223": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

\t described in: Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.
\t Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN.;Mol Syst Biol. 2009;5:256. Epub 2009 Apr 7. PMID:19357636; doi:10.1038/msb.2009.19
Abstract:
\t Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues. \t

\t An extracellular compartment with 34 times the volume of the cell was added and the association rate as well as the dissociation constants for Insulin and EGF binding were altered (kon'=34*kon, KD'=KD/34). This was done to allow using the concentrations for those species given in the article and retaining the same dynamics and Ligand depletion as in the matlab file the SBML file was exported from. \t

SBML model exported from PottersWheel on 2008-10-14 16:26:44.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues.", + "title": "Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.", + "type": "PubMed ID", + "volume": "5", + "year": 2009 + }, + "publicationId": "BIOMD0000000223", + "submissionId": "MODEL6194251662", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000224": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Stanford University, University of Heidelberg", + "email": "p.bayer@stud.uni-heidelberg.de", + "external": false, + "name": "Philipp Bayer" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Calcium spiking.
Meyer T, Stryer L Annu Rev Biophys Biophys Chem1991:20:153-74 1867714,
Abstract:
No Abstract Available

The IP3-Ca2+ Crosscoupling Model (ICC) is reviewed by Meyer and Stryer in 1991, originally from Meyer and Stryer, 1988. PMID - 2455890 Parameters refer to figures 5 and 6 of the article which were reproduced by using Copasi 4.5 (Build 30).Species CaI and IP3 are buffered to 1% and 50% percent, respectively.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the basic model described in eq. 1 of the article:
A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.
Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9
Abstract:
We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.", + "title": "A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.", + "type": "PubMed ID", + "volume": "85", + "year": 2003 + }, + "publicationId": "BIOMD0000000225", + "submissionId": "MODEL5950995486", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000226": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institut Curie", + "email": "andrei.zinovyev@curie.fr", + "external": false, + "name": "Andrei Zinovyev" + } + ] + }, + "curationStatus": "CURATED", + "description": "

NFkB model M(14,25,28) - Lipniacky's NFkB model

This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,created to demonstrate application of model reduction methods proposed in

This a model from the article:
Robust simplifications of multiscale biochemical networks.
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A. BMC Syst Biol2008:2:86 18854041,
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

This model is originally proposed by Lipniacki 2004 (Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M.(2004). Mathematical model of NF-kappaB regulatory module. J. Theor. Biol. 228 (2): 195-215. 15094015

The models are provided in CellDesigner v3.5format. The name of the model M(x,y,z) should bedeciphered as following:

x - number of speciesy - number of reactionsz - number of parameters

Simulation protocol:The model can be simulated in CellDesignerdirectly, or in any simulator supportingevents. The simulation period should beset up in 20 hours (t=72000 sec). This model reproduces Figure 3b (M(14,25,28)) of the publication.

For additional information please contactAndrei.Zinovyev at curie.fr

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Gorban", + "orcid": "0000-0001-6224-1430" + }, + { + "name": "Andrei Zinovyev", + "orcid": "0000-0002-9517-7284" + }, + { + "name": "Alain Lilienbaum", + "orcid": "0000-0002-6437-3186" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/18854041", + "month": "10", + "pages": "86", + "synopsis": "

Background

Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.

Results

We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway.

Conclusion

Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.", + "title": "Robust simplifications of multiscale biochemical networks.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000226", + "submissionId": "MODEL7743386835", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000227": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institut Curie", + "email": "andrei.zinovyev@curie.fr", + "external": false, + "name": "Andrei Zinovyev" + } + ] + }, + "curationStatus": "CURATED", + "description": "

NFkB model M(39,65,90) - most complex model

This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,created to demonstrate application of model reduction methods proposed in

Robust simplifications of multiscale biochemical networks.
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A. BMC Syst Biol2008:2:86 18854041,
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology withpotential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

The models are provided in CellDesigner v3.5format. The name of the model M(x,y,z) should bedeciphered as following:

x - number of speciesy - number of reactionsz - number of parameters

Simulation protocol:The model can be simulated in CellDesignerdirectly, or in any simulator supportingevents. The simulation period should beset up in 40 hours (t=144000 sec).The 'signal' event applies signal to thepathway at the moment t=20 hours=72000 sec. This model reproduces Figure 7c (M(39,65,90)) of the publication.

For additional information please contactAndrei.Zinovyev at curie.fr

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Background

Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed.

Results

We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway.

Conclusion

Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.", + "title": "Robust simplifications of multiscale biochemical networks.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000227", + "submissionId": "MODEL7743656488", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000228": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the extended model described the article:
Bifurcation analysis of the regulatory modules of the mammalian G1/S transition.
Swat M, Kel A, Herzel H. Bioinformatics 2004 Jul 10;20(10):1506-11. PMID: 15231543 , doi: 10.1093/bioinformatics/bth110
Abstract:
MOTIVATION: Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extremely complex interaction network.
RESULTS: We identify various positive and negative feedback loops in the network controlling the G1/S transition. It is shown that the positive feedback regulation of E2F1 and a double activator-inhibitor module can lead to bistability. Extensions of the core module preserve the essential features such as bistability. The complete model exhibits a transcritical bifurcation in addition to bistability. We relate these bifurcations to the cell cycle checkpoint and the G1/S phase transition point. Thus, core modules can explain major features of the complex G1/S network and have a robust decision taking function.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Motivation

Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extremely complex interaction network.

Results

We identify various positive and negative feedback loops in the network controlling the G1/S transition. It is shown that the positive feedback regulation of E2F1 and a double activator-inhibitor module can lead to bistability. Extensions of the core module preserve the essential features such as bistability. The complete model exhibits a transcritical bifurcation in addition to bistability. We relate these bifurcations to the cell cycle checkpoint and the G1/S phase transition point. Thus, core modules can explain major features of the complex G1/S network and have a robust decision taking function.", + "title": "Bifurcation analysis of the regulatory modules of the mammalian G1/S transition.", + "type": "PubMed ID", + "volume": "20", + "year": 2004 + }, + "publicationId": "BIOMD0000000228", + "submissionId": "MODEL3897709120", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000229": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Quantifying robustness of biochemical network models.
Ma L, Iglesias PA. BMC Bioinformatics.2002 Dec 13;3:38. 12482327,
Abstract:
BACKGROUND: Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed. RESULTS: Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary. CONCLUSION: The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.


This model is originally proposed by Laub and Loomis (1998).[Laub MT, Loomis WF (1998). A molecular network that produces spontaneous oscillations in excitable cells of Dictyostelium. Mol Biol Cell. 9(12):3521-32. PubMED: 12482327.
The parameters used in this model (Ma and Iglesias, 2002), are different from that used in the original model (Laub and Loomis, 1998), because of the typographical errors in the original paper. The parameters used in the model presented by Ma and Iglesias, are obtained directly from the authors of original publication (Laub and Loomis, 1998). These parameters are also used in the website for the Laub-Loomis model, http://www-biology.ucsd.edu/labs/loomis/network/laubloomis.html.
By using this model, Kim et al., 2006 [Kim J, Bates DG, Postlethwaite I, Ma L, Iglesias PA. (2006) Robustness analysis of biochemical network models. Syst Biol (Stevenage). 153(3):96-104. PubMED: 16984084], validate and extend the analysis approach proposed by Ma and Iglesias (2002), by showing how hybrid optimisation can be used to compute worst-case parameter combinations in the model.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Background

Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed.

Results

Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary.

Conclusion

The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.", + "title": "Quantifying robustness of biochemical network models.", + "type": "PubMed ID", + "volume": "3", + "year": 2002 + }, + "publicationId": "BIOMD0000000229", + "submissionId": "MODEL0606755064", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000230": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha.
Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB Syst Biol (Stevenage) [2004 Jun;1(1):93-103 17052119 ,
Abstract:
Analysis of cellular signalling interactions is expected to create an enormous informatics challenge, perhaps even greater than that of analysing the genome. A key step in the evolution towards a more quantitative understanding of signalling is to specify explicitly the kinetics of all chemical reaction steps in a pathway. We have reconstructed a model of the nuclear factor, kappaB (NF-kappaB) signalling pathway, containing 64 parameters and 26 variables, including steps in which the activation of the NF-kappaB transcription factor is intimately associated with the phosphorylation and ubiquitination of its inhibitor kappaB by a membrane-associated kinase, and its translocation from the cytoplasm to the nucleus. We apply sensitivity analysis to the model. This identifies those parameters in this (IkappaB)/NF-kappaB signalling system (containing only induced IkappaBalpha isoform) that most affect the oscillatory concentration of nuclear NF-kappaB (in terms of both period and amplitude). The intention is to provide guidance on which proteins are likely to be most significant as drug targets or should be exploited for further, more detailed experiments. The sensitivity coefficients were found to be strongly dependent upon the magnitude of the parameter change studied, indicating the highly non-linear nature of the system. Of the 64 parameters in the model, only eight to nine exerted a major control on nuclear NF-kappaB oscillations, and each of these involved as reaction participants either the IkappaB kinase (IKK) or IkappaBalpha, directly. This means that the dominant dynamics of the pathway can be reflected, in addition to that of nuclear NF-kappaB itself, by just two of the other pathway variables. This is conveniently observed in a phase-plane plot.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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of cellular signalling interactions is expected to create an enormous informatics challenge, perhaps even greater than that of analysing the genome. A key step in the evolution towards a more quantitative understanding of signalling is to specify explicitly the kinetics of all chemical reaction steps in a pathway. We have reconstructed a model of the nuclear factor, kappaB (NF-kappaB) signalling pathway, containing 64 parameters and 26 variables, including steps in which the activation of the NF-kappaB transcription factor is intimately associated with the phosphorylation and ubiquitination of its inhibitor kappaB by a membrane-associated kinase, and its translocation from the cytoplasm to the nucleus. We apply sensitivity analysis to the model. This identifies those parameters in this (IkappaB)/NF-kappaB signalling system (containing only induced IkappaBalpha isoform) that most affect the oscillatory concentration of nuclear NF-kappaB (in terms of both period and amplitude). 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This is conveniently observed in a phase-plane plot.", + "title": "Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha.", + "type": "PubMed ID", + "volume": "1", + "year": 2004 + }, + "publicationId": "BIOMD0000000230", + "submissionId": "MODEL5952988280", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000231": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A kinetic study of a ternary cycle between adenine nucleotides.
Valero E, Var\u00f3n R, Garc\u00eda-Carmona F FEBS J. [2006 Aug;273(15):3598-613 16884499 ,
Abstract:
In the present paper, a kinetic study is made of the behavior of a moiety-conserved ternary cycle between the adenine nucleotides. The system contains the enzymes S-acetyl coenzyme A synthetase, adenylate kinase and pyruvate kinase, and converts ATP into AMP, then into ADP and finally back to ATP. L-Lactate dehydrogenase is added to the system to enable continuous monitoring of the progress of the reaction. The cycle cannot work when the only recycling substrate in the reaction medium is AMP. A mathematical model is proposed whose kinetic behavior has been analyzed both numerically by integration of the nonlinear differential equations describing the kinetics of the reactions involved, and analytically under steady-state conditions, with good agreement with the experimental results being obtained. The data obtained showed that there is a threshold value of the S-acetyl coenzyme A synthetase/adenylate kinase ratio, above which the cycle stops because all the recycling substrate has been accumulated as AMP, never reaching the steady state. In addition, the concept of adenylate energy charge has been applied to the system, obtaining the enabled values of the rate constants for a fixed adenylate energy charge value and vice versa.

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This a model from the article:
Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production.
Nazaret C, Heiske M, Thurley K, Mazat JP J. Theor. Biol. 2009 Jun;258(3):455-64 19007794 ,
Abstract:
Mitochondria play a central role in cellular energetic metabolism. The essential parts of this metabolism are the tricarboxylic acid (TCA) cycle, the respiratory chain and the adenosine triphosphate (ATP) synthesis machinery. Here a simplified model of these three metabolic components with a limited set of differential equations is presented. The existence of a steady state is demonstrated and results of numerical simulations are presented. The relevance of a simple model to represent actual in vivo behavior is discussed.

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The relevance of a simple model to represent actual in vivo behavior is discussed.", + "title": "Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production.", + "type": "PubMed ID", + "volume": "258", + "year": 2009 + }, + "publicationId": "BIOMD0000000232", + "submissionId": "MODEL2594602728", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000233": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "IFR, Norwich, UK", + "email": "thomas.wilhelm@bbsrc.ac.uk", + "external": false, + "name": "Thomas Wilhelm" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
The smallest chemical reaction system with bistability
Thomas Wilhelm BMC Systems Biology2009;Sep 8;3:90. 19737387,
Abstract:
Background
Bistability underlies basic biological phenomena, such as cell division, differentiation, cancer onset, and apoptosis. So far biologists identified two necessary conditions for bistability: positive feedback and ultrasensitivity.
Results
Biological systems are based upon elementary mono- and bimolecular chemical reactions. In order to definitely clarify all necessary conditions for bistability we here present the corresponding minimal system. According to our definition, it contains the minimal number of (i) reactants, (ii) reactions, and (iii) terms in the corresponding ordinary differential equations (decreasing importance from i-iii). The minimal bistable system contains two reactants and four irreversible reactions (three bimolecular, one monomolecular).We discuss the roles of the reactions with respect to the necessary conditions for bistability: two reactions comprise the positive feedback loop, a third reaction filters out small stimuli thus enabling a stable 'off' state, and the fourth reaction prevents explosions. We argue that prevention of explosion is a third general necessary condition for bistability, which is so far lacking discussion in the literature.Moreover, in addition to proving that in two-component systems three steady states are necessary for bistability (five for tristability, etc.), we also present a simple general method to design such systems: one just needs one production and three different degradation mechanisms (one production, five degradations for tristability, etc.). This helps modelling multistable systems and it is important for corresponding synthetic biology projects.
Conclusion
The presented minimal bistable system finally clarifies the often discussed question for the necessary conditions for bistability. The three necessary conditions are: positive feedback, a mechanism to filter out small stimuli and a mechanism to prevent explosions. This is important for modelling bistability with simple systems and for synthetically designing new bistable systems. Our simple model system is also well suited for corresponding teaching purposes.


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This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Research Park, Colney Lane, Norwich NR4 7UA, UK. thomas.wilhelm@bbsrc.ac.uk", + "authors": [ + { + "institution": "Theoretical Systems Biology, Institute of Food Research, Norwich Research Park, Colney Lane, Norwich NR4 7UA, UK. thomas.wilhelm@bbsrc.ac.uk", + "name": "Thomas Wilhelm" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/19737387", + "month": "9", + "pages": "90", + "synopsis": "

Background

Bistability underlies basic biological phenomena, such as cell division, differentiation, cancer onset, and apoptosis. So far biologists identified two necessary conditions for bistability: positive feedback and ultrasensitivity.

Results

Biological systems are based upon elementary mono- and bimolecular chemical reactions. In order to definitely clarify all necessary conditions for bistability we here present the corresponding minimal system. According to our definition, it contains the minimal number of (i) reactants, (ii) reactions, and (iii) terms in the corresponding ordinary differential equations (decreasing importance from i-iii). The minimal bistable system contains two reactants and four irreversible reactions (three bimolecular, one monomolecular).We discuss the roles of the reactions with respect to the necessary conditions for bistability: two reactions comprise the positive feedback loop, a third reaction filters out small stimuli thus enabling a stable 'off' state, and the fourth reaction prevents explosions. We argue that prevention of explosion is a third general necessary condition for bistability, which is so far lacking discussion in the literature.Moreover, in addition to proving that in two-component systems three steady states are necessary for bistability (five for tristability, etc.), we also present a simple general method to design such systems: one just needs one production and three different degradation mechanisms (one production, five degradations for tristability, etc.). This helps modelling multistable systems and it is important for corresponding synthetic biology projects.

Conclusion

The presented minimal bistable system finally clarifies the often discussed question for the necessary conditions for bistability. The three necessary conditions are: positive feedback, a mechanism to filter out small stimuli and a mechanism to prevent explosions. This is important for modelling bistability with simple systems and for synthetically designing new bistable systems. Our simple model system is also well suited for corresponding teaching purposes.", + "title": "The smallest chemical reaction system with bistability.", + "type": "PubMed ID", + "volume": "3", + "year": 2009 + }, + "publicationId": "BIOMD0000000233", + "submissionId": "MODEL2425356597", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000234": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ], + "CodeCurator": [ + { + "affiliation": "Nova In Silico", + "email": "elea.greugny@novadiscovery.com", + "external": true, + "name": "El\u00e9a Thibault-Greugny", + "orcid": "0000-0001-5812-3722" + } + ], + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Nova In Silico", + "email": "elea.greugny@novadiscovery.com", + "external": true, + "name": "El\u00e9a Thibault-Greugny", + "orcid": "0000-0001-5812-3722" + }, + { + "affiliation": "University of Auckland", + "email": "n.holford@auckland.ac.nz", + "external": true, + "name": "Nick Holford", + "orcid": "0000-0002-4031-2514" + } + ], + "Other": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

PURPOSE: This tumor response pharmacodynamic model aims to describe primary lesion shrinkage in non-small cell lung cancer over time and determine if concentration-based exposure metrics for gemcitabine or that of its metabolites,n2',2'-difluorodeoxyuridine or gemcitabine triphosphate, are better than gemcitabine dose for prediction of individual response. EXPERIMENTAL DESIGN: Gemcitabine was given thrice weekly on days 1 and 8 in combination with carboplatin, which was given only on day 1 of every cycle. Gemcitabine amount in the body and area under the concentration-time curves of plasma gemcitabine, 2',2'-difluorodeoxyuridine, and intracellular gemcitabine triphosphate in white cells were compared to determine which best describes tumor shrinkage over time. Tumor growth kinetics were described using a Gompertz-like model.RESULTS: The apparent half-life for the effect of gemcitabine was 7.67 weeks. The tumor turnover time constant was 21.8 week.cm. Baseline tumor size and gemcitabine amount in the body to attain 50% of tumor shrinkage were estimated to be 6.66 cm and 10,600 mg. There was no evidence of relapse during treatment.

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Tham_lai_san@lilly.com", + "authors": [ + { + "institution": "Department of Hematology-Oncology, National University Hospital, Singapore. Tham_lai_san@lilly.com", + "name": "Lai-San Tham" + }, + { + "name": "Lingzhi Wang" + }, + { + "name": "Ross A Soo" + }, + { + "name": "Soo-Chin Lee" + }, + { + "name": "How-Sung Lee" + }, + { + "name": "Wei-Peng Yong", + "orcid": "0000-0003-4404-3777" + }, + { + "name": "Boon-Cher Goh" + }, + { + "institution": "University of Auckland", + "name": "Nicholas H G Holford", + "orcid": "0000-0002-4031-2514" + } + ], + "issue": "13", + "journal": "Clinical cancer research : an official journal of the American Association for Cancer Research", + "link": "http://identifiers.org/pubmed/18594002", + "month": "7", + "pages": "4213-4218", + "synopsis": "

Purpose

This tumor response pharmacodynamic model aims to describe primary lesion shrinkage in non-small cell lung cancer over time and determine if concentration-based exposure metrics for gemcitabine or that of its metabolites, 2',2'-difluorodeoxyuridine or gemcitabine triphosphate, are better than gemcitabine dose for prediction of individual response.

Experimental design

Gemcitabine was given thrice weekly on days 1 and 8 in combination with carboplatin, which was given only on day 1 of every cycle. Gemcitabine amount in the body and area under the concentration-time curves of plasma gemcitabine, 2',2'-difluorodeoxyuridine, and intracellular gemcitabine triphosphate in white cells were compared to determine which best describes tumor shrinkage over time. Tumor growth kinetics were described using a Gompertz-like model.

Results

The apparent half-life for the effect of gemcitabine was 7.67 weeks. The tumor turnover time constant was 21.8 week.cm. Baseline tumor size and gemcitabine amount in the body to attain 50% of tumor shrinkage were estimated to be 6.66 cm and 10,600 mg. There was no evidence of relapse during treatment.

Conclusions

Concentration-based exposure metrics for gemcitabine and its metabolites were no better than gemcitabine amount in predicting tumor shrinkage in primary lung cancer lesions. Gemcitabine dose-based models did marginally better than treatment-based models that ignored doses of drug administered to patients. Modeling tumor shrinkage in primary lesions can be used to quantify individual sensitivity and response to antitumor effects of anticancer drugs.", + "title": "A pharmacodynamic model for the time course of tumor shrinkage by gemcitabine + carboplatin in non-small cell lung cancer patients.", + "type": "PubMed ID", + "volume": "14", + "year": 2008 + }, + "publicationId": "BIOMD0000000234", + "submissionId": "MODEL0911120001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000235": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Humboldt University Berlin", + "email": "clemenskuehn@posteo.de", + "external": false, + "name": "Clemens K\u00fchn" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network.
K\u00fchn C, Wierling C, K\u00fchn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ. BMC Syst Biol.2009 Aug 23;3:83. 19698179,
Abstract:
BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.

The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"Max-Planck-Institute for Molecular Genetics, Ihnestr 63-73, 14195 Berlin, Germany. clemens.kuehn@biologie.hu-berlin.de", + "name": "Clemens K\u00fchn" + }, + { + "name": "Christoph Wierling", + "orcid": "0000-0002-8856-8412" + }, + { + "name": "Alexander K\u00fchn" + }, + { + "name": "Edda Klipp" + }, + { + "name": "Georgia Panopoulou" + }, + { + "name": "Hans Lehrach" + }, + { + "name": "Albert J Poustka" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/19698179", + "month": "8", + "pages": "83", + "synopsis": "

Background

Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs.

Results

We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data.

Conclusion

The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.", + "title": "Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network.", + "type": "PubMed ID", + "volume": "3", + "year": 2009 + }, + "publicationId": "BIOMD0000000235", + "submissionId": "MODEL2133240427", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000236": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the extended model described in eq. 2 of the article:
A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.
Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9
Abstract:
We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.", + "title": "A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.", + "type": "PubMed ID", + "volume": "85", + "year": 2003 + }, + "publicationId": "BIOMD0000000236", + "submissionId": "MODEL9574422639", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000237": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast.
Schaber J, Kofahl B, Kowald A, Klipp E FEBS J.2006 Aug; 273(15):3520-33 16884493,
Abstract:
Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"accession": "16884493", + "affiliation": "Max Planck Institute for Molecular Genetics, Berlin, Germany.", + "authors": [ + { + "institution": "Max Planck Institute for Molecular Genetics, Berlin, Germany.", + "name": "J\u00f6rg Schaber", + "orcid": "0000-0001-6971-2530" + }, + { + "name": "Bente Kofahl" + }, + { + "name": "Axel Kowald", + "orcid": "0000-0001-6448-4364" + }, + { + "name": "Edda Klipp" + } + ], + "issue": "15", + "journal": "The FEBS journal", + "link": "http://identifiers.org/pubmed/16884493", + "month": "8", + "pages": "3520-3533", + "synopsis": "Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other.", + "title": "A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast.", + "type": "PubMed ID", + "volume": "273", + "year": 2006 + }, + "publicationId": "BIOMD0000000237", + "submissionId": "MODEL5952001443", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000238": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.
Overgaard RV, Holford N, Rytved KA, Madsen H. Pharm Res.2007 Feb;24(2):298-309. PUBMED,
Abstract:
PURPOSE: To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling. METHODS: A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM. RESULTS: The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function. CONCLUSIONS: The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Purpose

To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling.

Methods

A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM.

Results

The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function.

Conclusions

The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.", + "title": "PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.", + "type": "PubMed ID", + "volume": "24", + "year": 2007 + }, + "publicationId": "BIOMD0000000238", + "submissionId": "MODEL0911110000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000239": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Jiang2007 - GSIS system, Pancreatic Beta Cells
Description of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells.

This model is described in the article:

Jiang N, Cox RD, Hancock JM.
Mamm Genome 2007 Jul; 18(6-7):508-20.

Abstract:

The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic beta cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state.

The model reproduces Figure 2 of the paper, and is built using files 'ModelNNT11.xml' and 'changed.m' available from http://www.har.mrc.ac.uk/research/bioinformatics/research_areas/systems_biology.html .

A couple of small errors in the model (in the original SBML file 'ModelNNT11.xml') have been corrected. The errors are:

This model is hosted on BioModels Database and identified by: BIOMD0000000239 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Group, MRC Mammalian Genetics Unit, Harwell, Oxfordshire, OX11 0RD, UK.", + "name": "Nan Jiang" + }, + { + "name": "Roger D Cox", + "orcid": "0000-0001-7170-5014" + }, + { + "name": "John M Hancock", + "orcid": "0000-0003-2991-2217" + } + ], + "issue": "6-7", + "journal": "Mammalian genome : official journal of the International Mammalian Genome Society", + "link": "http://identifiers.org/pubmed/17514510", + "month": "7", + "pages": "508-520", + "synopsis": "The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic beta cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state.", + "title": "A kinetic core model of the glucose-stimulated insulin secretion network of pancreatic beta cells.", + "type": "PubMed ID", + "volume": "18", + "year": 2007 + }, + "publicationId": "BIOMD0000000239", + "submissionId": "MODEL1469251725", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000240": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Transient heterogeneity in extracellular protease production by Bacillus subtilis.
Veening JW, Igoshin OA, Eijlander RT, Nijland R, Hamoen LW, Kuipers OP Mol. Syst. Biol. 2008 ; Volume: 4 : 184 18414485,
Abstract:
The most sophisticated survival strategy Bacillus subtilis employs is the differentiation of a subpopulation of cells into highly resistant endospores. To examine the expression patterns of non-sporulating cells within heterogeneous populations, we used buoyant density centrifugation to separate vegetative cells from endospore-containing cells and compared the transcriptome profiles of both subpopulations. This demonstrated the differential expression of various regulons. Subsequent single-cell analyses using promoter-gfp fusions confirmed our microarray results. Surprisingly, only part of the vegetative subpopulation highly and transiently expresses genes encoding the extracellular proteases Bpr (bacillopeptidase) and AprE (subtilisin), both of which are under the control of the DegU transcriptional regulator. As these proteases and their degradation products freely diffuse within the liquid growth medium, all cells within the clonal population are expected to benefit from their activities, suggesting that B. subtilis employs cooperative or even altruistic behavior. To unravel the mechanisms by which protease production heterogeneity within the non-sporulating subpopulation is established, we performed a series of genetic experiments combined with mathematical modeling. Simulations with our model yield valuable insights into how population heterogeneity may arise by the relatively long and variable response times within the DegU autoactivating pathway.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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\t described in: Pharmacokinetic-pharmacodynamic modeling of caffeine: Tolerance to pressor effects
\t Shi J, Benowitz NL, Denaro CP and Sheiner LB. ;Clin. Pharmacol. Ther. 1993 Jan;53(1):6-14. PMID:8422743;
Abstract:
\t We propose a parametric pharmacokinetic-pharmacodynamic model for caffeine that quantifies the development of tolerance to the pressor effect of the drug and characterizes the mean behavior and inter-individual variation of both pharmacokinetics and pressor effect. Our study in a small group of subjects indicates that acute tolerance develops to the pressor effect of caffeine and that both the pressor effect and tolerance occur after some time delay relative to changes in plasma caffeine concentration. The half-life of equilibration of effect with plasma caffeine concentration is about 20 minutes. The half-life of development and regression of tolerance is estimated to be about 1 hour, and the model suggests that tolerance, at its fullest, causes more than a 90 percent reduction of initial (nontolerant) effect. Whereas tolerance to the pressor effect of caffeine develops in habitual coffee drinkers, the pressor response is regained after relatively brief periods of abstinence. Because of the rapid development and regression of tolerance, the pressor response to caffeine depends on how much caffeine is consumed, the schedule of consumption, and the elimination half-life of caffeine. \t

Caffeine intake in this version is modelled as cups of coffee drunk at regular intervals (parameter t_interval). The amount of caffeine per cup is determined by the parameter cupsize. The body weight of the person drinking is given by the parameter bodyweight.
The even coffee cup occures delayed to the drinking of each cup, as the availability of the caffeine in the digestive tract is assumed to be delayed to the ingestion by the time t_lag.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Theoretical and experimental evidence for hysteresis in cell proliferation.
Bai S, Goodrich D, Thron CD, Tecarro E, Obeyesekere M. Cell Cycle. 2003 Jan-Feb;2(1):46-52. 12695688 ,
Abstract:
We propose a mathematical model for the regulation of the G1-phase of the mammalian cell cycle taking into account interactions of cyclin D/cdk4, cyclin E/cdk2, Rb and E2F. Mathematical analysis of this model predicts that a change in the proliferative status in response to a change in concentrations of serum growth factors will exhibit the property of hysteresis: the concentration of growth factors required to induce proliferation is higher than the concentration required to maintain proliferation. We experimentally confirmed this prediction in mouse embryonic fibroblasts in vitro. In agreement with the mathematical model, this indicates that changes in proliferative mode caused by small changes in concentrations of growth factors are not easily reversible. Based on this study, we discuss the importance of proliferation hysteresis for cell cycle regulation.


The original model was taken from the Cell Cycle DataBase (CCDB).

Variable added: assignment rule for denoting phosphorylated Rb (Rb_phosphorylated i.e(RT-RS-R)) created.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, USA.", + "authors": [ + { + "institution": "Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, USA.", + "name": "Shaochun Bai" + }, + { + "name": "David Goodrich" + }, + { + "name": "C Dennis Thron" + }, + { + "name": "Edwin Tecarro" + }, + { + "name": "Mandri Obeyesekere" + } + ], + "issue": "1", + "journal": "Cell cycle (Georgetown, Tex.)", + "link": "http://identifiers.org/pubmed/12695688", + "month": "0", + "pages": "46-52", + "synopsis": "We propose a mathematical model for the regulation of the G1-phase of the mammalian cell cycle taking into account interactions of cyclin D/cdk4, cyclin E/cdk2, Rb and E2F. 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Based on this study, we discuss the importance of proliferation hysteresis for cell cycle regulation.", + "title": "Theoretical and experimental evidence for hysteresis in cell proliferation.", + "type": "PubMed ID", + "volume": "2", + "year": 2003 + }, + "publicationId": "BIOMD0000000242", + "submissionId": "MODEL1003050000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000243": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

\t This is the reduced model (model 8) described in: Dynamics within the CD95 death-inducing signaling complex decide life and death of cells.
Leo Neumann, Carina Pforr, Joel Beaudouin, Alexander Golks, Peter H. Krammer, Inna N. Lavrik and Roland Eils (German Cancer Research Center (DKFZ), http://www.dkfz.de);Mol Sys Biol2010;6:352. doi:10.1038/msb.2010.6;

Abstract:
This study explores the dilemma in cellular signaling that triggering of CD95 (Fas/APO-1) in some situations results in cell death and in others leads to the activation of NF-\u03baB. We established an integrated kinetic mathematical model for CD95-mediated apoptotic and NF-\u03baB signaling. Systematic model reduction resulted in a surprisingly simple model well approximating experimentally observed dynamics. The model postulates a new link between c-FLIPL cleavage in the death-inducing signaling complex (DISC) and the NF-\u03baB pathway. We validated experimentally that CD95 stimulation resulted in an interaction of p43-FLIP with the IKK complex followed by its activation. Furthermore, we showed that the apoptotic and NF-\u03baB pathways diverge already at the DISC. Model and experimental analysis of DISC formation showed that a subtle balance of c-FLIPL and procaspase-8 determines life/death decisions in a nonlinear manner. We present an integrated model describing the complex dynamics of CD95-mediated apoptosis and NF-\u03baB signaling.

The original was taken from the MSB article supplementary material site msb20106-s2.xml. All the species ids were changed since the model was not a valid SBML with its original ids - Lukas.

Notes added to the species [L] (the initial concentration of Anti-CD95), regarding changes to be made in the initial concentration of [L], to obtain figure 5D.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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\t This is the model described in: Bacterial adaptation through distributed sensing of metabolic fluxes
\tOliver Kotte, Judith B Zaugg and Matthias Heinemann;Mol Sys Biol2010;6:355. doi:10.1038/msb.2010.10;
Abstract:
\t The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.\t

In its current form this SBML model is parametrized for the glucose to acetate transition and to simulate the extended diauxic shift as shown in figure 3 and scenario 6 of the attached matlab file. In this scenario the cells first are grown from an OD600 (BM) of 0.03 with a starting glucose concentration of 0.5 g/l for 8.15 h (29340 sec). Then a medium containing 5 g/l acetate is inoculated with these cells to an OD600 of 0.03 and grown for another 19.7 hours (70920 sec). Finally the cells are shifted to a medium containing both glucose and acetate at a concentration of 3 g/l with a starting OD600 of 0.0005.
The shifts where implemented using events triggering at the times determined by the parameters shift1 and shift2 (in hours). To simulate other scenarios the initial conditions need to be changed as described in the supplemental materials (supplement 1)
The original SBML model and the MATLAB file used for the calculations can be down loaded as supplementary materials of the publication from the MSB website. (supplement 2).

The units of the external metabolites are in [g/l], those of the biomass in optical density,OD600, taken as dimensionless, and [micromole/(gramm dry weight)] for all intracellular metabolites. As the latter cannot be implemented in SBML, it was chosen to be micromole only and the units of the parameters are left mostly undefined.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This the model from the article:
A biochemically structured model for Saccharomyces cerevisiae.
Lei F, Rotb\u00f8ll M, J\u00f8rgensen SB. J Biotechnol. 2001 Jul 12;88(3):205-21. \t PMID: 11434967 ,DOI: 10.1016/S0168-1656(01)00269-3

Abstract:
A biochemically structured model for the aerobic growth of Saccharomyces cerevisiae on glucose and ethanol is presented. The model focuses on the pyruvate and acetaldehyde branch points where overflow metabolism occurs when the growth changes from oxidative to oxido-reductive. The model is designed to describe the onset of aerobic alcoholic fermentation during steady-state as well as under dynamical conditions, by triggering an increase in the glycolytic flux using a key signalling component which is assumed to be closely related to acetaldehyde. An investigation of the modelled process dynamics in a continuous cultivation revealed multiple steady states in a region of dilution rates around the transition between oxidative and oxido-reductive growth. A bifurcation analysis using the two external variables, the dilution rate, D, and the inlet concentration of glucose, S(f), as parameters, showed that a fold bifurcation occurs close to the critical dilution rate resulting in multiple steady-states. The region of dilution rates within which multiple steady states may occur depends strongly on the substrate feed concentration. Consequently a single steady state may prevail at low feed concentrations, whereas multiple steady states may occur over a relatively wide range of dilution rates at higher feed concentrations.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This the single cell model from the article:
A multiscale model to investigate circadian rhythmicity of pacemaker neurons in the suprachiasmatic nucleus.
Vasalou C, Henson MA. PLoS Comput Biol 2010 Mar 12;6(3):e1000706.\t PMID: 20300645 , DOI: 10.1371/journal.pcbi.1000706 ;

Abstract:
The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels.

Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts, United States of America.", + "authors": [ + { + "institution": "Department of Chemical Engineering, University of Massachusetts, Amherst, Massachusetts, United States of America.", + "name": "Christina Vasalou" + }, + { + "name": "Michael A Henson" + } + ], + "issue": "3", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/20300645", + "month": "3", + "pages": "e1000706", + "synopsis": "The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels.", + "title": "A multiscale model to investigate circadian rhythmicity of pacemaker neurons in the suprachiasmatic nucleus.", + "type": "PubMed ID", + "volume": "6", + "year": 2010 + }, + "publicationId": "BIOMD0000000246", + "submissionId": "MODEL1004080000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000247": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model with unfitted parameters described in the article
Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress
Markus Ralser, Mirjam M Wamelink, Axel Kowald, Birgit Gerisch, Gino Heeren, Eduard A Struys, Edda Klipp, Cornelis Jakobs, Michael Breitenbach, Hans Lehrach and Sylvia Krobitsch, J Biol 2007 6(4):10; PMID: 18154684 , doi: 10.1186/jbiol61
Abstract:
BACKGROUND: Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.
RESULTS: We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.
CONCLUSION: The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions.

Different realtive enzyme velocities can be simulated by varying the parameters k_rel_TPI and k_rel_GAPDH .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Institute for Molecular Genetics, Ihnestrasse 73, 14195 Berlin, Germany. ralser@molgen.mpg.de", + "name": "Markus Ralser", + "orcid": "0000-0001-9535-7413" + }, + { + "name": "Mirjam M Wamelink", + "orcid": "0000-0002-9382-613X" + }, + { + "name": "Axel Kowald", + "orcid": "0000-0001-6448-4364" + }, + { + "name": "Birgit Gerisch" + }, + { + "name": "Gino Heeren" + }, + { + "name": "Eduard A Struys" + }, + { + "name": "Edda Klipp" + }, + { + "name": "Cornelis Jakobs" + }, + { + "name": "Michael Breitenbach" + }, + { + "name": "Hans Lehrach" + }, + { + "name": "Sylvia Krobitsch" + } + ], + "issue": "4", + "journal": "Journal of biology", + "link": "http://identifiers.org/pubmed/18154684", + "month": "12", + "pages": "10", + "synopsis": "

Background

Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.

Results

We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.

Conclusion

The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions.", + "title": "Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress.", + "type": "PubMed ID", + "volume": "6", + "year": 2007 + }, + "publicationId": "BIOMD0000000247", + "submissionId": "MODEL1003300000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000248": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Case Western Reserve University", + "email": "nicola.lai@case.edu", + "external": false, + "name": "Nicola Lai" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This file describes the SBML version of the mathematical model in the following journal article: Linking Pulmonary Oxygen Uptake, Muscle Oxygen Utilization and Cellular Metabolism during Exercise, Ann Biomed Eng. 2007 Jun;35(6):956-69. (Pubmed ID: 17380394). This mathematical model simulates oxygen transport and metabolism in skeletal muscle in response to a step change from a warm-up steady state to a higher work rate corresponding to exercise at different levels of intensity: moderate (M), heavy (H) and very heavy (VH). The model parameter values are listed in the tables of this article. The parameter values that are independent of the exercise level are reported in Table 2. The parameter values that depend on the exercise level are reported in Tables 1A, 3 and 4. The model simulations (Figures 2, 3, 4 and 5) were obtained for a representative subject with a set of parameter values different from those in Table 1A, 3 and 4. In the sbml model, these model parameters are used to simulate exercise at a very heavy (VH) intensity for the representative subject. Additionally, the parameter values needed to simulate exercise at moderate (M) and heavy (H) intensity are reported in the list of parameters of the file. The model simulates dynamics of (1) the concentrations of free (F) and total (T) oxygen concentration in blood (CFcap, CTcap) and tissue (CFtis, CTtis), Adenosine Triphosphate (ATP), Adenosine Diphosphate (ADP), Phosphocreatine (PCr) and Creatine (Cr); (2) the metabolic flux of oxidative phosphorylation, creatine kinase and ATPase; (3) the oxygen uptake in blood and oxygen transport rate from blood to tissue during exercise. The simulation also computes muscle oxygen saturation (StO2m) and relative muscle oxygen saturation (RStO2m) in order to compare simulated and experimental responses of human muscle oxygenation during exercise. The model was successfully tested with Roadrunner of the Systems Biology Workbench (SBW). The model simulations obtained with Roadrunner match those obtained with the mathematical model represented in Fortran and Matlab for relative and absolute tolerance smaller than 10-7.

To allow for simulations at varying levels of exercise, the parameter exercise_level was introduced. A value of 1 means medium, 2 heavy and 3 very heavy exercise. Setting this parameter assigns the parameters Vmax, KatpaseE, dQMm and tauQm with the relevant parameters. The warmup steady state is influenced by the parameter changes for this representative subject and the model has to be brought into steady state after each change of exercise level.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\ufffd\ufffdre N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Restif2006 - Whooping cough

This model is described in the article:

Restif O, Grenfell BT.
Proc. Biol. Sci. 2006 Feb; 273(1585): 409-416

Abstract:

Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), interfere through a given level of cross-immunity. We used whooping cough as a potential example, but the framework proposed here could be applied to other acute infectious diseases. Specifically, we analysed the effects of these parameters on the invasion dynamics of one strain into a population, where the second strain is endemic. Whereas the deterministic version of the model converges towards stable coexistence of the two strains in most cases, stochastic simulations showed that transient epidemic dynamics can cause the extinction of either strain. Thus ecological dynamics, modulated by the immune parameters, eventually determine the adaptive value of different pathogen genotypes. We advocate an integrative view of pathogen dynamics at the crossroads of immunology, epidemiology and evolution, as a way towards efficient control of infectious diseases.

This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size\u00a0 N\u00a0= 1. The model reproduces the deterministic time courses. Stochastic interpretation with Copasi UI gave results similar to the article, but was not extensively tested. The initial conditions for competition simulations can be derived by equilibrating the system for one pathogen and then adding a starting concentration for the other.

Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1)

This model is hosted on BioModels Database and identified by: BIOMD0000000249.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. This mechanistic model comprises the explicit reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. In the below article, two different mechanistic models have been studied, the first one based on previously known interactions but failing to account for the experimental data and the second one including additional interactions which were discovered and confirmed by new experiments. The mechanistic model encoded here is the second one, the extended and at the time of creation most complete model of cell fate decision making in response to different doses of EGF or HRG stimulation. The encoded parameter set corresponds to 10mM HRG stimulation as shown in Fig.1 of the article. The Supplementary Methods of the article provide further parameter sets that allow simulations for different ligands and different doses. A corresponding core model is available from http://www.ebi.ac.uk/biomodels/ as MODEL1003170000.

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010 , doi: 10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

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Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.", + "name": "Takashi Nakakuki" + }, + { + "name": "Marc R Birtwistle", + "orcid": "0000-0002-0341-0705" + }, + { + "name": "Yuko Saeki" + }, + { + "name": "Noriko Yumoto" + }, + { + "name": "Kaori Ide" + }, + { + "name": "Takeshi Nagashima" + }, + { + "institution": "Center for Information Services and High Performance Computing (ZIH), TUD Dresden University of Technology", + "name": "Lutz Brusch", + "orcid": "0000-0003-0137-5106" + }, + { + "name": "Babatunde A Ogunnaike", + "orcid": "0000-0002-8246-070X" + }, + { + "name": "Mariko Okada-Hatakeyama" + }, + { + "name": "Boris N Kholodenko", + "orcid": "0000-0002-9483-4975" + } + ], + "issue": "5", + "journal": "Cell", + "link": "http://identifiers.org/pubmed/20493519", + "month": "5", + "pages": "884-896", + "synopsis": "Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands.", + "title": "Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.", + "type": "PubMed ID", + "volume": "141", + "year": 2010 + }, + "publicationId": "BIOMD0000000250", + "submissionId": "MODEL1004300000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000251": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Center for Information Services and High Performance Computing (ZIH), TUD Dresden University of Technology", + "email": "lutz.brusch@tu-dresden.de", + "external": false, + "name": "Lutz Brusch", + "orcid": "0000-0003-0137-5106" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. The model comprises lumped reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. The parameter set implemented here corresponds to that used for generating Figs. 4 B,C,D (red curves for 10nM HRG) of the below article in Cell (2010). Moreover, we found that the same model described well the dynamics in different cell types (MCF-7 and PC-12), of different ligands (EGF and HRG) and at different doses (0.1nM, 1nM, 10nM) for a unique set of parameter values (as implemented here and reported in Table SD4_1 of the article) except for four parameters characterising the input, cytoplasmic ppERK. These four parameters K1, K2, tau1 and tau2 are used in the two equations involving species x1 and x2. These two equations define a phenomenological input module to describe the ligand-, dose- and cell type-dependent dynamics of ppERKc which are not modelled in mechanistic detail here. The four parameter values can be adjusted to model a specific ligand, dose and cell type. 8 parameter sets for different experiments are given in Table SD4_2 of the article. This SBML file, however, carries just one such parameter set. We have chosen that of MCF-7 cells stimulated by 10nM of HRG. To reproduce all simulations from the article, please replace the parameter values for K1, K2, tau1, tau2 as needed.

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010, doi:10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Research Group, Advanced Computational Sciences Department, RIKEN Advanced Science Institute, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.", + "name": "Takashi Nakakuki" + }, + { + "name": "Marc R Birtwistle", + "orcid": "0000-0002-0341-0705" + }, + { + "name": "Yuko Saeki" + }, + { + "name": "Noriko Yumoto" + }, + { + "name": "Kaori Ide" + }, + { + "name": "Takeshi Nagashima" + }, + { + "institution": "Center for Information Services and High Performance Computing (ZIH), TUD Dresden University of Technology", + "name": "Lutz Brusch", + "orcid": "0000-0003-0137-5106" + }, + { + "name": "Babatunde A Ogunnaike", + "orcid": "0000-0002-8246-070X" + }, + { + "name": "Mariko Okada-Hatakeyama" + }, + { + "name": "Boris N Kholodenko", + "orcid": "0000-0002-9483-4975" + } + ], + "issue": "5", + "journal": "Cell", + "link": "http://identifiers.org/pubmed/20493519", + "month": "5", + "pages": "884-896", + "synopsis": "Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are transient for both ligands. Knockdown of dual-specificity phosphatases extends HRG-stimulated nuclear ERK activation, but not c-fos mRNA expression, implying the existence of a HRG-induced repressor of c-fos transcription. Further experiments confirmed that this repressor is mainly induced by HRG, but not EGF, and requires new protein synthesis. We show how a spatially distributed, signaling-transcription cascade robustly discriminates between transient and sustained ERK activities at the c-Fos system level. The proposed control mechanisms are general and operate in different cell types, stimulated by various ligands.", + "title": "Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.", + "type": "PubMed ID", + "volume": "141", + "year": 2010 + }, + "publicationId": "BIOMD0000000251", + "submissionId": "MODEL1003170000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000252": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Niels Bohr Institute", + "email": "alex.hunziker@nbi.dk", + "external": false, + "name": "Alexander Hunziker" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Stress-specific response of the p53-Mdm2 feedback loop
Alexander Hunziker, Mogens H Jensen and Sandeep Krishna BMC Systems Biology 2010, Jul 12;4(1):94 20624280,
Abstract:
ABSTRACT: BACKGROUND: The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses. RESULTS: We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour -- steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels. CONCLUSIONS: We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms.

The parameters of the model corresponds to the resting state, with delta = 11hr-1, gamma = 0.2hr-1, kt = 0.03nM-1hr-1 and kf = 5000nM-1hr-1.

To simulate different stress conditions as in figure 2A (also look at the curation figure of this model) of the reference publication, the above parameter should be changed. The parameter values corresponding to different stress conditions are shown in the following table.


Stress Condition/Parameter delta gamma kt kf
Nutlin 11hr-1 0.2hr-1 0.03nM-1hr-1 500nM-1hr-1
Oncogene 2hr-10.2hr-10.03nM-1hr-15000nM-1hr-1
DNA damage 2hr-10.5hr-10.03nM-1hr-12500nM-1hr-1
Hypoxia 2hr-10.2hr-10.01nM-1hr-15000nM-1hr-1

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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{ + "accession": "20624280", + "affiliation": "Center for Models of Life, Niels Bohr Institute, Copenhagen, Denmark.", + "authors": [ + { + "institution": "Center for Models of Life, Niels Bohr Institute, Copenhagen, Denmark.", + "name": "Alexander Hunziker" + }, + { + "name": "Mogens H Jensen" + }, + { + "name": "Sandeep Krishna" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/20624280", + "month": "7", + "pages": "94", + "synopsis": "

Background

The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses.

Results

We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour - steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels.

Conclusions

We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms.", + "title": "Stress-specific response of the p53-Mdm2 feedback loop.", + "type": "PubMed ID", + "volume": "4", + "year": 2010 + }, + "publicationId": "BIOMD0000000252", + "submissionId": "MODEL1006280000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000253": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model described in the article:
The danger of metabolic pathways with turbo design
Teusink B, Walsh MC, van Dam K, Westerhoff HV Trends Biochem. Sci. 1998 May; Volume: 23 (Issue: 5 ): 162-9 9612078 ,
Abstract:
Many catabolic pathways begin with an ATP-requiring activation step, after which further metabolism yields a surplus of ATP. Such a 'turbo' principle is useful but also contains an inherent risk. This is illustrated by a detailed kinetic analysis of a paradoxical Saccharomyces cerevisiae mutant; the mutant fails to grow on glucose because of overactive initial enzymes of glycolysis, but is defective only in an enzyme (trehalose 6-phosphate synthase) that appears to have little relevance to glycolysis. The ubiquity of pathways that possess an initial activation step, suggests that there might be many more genes that, when deleted, cause rather paradoxical regulation phenotypes (i.e. growth defects caused by enhanced utilization of growth substrate).

The model represents the wild-type cell: 'guarded' glycolysis, which is the inhibition of the HK module by hexose monophosphate. The model reproduces figures 3c and 3d of the reference publication.

To reproduce unguarded glycolysis, set parameter wild_type to '0'.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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C. Slater Institute, BioCentrum Amsterdam, University of Amsterdam, The Netherlands.", + "authors": [ + { + "institution": "Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU Amsterdam, Amsterdam, The Netherlands.", + "name": "B Teusink", + "orcid": "0000-0003-3929-0423" + }, + { + "name": "M C Walsh" + }, + { + "name": "K van Dam" + }, + { + "institution": "Infrastructure for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "H V Westerhoff", + "orcid": "0000-0002-0443-6114" + } + ], + "issue": "5", + "journal": "Trends in biochemical sciences", + "link": "http://identifiers.org/pubmed/9612078", + "month": "5", + "pages": "162-169", + "synopsis": "Many catabolic pathways begin with an ATP-requiring activation step, after which further metabolism yields a surplus of ATP. Such a 'turbo' principle is useful but also contains an inherent risk. This is illustrated by a detailed kinetic analysis of a paradoxical Saccharomyces cerevisiae mutant; the mutant fails to grow on glucose because of overactive initial enzymes of glycolysis, but is defective only in an enzyme (trehalose 6-phosphate synthase) that appears to have little relevance to glycolysis. The ubiquity of pathways that possess an initial activation step, suggests that there might be many more genes that, when deleted, cause rather paradoxical regulation phenotypes (i.e. growth defects caused by enhanced utilization of growth substrate).", + "title": "The danger of metabolic pathways with turbo design.", + "type": "PubMed ID", + "volume": "23", + "year": 1998 + }, + "publicationId": "BIOMD0000000253", + "submissionId": "MODEL1007200002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000254": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
How yeast cells synchronize their glycolytic oscillations: a perturbation analytic treatment
Bier M, Bakker BM, Westerhoff HV. Biophys. J2000 Mar;78(3):1087-93. 10692299,
Abstract:
Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating metabolite at the bottom of the anaerobic part of the glycolytic chain. Here we address the issue of how a metabolite remote from the pacemaking origin of the oscillation may nevertheless control the synchronization. We present a quantitative model for glycolytic oscillations and their synchronization in terms of chemical kinetics. We show that, in essence, the common acetaldehyde concentration can be modeled as a small perturbation on the \"pacemaker\" whose effect on the period of the oscillations of cells in the same suspension is indeed such that a synchronization develops.

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H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "H V Westerhoff", + "orcid": "0000-0002-0443-6114" + } + ], + "issue": "3", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/10692299", + "month": "3", + "pages": "1087-1093", + "synopsis": "Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating metabolite at the bottom of the anaerobic part of the glycolytic chain. Here we address the issue of how a metabolite remote from the pacemaking origin of the oscillation may nevertheless control the synchronization. We present a quantitative model for glycolytic oscillations and their synchronization in terms of chemical kinetics. 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This is A431 IERMv1.0 model described in the article
Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.
William W Chen, Birgit Schoeberl, Paul J Jasper, Mario Niepel, Ulrik B Nielsen, Douglas A Lauffenburger and Peter K Sorger. Molecular Systems Biology 2009; 5:239. PMID: 19156131 , DOI: 10.1038/msb.2008.74

Abstract:
The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.

The sbml model is available as supplemental material to the article and at http://www.cdpcenter.org/resources/models/chen-et-al-2008/ . It was slightly changed to make it valid SBML and to incorporate the step functions, described in the readme file and needed for inhibitor preincubation. the equilibration processes end at 1800 sec, so to reproduce the dynamics shown in the publication and supplemental material, only the time points after 1800 need to be considered. The parameter set is the hand fitted one used for Sfigure 3 in the supplemental materials. All species are in molecules, apart from HRG, EGF and Inh, which are in M.

The results shown in SFigure 3 can be calculated dividing the parameters ERK_PP , AKT_PP and ERB_B1_P_tot by ERK_t , AKT_t and EGFR_t , respectively. Somehow we did not find the right scaleing factor for the phosphorylated ErbB1 receptor. Therefore the model does only qualitatively reproduces the timecourses shown in the first row of Sfigure 3.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\ufffd\ufffd\ufffd\ufffd\ufffd\ufffdre N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.", + "title": "Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.", + "type": "PubMed ID", + "volume": "5", + "year": 2009 + }, + "publicationId": "BIOMD0000000255", + "submissionId": "MODEL1007260001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000256": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Royal College of Surgeons Ireland . 123 St Stephens Green . Dublin 2 . Ireland", + "email": "ggomezestrada@rcsi.ie", + "external": false, + "name": "G Gomez Estrada" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the standard model described in the article:
Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein.
Rehm M, Huber HJ, Dussmann H, Prehn JH. EMBO J. 2006 Sep 20;25(18):4338-49. Epub 2006 Aug 24. PMID:16932741, doi:10.1038/sj.emboj.7601295;
Abstract:
Activation of effector caspases is a final step during apoptosis. Single-cell imaging studies have demonstrated that this process may occur as a rapid, all-or-none response, triggering a complete substrate cleavage within 15 min. Based on biochemical data from HeLa cells, we have developed a computational model of apoptosome-dependent caspase activation that was sufficient to remodel the rapid kinetics of effector caspase activation observed in vivo. Sensitivity analyses predicted a critical role for caspase-3-dependent feedback signalling and the X-linked-inhibitor-of-apoptosis-protein (XIAP), but a less prominent role for the XIAP antagonist Smac. Single-cell experiments employing a caspase fluorescence resonance energy transfer substrate verified these model predictions qualitatively and quantitatively. XIAP was predicted to control this all-or-none response, with concentrations as high as 0.15 microM enabling, but concentrations >0.30 microM significantly blocking substrate cleavage. Overexpression of XIAP within these threshold concentrations produced cells showing slow effector caspase activation and submaximal substrate cleavage. Our study supports the hypothesis that high levels of XIAP control caspase activation and substrate cleavage, and may promote apoptosis resistance and sublethal caspase activation in vivo.\t

This model is slightly altered from the description in the article. Cytochrome C and SMAC release from the mitochondrion is modelled as simple first order kinetics, giving the same form as the (integrated) equations in the supplement of the article. The apoptosome formation is modelled equally - and independent of the Cytochrome C release. The speed is either limited by the Apaf1 or ProCaspase9 concentration, whichever is higher, symbolised via the parameter with the ID apolim.
Also, once the substrate concentration falls below 1 percent, the event Production_Breakdown is triggered, leading to a breakdown of XIAP and procaspase3 production and turning off of the enhanced/proteosomal degradation (degradation rate for reactions 38,39,40,43,44,46,48,50,51 changes from 0.0347 to 0.0058).

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the self maintaining metabolism model described in the article:
A Simple Self-Maintaining Metabolic System: Robustness, Autocatalysis, Bistability.
Piedrafita G, Montero F, Mor\u00e1n F, C\u00e1rdenas ML, Cornish-Bowden A, PLoS Computational Biology 2010, 6(8):e1000872. doi:10.1371/journal.pcbi.1000872
Abstract:
A living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. We show here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation. As long as at least one catalyst is present at a sufficient concentration in the initial state, the others can be produced and maintained. The system shows bistability, because if the amount of catalyst in the initial state is insufficient to reach the non-trivial steady state the system collapses to a trivial steady state in which all fluxes are zero. It is also robust, because if one catalyst is catastrophically lost when the system is in steady state it can recreate the same state. There are three elementary flux modes, but none of them is an enzyme-maintaining mode, the entire network being necessary to maintain the two catalysts

As this is a theoretical model and no units are given in the article, the standard units (mol, seconds and litre) are used for the parameters. k8 and k11 are set equal to k4.

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Bioqu\u00edmica y Biolog\u00eda Molecular I, Facultad de Ciencias Qu\u00edmicas, Universidad Complutense de Madrid, Ciudad Universitaria, Madrid 28045, Spain.", + "name": "Francisco Montero", + "orcid": "0000-0003-2366-3635" + }, + { + "name": "Federico Mor\u00e1n", + "orcid": "0000-0003-3992-8829" + }, + { + "name": "Mar\u00eda Luz C\u00e1rdenas" + }, + { + "name": "Athel Cornish-Bowden", + "orcid": "0000-0001-6670-3006" + } + ], + "issue": "8", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/20700491", + "month": "8", + "pages": "e1000872", + "synopsis": "A living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. 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Ortega2006 - bistability from doublephosphorylation in signal transduction

This model is described in the article:

Ortega F, Garc\u00e9s JL, Mas F, Kholodenko BN, Cascante M.
FEBS J. 2006 Sep; 273(17): 3915-3926

Abstract:

Previous studies have suggested that positive feedback loops and ultrasensitivity are prerequisites for bistability in covalent modification cascades. However, it was recently shown that bistability and hysteresis can also arise solely from multisite phosphorylation. Here we analytically demonstrate that double phosphorylation of a protein (or other covalent modification) generates bistability only if: (a) the two phosphorylation (or the two dephosphorylation) reactions are catalyzed by the same enzyme; (b) the kinetics operate at least partly in the zero-order region; and (c) the ratio of the catalytic constants of the phosphorylation and dephosphorylation steps in the first modification cycle is less than this ratio in the second cycle. We also show that multisite phosphorylation enlarges the region of kinetic parameter values in which bistability appears, but does not generate multistability. In addition, we conclude that a cascade of phosphorylation/dephosphorylation cycles generates multiple steady states in the absence of feedback or feedforward loops. Our results show that bistable behavior in covalent modification cascades relies not only on the structure and regulatory pattern of feedback/feedforward loops, but also on the kinetic characteristics of their component proteins.

This model is hosted on BioModels Database and identified by: BIOMD0000000258.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"synopsis": "Previous studies have suggested that positive feedback loops and ultrasensitivity are prerequisites for bistability in covalent modification cascades. However, it was recently shown that bistability and hysteresis can also arise solely from multisite phosphorylation. Here we analytically demonstrate that double phosphorylation of a protein (or other covalent modification) generates bistability only if: (a) the two phosphorylation (or the two dephosphorylation) reactions are catalyzed by the same enzyme; (b) the kinetics operate at least partly in the zero-order region; and (c) the ratio of the catalytic constants of the phosphorylation and dephosphorylation steps in the first modification cycle is less than this ratio in the second cycle. We also show that multisite phosphorylation enlarges the region of kinetic parameter values in which bistability appears, but does not generate multistability. In addition, we conclude that a cascade of phosphorylation/dephosphorylation cycles generates multiple steady states in the absence of feedback or feedforward loops. Our results show that bistable behavior in covalent modification cascades relies not only on the structure and regulatory pattern of feedback/feedforward loops, but also on the kinetic characteristics of their component proteins.", + "title": "Bistability from double phosphorylation in signal transduction. Kinetic and structural requirements.", + "type": "PubMed ID", + "volume": "273", + "year": 2006 + }, + "publicationId": "BIOMD0000000258", + "submissionId": "MODEL1008100000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000259": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max Delbrueck Center for Molecular Medicine", + "email": "tiagojab@yahoo.com.br", + "external": false, + "name": "Tiago Jose da Silva Lopes" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which cancatalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Deficient condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"pages": "112", + "synopsis": "

Background

Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.

Results

Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.

Conclusions

This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.", + "title": "Systems analysis of iron metabolism: the network of iron pools and fluxes.", + "type": "PubMed ID", + "volume": "4", + "year": 2010 + }, + "publicationId": "BIOMD0000000259", + "submissionId": "MODEL4152801381", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000260": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max Delbrueck Center for Molecular Medicine", + "email": "tiagojab@yahoo.com.br", + "external": false, + "name": "Tiago Jose da Silva Lopes" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which cancatalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Adequate condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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+ "synopsis": "

Background

Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.

Results

Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.

Conclusions

This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.", + "title": "Systems analysis of iron metabolism: the network of iron pools and fluxes.", + "type": "PubMed ID", + "volume": "4", + "year": 2010 + }, + "publicationId": "BIOMD0000000260", + "submissionId": "MODEL4152760573", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000261": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max Delbrueck Center for Molecular Medicine", + "email": "tiagojab@yahoo.com.br", + "external": false, + "name": "Tiago Jose da Silva Lopes" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Loaded condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"synopsis": "

Background

Every cell of the mammalian organism needs iron as trace element in numerous oxido-reductive processes as well as for transport and storage of oxygen. The very versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signalling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.

Results

Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.

Conclusions

This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.", + "title": "Systems analysis of iron metabolism: the network of iron pools and fluxes.", + "type": "PubMed ID", + "volume": "4", + "year": 2010 + }, + "publicationId": "BIOMD0000000261", + "submissionId": "MODEL4152822384", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000262": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Tokyo", + "email": "kfujita@cb.k.u-tokyo.ac.jp", + "external": false, + "name": "Kazuhiro Fujita" + } + ] + }, + "curationStatus": "CURATED", + "description": "

EGF dependent Akt pathway model

made by Kazuhiro A. Fujita.

This is the EGF dependent Akt pathway model described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters EGF_conc_pulse , EGF_conc_step and EGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value EGF_conc_pulse , a 60 second step with EGF_conc_step or a signal increasing from 0 to EGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter are set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Electronic address: skuroda@bs.s.u-tokyo.ac.jp.", + "name": "Shinya Kuroda", + "orcid": "0000-0001-5059-8299" + } + ], + "issue": "132", + "journal": "Science signaling", + "link": "http://identifiers.org/pubmed/20664065", + "month": "7", + "pages": "ra56", + "synopsis": "In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)-dependent Akt pathway in PC12 cells on the basis of experimental results. 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Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.", + "title": "Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.", + "type": "PubMed ID", + "volume": "3", + "year": 2010 + }, + "publicationId": "BIOMD0000000262", + "submissionId": "MODEL1004060001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000263": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Tokyo", + "email": "kfujita@cb.k.u-tokyo.ac.jp", + "external": false, + "name": "Kazuhiro Fujita" + } + ] + }, + "curationStatus": "CURATED", + "description": "

NGF dependent Akt pathway model

made by Kazuhiro A. Fujita.

This is the NGF dependent Akt pathway model described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters NGF_conc_pulse , NGF_conc_step and NGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value NGF_conc_pulse , a 60 second step with NGF_conc_step or a signal increasing from 0 to NGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter is set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Kshiwanoha 5-1-5, Kashiwa, Chiba 277-8568, Japan.", + "name": "Kazuhiro A Fujita" + }, + { + "name": "Yu Toyoshima" + }, + { + "name": "Shinsuke Uda" + }, + { + "name": "Yu-ichi Ozaki" + }, + { + "name": "Hiroyuki Kubota" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo 113-0033, Japan. Electronic address: skuroda@bs.s.u-tokyo.ac.jp.", + "name": "Shinya Kuroda", + "orcid": "0000-0001-5059-8299" + } + ], + "issue": "132", + "journal": "Science signaling", + "link": "http://identifiers.org/pubmed/20664065", + "month": "7", + "pages": "ra56", + "synopsis": "In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)-dependent Akt pathway in PC12 cells on the basis of experimental results. 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Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.", + "title": "Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.", + "type": "PubMed ID", + "volume": "3", + "year": 2010 + }, + "publicationId": "BIOMD0000000263", + "submissionId": "MODEL1004060002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000264": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Tokyo", + "email": "kfujita@cb.k.u-tokyo.ac.jp", + "external": false, + "name": "Kazuhiro Fujita" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Akt pathway model with EGFR inhibitor

made by Kazuhiro A. Fujita.

This is the Akt pathway model with an EGFR inhibitor described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters EGF_conc_pulse , EGF_conc_step and EGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value EGF_conc_pulse , a 60 second step with EGF_conc_step or a signal increasing from 0 to EGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter are set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo 113-0033, Japan. 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This model is from the article:
Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex.
Conradie R, Bruggeman FJ, Ciliberto A, Csik\u00e1sz-Nagy A, Nov\u00e1k B, Westerhoff HV, Snoep JL FEBS J.2010 Jan; 277(2): 357-67 20015233,
Abstract:
Numerous top-down kinetic models have been constructed to describe the cell cycle. These models have typically been constructed, validated and analyzed using model species (molecular intermediates and proteins) and phenotypic observations, and therefore do not focus on the individual model processes (reaction steps). We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. The reactions in the model with the highest RP control were those involved in: (a) the interplay between retinoblastoma protein and E2F transcription factor; (b) those synthesizing the delayed response genes and cyclin D/Cdk4 in response to growth signals; (c) the E2F-dependent cyclin E/Cdk2 synthesis reaction; as well as (d) p27 formation reactions. Nine of the 23 intermediates were shown to have a good correlation between their concentration control and RP control. Sensitivity and co-control analysis indicated that the strongest control of the RP is mediated via the cyclin E/Cdk2:p27 complex concentration. Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP.

The rate constants presented in the paper have units [per tenth of an hour] and have been changed here to [per hour] (e.g. k16 = 0.25 not 0.025); for further confirmation of the correctness of this change, see the original model (Novak, J Theor Biol 2004 230:563).

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We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. 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Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP.", + "title": "Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex.", + "type": "PubMed ID", + "volume": "277", + "year": 2010 + }, + "publicationId": "BIOMD0000000265", + "submissionId": "MODEL1008240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000266": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Voit2003 - Trehalose Cycle

This model is described in the article:

Voit EO.
J. Theor. Biol. 2003 Jul; 223(1): 55-78

Abstract:

The physiological hallmark of heat-shock response in yeast is a rapid, enormous increase in the concentration of trehalose. Normally found in growing yeast cells and other organisms only as traces, trehalose becomes a crucial protector of proteins and membranes against a variety of stresses, including heat, cold, starvation, desiccation, osmotic or oxidative stress, and exposure to toxicants. Trehalose is produced from glucose 6-phosphate and uridine diphosphate glucose in a two-step process, and recycled to glucose by trehalases. Even though the trehalose cycle consists of only a few metabolites and enzymatic steps, its regulatory structure and operation are surprisingly complex. The article begins with a review of experimental observations on the regulation of the trehalose cycle in yeast and proposes a canonical model for its analysis. The first part of this analysis demonstrates the benefits of the various regulatory features by means of controlled comparisons with models of otherwise equivalent pathways lacking these features. The second part elucidates the significance of the expression pattern of the trehalose cycle genes in response to heat shock. Interestingly, the genes contributing to trehalose formation are up-regulated to very different degrees, and even the trehalose degrading trehalases show drastically increased activity during heat-shock response. Again using the method of controlled comparisons, the model provides rationale for the observed pattern of gene expression and reveals benefits of the counterintuitive trehalase up-regulation.

Toinduce a heat shock, set the parameter heat_shock from 0 to 1. Thischanges the parameter values of X8 to X19 from 1 to the valuesgiven in table 3 of the original publication.
Asthis is an S-systems model, it does not contain any reactionsencoded in SBML.

This model is hosted on BioModels Database and identified by: BIOMD0000000266.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Normally found in growing yeast cells and other organisms only as traces, trehalose becomes a crucial protector of proteins and membranes against a variety of stresses, including heat, cold, starvation, desiccation, osmotic or oxidative stress, and exposure to toxicants. Trehalose is produced from glucose 6-phosphate and uridine diphosphate glucose in a two-step process, and recycled to glucose by trehalases. Even though the trehalose cycle consists of only a few metabolites and enzymatic steps, its regulatory structure and operation are surprisingly complex. The article begins with a review of experimental observations on the regulation of the trehalose cycle in yeast and proposes a canonical model for its analysis. The first part of this analysis demonstrates the benefits of the various regulatory features by means of controlled comparisons with models of otherwise equivalent pathways lacking these features. 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Lebeda2008 - BoNT paralysis (3 step model)
The onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction is describing in the model. This is the 3-step model described in the paper. This model is the reduced form of the model developed my Simpson 1980; PMID:\u00a0\u00a0 6243359 \u00a0, i.e., it omits three unknown parameters that represents the binding sites for each species of the toxin. The extension to this model,\u00a0i.e. the 4-step model described in the paper is BIOMD0000000178.

This model is described in the article:

Lebeda FJ, Adler M, Erickson K, Chushak Y.
J Pharmacokinet Pharmacodyn 2008 Jun; 35(3): 251-267

Abstract:

Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000267.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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BoNT paralysis (3 step model)", + "publication": { + "accession": "18551355", + "affiliation": "Integrated Toxicology Division, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702-5011, USA. Frank.Lebeda@amedd.army.mil", + "authors": [ + { + "institution": "Integrated Toxicology Division, US Army Medical Research Institute of Infectious Diseases, Fort Detrick, MD 21702-5011, USA. Frank.Lebeda@amedd.army.mil", + "name": "Frank J Lebeda" + }, + { + "name": "Michael Adler" + }, + { + "name": "Keith Erickson" + }, + { + "name": "Yaroslav Chushak" + } + ], + "issue": "3", + "journal": "Journal of pharmacokinetics and pharmacodynamics", + "link": "http://identifiers.org/pubmed/18551355", + "month": "6", + "pages": "251-267", + "synopsis": "Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.", + "title": "Onset dynamics of type A botulinum neurotoxin-induced paralysis.", + "type": "PubMed ID", + "volume": "35", + "year": 2008 + }, + "publicationId": "BIOMD0000000267", + "submissionId": "MODEL1009070000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000268": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model described in the article:
A mathematical model of glutathione metabolism.
Michael C Reed, Rachel L Thomas, Jovana Pavisic, S. Jill James, Cornelia M Ulrich and H. Frederik Nijhout, Theor Biol Med Model 2008,5:8; PubmedID:18442411 ; DOI:10.1186/1742-4682-5-8;
Abstract:
BACKGROUND: Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.
APPROACH: We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.
CONCLUSION: We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.\t

parameter orig. article this model
Vm_CBS 700000 420000
Vm_GNMT 245 260
K_sam_GNMT 32 63
Vr_MTD(mito) 600000 595000
V_CBS kinetic law rearranged
V_bmetc 913 913.4
Vm_GR 8925 892.5

This version of the model contains a feeding rhythm as used in figure 5 of the original article. Four parameters, breakfast, lunch dinner and fasting, describe the relative level of amino acids, described by the parameter aa_input or Aminoacid_input, in the blood. To remove the daily feeding rhythm, either set the parameters for meals and fasting to 1 (or for figure 3 to 0.333), or remove the assignment rule for the Aminoacid_input. For the steady state evaluations for figure 6, the mealtime parameters were set to one, which, while making Copasi complain about explicit time dependency, still gives valid results.

This version of the model differs slightly from the version described in the supplement, in which contains some typos. It was corrected using the version of JWS-online, created using the original matlab files, thankfully provided by the articles authors. Many thanks to Jacky Snoep for his help and support.

In the SBML version of the model the volumes of the mitochondrion, the cytoplasm and the cell were all set to one to obtain the same equations as described in the supplemental materials of the article. The total folate is equally split between the cytosol and the mitochondrion and divided by 3/4 for the cytosol and 1/4 for the mitochondrion, respectively. To obtain an SBML model in which the volumes of the compartments, cytosol and mito, are used, the model needs to be altered as follows:

While the concentrations stay the same after these alteration, the reaction fluxes change by a factor of cytosol and mito for cytosolic and mitchondrial reactions, respectively.

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

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"http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Reed2008_Glutathione_Metabolism", + "publication": { + "accession": "18442411", + "affiliation": "Department of Mathematics, Duke University, Durham, NC 27708, USA. reed@math.duke.edu", + "authors": [ + { + "institution": "Department of Mathematics, Duke University, Durham, NC 27708, USA. reed@math.duke.edu", + "name": "Michael C Reed" + }, + { + "name": "Rachel L Thomas" + }, + { + "name": "Jovana Pavisic" + }, + { + "name": "S Jill James" + }, + { + "name": "Cornelia M Ulrich" + }, + { + "name": "H Frederik Nijhout" + } + ], + "journal": "Theoretical biology & medical modelling", + "link": "http://identifiers.org/pubmed/18442411", + "month": "4", + "pages": "8", + "synopsis": "

Background

Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.

Approach

We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.

Conclusion

We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.", + "title": "A mathematical model of glutathione metabolism.", + "type": "PubMed ID", + "volume": "5", + "year": 2008 + }, + "publicationId": "BIOMD0000000268", + "submissionId": "MODEL1007200001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000269": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the single cell model for analysis of hormonal crosstalk in Arabidopsis described in the article:
Modelling and experimental analysis of hormonal crosstalk in Arabidopsis.
Liu J, Mehdi S, Topping J, Tarkowski P and Lindsey K. Mol Syst Biol. 2010 Jun 8;6:373; PmID: 20531403 , DOI: 10.1038/msb.2010.26
Abstract:
An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS-ethylene pathway interactions is developed, and used to make quantitative predictions about PLS-hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose-response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants.

This model was originally created using Copasi and taken from the supplementary materials of the MSB article. It uses equation 5 for the auxin biosynthesis and was altered to also contain the reactions for ACC, IAA and cytokinine import. Different from the supplementary material, the parameters for the auxin synthesis, v2, are set to k2c = 0.01 uM and k2=0.2 uM_per_sec and for the WT PLS transcription k6=0.3 . To obtain the model described in the first table of the supplementary materials, set k2c=k2=0 and k6=0.9 . For the pls and PLSox mutants, k6 should be set to 0 and 0.45, respectively.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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"0000-0002-7992-6804" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/20531403", + "month": "6", + "pages": "373", + "synopsis": "An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS-ethylene pathway interactions is developed, and used to make quantitative predictions about PLS-hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose-response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants.", + "title": "Modelling and experimental analysis of hormonal crosstalk in Arabidopsis.", + "type": "PubMed ID", + "volume": "6", + "year": 2010 + }, + "publicationId": "BIOMD0000000269", + "submissionId": "MODEL1008260000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000270": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "German Cancer Research Center", + "email": "m.schilling@dkfz.de", + "external": false, + "name": "Marcel Schilling" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Schilling2009 - ERK distributive

This model has been exported from PottersWheel on 2009-04-20 18:57:44.\u00a0 The PottersWheel Model Definition file can be obtained from the curation tab.

This model is described in the article:

Schilling M, Maiwald T, Hengl S, Winter D, Kreutz C, Kolch W, Lehmann WD, Timmer J, Klingm\u00fcller U.
Mol. Syst. Biol. 2009; 5: 334

Abstract:

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.

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To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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ERK distributive", + "publication": { + "accession": "20029368", + "affiliation": "Division Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany.", + "authors": [ + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "name": "Thomas Maiwald" + }, + { + "name": "Stefan Hengl" + }, + { + "name": "Dominic Winter", + "orcid": "0000-0001-6788-6641" + }, + { + "name": "Clemens Kreutz", + "orcid": "0000-0002-8796-5766" + }, + { + "institution": "1] Systems Biology Ireland, University College Dublin, Dublin 4, Ireland [2] Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin 4, Ireland [3] School of Medicine and Medical Sciences, University College Dublin, Dublin 4, Ireland [4].", + "name": "Walter Kolch", + "orcid": "0000-0001-5777-5016" + }, + { + "name": "Wolf D Lehmann" + }, + { + "name": "Jens Timmer" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/20029368", + "month": "0", + "pages": "334", + "synopsis": "Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.", + "title": "Theoretical and experimental analysis links isoform-specific ERK signalling to cell fate decisions.", + "type": "PubMed ID", + "volume": "5", + "year": 2009 + }, + "publicationId": "BIOMD0000000270", + "submissionId": "MODEL1001120000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000271": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department of Systems Biology, Harvard Medical School, Boston, USA", + "email": "verena_becker@hms.harvard.edu", + "external": false, + "name": "Verena Becker" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the core model described in the article:
Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor
Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingm\u00fcller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988
Abstract:
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

SBML model exported from PottersWheel.

% PottersWheel model definition filefunction m = BeckerSchilling2010_EpoR_CoreModel()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'BeckerSchilling2010_EpoR_CoreModel';m.name        = 'BeckerSchilling2010_EpoR_CoreModel';m.description = 'BeckerSchilling2010_EpoR_CoreModel';m.authors     = {'Verena Becker',' Marcel Schilling'};m.dates       = {'2010'};m.type        = 'PW-2-0-42';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'EpoR'     ,     516, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo'      , 2030.19, 'global', 1890,  2310,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo_EpoR' ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo_EpoRi',       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dEpoi'    ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dEpoe'    ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {            }, {'EpoR'      }, {  }, 'C' , [] , 'k1*k2', {'kt','Bmax'}, [], 'reaction0001');m = pwAddR(m, {'EpoR'      }, {            }, {  }, 'MA', [] , []     , {'kt'       }, [], 'reaction0002');m = pwAddR(m, {'Epo','EpoR'}, {'Epo_EpoR'  }, {  }, 'MA', [] , []     , {'kon'      }, [], 'reaction0003');m = pwAddR(m, {'Epo_EpoR'  }, {'Epo','EpoR'}, {  }, 'MA', [] , []     , {'koff'     }, [], 'reaction0004');m = pwAddR(m, {'Epo_EpoR'  }, {'Epo_EpoRi' }, {  }, 'MA', [] , []     , {'ke'       }, [], 'reaction0005');m = pwAddR(m, {'Epo_EpoRi' }, {'Epo','EpoR'}, {  }, 'MA', [] , []     , {'kex'      }, [], 'reaction0006');m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoi'     }, {  }, 'MA', [] , []     , {'kdi'      }, [], 'reaction0007');m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoe'     }, {  }, 'MA', [] , []     , {'kde'      }, [], 'reaction0008');%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'kt'  , 0.0329366 , 'global', 1e-007, 1000);m = pwAddK(m, 'Bmax', 516       , 'fix'   , 492   , 540 );m = pwAddK(m, 'kon' , 0.00010496, 'global', 1e-007, 1000);m = pwAddK(m, 'koff', 0.0172135 , 'global', 1e-007, 1000);m = pwAddK(m, 'ke'  , 0.0748267 , 'global', 1e-007, 1000);m = pwAddK(m, 'kex' , 0.00993805, 'global', 1e-007, 1000);m = pwAddK(m, 'kdi' , 0.00317871, 'global', 1e-007, 1000);m = pwAddK(m, 'kde' , 0.0164042 , 'global', 1e-007, 1000);%% Default sampling time pointsm.t = 0:3:99;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Epo + dEpoe'      , 'Epo_extracellular_obs');m = pwAddY(m, 'Epo_EpoR'         , 'Epo_cellsurface_obs'  );m = pwAddY(m, 'Epo_EpoRi + dEpoi', 'Epo_intracellular_obs');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_Epo_extracellular_obs', 1, 'fix', 0, 100);m = pwAddS(m, 'scale_Epo_cellsurface_obs'  , 1, 'fix', 0, 100);m = pwAddS(m, 'scale_Epo_intracellular_obs', 1, 'fix', 0, 100);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"institution": "Division Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, 69120 Heidelberg, Germany.", + "name": "Verena Becker" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "name": "Julie Bachmann" + }, + { + "name": "Ute Baumann" + }, + { + "name": "Andreas Raue" + }, + { + "name": "Thomas Maiwald" + }, + { + "name": "Jens Timmer" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + } + ], + "issue": "5984", + "journal": "Science (New York, N.Y.)", + "link": "http://identifiers.org/pubmed/20488988", + "month": "6", + "pages": "1404-1408", + "synopsis": "Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.", + "title": "Covering a broad dynamic range: information processing at the erythropoietin receptor.", + "type": "PubMed ID", + "volume": "328", + "year": 2010 + }, + "publicationId": "BIOMD0000000271", + "submissionId": "MODEL1005260000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000272": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department of Systems Biology, Harvard Medical School, Boston, USA", + "email": "verena_becker@hms.harvard.edu", + "external": false, + "name": "Verena Becker" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the auxiliary model described in the article:
Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor
Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingm\u00fcller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988
Abstract:
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

SBML model exported from PottersWheel.

% PottersWheel model definition filefunction m = BeckerSchilling2010_EpoR_AuxiliaryMode()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'BeckerSchilling2010_EpoR_AuxiliaryMode';m.name        = 'BeckerSchilling2010_EpoR_AuxiliaryModel';m.description = 'BeckerSchilling2010_EpoR_AuxiliaryModel';m.authors     = {'Verena Becker',' Marcel Schilling'};m.dates       = {'2010'};m.type        = 'PW-2-0-42';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'EpoR'     ,      76, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv'      , 999.293, 'global', 900,  1100,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv_EpoR' ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv_EpoRi',       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dSAvi'    ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dSAve'    ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {            }, {'EpoR'      }, {  }, 'C' , [] , 'k1*k2', {'kt','Bmax_SAv'}, [], 'reaction0001');m = pwAddR(m, {'EpoR'      }, {            }, {  }, 'MA', [] , []     , {'kt'           }, [], 'reaction0002');m = pwAddR(m, {'SAv','EpoR'}, {'SAv_EpoR'  }, {  }, 'MA', [] , []     , {'kon_SAv'      }, [], 'reaction0003');m = pwAddR(m, {'SAv_EpoR'  }, {'SAv','EpoR'}, {  }, 'MA', [] , []     , {'koff_SAv'     }, [], 'reaction0004');m = pwAddR(m, {'SAv_EpoR'  }, {'SAv_EpoRi' }, {  }, 'MA', [] , []     , {'kt'           }, [], 'reaction0005');m = pwAddR(m, {'SAv_EpoRi' }, {'SAv'       }, {  }, 'MA', [] , []     , {'kex_SAv'      }, [], 'reaction0006');m = pwAddR(m, {'SAv_EpoRi' }, {'dSAvi'     }, {  }, 'MA', [] , []     , {'kdi'          }, [], 'reaction0007');m = pwAddR(m, {'SAv_EpoRi' }, {'dSAve'     }, {  }, 'MA', [] , []     , {'kde'          }, [], 'reaction0008');%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'kt'      , 0.0329366   , 'global', 1e-007, 1000);m = pwAddK(m, 'Bmax_SAv', 76          , 'fix'   , 61    , 91  );m = pwAddK(m, 'kon_SAv' , 2.29402e-006, 'global', 1e-007, 1000);m = pwAddK(m, 'koff_SAv', 0.00679946  , 'global', 1e-007, 1000);m = pwAddK(m, 'kex_SAv' , 0.01101     , 'global', 1e-007, 1000);m = pwAddK(m, 'kdi'     , 0.00317871  , 'global', 1e-007, 1000);m = pwAddK(m, 'kde'     , 0.0164042   , 'global', 1e-007, 1000);%% Default sampling time pointsm.t = 0:3:99;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'SAv + dSAve'      , 'SAv_extracellular_obs');m = pwAddY(m, 'SAv_EpoR'         , 'SAv_cellsurface_obs'  );m = pwAddY(m, 'SAv_EpoRi + dSAvi', 'SAv_intracellular_obs');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_SAv_extracellular_obs', 1, 'fix', 0, 100);m = pwAddS(m, 'scale_SAv_cellsurface_obs'  , 1, 'fix', 0, 100);m = pwAddS(m, 'scale_SAv_intracellular_obs', 1, 'fix', 0, 100);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"institution": "Division Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance, German Cancer Research Center, 69120 Heidelberg, Germany.", + "name": "Verena Becker" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "name": "Julie Bachmann" + }, + { + "name": "Ute Baumann" + }, + { + "name": "Andreas Raue" + }, + { + "name": "Thomas Maiwald" + }, + { + "name": "Jens Timmer" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + } + ], + "issue": "5984", + "journal": "Science (New York, N.Y.)", + "link": "http://identifiers.org/pubmed/20488988", + "month": "6", + "pages": "1404-1408", + "synopsis": "Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of the cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input; this process is carried out over a broad range of ligand concentrations. This relation depends solely on EpoR turnover independent of ligand binding, which suggests an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.", + "title": "Covering a broad dynamic range: information processing at the erythropoietin receptor.", + "type": "PubMed ID", + "volume": "328", + "year": 2010 + }, + "publicationId": "BIOMD0000000272", + "submissionId": "MODEL1005260001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000273": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Edinburgh University", + "email": "apokhilk@staffmail.ed.ac.uk", + "external": false, + "name": "Alexandra Pokhilko" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model.
Pokhilko A, Hodge SK, Stratford K, Knox K, Edwards KD, Thomson AW, Mizuno T, Millar AJ. Mol Syst Biol.2010 Sep 21;6:416. 20865009,
Abstract:
Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation.

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"bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Pokhilko2010_CircClock", + "publication": { + "accession": "20865009", + "affiliation": "School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh, UK.", + "authors": [ + { + "institution": "School of Biological Sciences, University of Edinburgh, Mayfield Road, Edinburgh, UK.", + "name": "Alexandra Pokhilko" + }, + { + "name": "Sarah K Hodge" + }, + { + "name": "Kevin Stratford" + }, + { + "name": "Kirsten Knox", + "orcid": "0000-0002-5487-1763" + }, + { + "name": "Kieron D Edwards", + "orcid": "0000-0002-7202-4082" + }, + { + "name": "Adrian W Thomson" + }, + { + "name": "Takeshi Mizuno" + }, + { + "name": "Andrew J Millar", + "orcid": "0000-0003-1756-3654" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/20865009", + "month": "9", + "pages": "416", + "synopsis": "Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. 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This a model from the article:
Modeling of bone formation and resorption mediated by parathyroid hormone: response to estrogen/PTH therapy.
\tRattanakul C, Lenbury Y, Krishnamara N, Wollkind DJ. Biosystems 2003:70(1):55-72. 12753937,
Abstract:
Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: rattananakul, lenbury, krishnamara, wollkind. (2003) - version01
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"synopsis": "Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process.", + "title": "Modeling of bone formation and resorption mediated by parathyroid hormone: response to estrogen/PTH therapy.", + "type": "PubMed ID", + "volume": "70", + "year": 2003 + }, + "publicationId": "BIOMD0000000274", + "submissionId": "MODEL7909987998", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000275": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the simple model without diffusion described in th epublication
Sharp developmental thresholds defined through bistability by antagonistic gradients of retinoic acid and FGF signaling.
Goldbeter A, Gonze D, Pourqui\u00e9 O. Dev Dyn. 2007 Jun;236(6):1495-508. PMID: 17497689, doi:10.1016/j.jtbi.2008.01.006
Abstract:
The establishment of thresholds along morphogen gradients in the embryo is poorly understood. Using mathematical modeling, we show that mutually inhibitory gradients can generate and position sharp morphogen thresholds in the embryonic space. Taking vertebrate segmentation as a paradigm, we demonstrate that the antagonistic gradients of retinoic acid (RA) and Fibroblast Growth Factor (FGF) along the presomitic mesoderm (PSM) may lead to the coexistence of two stable steady states. Here, we propose that this bistability is associated with abrupt switches in the levels of FGF and RA signaling, which permit the synchronized activation of segmentation genes, such as mesp2, in successive cohorts of PSM cells in response to the segmentation clock, thereby defining the future segments. Bistability resulting from mutual inhibition of RA and FGF provides a molecular mechanism for the all-or-none transitions assumed in the \"clock and wavefront\" somitogenesis model. Given that mutually antagonistic signaling gradients are common in development, such bistable switches could represent an important principle underlying embryonic patterning.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Given that mutually antagonistic signaling gradients are common in development, such bistable switches could represent an important principle underlying embryonic patterning.", + "title": "Sharp developmental thresholds defined through bistability by antagonistic gradients of retinoic acid and FGF signaling.", + "type": "PubMed ID", + "volume": "236", + "year": 2007 + }, + "publicationId": "BIOMD0000000275", + "submissionId": "MODEL1011170000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000276": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.
Shrestha RP, Hollot CV, Chipkin SR, Schmitt CP, Chait Y. Math Biosci.2010 Jul;226(1):46-57. 20406649,
Abstract:
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.

The model corresponds to hypocalcemic clamp test explained in the paper and parameter values used in the model are that of \"subject 1\". In order to obtain the plots corresponding to \"subject 2\" and \"subject 3\" the following parameters to be changed: lambda_1, lambda_2, m1, m2, R, beta, x1_n, x2_n, x2_min, x2_max, Ca0, Ca1, t0 and alpha.

parameter Subject 1 Subject 2 Subject 3
lambda_1 0.0125 0.0122 0.0269
lambda_2 0.5595 0.4642 0.4935
m1 112.5200 150.0000 90.8570
m2 15.0000 15.0000 15.0000
R 1.2162 1.1627 1.1889
beta 10e+06 10e+06 10e+06
x1_n 490.7800 452.8200 298.8200
x2_n 6.6290 9.5894 5.4600
x2_min 0.6697 1.4813 0.8287
x2_max 14.0430 17.8710 15.1990
Ca0 1.2550 1.2369 1.2475
Ca1 0.1817 0.2211 0.1985
t0 575 577 575
alpha 0.0442 0.0488 0.0472

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"link": "http://identifiers.org/pubmed/20406649", + "month": "7", + "pages": "46-57", + "synopsis": "A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.", + "title": "A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.", + "type": "PubMed ID", + "volume": "226", + "year": 2010 + }, + "publicationId": "BIOMD0000000276", + "submissionId": "MODEL1011170001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000277": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.
Shrestha RP, Hollot CV, Chipkin SR, Schmitt CP, Chait Y. Math Biosci.2010 Jul;226(1):46-57. 20406649,
Abstract:
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.

The model corresponds to hypercalcemic clamp test explained in the paper and parameter values used in the model are that of \"subject 1\". In order to obtain the plots corresponding to \"subject 2\" and \"subject 3\" the following parameters to be changed: lambda_1, lambda_2, m1, m2, R, beta, x1_n, x2_n, x2_min, x2_max, t0, Ca0, Ca1 and alpha.

parameter Subject 1 Subject 2 Subject 3
lambda_1 0.0125 0.0122 0.0269
lambda_2 0.5595 0.4642 0.4935
m1 112.5200 150.0000 90.8570
m2 15.0000 15.0000 15.0000
R 1.2162 1.1627 1.1889
beta 10e+06 10e+06 10e+06
x1_n 490.7800 452.8200 298.8200
x2_n 6.6290 9.5894 5.4600
x2_min 0.6697 1.4813 0.8287
x2_max 14.0430 17.8710 15.1990
Ca0 1.2200 1.2513 1.2480
Ca1 0.2624 0.2267 0.2132
t0 575 575 575
alpha 0.0569 0.0563 0.0421

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"link": "http://identifiers.org/pubmed/20406649", + "month": "7", + "pages": "46-57", + "synopsis": "A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.", + "title": "A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.", + "type": "PubMed ID", + "volume": "226", + "year": 2010 + }, + "publicationId": "BIOMD0000000277", + "submissionId": "MODEL1011170002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000278": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.
Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ. J Theor Biol.2004 Aug 7;229(3):293-309. 15234198,
Abstract:
We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling.

This model corresponds to the core model published in the paper. There is no corresponding plot to reproduce for this model.To obtain each of the 9 plots in the Figure 2 of the reference publication, there are some changes to be made to the core model.The curation figure reproduces figure 2 of the reference publication. There is a corresponding SBML and Copasi files for each of the plot. See curation tab for more details.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Role of RANK/RANKL/OPG pathway in bone remodelling process", + "publication": { + "accession": "15234198", + "affiliation": "Scientific Computing and Mathematical Modeling, GlaxoSmithKline, King of Prussia, PA, USA. lemaire@cnd.mcgill.ca", + "authors": [ + { + "institution": "Scientific Computing and Mathematical Modeling, GlaxoSmithKline, King of Prussia, PA, USA. lemaire@cnd.mcgill.ca", + "name": "Vincent Lemaire", + "orcid": "0000-0003-2086-6787" + }, + { + "name": "Frank L Tobin" + }, + { + "name": "Larry D Greller" + }, + { + "name": "Carolyn R Cho" + }, + { + "name": "Larry J Suva", + "orcid": "0000-0002-2892-9757" + } + ], + "issue": "3", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/15234198", + "month": "8", + "pages": "293-309", + "synopsis": "We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling.", + "title": "Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.", + "type": "PubMed ID", + "volume": "229", + "year": 2004 + }, + "publicationId": "BIOMD0000000278", + "submissionId": "MODEL1006230067", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000279": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Mathematical model of paracrine interactions between osteoclasts and osteoblasts predicts anabolic action of parathyroid hormone on bone.
Komarova SV. Endocrinology.2005 Aug;146(8):3589-95. 15860557,
Abstract:
To restore falling plasma calcium levels, PTH promotes calcium liberation from bone. PTH targets bone-forming cells, osteoblasts, to increase expression of the cytokine receptor activator of nuclear factor kappaB ligand (RANKL), which then stimulates osteoclastic bone resorption. Intriguingly, whereas continuous administration of PTH decreases bone mass, intermittent PTH has an anabolic effect on bone, which was proposed to arise from direct effects of PTH on osteoblastic bone formation. However, antiresorptive therapies impair the ability of PTH to increase bone mass, indicating a complex role for osteoclasts in the process. We developed a mathematical model that describes the actions of PTH at a single site of bone remodeling, where osteoclasts and osteoblasts are regulated by local autocrine and paracrine factors. It was assumed that PTH acts only to increase the production of RANKL by osteoblasts. As a result, PTH stimulated osteoclasts upon application, followed by compensatory osteoblast activation due to the coupling of osteoblasts to osteoclasts through local paracrine factors. Continuous PTH administration resulted in net bone loss, because bone resorption preceded bone formation at all times. In contrast, over a wide range of model parameters, short application of PTH resulted in a net increase in bone mass, because osteoclasts were rapidly removed upon PTH withdrawal, enabling osteoblasts to rebuild the bone. In excellent agreement with experimental findings, increase in the rate of osteoclast death abolished the anabolic effect of PTH on bone. This study presents an original concept for the regulation of bone remodeling by PTH, currently the only approved anabolic treatment for osteoporosis.

The model reproduces Figures 1B and 2A of the reference publication. To obtain the figures 1B, the parameter g21 needs changes. To obtain the figures 1A, the parameters g21, g12 and k2 need to changed. For details look at the curation tab.

The initial concentration of Osteoclasts (x1) is corrected to 1.06066 from 10.06066.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: CellMLdetails
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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[refer Figure 1B of the reference publication]. * Panel A: g21 = -0.1 (green). [figA_green.cps] * Panel A: g21 = 0.1 (blue). [figA_blue.cps] * Panel A: g21 = 0.3 (red). [figA_red.cps]", + "fileSize": "37577", + "md5sum": "6735948cbc43009cad60eb58a475bd65", + "mimeType": "application/xml", + "name": "figA_blue.cps", + "sha1sum": "c28bbff7110fa09f6d6c7e9df88999bb49ac4aa1", + "sha256sum": "bb1e40ee92837af9959a37b5178413388b5e132ac1dfe2b07ae441f97d22764b" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13934", + "md5sum": "ccb09d2d9ebdb5609b35cb24b6324c0e", + "mimeType": "application/xml", + "name": "figA_blue.sedml", + "sha1sum": "21e3f7bb33afadbcf518e4b15019c6f550eb6fab", + "sha256sum": "1e79e4791bed31a9aff353361c455f0d304dc1d02780b4ad8ce6c5169fb87530" + }, + { + "description": "Komarova (2005): Simulation of Osteoclasts, Osteoblasts and Bone Mass upon stimulation by PTH with an increase in g21 from -0.5 to -0.1, 0.1 and 0.3 as a 1-d burst. [refer Figure 1B of the reference publication].\r\n* Panel A: g21 = -0.1 (green). [figA_green.cps]\r\n* Panel A: g21 = 0.1 (blue). [figA_blue.cps]\r\n* Panel A: g21 = 0.3 (red). [figA_red.cps]", + "fileSize": "37579", + "md5sum": "a8041067fdcc0cda6714a1ff4937d872", + "mimeType": "application/xml", + "name": "figA_green.cps", + "sha1sum": "17de46317f1752bcf1bde2149d9a22266cadf32f", + "sha256sum": "becbe68e05c1ab66d1256ab667cda024acbe1df3dd605d933cad704ae1dc356f" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13938", + "md5sum": "635fb5bd220929fc77bd4b5e22c0cbfc", + "mimeType": "application/xml", + "name": "figA_green.sedml", + "sha1sum": "958e00412a94293a5c2067bbb3a8e0f406613175", + "sha256sum": "8af7f0402e86d69d3458856cce814f99db5a610c5647755e0300f2c44a4d0a9c" + }, + { + "description": "Komarova (2005): Simulation of Osteoclasts, Osteoblasts and Bone Mass upon stimulation by PTH with an increase in g21 from -0.5 to -0.1, 0.1 and 0.3 as a 1-d burst. [refer Figure 1B of the reference publication]. * Panel A: g21 = -0.1 (green). [figA_green.cps] * Panel A: g21 = 0.1 (blue). [figA_blue.cps] * Panel A: g21 = 0.3 (red). [figA_red.cps]", + "fileSize": "37576", + "md5sum": "1df31318d91e3447e4f3252e19037f22", + "mimeType": "application/xml", + "name": "figA_red.cps", + "sha1sum": "fbcdaae7a4e0513427a0ce791127f9f5aa8e2715", + "sha256sum": "0ad4fefbd62a429264697be20b0dffe65230342b786cdc31fa858a2c3e15ae04" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13938", + "md5sum": "828ce66bde8a5baeadb5f7459eb66e8b", + "mimeType": "application/xml", + "name": "figA_red.sedml", + "sha1sum": "00d38a2c97411aaf21ed92150fbad13f521d3357", + "sha256sum": "6539759b873e7d4c75bfd5ec8867f1f923eaf4b1b0f65b9c469e4ed9cf6abacd" + }, + { + "description": "Komarova (2005): The changes in bone mass as observed by an increase in g21 from -0.5 to 0, 0.1 and 0.15 for 1-day, with 3 sets of g12 and k2 values. [refer Figure 2A of the reference publication]. * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.0 (green). [figB1_green.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red).", + "fileSize": "38413", + "md5sum": "8dedb65a28e7101cac3911e63e31a866", + "mimeType": "application/xml", + "name": "figB1_blue.cps", + "sha1sum": "6d74d34d7b7f6f44db0f307debecf9dcada03065", + "sha256sum": "176cea14ac94af0e99f151f4d848195feab88b3325094c4835b6b22f038112ee" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13920", + "md5sum": "687bbcebaf19b361ab272c40ddecfecd", + "mimeType": "application/xml", + "name": "figB1_blue.sedml", + "sha1sum": "82afe164009322f33b85cda68181797e46763161", + "sha256sum": "e3f9c09759717f59b1c5eda52ca2171db25517f070ffc3ad03634a05d2c7ff4b" + }, + { + "description": "Komarova (2005): The changes in bone mass as observed by an increase in g21 from -0.5 to 0, 0.1 and 0.15 for 1-day, with 3 sets of g12 and k2 values. [refer Figure 2A of the reference publication].\r\n* Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.0 (green). [figB1_green.cps]\r\n* Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps]\r\n* Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps]\r\n\r\n* Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps]\r\n* Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps]\r\n* Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps]\r\n\r\n* Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps]\r\n* Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps]\r\n* Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red). [figB3_red.cps]", + "fileSize": "38412", + "md5sum": "04af9499e0e8b80535ae1db0a4fa57b6", + "mimeType": "application/xml", + "name": "figB1_green.cps", + "sha1sum": "46bbd4ffcf3d34bbd3de9cff5efe8e15daddf141", + "sha256sum": "1626b8c8dc7bf0d78c791e57f5bbf1c594b43627a29348ffeef40e53c66a6633" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13922", + "md5sum": "2b56e7669375c597e0cb7e2f24b23b1e", + "mimeType": "application/xml", + "name": "figB1_green.sedml", + "sha1sum": "8d4b137fa60245bb7a3fad5bf29b62743ea1516f", + "sha256sum": "133ca5fa6e8b139af826c0dc8d59b4206aedcb969f1033bd2403c1a0640ef53e" + }, + { + "description": "Komarova (2005): The changes in bone mass as observed by an increase in g21 from -0.5 to 0, 0.1 and 0.15 for 1-day, with 3 sets of g12 and k2 values. [refer Figure 2A of the reference publication]. * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.0 (green). [figB1_green.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red). [figB3_red.cps]", + "fileSize": "38413", + "md5sum": "4a6205be24fefe69b6579d8faec545f9", + "mimeType": "application/xml", + "name": "figB1_red.cps", + "sha1sum": "b5edc02329da69d3b7fb3fc6bed0bcd0254eeff5", + "sha256sum": "00371ba43f5a94d1c832f36aeb54a434743fdc7e0f2e595b4934fe1e8a56777a" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13918", + "md5sum": "e332a84ae176be270d1ceb6cc9907dac", + "mimeType": "application/xml", + "name": "figB1_red.sedml", + "sha1sum": "99f242a63710d5360b2742f923b8c35c3cbfa624", + "sha256sum": "57d602798e27a8743a438624a4b635ad3b69d414d16107d463fc8d3033b1332d" + }, + { + "description": "Komarova (2005): The changes in bone mass as observed by an increase in g21 from -0.5 to 0, 0.1 and 0.15 for 1-day, with 3 sets of g12 and k2 values. [refer Figure 2A of the reference publication]. * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.0 (green). [figB1_green.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red). [figB3_red.cps]", + "fileSize": "38415", + "md5sum": "eeefe41fb4854e695d22fcc2b16dcb7b", + "mimeType": "application/xml", + "name": "figB2_blue.cps", + "sha1sum": "d28c2bfd4e16097723f2602119e435d0e8028600", + "sha256sum": "e2e23e4ff6e47d183385039ccc26fc6c51c3de917252ef978c5d1966090e6151" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13920", + "md5sum": "dbc03117bab13cd560b0dd7b8e43ca88", + "mimeType": "application/xml", + "name": "figB2_blue.sedml", + "sha1sum": "aedc7bc7e2c8d82265845cc01fc70ef5a0b874fb", + "sha256sum": "6969c9a39a4601caafd8ae5072941ad5b2bfb113bd63ee3c329fa41c6077d5f0" + }, + { + "description": "Komarova (2005): The changes in bone mass as observed by an increase in g21 from -0.5 to 0, 0.1 and 0.15 for 1-day, with 3 sets of g12 and k2 values. [refer Figure 2A of the reference publication]. * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.0 (green). [figB1_green.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red). [figB3_red.cps]", + "fileSize": "38414", + "md5sum": "7d48b2841077ae5a74ed5c1afb116f39", + "mimeType": "application/xml", + "name": "figB2_green.cps", + "sha1sum": "5b03240fa4b4d562ec42b5da2ff976c21365a122", + "sha256sum": "aee306f5db568229c1af55870e7d2ecc9f8a5250aa548b14ae162581e7748a82" + }, + { + "description": "CRBM-processed SED-ML file, based on original COPASI save file export.", + "fileSize": "13922", + "md5sum": "1888df5640ed8408ba8d368a01b4c89e", + "mimeType": "application/xml", + "name": "figB2_green.sedml", + "sha1sum": "25837eb312b250d63bd6caf8320dbe9b3028eabd", + "sha256sum": "e01777a3130694bef975bb41157bac4176d21795d850621b517cac0fe6fd6dd3" + }, + { + "description": "Komarova (2005): The changes in bone mass as observed by an increase in g21 from -0.5 to 0, 0.1 and 0.15 for 1-day, with 3 sets of g12 and k2 values. [refer Figure 2A of the reference publication]. * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.0 (green). [figB1_green.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red). 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[figB1_green.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.1 (blue). [figB1_blue.cps] * Panel B1: g12 = 0.5; k2 = 0.005; g21 = 0.15 (red). [figB1_red.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.0 (green). [figB2_green.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.1 (blue). [figB2_blue.cps] * Panel B2: g12 = 0.98; k2 = 0.0017; g21 = 0.15 (red). [figB2_red.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.0 (green). [figB3_green.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.1 (blue). [figB3_blue.cps] * Panel B3: g12 = 1.4; k2 = 0.00075; g21 = 0.15 (red). 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"authors": [ + { + "institution": "Faculty of Dentistry, McGill University, 3640 University Street, Montreal, Quebec, Canada H3A 2B2. svetlana.komarova@mcgill.ca", + "name": "Svetlana V Komarova" + } + ], + "issue": "8", + "journal": "Endocrinology", + "link": "http://identifiers.org/pubmed/15860557", + "month": "8", + "pages": "3589-3595", + "synopsis": "To restore falling plasma calcium levels, PTH promotes calcium liberation from bone. PTH targets bone-forming cells, osteoblasts, to increase expression of the cytokine receptor activator of nuclear factor kappaB ligand (RANKL), which then stimulates osteoclastic bone resorption. Intriguingly, whereas continuous administration of PTH decreases bone mass, intermittent PTH has an anabolic effect on bone, which was proposed to arise from direct effects of PTH on osteoblastic bone formation. However, antiresorptive therapies impair the ability of PTH to increase bone mass, indicating a complex role for osteoclasts in the process. We developed a mathematical model that describes the actions of PTH at a single site of bone remodeling, where osteoclasts and osteoblasts are regulated by local autocrine and paracrine factors. It was assumed that PTH acts only to increase the production of RANKL by osteoblasts. As a result, PTH stimulated osteoclasts upon application, followed by compensatory osteoblast activation due to the coupling of osteoblasts to osteoclasts through local paracrine factors. Continuous PTH administration resulted in net bone loss, because bone resorption preceded bone formation at all times. In contrast, over a wide range of model parameters, short application of PTH resulted in a net increase in bone mass, because osteoclasts were rapidly removed upon PTH withdrawal, enabling osteoblasts to rebuild the bone. In excellent agreement with experimental findings, increase in the rate of osteoclast death abolished the anabolic effect of PTH on bone. This study presents an original concept for the regulation of bone remodeling by PTH, currently the only approved anabolic treatment for osteoporosis.", + "title": "Mathematical model of paracrine interactions between osteoclasts and osteoblasts predicts anabolic action of parathyroid hormone on bone.", + "type": "PubMed ID", + "volume": "146", + "year": 2005 + }, + "publicationId": "BIOMD0000000279", + "submissionId": "MODEL1011230000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000280": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the reduced model of the voltage oscillations in barnacle muscle fibers, generally known as the Morris-Lecar model (eg. wikipedia), described in the article:
Voltage oscillations in the barnacle giant muscle fiber.
\tMorris C, Lecar H. Biophys J. 1981 Jul;35(1):193-213. PubmedID:7260316; DOI:10.1016/S0006-3495(81)84782-0
\tAbstract:
\tBarnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.

The model consists of the differential equations (9) and (2) given on pages 205 and 196 of the article. There seems to be a typo in the figure caption of figure 9. Using V2 = 15 instead of -15 allows to reproduce the results.

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"accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Morris1981_MuscleFibre_Voltage_reduced", + "publication": { + "accession": "7260316", + "authors": [ + { + "name": "C Morris", + "orcid": "0000-0002-7401-6545" + }, + { + "name": "H Lecar" + } + ], + "issue": "1", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/7260316", + "month": "7", + "pages": "193-213", + "synopsis": "Barnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.", + "title": "Voltage oscillations in the barnacle giant muscle fiber.", + "type": "PubMed ID", + "volume": "35", + "year": 1981 + }, + "publicationId": "BIOMD0000000280", + "submissionId": "MODEL1011240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000281": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is described inthe article:
Metabolic control mechanisms. 5. A solution for the equations representing interaction between glycolysis and respiration in ascites tumor cells.
Britton Chance, David Garfinkel, Joseph Higgins and Benno Hess, J Biol Chem. 1960 35:2426-2439. PubmedID: 13692276
Abstract:
The other papers of this series present experimentalevidence for possible relationships between the kinetics of oxygen,glucose, adenosine diphosphate, adenosine triphosphate, andphosphate and those of the cytochromes and pyridine nucleotidesof the ascites tumor cell. From these general experiments weare able to formulate, under the law of mass action, a minimumhypothesis under which the four metabolic regulations previouslydescribed can be observed. In brief, the system can be represented by the known enzyme systems, a relatively higher ADP affinity in respiration than in glycolysis, the mitochondrial membrane, a segregation of ATP into two compartments, and an ATP-utilizing system that is responsive to small decreases of the intracellular ADP level. The chemical equations for the pathway from glucose to oxygen are solved by a digital computer method so that the responses of the chemical equations and of the living cell can be accurately compared.
For reasons already described, we greatly prefer a com-puter representation based upon a physical or chemical lawrepresenting the action of the system to a model simulating theoperation of the chemical system but not based upon funda-mental laws for the reactions involved; such a representationwould not adequately represent the kinetics of the system, as inan electric circuit network or in some types of hydraulic ana-logues.

The model gives solutions of the reaction kinetics for three types of metabolism:

  1. 0 - 64s, metabolism of endogenous substrate
  2. 64s - 119s, metabolism of added glucose, illustrating the activated and inhibited aspects of glucose metabolism
  3. 119s - 153s, relief of glucose and oxygen inhibition by the addition of an uncoupling agent

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A solution for the equations representing interaction between glycolysis and respiration in ascites tumor cells.", + "type": "PubMed ID", + "volume": "235", + "year": 1960 + }, + "publicationId": "BIOMD0000000281", + "submissionId": "MODEL1010190002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000282": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is described in the article:
The mechanism of catalase action. II. Electric analog computer studies.
Britton Chance, David S Greenstein, Joseph Higgins, CC Yang, Arch Biochem. 1952 37:322-39. PubmedID:14953444
Summary:
An electric analog computer has been constructed for a study of the kinetics of catalase action. This computer gives results for the formation and disappearance of the catalase-hydrogen peroxide complex that are in good agreement with the experimental data. The computer study verifies an approximate method for the computation of the velocity constant for the combination of hydrogen peroxide and catalase and justifies the simple formula used previously to compute the velocity constant for the reaction of the catalase-hydrogen peroxide complex with donor molecules. Finally, the computer data show that the binding of peroxide to catalase is a practically irreversible reaction.

The reaction of the enzyme-substrate complex, p, with the electron donor, a, is bimolecular, although in the article, as a is assumed to be constant, it is modelled using an apparent rate constant consisting of the product of the rate constant, k4, and the concentration of a. In this implementation, the concentration of a is set to 1 and the value of k4 just adapted so that the product equals the values given for k4*a in the article. The specific parameter values are taken from Fig 3. The graphs do not exactly match those in the paper, this may be due to the different simulators used.

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II. Electric analog computer studies.", + "type": "PubMed ID", + "volume": "37", + "year": 1952 + }, + "publicationId": "BIOMD0000000282", + "submissionId": "MODEL1010190001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000283": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Default parameter values are those in the right hand panel of Fig 12. The other panels may be obtained by setting X to 1, 2 or 4, and K3 to 0, 1/2 or 1.

This model is described in:
The kinetics of the enzyme-substrate compound of peroxidase.
Britton Chance, Journal of Biological Chemistry, 151, 553-577, 1943. PDF at JBC
reprinted in: Adv Enzymol Relat Areas Mol Biol. 1999;73:3-23. PubmedID:10218104>
Abstract:
Under the narrow range of experimental conditions, and at a temperature of approximately 25 degrees, the following data were obtained. 1. The equilibrium constant of peroxidase and hydrogen peroxide has a minimum value of 2 x 10(-8). 2. The velocity constant for the formation of peroxidase-H2O2 Complex I is 1.2 x 10(7) liter mole-1 sec.-1, +/- 0.4 x 10(7). 3. The velocity constant for the reversible breakdown of peroxidase-H2O2 Complex I is a negligible factor in the enzyme-substrate kinetics and is calculated to be less than 0.2 sec.-1. 4. The velocity constant, k3, for the enzymatic breakdown of peroxidase-H2O2 Complex I varies from nearly zero to higher than 5 sec.-1, depending upon the acceptor and its concentration. The quotient of k3 and the leucomalachite green concentration is 3.0 x 10(4) liter mole-1 sec.-1. For ascorbic acid this has a value of 1.8 x 10(5) liter mole-1 sec.-1. 5. For a particular acceptor concentration, k3 is determined solely from the enzyme-substrate kinetics and is found to be 4.2 sec.-1. 6. For the same conditions, k3 is determined from a simple relationship derived from mathematical solutions of the Michaelis theory and is found to be 5.2 sec.-1. 7. For the same conditions, k3 is determined from the over-all enzyme action and is found to be 5.1 sec.-1. 8. The Michaelis constant determined from kinetic data alone is found to be 0.44 x 10(-6). 9. The Michaelis constant determined from steady state measurements is found to be 0.41 x 10(-6). 10. The Michaelis constant determined from measurement of the overall enzyme reaction is found to be 0.50 x 10(-6). 11. The kinetics of the enzyme-substrate compound closely agree with mathematical solutions of an extension of the Michaelis theory obtained for experimental values of concentrations and reaction velocity constants. 12. The adequacy of the criteria by which experiment and theory were correlated has been examined critically and the mathematical solutions have been found to be sensitive to variations in the experimental conditions. 13. The critical features of the enzyme-substrate kinetics are Pmax, and curve shape, rather than t1/2. t1/2 serves as a simple measure of dx/dt. 14. A second order combination of enzyme and substrate to form the enzyme-substrate compound, followed by a first order breakdown of the compound, describes the activity of peroxidase for a particular acceptor concentration. 15. The kinetic data indicate a bimolecular combination of acceptor and enzyme-substrate compound.

This model is the one described in the appendix of the article. It reproduces, amongst others, figure 12. The parameters and concentrations used are rescaled as stated in the article. K2 and K3 stand for k2 and k3, respectively, divided by k1.

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The equilibrium constant of peroxidase and hydrogen peroxide has a minimum value of 2 x 10(-8). 2. The velocity constant for the formation of peroxidase-H2O2 Complex I is 1.2 x 10(7) liter mole-1 sec.-1, +/- 0.4 x 10(7). 3. The velocity constant for the reversible breakdown of peroxidase-H2O2 Complex I is a negligible factor in the enzyme-substrate kinetics and is calculated to be less than 0.2 sec.-1. 4. The velocity constant, k3, for the enzymatic breakdown of peroxidase-H2O2 Complex I varies from nearly zero to higher than 5 sec.-1, depending upon the acceptor and its concentration. The quotient of k3 and the leucomalachite green concentration is 3.0 x 10(4) liter mole-1 sec.-1. For ascorbic acid this has a value of 1.8 x 10(5) liter mole-1 sec.-1. 5. For a particular acceptor concentration, k3 is determined solely from the enzyme-substrate kinetics and is found to be 4.2 sec.-1. 6. 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The kinetic data indicate a bimolecular combination of acceptor and enzyme-substrate compound.", + "title": "The kinetics of the enzyme-substrate compound of peroxidase. 1943.", + "type": "PubMed ID", + "volume": "73", + "year": 1999 + }, + "publicationId": "BIOMD0000000283", + "submissionId": "MODEL1010190000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000284": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is the reaction sequence SEQFB, a model pathway of a branched system with sequential feedback interactions found in bacterial amino acid synthesis. Its steady state is presented in Fig 4.

The model is described in:
METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer.
JHS Hofmeyr and KJ van der Merwe, Comput Appl Biosci 1986 2:243-9; PubmedID: 3450367
Abstract:
METAMOD, a BBC microcomputer-based software package for steady-state modelling and control analysis of model metabolic pathways, is described, The package consists of two programs. METADEF allows the user to define the pathway in terms of reactions, rate equations and initial concentrations of metabolites. METACAL uses one of two algorithms to calculate the steady-state concentrations and fluxes. One algorithm uses the current ratio of production and consumption rates of variable metabolites to adjust iteratively their concentrations in such a way that they converge towards the steady state. The other algorithm solves the roots of the system equations by means of a quasi-Newtonian procedure. Control analysis allows the calculation of elasticity, control and response coefficients, by means of finite difference approximation. METAMOD is interactive and easy to use, and suitable for teaching and research purposes.


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novere N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Experimental and computational analysis of polyglutamine-mediated cytotoxicity.
Tang MY, Proctor CJ, Woulfe J, Gray DA. PLoS Comput Biol.2010 Sep 23;6(9). 20885783,
Abstract:
Expanded polyglutamine (polyQ) proteins are known to be the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activation of stress kinases such as p38MAPK, which can be cytotoxic. The relationships of these systems are not well understood. We have used fluorescent reporter systems imaged in living cells, and stochastic computer modeling to explore the relationships of polyQ, p38MAPK activation, generation of reactive oxygen species (ROS), proteasome inhibition, and inclusion body formation. In cells expressing a polyQ protein inclusion, body formation was preceded by proteasome inhibition but cytotoxicity was greatly reduced by administration of a p38MAPK inhibitor. Computer simulations suggested that without the generation of ROS, the proteasome inhibition and activation of p38MAPK would have significantly reduced toxicity. Our data suggest a vicious cycle of stress kinase activation and proteasome inhibition that is ultimately lethal to cells. There was close agreement between experimental data and the predictions of a stochastic computer model, supporting a central role for proteasome inhibition and p38MAPK activation in inclusion body formation and ROS-mediated cell death.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activation of stress kinases such as p38MAPK, which can be cytotoxic. The relationships of these systems are not well understood. We have used fluorescent reporter systems imaged in living cells, and stochastic computer modeling to explore the relationships of polyQ, p38MAPK activation, generation of reactive oxygen species (ROS), proteasome inhibition, and inclusion body formation. In cells expressing a polyQ protein inclusion, body formation was preceded by proteasome inhibition but cytotoxicity was greatly reduced by administration of a p38MAPK inhibitor. Computer simulations suggested that without the generation of ROS, the proteasome inhibition and activation of p38MAPK would have significantly reduced toxicity. Our data suggest a vicious cycle of stress kinase activation and proteasome inhibition that is ultimately lethal to cells. There was close agreement between experimental data and the predictions of a stochastic computer model, supporting a central role for proteasome inhibition and p38MAPK activation in inclusion body formation and ROS-mediated cell death.", + "title": "Experimental and computational analysis of polyglutamine-mediated cytotoxicity.", + "type": "PubMed ID", + "volume": "6", + "year": 2010 + }, + "publicationId": "BIOMD0000000285", + "submissionId": "MODEL1002250000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000286": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model described the article:
GSK3 and p53 - is there a link in Alzheimer's disease?
Carole J Proctor and Douglas A Gray Molecular Neurodegeneration 2010, 5:7; doi: 10.1186/1750-1326-5-7
Abstract:
Background: Recent evidence suggests that glycogen synthase kinase-3beta (GSK3beta) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3beta and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3beta. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model.
Results: The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3beta and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3beta and a vicious cycle ensues.
Conclusions: Since p53 and GSK3beta are both involved in the apoptotic pathway, and GSK3beta overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration.


Notes: The original model submitted by the author had events in it. Since, this model is intended for Stochastic Simulation run and Copasi cannot handle events in Stochastic run, I have replaced the events with piecewise assignment rule. -Viji

This model is an extension of Proctor_p53_Mdm2_ATM ( BIOMD0000000188 ).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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a link between GSK3 and p53 in Alzheimer's Disease", + "publication": { + "accession": "20181016", + "affiliation": "Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK. c.j.proctor@ncl.ac.uk.", + "authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "Douglas A Gray", + "orcid": "0000-0002-8634-4984" + } + ], + "journal": "Molecular neurodegeneration", + "link": "http://identifiers.org/pubmed/20181016", + "month": "1", + "pages": "7", + "synopsis": "

Background

Recent evidence suggests that glycogen synthase kinase-3beta (GSK3beta) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3beta and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3beta. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model.

Results

The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3beta and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3beta and a vicious cycle ensues.

Conclusions

Since p53 and GSK3beta are both involved in the apoptotic pathway, and GSK3beta overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration.", + "title": "GSK3 and p53 - is there a link in Alzheimer's disease?", + "type": "PubMed ID", + "volume": "5", + "year": 2010 + }, + "publicationId": "BIOMD0000000286", + "submissionId": "MODEL0910130002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000287": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "

\t This is the model described in: Feedback between p21 and reactive oxygen production is necessary for cell senescence.
\tPassos JF, Nelson G, Wang C, Richter T, Simillion C, Proctor CJ, Miwa S, Olijslagers S, Hallinan J, Wipat A, Saretzki G, Rudolph KL, Kirkwood TB, von Zglinicki T. ;Mol Sys Biol2010;6:347. Epub 2010 Feb 16. PMID:20160708 doi:10.1038/msb.2010.5;
Abstract:
\t Cellular senescence--the permanent arrest of cycling in normally proliferating cells such as fibroblasts--contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of 'deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFbeta. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype. \t

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"affiliation": "Ageing Research Laboratories, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK.", + "authors": [ + { + "institution": "Ageing Research Laboratories, Institute for Ageing, Newcastle University, Newcastle upon Tyne, UK Jeanne.Perez@inserm.fr Gavin.Richardson@ncl.ac.uk Passos.Joao@mayo.edu.Institute for Cell and Molecular Biosciences, Newcastle University, Newcastle upon Tyne, UK.Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.", + "name": "Jo\u00e3o F Passos", + "orcid": "0000-0001-8765-1890" + }, + { + "name": "Glyn Nelson", + "orcid": "0000-0002-1895-4772" + }, + { + "name": "Chunfang Wang" + }, + { + "name": "Torsten Richter" + }, + { + "name": "Cedric Simillion", + "orcid": "0000-0002-2026-6741" + }, + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK 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This model is from the article:
PI3K-dependent cross-talk interactions converge with Ras as quantifiable inputs integrated by Erk.
Wang CC, Cirit M, Haugh JM Mol. Syst. Biol. 2009;5:246. 19225459 ,
Abstract:
Although it is appreciated that canonical signal-transduction pathways represent dominant modes of regulation embedded in larger interaction networks, relatively little has been done to quantify pathway cross-talk in such networks. Through quantitative measurements that systematically canvas an array of stimulation and molecular perturbation conditions, together with computational modeling and analysis, we have elucidated cross-talk mechanisms in the platelet-derived growth factor (PDGF) receptor signaling network, in which phosphoinositide 3-kinase (PI3K) and Ras/extracellular signal-regulated kinase (Erk) pathways are prominently activated. We show that, while PI3K signaling is insulated from cross-talk, PI3K enhances Erk activation at points both upstream and downstream of Ras. The magnitudes of these effects depend strongly on the stimulation conditions, subject to saturation effects in the respective pathways and negative feedback loops. Motivated by those dynamics, a kinetic model of the network was formulated and used to precisely quantify the relative contributions of PI3K-dependent and -independent modes of Ras/Erk activation.


This model is parameterized with the median of the estimated parameters given in the supplementary material of the original publication's (doi: 10.1038/msb.2009.4 ) supplement on pages 8 and 9.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is system 1, the model with linear antigen uptake by pAPCs, described in the article:
Self-tolerance and Autoimmunity in a Regulatory T Cell Model.
Alexander HK, Wahl LM. Bull Math Biol. 2010 Mar 2. PMID:20195912, doi:10.1007/s11538-010-9519-2;
Abstract:
The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R (0) for each system. When R (0) < 1, only the trivial equilibrium exists and is stable; when R (0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

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This is system 2, the model with Michelis Menten type antigen uptake by pAPCs, described in the article:
Self-tolerance and Autoimmunity in a Regulatory T Cell Model.
Alexander HK, Wahl LM. Bull Math Biol. 2010 Mar 2. PMID: 20195912 , doi: 10.1007/s11538-010-9519-2 ;
Abstract:
The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R (0) for each system. When R (0) < 1, only the trivial equilibrium exists and is stable; when R (0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

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Ontario, London, Ontario, Canada.", + "name": "H K Alexander", + "orcid": "0000-0002-4104-0418" + }, + { + "name": "L M Wahl" + } + ], + "issue": "1", + "journal": "Bulletin of mathematical biology", + "link": "http://identifiers.org/pubmed/20195912", + "month": "1", + "pages": "33-71", + "synopsis": "The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. 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The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).", + "title": "Self-tolerance and autoimmunity in a regulatory T cell model.", + "type": "PubMed ID", + "volume": "73", + "year": 2011 + }, + "publicationId": "BIOMD0000000290", + "submissionId": "MODEL1012220001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000291": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Mathematical model of binding of albumin-bilirubin complex to the surface of carbon pyropolymer.
Nikolaev AV, Rozhilo YA, Starozhilova TK, Sarnatskaya VV, Yushko LA, Mikhailovskii SV, Kholodov AS, Lobanov AI. Bull Exp Biol Med2005 Sep;140(3):365-9. 16307060,
Abstract:
We proposed a mathematical model and estimated the parameters of adsorption of albumin-bilirubin complex to the surface of carbon pyropolymer. Design data corresponded to the results of experimental studies. Our findings indicate that modeling of this process should take into account fractal properties of the surface of carbon pyropolymer.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Design data corresponded to the results of experimental studies. Our findings indicate that modeling of this process should take into account fractal properties of the surface of carbon pyropolymer.", + "title": "Mathematical model of binding of albumin-bilirubin complex to the surface of carbon pyropolymer.", + "type": "PubMed ID", + "volume": "140", + "year": 2005 + }, + "publicationId": "BIOMD0000000291", + "submissionId": "MODEL1101060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000292": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model described in the article:
Photosynthetic oscillations and the interdependence of photophosphorylation and electron transport as studied by a mathematical model.
Rovers W, Giersch C. Biosystems. 1995;35(1):63-73. PMID: 7772723
Abstract:
A simple mathematical model of photosynthetic carbon metabolism as driven by ATP and NADPH has been formulated to analyse photosynthetic oscillations. Two essential assumptions of this model are: (i) reduction of 3-phosphoglycerate to triosephosphate in the Clavin cycle is limited by ATP, not by NADPH, and (ii) photophosphorylation is affected by the availability of both ADP and NADP, while electron transport is limited by NADP only. The model produces oscillations of observed damping and period in ATP and NADP concentrations which are about 180 degrees out of phase, while three alternative proposals regarding coupling of electron transport and photophosphorylation do not produce oscillatory model solutions. The phases of ATP and NADPH are in reasonable agreement with the available experimental data. The model (which assumes that redox control of photophosphorylation is part of the oscillatory mechanism) is compared with an alternative proposal (that oscillations are due to interdependence of turnover of adenylates and Calvin cycle intermediates). From the similarity of the mathematical structures of both models it is inviting to speculate that both models are partial aspects of the oscillatory mechanism.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"name": "C Giersch" + } + ], + "issue": "1", + "journal": "Bio Systems", + "link": "http://identifiers.org/pubmed/7772723", + "month": "0", + "pages": "63-73", + "synopsis": "A simple mathematical model of photosynthetic carbon metabolism as driven by ATP and NADPH has been formulated to analyse photosynthetic oscillations. 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From the similarity of the mathematical structures of both models it is inviting to speculate that both models are partial aspects of 'the oscillatory mechanism'.", + "title": "Photosynthetic oscillations and the interdependence of photophosphorylation and electron transport as studied by a mathematical model.", + "type": "PubMed ID", + "volume": "35", + "year": 1995 + }, + "publicationId": "BIOMD0000000292", + "submissionId": "MODEL1101060001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000293": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Newcastle", + "email": "c.j.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Modelling the Role of UCH-L1 on Protein Aggregation inAge-Related Neurodegeneration.
Proctor CJ, Tangeman PJ, Ardley HC. PLoS One. 2010 Oct 6;5(10):e13175 20949132 ,
Abstract:
Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and \u03b1-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Restif2007 - Vaccination invasion

This model is described in the article:

Restif O, Grenfell BT.
J R Soc Interface 2007 Feb; 4(12): 143-153

Abstract:

Vaccines exert strong selective pressures on pathogens, favouring the spread of antigenic variants. We propose a simple mathematical model to investigate the dynamics of a novel pathogenic strain that emerges in a population where a previous strain is maintained at low endemic level by a vaccine. We compare three methods to assess the ability of the novel strain to invade and persist: algebraic rate of invasion; deterministic dynamics; and stochastic dynamics. These three techniques provide complementary predictions on the fate of the system. In particular, we emphasize the importance of stochastic simulations, which account for the possibility of extinctions of either strain. More specifically, our model suggests that the probability of persistence of an invasive strain (i) can be minimized for intermediate levels of vaccine cross-protection (i.e. immune protection against the novel strain) and (ii) is lower if cross-immunity acts through a reduced infectious period rather than through reduced susceptibility.

This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size\u00a0 N\u00a0= 1. The model does reproduces the deterministic time course. The initial values are set to the steady state values for a latent infection with strain 1 with an invading infection of strain 2 (I2=1e-06), 100 percent vaccination with a susceptibility reduction \u03c4=0.7 at birth (p=1), and all other parameters as in figure 3 of the publication.\u00a0

To be compatible with older software tools, the english letter names instead of the greek symbols were used for parameter names:

parameter symbol name
transmission rate \u03b2 beta
recovery rate \u03b3 gamma
birth/death rate \u03bc mu
rate of loss of natural immunity \u03c3 sigma
rate of loss of vaccine immunity \u03c3 v sigmaV
reduction of susceptibility by primary infection \u03b8 theta
reduction of infection period by primary infection \u03bd nu
reduction of susceptibility by vaccination \u03c4 tau
reduction of infection period by vaccination \u03b7 eta

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model is hosted on BioModels Database and identified by: BIOMD0000000294.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Vaccination invasion", + "publication": { + "accession": "17210532", + "affiliation": "Department of Veterinary Medicine, University of Cambridge, Cambridge Infectious Diseases Consortium, Madingley Road, Cambridge CB3 0ES, UK. or226@cam.ac.uk", + "authors": [ + { + "institution": "Department of Veterinary Medicine, University of Cambridge, Cambridge Infectious Diseases Consortium, Madingley Road, Cambridge CB3 0ES, UK. or226@cam.ac.uk", + "name": "Olivier Restif", + "orcid": "0000-0001-9158-853X" + }, + { + "name": "Bryan T Grenfell", + "orcid": "0000-0003-3227-5909" + } + ], + "issue": "12", + "journal": "Journal of the Royal Society, Interface", + "link": "http://identifiers.org/pubmed/17210532", + "month": "2", + "pages": "143-153", + "synopsis": "Vaccines exert strong selective pressures on pathogens, favouring the spread of antigenic variants. We propose a simple mathematical model to investigate the dynamics of a novel pathogenic strain that emerges in a population where a previous strain is maintained at low endemic level by a vaccine. We compare three methods to assess the ability of the novel strain to invade and persist: algebraic rate of invasion; deterministic dynamics; and stochastic dynamics. These three techniques provide complementary predictions on the fate of the system. In particular, we emphasize the importance of stochastic simulations, which account for the possibility of extinctions of either strain. More specifically, our model suggests that the probability of persistence of an invasive strain (i) can be minimized for intermediate levels of vaccine cross-protection (i.e. immune protection against the novel strain) and (ii) is lower if cross-immunity acts through a reduced infectious period rather than through reduced susceptibility.", + "title": "Vaccination and the dynamics of immune evasion.", + "type": "PubMed ID", + "volume": "4", + "year": 2007 + }, + "publicationId": "BIOMD0000000294", + "submissionId": "MODEL1012210000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000295": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Isoform switching facilitates period control in the Neurospora crassa circadian clock.
Akman OE, Locke JC, Tang S, Carr\u00e9 I, Millar AJ, Rand DA Mol. Syst. Biol. 2008;Vol 4: 164 18277380 ,
Abstract:
A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"0000-0002-2217-3274" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/18277380", + "month": "0", + "pages": "164", + "synopsis": "A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.", + "title": "Isoform switching facilitates period control in the Neurospora crassa circadian clock.", + "type": "PubMed ID", + "volume": "4", + "year": 2008 + }, + "publicationId": "BIOMD0000000295", + "submissionId": "MODEL8306248909", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000296": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the reduced model described in the article:
A synthetic Escherichia coli predator\u2013prey ecosystem
Balagadd\u00e9 FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L.Mol Syst Biol. 2008;4:187. Epub 2008 Apr 15. PMID: 18414488; DOI:10.1038/msb.2008.24

Abstract:
We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator\u2013prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner.

In the article the cell density is given in per 103 cells per microlitre. To evade a conversion factor in the SBML implementation, the unit for the cell densities was just left the same as for the AHLs A and A2 (nM).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. 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This a model from the article:
Mathematical model of the morphogenesis checkpoint in budding yeast.
Ciliberto A, Novak B, Tyson JJ J. Cell Biol. [2003 Dec; Volume: 163 (Issue: 6 )] Page info: 1243-54 14691135 ,
Abstract:
The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next.

The model reproduces Fig 3 of the paper.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. 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This a model from the article:
Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.
Leloup JC, Gonze D, Goldbeter A. J Biol Rhythms.1999 Dec;14(6):433-48. 10643740,
Abstract:
We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.

This particular version of the model has been translated from equations 1a-1j (Drosophila).

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Leloup JC, Gonze D, Goldbeter A. (1999) - version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Sciences, Universit\u00e9 Libre de Bruxelles, Brussels, Belgium.", + "authors": [ + { + "institution": "Unit\u00e9 de Chronobiologie Th\u00e9orique, Facult\u00e9 des Sciences, Universit\u00e9 Libre de Bruxelles, Campus Plaine, C. P. 231, B-1050 Brussels, Belgium.", + "name": "J C Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "D Gonze" + }, + { + "name": "A Goldbeter" + } + ], + "issue": "6", + "journal": "Journal of biological rhythms", + "link": "http://identifiers.org/pubmed/10643740", + "month": "12", + "pages": "433-448", + "synopsis": "We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. 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Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.", + "title": "Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.", + "type": "PubMed ID", + "volume": "14", + "year": 1999 + }, + "publicationId": "BIOMD0000000298", + "submissionId": "MODEL0478965170", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000299": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.
Leloup JC, Gonze D, Goldbeter A. J Biol Rhythms. 1999 Dec;14(6):433-48. 10643740 ,
Abstract:
We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.

This particular version of the model has been translated from equations 4a-4c (Neurospora).

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Leloup JC, Gonze D, Goldbeter A. (1999) - version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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P. 231, B-1050 Brussels, Belgium.", + "name": "J C Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "D Gonze" + }, + { + "name": "A Goldbeter" + } + ], + "issue": "6", + "journal": "Journal of biological rhythms", + "link": "http://identifiers.org/pubmed/10643740", + "month": "12", + "pages": "433-448", + "synopsis": "We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.", + "title": "Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.", + "type": "PubMed ID", + "volume": "14", + "year": 1999 + }, + "publicationId": "BIOMD0000000299", + "submissionId": "MODEL1101140000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000300": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Developmental Signalling Lab, Cancer Research UK London Research Institute", + "email": "Bernhard.Schmierer@ymail.com", + "external": false, + "name": "Bernhard Schmierer" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Hypoxia-dependent sequestration of an oxygen sensor by a widespread structural motif can shape the hypoxic response - a predictive kinetic model
Bernhard Schmierer, B\u00e9la Nov\u00e1k1 and Christopher J Schofield BMC Systems Biology2010, 4:139 20955552,
Abstract:
Background
The activity of the heterodimeric transcription factor hypoxia inducible factor (HIF) is regulated by the post-translational, oxygen-dependent hydroxylation of its \u03b1-subunit by members of the prolyl hydroxylase domain (PHD or EGLN)-family and by factor inhibiting HIF (FIH). PHD-dependent hydroxylation targets HIF\u03b1 for rapid proteasomal degradation; FIH-catalysed asparaginyl-hydroxylation of the C-terminal transactivation domain (CAD) of HIF\u03b1 suppresses the CAD-dependent subset of the extensive transcriptional responses induced by HIF. FIH can also hydroxylate ankyrin-repeat domain (ARD) proteins, a large group of proteins which are functionally unrelated but share common structural features. Competition by ARD proteins for FIH is hypothesised to affect FIH activity towards HIF\u03b1; however the extent of this competition and its effect on the HIF-dependent hypoxic response are unknown.
Results
To analyse if and in which way the FIH/ARD protein interaction affects HIF-activity, we created a rate equation model. Our model predicts that an oxygen-regulated sequestration of FIH by ARD proteins significantly shapes the input/output characteristics of the HIF system. The FIH/ARD protein interaction is predicted to create an oxygen threshold for HIF\u03b1 CAD-hydroxylation and to significantly sharpen the signal/response curves, which not only focuses HIF\u03b1 CAD-hydroxylation into a defined range of oxygen tensions, but also makes the response ultrasensitive to varying oxygen tensions. Our model further suggests that the hydroxylation status of the ARD protein pool can encode the strength and the duration of a hypoxic episode, which may allow cells to memorise these features for a certain time period after reoxygenation.
Conclusions
The FIH/ARD protein interaction has the potential to contribute to oxygen-range finding, can sensitise the response to changes in oxygen levels, and can provide a memory of the strength and the duration of a hypoxic episode. These emergent properties are predicted to significantly shape the characteristics of HIF activity in animal cells. We argue that the FIH/ARD interaction should be taken into account in studies of the effect of pharmacological inhibition of the HIF-hydroxylases and propose that the interaction of a signalling sensor with a large group of proteins might be a general mechanism for the regulation of signalling pathways.

There are there models described in the paper. 1) Skeleton Model 1 (SKM1) - HIF\u03b1 CAD-hydroxylation in the absence of the FIH/AR-interaction. 2) Skeleton Model 2 (SKM2) - FIG sequestration by ARD proteins and oxygen-dependent FIH-release. 3) Full Model (Fusion of SKM1 and SKM2) - the effects of the FIH/ARD proteins interaction on HIF\u03b1 CAD-hydroxylation.

This model corresponds to the \"Full Model\" described in the paper. The model reproduces figure 5 of the publication.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

The activity of the heterodimeric transcription factor hypoxia inducible factor (HIF) is regulated by the post-translational, oxygen-dependent hydroxylation of its \u03b1-subunit by members of the prolyl hydroxylase domain (PHD or EGLN)-family and by factor inhibiting HIF (FIH). PHD-dependent hydroxylation targets HIF\u03b1 for rapid proteasomal degradation; FIH-catalysed asparaginyl-hydroxylation of the C-terminal transactivation domain (CAD) of HIF\u03b1 suppresses the CAD-dependent subset of the extensive transcriptional responses induced by HIF. FIH can also hydroxylate ankyrin-repeat domain (ARD) proteins, a large group of proteins which are functionally unrelated but share common structural features. Competition by ARD proteins for FIH is hypothesised to affect FIH activity towards HIF\u03b1; however the extent of this competition and its effect on the HIF-dependent hypoxic response are unknown.

Results

To analyse if and in which way the FIH/ARD protein interaction affects HIF-activity, we created a rate equation model. Our model predicts that an oxygen-regulated sequestration of FIH by ARD proteins significantly shapes the input/output characteristics of the HIF system. The FIH/ARD protein interaction is predicted to create an oxygen threshold for HIF\u03b1 CAD-hydroxylation and to significantly sharpen the signal/response curves, which not only focuses HIF\u03b1 CAD-hydroxylation into a defined range of oxygen tensions, but also makes the response ultrasensitive to varying oxygen tensions. Our model further suggests that the hydroxylation status of the ARD protein pool can encode the strength and the duration of a hypoxic episode, which may allow cells to memorise these features for a certain time period after reoxygenation.

Conclusions

The FIH/ARD protein interaction has the potential to contribute to oxygen-range finding, can sensitise the response to changes in oxygen levels, and can provide a memory of the strength and the duration of a hypoxic episode. These emergent properties are predicted to significantly shape the characteristics of HIF activity in animal cells. We argue that the FIH/ARD interaction should be taken into account in studies of the effect of pharmacological inhibition of the HIF-hydroxylases and propose that the interaction of a signalling sensor with a large group of proteins might be a general mechanism for the regulation of signalling pathways.", + "title": "Hypoxia-dependent sequestration of an oxygen sensor by a widespread structural motif can shape the hypoxic response--a predictive kinetic model.", + "type": "PubMed ID", + "volume": "4", + "year": 2010 + }, + "publicationId": "BIOMD0000000300", + "submissionId": "MODEL1008170000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000301": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

\t This is the model of the RTC3 counter described in the article:
Synthetic gene networks that count.
\t Friedland AE, Lu TK, Wang X, Shi D, Church G, Collins JJ. Science. 2009 May 29;324(5931):1199-202. PMID:19478183, DOI:10.1126/science.1172005

\t Abstract:
\t Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here, we report two complementary synthetic genetic counters in Escherichia coli that can count up to three induction events: the first, a riboregulated transcriptional cascade, and the second, a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.\t

\t The 3 arabinose pulses are implemented using events, one for the start of pulses and one for the end. The variable pulse_flag changes arabinose consumption to fit behaviour during pulses and in between. To simulate two pulses only, set the pulse length of the third pulse to a negative value (though with an absolute value smaller than the pulse intervall length). \t

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"pages": "1199-1202", + "synopsis": "Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here, we report two complementary synthetic genetic counters in Escherichia coli that can count up to three induction events: the first, a riboregulated transcriptional cascade, and the second, a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.", + "title": "Synthetic gene networks that count.", + "type": "PubMed ID", + "volume": "324", + "year": 2009 + }, + "publicationId": "BIOMD0000000301", + "submissionId": "MODEL1012220006", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000302": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is a model of one presynaptic and one postsynaptic cell, as described in the article:
Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.
Wang XJ, Buzs\u00e1ki G. J Neurosci. 1996 Oct 15;16(20):6402-13. PMID:8815919;\t

Abstract:
Fast neuronal oscillations (gamma, 20-80 Hz) have been observed in the neocortex and hippocampus during behavioral arousal. Using computer simulations, we investigated the hypothesis that such rhythmic activity can emerge in a random network of interconnected GABAergic fast-spiking interneurons. Specific conditions for the population synchronization, on properties of single cells and the circuit, were identified. These include the following: (1) that the amplitude of spike afterhyperpolarization be above the GABAA synaptic reversal potential; (2) that the ratio between the synaptic decay time constant and the oscillation period be sufficiently large; (3) that the effects of heterogeneities be modest because of a steep frequency-current relationship of fast-spiking neurons. Furthermore, using a population coherence measure, based on coincident firings of neural pairs, it is demonstrated that large-scale network synchronization requires a critical (minimal) average number of synaptic contacts per cell, which is not sensitive to the network size. By changing the GABAA synaptic maximal conductance, synaptic decay time constant, or the mean external excitatory drive to the network, the neuronal firing frequencies were gradually and monotonically varied. By contrast, the network synchronization was found to be high only within a frequency band coinciding with the gamma (20-80 Hz) range. We conclude that the GABAA synaptic transmission provides a suitable mechanism for synchronized gamma oscillations in a sparsely connected network of fast-spiking interneurons. In turn, the interneuronal network can presumably maintain subthreshold oscillations in principal cell populations and serve to synchronize discharges of spatially distributed neurons.\t

\t The presynaptic and postsynaptic cell have identical parameters and the variables in each cell are identified by using _pre or _post as a postfix to their names. The presynaptic cell influences the postsynaptic one via the synapse (variables and parameters: I_syn, E_syn, g_syn, F, theta_syn, alpha, beta). The applied current to the presynaptic cell, I_app_pre, is set to 2 microA/cm2 for 10 ms as in figure 1C of the article. The dependence of the postsynaptic cell on directly applied current can be investigated in isolation by setting I_app_pre to 0 and altering I_app_post. \t

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Using computer simulations, we investigated the hypothesis that such rhythmic activity can emerge in a random network of interconnected GABAergic fast-spiking interneurons. Specific conditions for the population synchronization, on properties of single cells and the circuit, were identified. These include the following: (1) that the amplitude of spike afterhyperpolarization be above the GABAA synaptic reversal potential; (2) that the ratio between the synaptic decay time constant and the oscillation period be sufficiently large; (3) that the effects of heterogeneities be modest because of a steep frequency-current relationship of fast-spiking neurons. Furthermore, using a population coherence measure, based on coincident firings of neural pairs, it is demonstrated that large-scale network synchronization requires a critical (minimal) average number of synaptic contacts per cell, which is not sensitive to the network size. By changing the GABAA synaptic maximal conductance, synaptic decay time constant, or the mean external excitatory drive to the network, the neuronal firing frequencies were gradually and monotonically varied. By contrast, the network synchronization was found to be high only within a frequency band coinciding with the gamma (20-80 Hz) range. We conclude that the GABAA synaptic transmission provides a suitable mechanism for synchronized gamma oscillations in a sparsely connected network of fast-spiking interneurons. In turn, the interneuronal network can presumably maintain subthreshold oscillations in principal cell populations and serve to synchronize discharges of spatially distributed neurons.", + "title": "Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.", + "type": "PubMed ID", + "volume": "16", + "year": 1996 + }, + "publicationId": "BIOMD0000000302", + "submissionId": "MODEL1101240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000303": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": " Model of the Complement System

This is the continuous deterministic (ODE) model of the complement system described in the article:
Computational and Experimental Study of the Regulatory Mechanisms of the Complement System.
Liu B, Zhang J, Tan PY, Hsu D, Blom AM, Leong B, Sethi S, Ho B, Ding JL and Thiagarajan PS. PLoS Comp. Bio. 2011 Jan. 7:1; doi:10.1371/journal.pcbi.1001059

Abstract:
The complement system is key to innate immunity and its activation is necessary for the clearance of bacteria and apoptotic cells. However, insufficient or excessive complement activation will lead to immune-related diseases. It is so far unknown how the complement activity is up- or down- regulated and what the associated pathophysiological mechanisms are. To quantitatively understand the modulatory mechanisms of the complement system, we built a computational model involving the enhancement and suppression mechanisms that regulate complement activity. Our model consists of a large system of Ordinary Differential Equations (ODEs) accompanied by a dynamic Bayesian network as a probabilistic approximation of the ODE dynamics. Applying Bayesian inference techniques, this approximation was used to perform parameter estimation and sensitivity analysis. Our combined computational and experimental study showed that the antimicrobial response is sensitive to changes in pH and calcium levels, which determines the strength of the crosstalk between CRP and L-ficolin. Our study also revealed differential regulatory effects of C4BP. While C4BP delays but does not decrease the classical complement activation, it attenuates but does not significantly delay the lectin pathway activation. We also found that the major inhibitory role of C4BP is to facilitate the decay of C3 convertase. In summary, the present work elucidates the regulatory mechanisms of the complement system and demonstrates how the bio-pathway machinery maintains the balance between activation and inhibition. The insights we have gained could contribute to the development of therapies targeting the complement system.

Comment:
Reproduction of figures in the article:
Figure 5: the effects of C4BP
Fig 5A: set initial concentrations PC=0.0327796, GlcNac=0, vary the initial concentration of C4BP from 2.6 to 2600 using parameter scan
Fig 5B: set initial concentrations PC=0, GlcNac=0.0327796, vary the initial concentration of C4BP from 2.6 to 2600 using parameter scan
Figure 6: knockout simulations
Set PC=0.0327796, GlcNac=0
Fig 6A: kf01=0, kf02=0
Fig 6B: kf04=0, kf06=0, kf07=0
Fig 6C: kf05=0
Fig 6D: kf03=0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"The complement system is key to innate immunity and its activation is necessary for the clearance of bacteria and apoptotic cells. However, insufficient or excessive complement activation will lead to immune-related diseases. It is so far unknown how the complement activity is up- or down- regulated and what the associated pathophysiological mechanisms are. To quantitatively understand the modulatory mechanisms of the complement system, we built a computational model involving the enhancement and suppression mechanisms that regulate complement activity. Our model consists of a large system of Ordinary Differential Equations (ODEs) accompanied by a dynamic Bayesian network as a probabilistic approximation of the ODE dynamics. Applying Bayesian inference techniques, this approximation was used to perform parameter estimation and sensitivity analysis. Our combined computational and experimental study showed that the antimicrobial response is sensitive to changes in pH and calcium levels, which determines the strength of the crosstalk between CRP and L-ficolin. 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The insights we have gained could contribute to the development of therapies targeting the complement system.", + "title": "A computational and experimental study of the regulatory mechanisms of the complement system.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000303", + "submissionId": "MODEL1101260000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000304": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Bifurcation and resonance in a model for bursting nerve cells.
Plant RE J Math Biol1981 Jan; 11(1): 15-32 7252375,
Abstract:
In this paper we consider a model for the phenomenon of bursting in nerve cells. Experimental evidence indicates that this phenomenon is due to the interaction of multiple conductances with very different kinetics, and the model incorporates this evidence. As a parameter is varied the model undergoes a transition between two oscillatory waveforms; a corresponding transition is observed experimentally. After establishing the periodicity of the subcritical oscillatory solution, the nature of the transition is studied. It is found to be a resonance bifurcation, with the solution branching at the critical point to another periodic solution of the same period. Using this result a comparison is made between the model and experimental observations. The model is found to predict and allow an interpretation of these observations.

Also, look athttp://www.scholarpedia.org/article/Plant_model

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Experimental evidence indicates that this phenomenon is due to the interaction of multiple conductances with very different kinetics, and the model incorporates this evidence. As a parameter is varied the model undergoes a transition between two oscillatory waveforms; a corresponding transition is observed experimentally. After establishing the periodicity of the subcritical oscillatory solution, the nature of the transition is studied. It is found to be a resonance bifurcation, with the solution branching at the critical point to another periodic solution of the same period. Using this result a comparison is made between the model and experimental observations. The model is found to predict and allow an interpretation of these observations.", + "title": "Bifurcation and resonance in a model for bursting nerve cells.", + "type": "PubMed ID", + "volume": "11", + "year": 1981 + }, + "publicationId": "BIOMD0000000304", + "submissionId": "MODEL6762427183", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000305": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the 2 state model of Myosin V movement described in the article:
A simple kinetic model describes the processivity of myosin-v.
Kolomeisky AB , Fisher ME Biophys. J. 84(3):1642-50 (2003); PubmedID: 12609867

Abstract:
Myosin-V is a motor protein responsible for organelle and vesicle transport in cells. Recent single-molecule experiments have shown that it is an efficient processive motor that walks along actin filaments taking steps of mean size close to 36 nm. A theoretical study of myosin-V motility is presented following an approach used successfully to analyze the dynamics of conventional kinesin but also taking some account of step-size variations. Much of the present experimental data for myosin-V can be well described by a two-state chemical kinetic model with three load-dependent rates. In addition, the analysis predicts the variation of the mean velocity and of the randomness-a quantitative measure of the stochastic deviations from uniform, constant-speed motion-with ATP concentration under both resisting and assisting loads, and indicates a substep of size d(0) approximately 13-14 nm (from the ATP-binding state) that appears to accord with independent observations.

The model differs slightly from the published version. The ATP and ADP bound forms of myosin are called S0 and S1. The state transition and binding constants are called k_1, k_2, k_3 and k_4 instead of k0 0, u0 1, k' 0 and w0 1. Similarly the state loading factors are named th_1, th_2, th_3 and th_4 instead of \u03b8+ 0, \u03b8+ 1, \u03b8- 0 and \u03b8- 1. The species fwd_step1, fwd_step2, back_step1 and back_step2 count the number of state changes of each kind the myosine molecules have taken over time.
The model can be evaluated in a deterministic continuous or stochastic discreet fashion. The parameter V holds the (forward) speed at each time point, the V_avg the overall way divided by the simulation time and the amount of myosine molecules.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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77005-1892, USA. tolya@rice.edu", + "name": "Anatoly B Kolomeisky" + }, + { + "name": "Michael E Fisher" + } + ], + "issue": "3", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/12609867", + "month": "3", + "pages": "1642-1650", + "synopsis": "Myosin-V is a motor protein responsible for organelle and vesicle transport in cells. 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This is an SBML implementation the model of the activator inhibitor oscillator (figure 2b) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is an SBML implementation the model of the substrate depletion oscillator (figure 2c) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This is an SBML implementation the model of negative feedback oscillator (figure 2a) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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of Biochemistry, University of Oxford, Oxford, United Kingdom.", + "name": "Bela Novak", + "orcid": "0000-0002-6961-1366" + } + ], + "issue": "2", + "journal": "Current opinion in cell biology", + "link": "http://identifiers.org/pubmed/12648679", + "month": "4", + "pages": "221-231", + "synopsis": "The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.", + "title": "Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.", + "type": "PubMed ID", + "volume": "15", + "year": 2003 + }, + "publicationId": "BIOMD0000000308", + "submissionId": "MODEL1102100004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000309": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is an SBML implementation the model of homeostastis by negative feedback (figure 1g) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is an SBML implementation the model of mutual inhibition (figure 1f) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

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This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is an SBML implementation the model of mutual activation (figure 1e) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

The article has a typo: the expression k2*X*R most likely should be k2*R

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"link": "http://identifiers.org/pubmed/12648679", + "month": "4", + "pages": "221-231", + "synopsis": "The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.", + "title": "Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.", + "type": "PubMed ID", + "volume": "15", + "year": 2003 + }, + "publicationId": "BIOMD0000000311", + "submissionId": "MODEL1102100001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000312": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is an SBML implementation the model of the perfect adaptor (figure 1d) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the model of IL13 induced signalling in MedB-1 cell described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, B\u00f6hm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, M\u00f6ller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingm\u00fcller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID:21127196; DOI:10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 () and the conversion factor 2.265 molecules IL13/cell = 1 ng/ml to be around 60 fl.

SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:

% PottersWheel model definition filefunction m = Raia2010_IL13_MedB1()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'Raia2010_IL13_MedB1';m.name        = 'Raia2010_IL13_MedB1';m.description = '';m.authors     = {'Raia et al'};m.dates       = {'2010'};m.type        = 'PW-2-0-47';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'Rec'         ,              1.3, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Rec_i'       , 113.193916718733, 'global',  0.001, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_Rec'    ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec'  ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec_i',                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'JAK2'        ,              2.8, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pJAK2'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SHP1'        ,               91, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'STAT5'       ,              165, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pSTAT5'      ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SOCS3mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'DecoyR'      ,             0.34, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_DecoyR' ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SOCS3'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'CD274mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {'Rec'         }, {'IL13_Rec'    }, {'IL13stimulation'   }, 'C' , [] , 'k1 * m1 * r1 * 2.265'        , {'Kon_IL13Rec'                             });m = pwAddR(m, {'Rec'         }, {'Rec_i'       }, {                    }, 'MA', [] , []                            , {'Rec_intern'                              });m = pwAddR(m, {'Rec_i'       }, {'Rec'         }, {                    }, 'MA', [] , []                            , {'Rec_recycle'                             });m = pwAddR(m, {'IL13_Rec'    }, {'p_IL13_Rec'  }, {'pJAK2'             }, 'E' , [] , []                            , {'Rec_phosphorylation'                     });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'IL13_Rec','SOCS3'  }, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'p_IL13_Rec','SOCS3'}, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});m = pwAddR(m, {'p_IL13_Rec'  }, {'p_IL13_Rec_i'}, {                    }, 'MA', [] , []                            , {'pRec_intern'                             });m = pwAddR(m, {'p_IL13_Rec_i'}, {              }, {                    }, 'MA', [] , []                            , {'pRec_degradation'                        });m = pwAddR(m, {'pJAK2'       }, {'JAK2'        }, {'SHP1'              }, 'E' , [] , []                            , {'pJAK2_dephosphorylation'                 });m = pwAddR(m, {'STAT5'       }, {'pSTAT5'      }, {'pJAK2'             }, 'E' , [] , []                            , {'STAT5_phosphorylation'                   });m = pwAddR(m, {'pSTAT5'      }, {'STAT5'       }, {'SHP1'              }, 'E' , [] , []                            , {'pSTAT5_dephosphorylation'                });m = pwAddR(m, {'DecoyR'      }, {'IL13_DecoyR' }, {'IL13stimulation'   }, 'C' , [] , 'k1 * m1 * r1 * 2.265'        , {'DecoyR_binding'                          });m = pwAddR(m, {              }, {'SOCS3mRNA'   }, {'pSTAT5'            }, 'C' , [] , 'm1*k1'                       , {'SOCS3mRNA_production'                    });m = pwAddR(m, {              }, {'SOCS3'       }, {'SOCS3mRNA'         }, 'C' , [] , 'm1*k1/(k2+m1)'               , {'SOCS3_translation','SOCS3_accumulation'  });m = pwAddR(m, {'SOCS3'       }, {              }, {                    }, 'MA', [] , []                            , {'SOCS3_degradation'                       });m = pwAddR(m, {              }, {'CD274mRNA'   }, {'pSTAT5'            }, 'C' , [] , 'm1*k1'                       , {'CD274mRNA_production'                    });%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'Kon_IL13Rec'             , 0.00341992477561527  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_phosphorylation'     , 999.630699390459     , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_intern'             , 0.152540135862128    , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_degradation'        , 0.17292753960894     , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_intern'              , 0.103345784175639    , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_recycle'             , 0.00135598001330518  , 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_phosphorylation'    , 0.157057142470047    , 'global', 1e-009, 1000);m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.000621906059346898 , 'global', 1e-009, 1000);m = pwAddK(m, 'STAT5_phosphorylation'   , 0.0382596267705733   , 'global', 1e-009, 1000);m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.000343391620492938 , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3mRNA_production'    , 0.00215826062955433  , 'global', 1e-009, 1000);m = pwAddK(m, 'DecoyR_binding'          , 0.000124391087466499 , 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_p_inhibition'       , 0.0168267797836881   , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3_translation'       , 11.9086462945188     , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3_accumulation'      , 3.70803336415341     , 'global', 1     , 1000);m = pwAddK(m, 'SOCS3_degradation'       , 0.0429185935645562   , 'global', 1e-009, 1000);m = pwAddK(m, 'CD274mRNA_production'    , 8.21752278733562e-005, 'global', 1e-009, 1000);%% U: Driving input% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0]  , [0 1]  , [], [], [], [], {}, [], 'protein.generic');%% Default sampling time pointsm.t = 0:1:120;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec'                       , 'RecSurf_obs'  , 'scale_RecSurf'  , '0.10 * y + 0.1 * max(y)');m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i + IL13_DecoyR', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i'                         , 'pIL4Ra_obs'   , 'scale_pIL4Ra'   , '0.1 * y + 0.15 * max(y)');m = pwAddY(m, 'pJAK2'                                             , 'pJAK2_obs'    , 'scale_pJAK2'    , '0.15 * y + 0.1 * max(y)');m = pwAddY(m, 'SOCS3mRNA'                                         , 'SOCS3mRNA_obs', 'scale_SOCS3mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'CD274mRNA'                                         , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'SOCS3'                                             , 'SOCS3_obs'    , 'scale_SOCS3'    , '0.1 * y + 0.15 * max(y)');m = pwAddY(m, 'pSTAT5'                                            , 'pSTAT5_obs'   , 'scale_pSTAT5'   , '0.15 * y + 0.1 * max(y)');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_pJAK2'    , 1.39039557075997, 'global', 0.001, 10000);m = pwAddS(m, 'scale_pIL4Ra'   , 1.88700484471494, 'global', 0.001, 10000);m = pwAddS(m, 'scale_RecSurf'  ,                1,    'fix', 0.001, 10000);m = pwAddS(m, 'scale_IL13-cell', 5.56750251420935, 'global', 0.001, 10000);m = pwAddS(m, 'scale_SOCS3mRNA', 17.6699101927908, 'global', 0.001, 10000);m = pwAddS(m, 'scale_CD274mRNA', 2.48547378765387, 'global', 0.001, 10000);m = pwAddS(m, 'scale_pSTAT5'   ,                1,    'fix', 0.001, 10000);m = pwAddS(m, 'scale_SOCS3'    ,                1,    'fix', 0.001, 10000);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\ufffd\ufffdre N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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IL13 Signalling MedB1", + "publication": { + "accession": "21127196", + "affiliation": "Division of Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance and Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.", + "authors": [ + { + "institution": "Division of Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance and Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.", + "name": "Valentina Raia" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "name": "Martin B\u00f6hm" + }, + { + "name": "Bettina Hahn" + }, + { + "name": "Andreas Kowarsch" + }, + { + "name": "Andreas Raue" + }, + { + "name": "Carsten Sticht" + }, + { + "name": "Sebastian Bohl" + }, + { + "name": "Maria Saile" + }, + { + "name": "Peter M\u00f6ller" + }, + { + "name": "Norbert Gretz" + }, + { + "name": "Jens Timmer" + }, + { + "name": "Fabian Theis", + "orcid": "0000-0002-2419-1943" + }, + { + "name": "Wolf-Dieter Lehmann" + }, + { + "name": "Peter Lichter" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + } + ], + "issue": "3", + "journal": "Cancer research", + "link": "http://identifiers.org/pubmed/21127196", + "month": "2", + "pages": "693-704", + "synopsis": "Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.", + "title": "Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.", + "type": "PubMed ID", + "volume": "71", + "year": 2011 + }, + "publicationId": "BIOMD0000000313", + "submissionId": "MODEL1102020001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000314": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center", + "email": "m.schilling@dkfz.de", + "external": false, + "name": "Marcel Schilling" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model of IL13 induced signalling in L1236 cells described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, B\u00f6hm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, M\u00f6ller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingm\u00fcller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID: 21127196 ; DOI: 10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 (15.8 kDa) and the conversion factor 3.776 molecules IL13/cell = 1 ng/ml to be around 100 fl.

SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:

% PottersWheel model definition filefunction m = Raia2010_IL13_L1236()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'Raia2010_IL13_L1236';m.name        = 'Raia2010_IL13_L1236';m.description = '';m.authors     = {'Raia et al'};m.dates       = {'2010'};m.type        = 'PW-2-0-47';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'Rec'         ,              1.8, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Rec_i'       , 118.598421286338, 'global',  0.001, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_Rec'    ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec'  ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec_i',                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'JAK2'        ,               24, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pJAK2'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SHP1'        ,              9.4, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'STAT5'       ,              209, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pSTAT5'      ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'CD274mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {'Rec'         }, {'IL13_Rec'    }, {'IL13stimulation'}, 'C' , [] , 'k1 * m1 * r1 * 3.776', {'Kon_IL13Rec'             });m = pwAddR(m, {'Rec'         }, {'Rec_i'       }, {                 }, 'MA', [] , []                    , {'Rec_intern'              });m = pwAddR(m, {'Rec_i'       }, {'Rec'         }, {                 }, 'MA', [] , []                    , {'Rec_recycle'             });m = pwAddR(m, {'IL13_Rec'    }, {'p_IL13_Rec'  }, {'pJAK2'          }, 'E' , [] , []                    , {'Rec_phosphorylation'     });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'IL13_Rec'       }, 'E' , [] , []                    , {'JAK2_phosphorylation'    });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'p_IL13_Rec'     }, 'E' , [] , []                    , {'JAK2_phosphorylation'    });m = pwAddR(m, {'p_IL13_Rec'  }, {'p_IL13_Rec_i'}, {                 }, 'MA', [] , []                    , {'pRec_intern'             });m = pwAddR(m, {'p_IL13_Rec_i'}, {              }, {                 }, 'MA', [] , []                    , {'pRec_degradation'        });m = pwAddR(m, {'pJAK2'       }, {'JAK2'        }, {'SHP1'           }, 'E' , [] , []                    , {'pJAK2_dephosphorylation' });m = pwAddR(m, {'STAT5'       }, {'pSTAT5'      }, {'pJAK2'          }, 'E' , [] , []                    , {'STAT5_phosphorylation'   });m = pwAddR(m, {'pSTAT5'      }, {'STAT5'       }, {'SHP1'           }, 'E' , [] , []                    , {'pSTAT5_dephosphorylation'});m = pwAddR(m, {              }, {'CD274mRNA'   }, {'pSTAT5'         }, 'C' , [] , 'm1*k1'               , {'CD274mRNA_production'    });%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'Kon_IL13Rec'             , 0.00174086832237195, 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_phosphorylation'     , 9.07540737285078   , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_intern'             , 0.324132341358502  , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_degradation'        , 0.417538218767296  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_intern'              , 0.259685756311325  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_recycle'             , 0.00392430355501153, 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_phosphorylation'    , 0.300019047540849  , 'global', 1e-009, 1000);m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.0981610557569751 , 'global', 1e-009, 1000);m = pwAddK(m, 'STAT5_phosphorylation'   , 0.00426766529531612, 'global', 1e-009, 1000);m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.0116388587580445 , 'global', 1e-009, 1000);m = pwAddK(m, 'CD274mRNA_production'    , 0.0115927572109515 , 'global', 1e-009, 1000);%% U: Driving input% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0]  , [0 1]  , [], [], [], [], {}, [], 'protein.generic');%% Default sampling time pointsm.t = 0:1:120;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec'         , 'RecSurf_obs'  , 'scale_RecSurf'  , '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i'           , 'pIL4Ra_obs'   , 'scale_pIL4Ra'   , '0.10 * y + 0.15 * max(y)');m = pwAddY(m, 'pJAK2'                               , 'pJAK2_obs'    , 'scale_pJAK2'    , '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'CD274mRNA'                           , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'pSTAT5'                              , 'pSTAT5_obs'   , 'scale_pSTAT5'   , '0.1 * y + 0.1 * max(y)');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_pJAK2'    , 0.469836894150194, 'global',  0.001, 10000);m = pwAddS(m, 'scale_pIL4Ra'   ,  1.80002942264669, 'global',  0.001, 10000);m = pwAddS(m, 'scale_RecSurf'  ,                 1,    'fix', 0.0001, 10000);m = pwAddS(m, 'scale_IL13-cell',  174.726805005048, 'global',  0.001, 10000);m = pwAddS(m, 'scale_CD274mRNA', 0.110568221201943, 'global',  0.001, 10000);m = pwAddS(m, 'scale_pSTAT5'   ,                 1,    'fix',  0.001, 10000);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];
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IL13 L1236", + "publication": { + "accession": "21127196", + "affiliation": "Division of Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance and Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.", + "authors": [ + { + "institution": "Division of Systems Biology of Signal Transduction, DKFZ-ZMBH Alliance and Molecular Genetics, German Cancer Research Center, Heidelberg, Germany.", + "name": "Valentina Raia" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "name": "Martin B\u00f6hm" + }, + { + "name": "Bettina Hahn" + }, + { + "name": "Andreas Kowarsch" + }, + { + "name": "Andreas Raue" + }, + { + "name": "Carsten Sticht" + }, + { + "name": "Sebastian Bohl" + }, + { + "name": "Maria Saile" + }, + { + "name": "Peter M\u00f6ller" + }, + { + "name": "Norbert Gretz" + }, + { + "name": "Jens Timmer" + }, + { + "name": "Fabian Theis", + "orcid": "0000-0002-2419-1943" + }, + { + "name": "Wolf-Dieter Lehmann" + }, + { + "name": "Peter Lichter" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + } + ], + "issue": "3", + "journal": "Cancer research", + "link": "http://identifiers.org/pubmed/21127196", + "month": "2", + "pages": "693-704", + "synopsis": "Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.", + "title": "Dynamic mathematical modeling of IL13-induced signaling in Hodgkin and primary mediastinal B-cell lymphoma allows prediction of therapeutic targets.", + "type": "PubMed ID", + "volume": "71", + "year": 2011 + }, + "publicationId": "BIOMD0000000314", + "submissionId": "MODEL1102020002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000315": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Keio University, Faculty of Science and Technology, Department of Applied Chemistry", + "email": "plasson@applc.keio.ac.jp", + "external": false, + "name": "Rapha\u00ebl Plasson" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model of the in vitro DNA oscillator called oligator with the optmized set of parameters described in the article:
Programming an in vitro DNA oscillator using a molecular networking strategy.
Montagne K, Plasson R, Sakai Y, Fujii T, Rondelez Y. Mol Syst Biol. 2011 Feb 1;7:466. PubmedID:21283142, Doi:10.1038/msb.2010.120

Abstract:
Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules.

The model reproduces the time courses in fig 2B. The parameter identifiers of the reaction constants are not the same as in the supplemental material, but are just called kXd and kXr for the forward and backwards constant of reaction X respectively.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the coherent feed forward loop with an AND-gate like control of the response operon described in the article:
Network motifs in the transcriptional regulation network of Escherichia coli
Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon, Nat Genet 2002 31:64-68; PMID: 11967538 ; DOI: 10.1038/ng881 ;

Abstract:
Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

This model reproduces the timecourse presented in Figure 2a. All species and parameters in the model are dimensionless.

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This is the single input module, SIM, described in the article:
Network motifs in the transcriptional regulation network of Escherichia coli
Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon, Nat Genet 2002 31:64-68; PMID:11967538; DOI:10.1038/ng881;

Abstract:
Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

This model reproduces the SIM timecourse presented in Figure 2b. All species and parameters in the model are dimensionless.

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This is the model described in the article:
A bistable Rb-E2F switch underlies the restriction point
Guang Yao, Tae Jun Lee, Seiichi Mori, Joseph R. Nevins, Lingchong You, Nat Cell Biol 2008 10:476-482; PMID: 18364697 ; DOI: 10.1038/ncb1711 .

Abstract:
The restriction point (R-point) marks the critical event when a mammalian cell commits to proliferation and becomes independent of growth stimulation. It is fundamental for normal differentiation and tissue homeostasis, and seems to be dysregulated in virtually all cancers. Although the R-point has been linked to various activities involved in the regulation of G1-S transition of the mammalian cell cycle, the underlying mechanism remains unclear. Using single-cell measurements, we show here that the Rb-E2F pathway functions as a bistable switch to convert graded serum inputs into all-or-none E2F responses. Once turned ON by sufficient serum stimulation, E2F can memorize and maintain this ON state independently of continuous serum stimulation. We further show that, at critical concentrations and duration of serum stimulation, bistable E2F activation correlates directly with the ability of a cell to traverse the R-point.

This model reproduces the serum-pulse stimulation-protocol in Figure 3(b).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novere N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Sciences and Policy, Duke University, Durham, NC 27708, USA.", + "authors": [ + { + "institution": "Institute for Genome Sciences and Policy, Duke University, Durham, NC 27708, USA.", + "name": "Guang Yao" + }, + { + "name": "Tae Jun Lee" + }, + { + "name": "Seiichi Mori" + }, + { + "name": "Joseph R Nevins" + }, + { + "name": "Lingchong You" + } + ], + "issue": "4", + "journal": "Nature cell biology", + "link": "http://identifiers.org/pubmed/18364697", + "month": "4", + "pages": "476-482", + "synopsis": "The restriction point (R-point) marks the critical event when a mammalian cell commits to proliferation and becomes independent of growth stimulation. It is fundamental for normal differentiation and tissue homeostasis, and seems to be dysregulated in virtually all cancers. Although the R-point has been linked to various activities involved in the regulation of G1-S transition of the mammalian cell cycle, the underlying mechanism remains unclear. Using single-cell measurements, we show here that the Rb-E2F pathway functions as a bistable switch to convert graded serum inputs into all-or-none E2F responses. Once turned ON by sufficient serum stimulation, E2F can memorize and maintain this ON state independently of continuous serum stimulation. We further show that, at critical concentrations and duration of serum stimulation, bistable E2F activation correlates directly with the ability of a cell to traverse the R-point.", + "title": "A bistable Rb-E2F switch underlies the restriction point.", + "type": "PubMed ID", + "volume": "10", + "year": 2008 + }, + "publicationId": "BIOMD0000000318", + "submissionId": "MODEL1102090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000319": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the scaled model described in the article:
Birhythmicity, chaos, and other patterns of temporal self-organization in a multiply regulated biochemical system
Olivier Decroly, Albert Goldbeter, Proc Natl Acad Sci USA 1982 79:6917-6921; PMID:6960354;

Abstract:
We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable steady state (hard excitation) or with chaos. The relationship between these patterns of temporal self-organization is analyzed as a function of the control parameters of the model. Chaos and birhythmicity appear to be rare events in comparison with simple periodic behavior. We discuss the relevance of these results with respect to the regularity of most biological rhythms.

The parameters q1 = 50 and q2 = 0.02 are explicitely included as the stoichiometric coefficients of beta and gamma in the reactions r2 and r3, respectively. Parameter values and initial conditions [ks=1.99/sec, alpha(0)=29.19988, beta(0)=188.8, gamma(0)=0.3367] are for the chaotic regime presented in the upper-curve of Figure 3b.

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differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Decroly1982_Enzymatic_Oscillator", + "publication": { + "accession": "6960354", + "authors": [ + { + "name": "O Decroly" + }, + { + "name": "A Goldbeter" + } + ], + "issue": "22", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/6960354", + "month": "11", + "pages": "6917-6921", + "synopsis": "We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable steady state (hard excitation) or with chaos. The relationship between these patterns of temporal self-organization is analyzed as a function of the control parameters of the model. Chaos and birhythmicity appear to be rare events in comparison with simple periodic behavior. We discuss the relevance of these results with respect to the regularity of most biological rhythms.", + "title": "Birhythmicity, chaos, and other patterns of temporal self-organization in a multiply regulated biochemical system.", + "type": "PubMed ID", + "volume": "79", + "year": 1982 + }, + "publicationId": "BIOMD0000000319", + "submissionId": "MODEL1102070000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000320": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Grange2001 - PK interaction of L-dopa and benserazide

A pharmacokinetics of L-dopa in rats after administration of L-dopa alone (BIOMD0000000321) or L-dopa combined with a peripheral AADC (amino-acid-decarboxylase) inhibitor (this model: BIOMD0000000320) has been studied using noncompartmental analysis.

This model is described in the article:

Grange S, Holford NH, Guentert TW
Pharmaceutical Research [2001, 18(8):1174-1184]

Abstract:

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The volumes and variables in this model are taken for a rat with 0.25 kg. The inital dose for L_Dopa (L_Dopa_per_kg_rat) and Benserazide (Benserazide_per_kg_rat) are to be given in umole per kg. 80 mg/kg L-Dopa correspond to 404 umol/kg, 20 mg/kg benserazide to 78 umol/kg. To change the model to a different mass of rat the compartment volumes, and the parameters rat_body_mass and Q have to changed accordingly.

The model has three species (A-dopa, A_B, A_M) whose initial concentrations are calculated from a listOfInitialAssignments . While running for the first time the time-course (24hrs) for this model in COPASI (up to version 4.6, Build 33), the resulting graph displays only straight lines for all the species. Any subsequent runs should provide proper plots (i.e. without making any change to the model, just by clicking the \"run\" button again).

The above issue is caused by some initial assignments which are not calculated when COPASI imports the file. This issue should not be present in newer releases of COPASI.

This model is hosted on BioModels Database and identified by: MODEL0910130001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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PK interaction of L-dopa and benserazide", + "publication": { + "accession": "11587490", + "affiliation": "PRNS Non-Clinical Drug Safety, F. Hoffmann-La Roche Ltd, Basel, Switzerland. susan.grange@roche.com", + "authors": [ + { + "institution": "PRNS Non-Clinical Drug Safety, F. Hoffmann-La Roche Ltd, Basel, Switzerland. susan.grange@roche.com", + "name": "S Grange" + }, + { + "institution": "University of Auckland", + "name": "N H Holford", + "orcid": "0000-0002-4031-2514" + }, + { + "name": "T W Guentert" + } + ], + "issue": "8", + "journal": "Pharmaceutical research", + "link": "http://identifiers.org/pubmed/11587490", + "month": "8", + "pages": "1174-1184", + "synopsis": "

Purpose

To study the PK interaction of L-dopa/benserazide in rats.

Methods

Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

Results

The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

Conclusions

Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.", + "title": "A pharmacokinetic model to predict the PK interaction of L-dopa and benserazide in rats.", + "type": "PubMed ID", + "volume": "18", + "year": 2001 + }, + "publicationId": "BIOMD0000000320", + "submissionId": "MODEL0910130001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000321": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Grange2001 - L-dopa PK model

A pharmacokinetics of L-dopa in rats after administration of L-dopa alone (this model: BIOMD0000000321) or L-dopa combined with a peripheral AADC (amino-acid-decarboxylase) inhibitor (BIOMD0000000320) has been studied using noncompartmental analysis.

This model is described in the article:

Grange S, Holford NH, Guentert TW
Pharmaceutical Research [2001, 18(8):1174-1184]

Abstract:

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The model has a species (A-dopa) whose initial concentration is calculated from a listOfInitialAssignments . While running for the first time the time-course (24hrs) for this model in COPASI (up to version 4.6, Build 33), the resulting graph displays only straight lines for all the species. Any subsequent runs should provide proper plots (i.e. without making any change to the model, just by clicking the \"run\" button again).

The above issue is caused by some initial assignments which are not calculated when COPASI imports the file. This issue should not be present in newer releases of COPASI.

This model is hosted on BioModels Database and identified by: MODEL1103250000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Hoffmann-La Roche Ltd, Basel, Switzerland. susan.grange@roche.com", + "authors": [ + { + "institution": "PRNS Non-Clinical Drug Safety, F. Hoffmann-La Roche Ltd, Basel, Switzerland. susan.grange@roche.com", + "name": "S Grange" + }, + { + "institution": "University of Auckland", + "name": "N H Holford", + "orcid": "0000-0002-4031-2514" + }, + { + "name": "T W Guentert" + } + ], + "issue": "8", + "journal": "Pharmaceutical research", + "link": "http://identifiers.org/pubmed/11587490", + "month": "8", + "pages": "1174-1184", + "synopsis": "

Purpose

To study the PK interaction of L-dopa/benserazide in rats.

Methods

Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

Results

The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

Conclusions

Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.", + "title": "A pharmacokinetic model to predict the PK interaction of L-dopa and benserazide in rats.", + "type": "PubMed ID", + "volume": "18", + "year": 2001 + }, + "publicationId": "BIOMD0000000321", + "submissionId": "MODEL1103250000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000322": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "california institute of technology", + "email": "jongmin@dna.caltech.edu", + "external": false, + "name": "jongmin kim" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Synthetic in vitro transcriptional oscillators.
Kim J, Winfree E Mol. Syst. Biol. 2011 Feb 1;7:465. 21283141 ,
Abstract:
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplifiedanalog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platformfor the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally,a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.

Note:

The paper describes 7 models (MODEL1012090000-6) and all these are submitted by the authors. Thismodel (MODEL1012090000) corresponds to the Simple model for both mode I and II (Design I and II). The model reproduces timecourse figure plotted in the supplementary material (page 10 of Supplementary material) of the reference publication.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.", + "title": "Synthetic in vitro transcriptional oscillators.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000322", + "submissionId": "MODEL1012090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000323": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "california institute of technology", + "email": "jongmin@dna.caltech.edu", + "external": false, + "name": "jongmin kim" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Synthetic in vitro transcriptional oscillators.
Kim J, Winfree E Mol. Syst. Biol. 2011 Feb 1;7:465. 21283141 ,
Abstract:
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.

Notes:

The paper describes 7 models (MODEL1012090000-6) and all these are submitted by the authors. This model (MODEL1012090001) corresponds to the Simple model of the three-switch ring oscillator (Design III). The model reproduces figure 6 (central figures) of the reference publication. The time is rescaled by s=v_d/K_I*t where K_I=0.333 and v_d=1 (for alpha = 1) and v_d=0.5 (for alpha = 0.5). i.e. For alpha = 1, s = 0.003 * t (roughly 10 unitless time = 1hr; the time-course should be run for 60 timeunits (6hrs) to get figure 6a). For alpha = 2, s= 0.0015 * t (roughly 5 unitless time = 1hr; the time-course shoue be run for 100 timesunits (20hrs) to get figure 6b).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.", + "title": "Synthetic in vitro transcriptional oscillators.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000323", + "submissionId": "MODEL1012090001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000324": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the full model (eq. 1 and 2) of the voltage oscillations in barnacle muscle fibers described in the article:
Voltage oscillations in the barnacle giant muscle fiber.
\tMorris C, Lecar H. Biophys J. 1981 Jul;35(1):193-213. PubmedID:7260316; DOI:10.1016/S0006-3495(81)84782-0
\tAbstract:
\tBarnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.

The model consists of the differential equations (1) and (2) given on pages 195 and 196 of the article. There is one typo in the equation for I in (1), gL(VL) should be gL(V - VL). This was changed in the SBML file. As there are no current values given, for reproducing the time courses in figure 6 an applied current of 50 uA was assumed. The legend for the broken and the full line in this figure seems to be confounded in the article.

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types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.", + "title": "Voltage oscillations in the barnacle giant muscle fiber.", + "type": "PubMed ID", + "volume": "35", + "year": 1981 + }, + "publicationId": "BIOMD0000000324", + "submissionId": "MODEL1011230001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000325": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Pennsylvania", + "email": "santhosh.palani@gmail.com", + "external": false, + "name": "Santhosh Palani" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model of the minmal 2 feedback switch described in the article:
Synthetic conversion of a graded receptor signal into a tunable, reversible switch.
Santhosh Palani and Casim A. Sarkar, 2011, Molecular Systems Biology 7:480; doi: 10.1038/msb.2011.13

The ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network switches have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (nH420), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties.Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways.

This model is parametrised for a transcription factor and receptor feedback strength of 3, TFs = 3 and Rs = 3. To reproduce figure 1 E, the parameters TFs and Rs have to be varied accordingly.

Nomenclature for the model:
L : Ligand
R : Receptor
C : Ligand-Receptor Complex
I : Inactive Transcription Factor
X : C bound to I
A : Active Transcription Factor

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Network-level analysis of light adaptation in rod cells under normal and altered conditions.
Dell'Orco D, Schmidt H, Mariani S, Fanelli F Mol Biosyst2009 Oct; 5(10):1232-46 19756313,
Abstract:
Photoreceptor cells finely adjust their sensitivity and electrical response according to changes in light stimuli as a direct consequence of the feedback and regulation mechanisms in the phototransduction cascade. In this study, we employed a systems biology approach to develop a dynamic model of vertebrate rod phototransduction that accounts for the details of the underlying biochemistry. Following a bottom-up strategy, we first reproduced the results of a robust model developed by Hamer et al. (Vis. Neurosci., 2005, 22(4), 417), and then added a number of additional cascade reactions including: (a) explicit reactions to simulate the interaction between the activated effector and the regulator of G-protein signalling (RGS); (b) a reaction for the reformation of the G-protein from separate subunits; (c) a reaction for rhodopsin (R) reconstitution from the association of the opsin apoprotein with the 11-cis-retinal chromophore; (d) reactions for the slow activation of the cascade by opsin. The extended network structure successfully reproduced a number of experimental conditions that were inaccessible to prior models. With a single set of parameters the model was able to predict qualitative and quantitative features of rod photoresponses to light stimuli ranging over five orders of magnitude, in normal and altered conditions, including genetic manipulations of the cascade components. In particular, the model reproduced the salient dynamic features of the rod from Rpe65(-/-) animals, a well established model for Leber congenital amaurosis and vitamin A deficiency. The results of this study suggest that a systems-level approach can help to unravel the adaptation mechanisms in normal and in disease-associated conditions on a molecular basis.


Note:

Figure 7 of the reference is reproduced here. Each plot is obtained by increasing flash strength. More details about generating the plots can be obtained from the comments in the curation figure (go to curation tab).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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regulation mechanisms in the phototransduction cascade. In this study, we employed a systems biology approach to develop a dynamic model of vertebrate rod phototransduction that accounts for the details of the underlying biochemistry. Following a bottom-up strategy, we first reproduced the results of a robust model developed by Hamer et al. (Vis. Neurosci., 2005, 22(4), 417), and then added a number of additional cascade reactions including: (a) explicit reactions to simulate the interaction between the activated effector and the regulator of G-protein signalling (RGS); (b) a reaction for the reformation of the G-protein from separate subunits; (c) a reaction for rhodopsin (R) reconstitution from the association of the opsin apoprotein with the 11-cis-retinal chromophore; (d) reactions for the slow activation of the cascade by opsin. The extended network structure successfully reproduced a number of experimental conditions that were inaccessible to prior models. With a single set of parameters the model was able to predict qualitative and quantitative features of rod photoresponses to light stimuli ranging over five orders of magnitude, in normal and altered conditions, including genetic manipulations of the cascade components. In particular, the model reproduced the salient dynamic features of the rod from Rpe65(-/-) animals, a well established model for Leber congenital amaurosis and vitamin A deficiency. The results of this study suggest that a systems-level approach can help to unravel the adaptation mechanisms in normal and in disease-associated conditions on a molecular basis.", + "title": "Network-level analysis of light adaptation in rod cells under normal and altered conditions.", + "type": "PubMed ID", + "volume": "5", + "year": 2009 + }, + "publicationId": "BIOMD0000000326", + "submissionId": "MODEL1103090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000327": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

A mathematical model of the pancreatic duct cell generating high bicarbonate concentrations in pancreatic juice
David C Whitcomb, G Bard Ermentrout, Pancreas 2004 29:e30-40; PubMedID:15257112

Abstract:
OBJECTIVE:To develop a simple, physiologically based mathematical model of pancreatic duct cell secretion using experimentally derived parameters that generates pancreatic fluid bicarbonate concentrations of >140 mM after CFTR activation.
METHODS:A new mathematical model was developed simulating a duct cell within a proximal pancreatic duct and included a sodium-2-bicarbonate cotransporter (NBC) and sodium-potassium pump (NaK pump) on a chloride-impermeable basolateral membrane, CFTR on the luminal membrane with 0.2 to 1 bicarbonate to chloride permeability ratio. Chloride-bicarbonate antiporters (Cl/HCO3 AP) were added or subtracted from the basolateral (APb) and luminal (APl) membranes. The model was integrated over time using XPPAUT.
RESULTS:This model predicts robust, NaK pump-dependent bicarbonate secretion with opening of the CFTR, generates and maintains pancreatic fluid secretion with bicarbonate concentrations >140 mM, and returns to basal levels with CFTR closure. Limiting CFTR permeability to bicarbonate, as seen in some CFTR mutations, markedly inhibited pancreatic bicarbonate and fluid secretion.
CONCLUSIONS:A simple CFTR-dependent duct cell model can explain active, high-volume, high-concentration bicarbonate secretion in pancreatic juice that reproduces the experimental findings. This model may also provide insight into why CFTR mutations that predominantly affect bicarbonate permeability predispose to pancreatic dysfunction in humans.

This SBML version of the model was created directly from the XPPAUT code found in the appendix with the exception of the parameter vr, the ratio between the duct cell volume and the duct lumen, which is defined inversely to the main text in the XPPAUT code. vr was defined as the ratio of the duct cell volume to the duct lumen volume as in the main text. The model reproduces the figures found in the article. The model uses initial assignments for the lumen volume and events to trigger CFTR opening, so only tools supporting these features can be used to simulate it (eg. Copasi and SBW/Roadrunner).

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"name": "Whitcomb2004_Bicarbonate_Pancreas", + "publication": { + "accession": "15257112", + "affiliation": "Department of Medicine, University of Pittsburgh, UPMC Presbyterian, Pittsburgh, Pennsylvania 15213, USA. whitcomb@pitt.edu", + "authors": [ + { + "institution": "Department of Medicine, University of Pittsburgh, UPMC Presbyterian, Pittsburgh, Pennsylvania 15213, USA. whitcomb@pitt.edu", + "name": "David C Whitcomb" + }, + { + "name": "G Bard Ermentrout" + } + ], + "issue": "2", + "journal": "Pancreas", + "link": "http://identifiers.org/pubmed/15257112", + "month": "8", + "pages": "e30-40", + "synopsis": "

Objective

To develop a simple, physiologically based mathematical model of pancreatic duct cell secretion using experimentally derived parameters that generates pancreatic fluid bicarbonate concentrations of >140 mM after CFTR activation.

Methods

A new mathematical model was developed simulating a duct cell within a proximal pancreatic duct and included a sodium-2-bicarbonate cotransporter (NBC) and sodium-potassium pump (NaK pump) on a chloride-impermeable basolateral membrane, CFTR on the luminal membrane with 0.2 to 1 bicarbonate to chloride permeability ratio. Chloride-bicarbonate antiporters (Cl/HCO3 AP) were added or subtracted from the basolateral (APb) and luminal (APl) membranes. The model was integrated over time using XPPAUT.

Results

This model predicts robust, NaK pump-dependent bicarbonate secretion with opening of the CFTR, generates and maintains pancreatic fluid secretion with bicarbonate concentrations >140 mM, and returns to basal levels with CFTR closure. Limiting CFTR permeability to bicarbonate, as seen in some CFTR mutations, markedly inhibited pancreatic bicarbonate and fluid secretion.

Conclusions

A simple CFTR-dependent duct cell model can explain active, high-volume, high-concentration bicarbonate secretion in pancreatic juice that reproduces the experimental findings. This model may also provide insight into why CFTR mutations that predominantly affect bicarbonate permeability predispose to pancreatic dysfunction in humans.", + "title": "A mathematical model of the pancreatic duct cell generating high bicarbonate concentrations in pancreatic juice.", + "type": "PubMed ID", + "volume": "29", + "year": 2004 + }, + "publicationId": "BIOMD0000000327", + "submissionId": "MODEL1104180000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000328": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institute of Biochemical Engineering, University of Stuttgart, Stuttgart, Germany", + "email": "bucher@ibvt.uni-stuttgart.de", + "external": false, + "name": "Joachim Bucher" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model of atorvastatin metabolism in hepaitc cells described in the article:
A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes
Joachim Bucher , Stephan Riedmaier , Anke Schnabel , Katrin Marcus , Gabriele Vacun , Thomas S Weiss , Wolfgang E Thasler , Andreas K Nussler , Ulrich M Zanger and Matthias Reuss. BMC Systems Biology 2011, 5:66. DOI:10.1186/1752-0509-5-66

Abstract:
Background:
The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.
Results:
In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated in primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n=150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.
Conclusions:
A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.

The model is parameterized for patient 1 and reproduces the time courses in figure 2 of the article.

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Background

The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.

Results

In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated on primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n = 150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.

Conclusions

A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.", + "title": "A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes.", + "type": "PubMed ID", + "volume": "5", + "year": 2011 + }, + "publicationId": "BIOMD0000000328", + "submissionId": "MODEL1103200000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000329": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kummer2000 - Oscillations in CalciumSignalling

Simplified (3-variable) calcium oscillation model Kummer et al. (2000) Biophys. J. 79, 1188-1195 This model is defined in a small compartment with low concentrations. You can run it first with the LSODA ODE solver and then with the Gillespie Monte Carlo method (in Time Course widget). This illustrates that at low particle numbers, as here, the stochastic simulation and the ODE approach produce different results (the stochastic approach is more correct in these circumstances). This file also demonstrates the use of several different plots to visualize results, including a histogram.

This model is described in the article:

Kummer U, Olsen LF, Dixon CJ, Green AK, Bornberg-Bauer E, Baier G.
Biophys. J. 2000 Sep; 79(3): 1188-1195

Abstract:

We present a new model for calcium oscillations based on experiments in hepatocytes. The model considers feedback inhibition on the initial agonist receptor complex by calcium and activated phospholipase C, as well as receptor type-dependent self-enhanced behavior of the activated G(alpha) subunit. It is able to show simple periodic oscillations and periodic bursting, and it is the first model to display chaotic bursting in response to agonist stimulations. Moreover, our model offers a possible explanation for the differences in dynamic behavior observed in response to different agonists in hepatocytes.

This model is hosted on BioModels Database and identified by: BIOMD0000000329.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Heidelberg, Germany. ursula.kummer@eml.villa-bosch.de", + "name": "U Kummer" + }, + { + "name": "L F Olsen", + "orcid": "0000-0001-7391-2340" + }, + { + "name": "C J Dixon" + }, + { + "name": "A K Green" + }, + { + "name": "E Bornberg-Bauer" + }, + { + "name": "G Baier", + "orcid": "0000-0002-2540-3100" + } + ], + "issue": "3", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/10968983", + "month": "9", + "pages": "1188-1195", + "synopsis": "We present a new model for calcium oscillations based on experiments in hepatocytes. 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Moreover, our model offers a possible explanation for the differences in dynamic behavior observed in response to different agonists in hepatocytes.", + "title": "Switching from simple to complex oscillations in calcium signaling.", + "type": "PubMed ID", + "volume": "79", + "year": 2000 + }, + "publicationId": "BIOMD0000000329", + "submissionId": "MODEL1105060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000330": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
On the encoding and decoding of calcium signals in hepatocytes
Ann Zahle Larsen, Lars Folke Olsen and Ursula Kummera Biophysical ChemistryVolume 107, Issue 1, 1 January 2004, Pages 83-99 14871603,
Abstract:
Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.

Note:

This model corresponds to the 5 variable receptor-operated model, as described by Larsen et al., 2004. This model is a modified version of the model described in Kummer 2000 (PMID:10968983)

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Syddansk Universitet, Campusvej 55, Odense M DK-5230, Denmark. zahle@odense.kollegienet.dk", + "authors": [ + { + "institution": "Celcom, Department of Biochemistry and Molecular Biology, Syddansk Universitet, Campusvej 55, Odense M DK-5230, Denmark. zahle@odense.kollegienet.dk", + "name": "Ann Zahle Larsen" + }, + { + "name": "L F Olsen", + "orcid": "0000-0001-7391-2340" + }, + { + "name": "Ursula Kummer" + } + ], + "issue": "1", + "journal": "Biophysical chemistry", + "link": "http://identifiers.org/pubmed/14871603", + "month": "1", + "pages": "83-99", + "synopsis": "Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.", + "title": "On the encoding and decoding of calcium signals in hepatocytes.", + "type": "PubMed ID", + "volume": "107", + "year": 2004 + }, + "publicationId": "BIOMD0000000330", + "submissionId": "MODEL1105060001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000331": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
On the encoding and decoding of calcium signals in hepatocytes
Ann Zahle Larsen, Lars Folke Olsen and Ursula Kummera Biophysical ChemistryVolume 107, Issue 1, 1 January 2004, Pages 83-99 14871603,
Abstract:
Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.

Note:

This model corresponds to the improved model eqn 1-7, as described by Larsen et al., 2004 implemented to investigate how the cell can decode different oscillations. This is done by introducing 2 more variables Enzyme and Product in addition to the 5 variables G-alpha, PLC, Ca_cyt, Ca_ER and Ca_mit receptor-operated model described in the first part of the paper. The receptor-operated model is itself a modified version of the model described in Kummer 2000 (PMID:10968983)

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This model is from the article:
Modelling thrombin generation in human ovarian follicular fluid
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Bulletin of Mathematical BiologyVolume 68, Issue 8, 12 July 2006, Pages 2283-302 16838084,
Abstract:
A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.

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sharene@cs.mun.ca", + "authors": [ + { + "institution": "Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1. sharene@cs.mun.ca", + "name": "Sharene D Bungay" + }, + { + "name": "Patricia A Gentry" + }, + { + "name": "Rodney D Gentry" + } + ], + "issue": "8", + "journal": "Bulletin of mathematical biology", + "link": "http://identifiers.org/pubmed/16838084", + "month": "11", + "pages": "2283-2302", + "synopsis": "A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.", + "title": "Modelling thrombin generation in human ovarian follicular fluid.", + "type": "PubMed ID", + "volume": "68", + "year": 2006 + }, + "publicationId": "BIOMD0000000332", + "submissionId": "MODEL1105090001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000333": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Modelling thrombin generation in human ovarian follicular fluid
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Bulletin of Mathematical BiologyVolume 68, Issue 8, 12 July 2006, Pages 2283-302 16838084,
Abstract:
A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.

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The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.", + "title": "Modelling thrombin generation in human ovarian follicular fluid.", + "type": "PubMed ID", + "volume": "68", + "year": 2006 + }, + "publicationId": "BIOMD0000000333", + "submissionId": "MODEL1105090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000334": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
A mathematical model of lipid-mediated thrombin generation
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Mathematical Medicine and BiologyVolume 20, Issue 1, 1 March 2003, Pages 105-29 12974500,
Abstract:
Thrombin is an enzyme that is generated in both vascular and non-vascular systems. In blood coagulation, a fundamental process in all species, thrombin induces the formation of a fibrin clot. A dynamical model of thrombin generation in the presence of lipid surfaces is presented. This model also includes the self-regulating thrombin feedback reactions, the thrombomodulin-protein C-protein S inhibitory system, tissue factor pathway inhibitor (TFPI), and the inhibitor, antithrombin (AT). The dynamics of this complex system were found to be highly lipid dependent, as would be expected from experimental studies. Simulations of this model indicate that a threshold lipid level is required to generate physiologically relevant amounts of thrombin. The dependence of the onset, the peak levels, and the duration of thrombin generation on lipid was saturable. The lipid concentration affects the way in which the inhibitors modulate thrombin production. A novel feature of this model is the inclusion of the dynamical protein C pathway, initiated by thrombin feedback. This inhibitory system exerts its effects on the lipid surface, where its substrates are formed. The maximum impact of TFPI occurs at intermediate vesicle concentrations. Inhibition by AT is only indirectly affected by the lipid since AT irreversibly binds only to solution phase proteins. In a system with normal plasma concentrations of the proteins involved in thrombin formation, the combination of these three inhibitors is sufficient both to effectively stop thrombin generation prior to the exhaustion of its precursor, prothrombin, and to inhibit all thrombin formed. This model can be used to predict thrombin generation under extreme lipid conditions that are difficult to implement experimentally and to examine thrombin generation in non-vascular systems.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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In blood coagulation, a fundamental process in all species, thrombin induces the formation of a fibrin clot. A dynamical model of thrombin generation in the presence of lipid surfaces is presented. This model also includes the self-regulating thrombin feedback reactions, the thrombomodulin-protein C-protein S inhibitory system, tissue factor pathway inhibitor (TFPI), and the inhibitor, antithrombin (AT). The dynamics of this complex system were found to be highly lipid dependent, as would be expected from experimental studies. Simulations of this model indicate that a threshold lipid level is required to generate physiologically relevant amounts of thrombin. The dependence of the onset, the peak levels, and the duration of thrombin generation on lipid was saturable. The lipid concentration affects the way in which the inhibitors modulate thrombin production. A novel feature of this model is the inclusion of the dynamical protein C pathway, initiated by thrombin feedback. This inhibitory system exerts its effects on the lipid surface, where its substrates are formed. The maximum impact of TFPI occurs at intermediate vesicle concentrations. Inhibition by AT is only indirectly affected by the lipid since AT irreversibly binds only to solution phase proteins. In a system with normal plasma concentrations of the proteins involved in thrombin formation, the combination of these three inhibitors is sufficient both to effectively stop thrombin generation prior to the exhaustion of its precursor, prothrombin, and to inhibit all thrombin formed. This model can be used to predict thrombin generation under extreme lipid conditions that are difficult to implement experimentally and to examine thrombin generation in non-vascular systems.", + "title": "A mathematical model of lipid-mediated thrombin generation.", + "type": "PubMed ID", + "volume": "20", + "year": 2003 + }, + "publicationId": "BIOMD0000000334", + "submissionId": "MODEL9852292468", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000335": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
A model for the stoichiometric regulation of blood coagulation.
Hockin MF, Jones KC, Everse SJ, Mann KG. Journal of Biological ChemistryVolume 277, Issue 21, 24 May 2002, Pages 18322 -18333 11893748,
Abstract:
We have developed a model of the extrinsic blood coagulation system that includes the stoichiometric anticoagulants. The model accounts for the formation, expression, and propagation of the vitamin K-dependent procoagulant complexes and extends our previous model by including: (a) the tissue factor pathway inhibitor (TFPI)-mediated inactivation of tissue factor (TF).VIIa and its product complexes; (b) the antithrombin-III (AT-III)-mediated inactivation of IIa, mIIa, factor VIIa, factor IXa, and factor Xa; (c) the initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; (d) factor VIIIa dissociation/activity loss; (e) the binding competition and kinetic activation steps that exist between TF and factors VII and VIIa; and (f) the activation of factor VII by IIa, factor Xa, and factor IXa. These additions to our earlier model generate a model consisting of 34 differential equations with 42 rate constants that together describe the 27 independent equilibrium expressions, which describe the fates of 34 species. Simulations are initiated by \"exposing\" picomolar concentrations of TF to an electronic milieu consisting of factors II, IX, X, VII, VIIa, V, and VIIII, and the anticoagulants TFPI and AT-III at concentrations found in normal plasma or associated with coagulation pathology. The reaction followed in terms of thrombin generation, proceeds through phases that can be operationally defined as initiation, propagation, and termination. The generation of thrombin displays a nonlinear dependence upon TF, AT-III, and TFPI and the combination of these latter inhibitors displays kinetic thresholds. At subthreshold TF, thrombin production/expression is suppressed by the combination of TFPI and AT-III; for concentrations above the TF threshold, the bolus of thrombin produced is quantitatively equivalent. A comparison of the model with empirical laboratory data illustrates that most experimentally observable parameters are captured, and the pathology that results in enhanced or deficient thrombin generation is accurately described.

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The model accounts for the formation, expression, and propagation of the vitamin K-dependent procoagulant complexes and extends our previous model by including: (a) the tissue factor pathway inhibitor (TFPI)-mediated inactivation of tissue factor (TF).VIIa and its product complexes; (b) the antithrombin-III (AT-III)-mediated inactivation of IIa, mIIa, factor VIIa, factor IXa, and factor Xa; (c) the initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; (d) factor VIIIa dissociation/activity loss; (e) the binding competition and kinetic activation steps that exist between TF and factors VII and VIIa; and (f) the activation of factor VII by IIa, factor Xa, and factor IXa. These additions to our earlier model generate a model consisting of 34 differential equations with 42 rate constants that together describe the 27 independent equilibrium expressions, which describe the fates of 34 species. Simulations are initiated by \"exposing\" picomolar concentrations of TF to an electronic milieu consisting of factors II, IX, X, VII, VIIa, V, and VIIII, and the anticoagulants TFPI and AT-III at concentrations found in normal plasma or associated with coagulation pathology. The reaction followed in terms of thrombin generation, proceeds through phases that can be operationally defined as initiation, propagation, and termination. The generation of thrombin displays a nonlinear dependence upon TF, AT-III, and TFPI and the combination of these latter inhibitors displays kinetic thresholds. At subthreshold TF, thrombin production/expression is suppressed by the combination of TFPI and AT-III; for concentrations above the TF threshold, the bolus of thrombin produced is quantitatively equivalent. A comparison of the model with empirical laboratory data illustrates that most experimentally observable parameters are captured, and the pathology that results in enhanced or deficient thrombin generation is accurately described.", + "title": "A model for the stoichiometric regulation of blood coagulation.", + "type": "PubMed ID", + "volume": "277", + "year": 2002 + }, + "publicationId": "BIOMD0000000335", + "submissionId": "MODEL1106010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000336": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Jones1994_BloodCoagulation
This model is built based on theexperimental findings described in Lawson et al., 1994(PMID:8083241)

This model is described in the article:

Jones KC, Mann KG.
J. Biol. Chem. 1994 Sep; 269(37): 23367-23373

Abstract:

A mathematical simulation of the tissue factor pathway to the generation of thrombin has been developed using a combination of empirical, estimated, and deduced rate constants for reactions involving the activation of factor IX, X, V, and VIII, in the formation of thrombin, as well as rate constants for the assembly of the coagulation enzyme complexes which involve factor VIIIa-factor IXa (intrinsic tenase) and factor Va-Xa (prothrombinase) assembled on phospholipid membrane. Differential equations describing the fate of each species in the reaction were developed and solved using an interactive procedure based upon the Runge-Kutta technique. In addition to the theoretical considerations involving the reactions of the tissue factor pathway, a physical constraint associated with the stability of the factor VIIIa-factor IXa complex has been incorporated into the model based upon the empirical observations associated with the stability of this complex. The model system provides a realistic accounting of the fates of each of the proteins in the coagulation reaction through a range of initiator (factor VIIa-tissue factor) concentrations ranging from 5 pM to 5 nM. The model is responsive to alterations in the concentrations of factor VIII, factor V, and their respective activated species, factor VIIIa and factor Va, and overall provides a reasonable approximation of empirical data. The computer model permits the assessment of the reaction over a broad range of conditions and provides a useful tool for the development and management of reaction studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000336.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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coagulation enzyme complexes which involve factor VIIIa-factor IXa (intrinsic tenase) and factor Va-Xa (prothrombinase) assembled on phospholipid membrane. 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This model is from the article:
Cooperation and Competition in the Evolution of ATP-Producing Pathways
Thomas Pfeiffer, Stefan Schuster, Sebastian Bonhoeffer Science 2001 Apr; Volume:292 (Issue:5516); Page info:504-7 11283355 ,
Abstract:
Heterotrophic organisms generally face a trade-off between rate and yield of adenosine triphosphate (ATP) production. This trade-off may result in an evolutionary dilemma, because cells with a higher rate but lower yield of ATP production may gain a selective advantage when competing for shared energy resources. Using an analysis of model simulations and biochemical observations, we show that ATP production with a low rate and high yield can be viewed as a form of cooperative resource use and may evolve in spatially structured environments. Furthermore, we argue that the high ATP yield of respiration may have facilitated the evolutionary transition from unicellular to undifferentiated multicellular organisms.


Note:

This model reproduces the competition and invasion described in Supplemental Figure 2.

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"journal": "Science (New York, N.Y.)", + "link": "http://identifiers.org/pubmed/11283355", + "month": "4", + "pages": "504-507", + "synopsis": "Heterotrophic organisms generally face a trade-off between rate and yield of adenosine triphosphate (ATP) production. 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Furthermore, we argue that the high ATP yield of respiration may have facilitated the evolutionary transition from unicellular to undifferentiated multicellular organisms.", + "title": "Cooperation and competition in the evolution of ATP-producing pathways.", + "type": "PubMed ID", + "volume": "292", + "year": 2001 + }, + "publicationId": "BIOMD0000000337", + "submissionId": "MODEL1105120000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000338": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

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"http://identifiers.org/pubmed/19516255", + "month": "9", + "pages": "290-298", + "synopsis": "Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.", + "title": "A comprehensive model for the humoral coagulation network in humans.", + "type": "PubMed ID", + "volume": "86", + "year": 2009 + }, + "publicationId": "BIOMD0000000338", + "submissionId": "MODEL1107010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000339": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

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This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

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This model is from the article:
A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes.
Topp B, Promislow K, deVries G, Miura RM, Finegood DT. J Theor Biol.2000 Oct 21;206(4):605-19. 11013117,
Abstract:
Diabetes is a disease of the glucose regulatory system that is associated with increased morbidity and early mortality. The primary variables of this system are beta-cell mass, plasma insulin concentrations, and plasma glucose concentrations. Existing mathematical models of glucose regulation incorporate only glucose and/or insulin dynamics. Here we develop a novel model of beta -cell mass, insulin, and glucose dynamics, which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics. For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point. Mild hyperglycemia leads to the growth of the beta -cell mass (negative feedback) while extreme hyperglycemia leads to the reduction of the beta-cell mass (positive feedback). The model predicts that there are three pathways in prolonged hyperglycemia: (1) the physiological fixed point can be shifted to a hyperglycemic level (regulated hyperglycemia), (2) the physiological and saddle points can be eliminated (bifurcation), and (3) progressive defects in glucose and/or insulin dynamics can drive glucose levels up at a rate faster than the adaptation of the beta -cell mass which can drive glucose levels down (dynamical hyperglycemia).

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"605-619", + "synopsis": "Diabetes is a disease of the glucose regulatory system that is associated with increased morbidity and early mortality. The primary variables of this system are beta-cell mass, plasma insulin concentrations, and plasma glucose concentrations. Existing mathematical models of glucose regulation incorporate only glucose and/or insulin dynamics. Here we develop a novel model of beta -cell mass, insulin, and glucose dynamics, which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics. For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point. Mild hyperglycemia leads to the growth of the beta -cell mass (negative feedback) while extreme hyperglycemia leads to the reduction of the beta-cell mass (positive feedback). The model predicts that there are three pathways in prolonged hyperglycemia: (1) the physiological fixed point can be shifted to a hyperglycemic level (regulated hyperglycemia), (2) the physiological and saddle points can be eliminated (bifurcation), and (3) progressive defects in glucose and/or insulin dynamics can drive glucose levels up at a rate faster than the adaptation of the beta -cell mass which can drive glucose levels down (dynamical hyperglycemia).", + "title": "A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes.", + "type": "PubMed ID", + "volume": "206", + "year": 2000 + }, + "publicationId": "BIOMD0000000341", + "submissionId": "MODEL1106270000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000342": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "BIOSS Centre for Biological Signalling Studies, University of Freiburg", + "email": "zhike.zi@bioss.uni-freiburg.de", + "external": false, + "name": "Zhike Zi" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Quantitative analysis of transient and sustained transforming growth factor-\u03b2 signaling dynamics.
Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 ,
Abstract:
Mammalian cells can decode the concentration of extracellular transforming growth factor-\u03b2 (TGF-\u03b2) and transduce this cue into appropriate cell fate decisions. How variable TGF-\u03b2 ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-\u03b2 stimulation. The TGF-\u03b2 pathway elicits a transient signaling response to a single pulse of TGF-\u03b2 stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-\u03b2 pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-\u03b2 levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-\u03b2 pathway might be critical for cell fate determination.

Note:

Developer of the model: Zhike Zi

Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011

1. The global parameter that set the type of stimulation

(a) for sustained TGF-beta stimulation: set stimulation_type = 1.

(b) for single pulse of TGF-beta stimulation: set stimulation_type = 2.

parameter \"single_pulse_duration\" is for the duration of stimulation, for example,

single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation.

*Note: make sure that the time course cover the time point when the event is triggered.

(c) for single pulse of TGF-beta stimulation in COPASI

change the trigger of event \"single_pulse_TGF_beta_washout\"

from

\"and(eq(stimulation_type, 2), eq(time, single_pulse_duration))\" (for SBML-SAT)

to

\"and(eq(stimulation_type, 2), gt(time, single_pulse_duration))\" (for COPASI)

2. Notes for TGF-beta dose in terms of molecules per cell

(a) The following equation applies for conversion of TGF-beta dose in molecules per cell

TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23

(b) for standard experimental setup 1e6 cells in 2 mL medium

0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell

0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell

(c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed.

initial Vmed = 1e-8 (1e6 cells in 10 mL medium)

Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium)

3. Please note that this model contains events and the medium compartment size is varied.

4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.

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Klipp" + }, + { + "name": "Aristidis Moustakas" + }, + { + "name": "Xuedong Liu", + "orcid": "0000-0001-7209-4964" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/21613981", + "month": "5", + "pages": "492", + "synopsis": "Mammalian cells can decode the concentration of extracellular transforming growth factor-\u03b2 (TGF-\u03b2) and transduce this cue into appropriate cell fate decisions. 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Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-\u03b2 pathway might be critical for cell fate determination.", + "title": "Quantitative analysis of transient and sustained transforming growth factor-\u03b2 signaling dynamics.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000342", + "submissionId": "MODEL1104050000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000343": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework.
Brannmark C, Palmer R, Glad ST, Cedersund G, Stralfors P. J Biol Chem.2010 Jun 25;285(26):20171-9. 20421297,
Abstract:
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between co-existing submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.

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Cell Biology, Department of Clinical and Experimental Medicine, Laboratory of Diabetes and Integrated Systems Biology, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden.", + "authors": [ + { + "institution": "Division of Cell Biology, Department of Clinical and Experimental Medicine, Laboratory of Diabetes and Integrated Systems Biology, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden.", + "name": "Cecilia Br\u00e4nnmark" + }, + { + "name": "Robert Palm\u00e9r" + }, + { + "name": "S Torkel Glad" + }, + { + "name": "Gunnar Cedersund" + }, + { + "institution": "Department of Clinical and Experimental Medicine, Link\u00f6ping University, SE-58185, Link\u00f6ping, Sweden.", + "name": "Peter Str\u00e5lfors", + "orcid": "0000-0002-1196-1806" + } + ], + "issue": "26", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/20421297", + "month": "6", + "pages": "20171-20179", + "synopsis": "Insulin and other hormones control target cells through a network of signal-mediating molecules. 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This model is from the article:
Modelling the Role of the Hsp70/Hsp90 System in the Maintenance of Protein Homeostasis
Proctor CJ, Lorimer IAJ PLoS ONE2011; 6(7): e22038. doi:10.1371/journal.pone.0022038,
Abstract:
Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis.

Note:

Simulations were performed under three different conditions: 1) normal condition (no stress), 2) moderate stress due to an increase in reactive oxygen species (ROS) i.e. ROS levels were increased by a factor of 4 at time=4hours for a period of 1 hour (not 2 hours as mentioned in the figure 5 legend of the reference publication. This is a typo in the paper and is clarified by the author) and 3) high stress due to sustained increase in reactive oxygen species (ROS) (here ROS increases with time).

The model that corresponds to the normal condition is submitted as a main model in the BioModels Database. The other two models, that corresponds to the moderate stress conditions and high stress conditions are available in SBML format as supporting files [go to Curation tab].

Supplementary figures S3 (normal condition), S4 (moderate stress condition) and S6 (high stress condition) are reproduced here.

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"Proctor2011_ProteinHomeostasis_NormalCondition", + "publication": { + "accession": "21779370", + "affiliation": "Centre for Integrated Systems Biology of Ageing and Nutrition, Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom. c.j.proctor@ncl.ac.uk", + "authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "Ian A J Lorimer" + } + ], + "issue": "7", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/21779370", + "month": "0", + "pages": "e22038", + "synopsis": "Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis.", + "title": "Modelling the role of the Hsp70/Hsp90 system in the maintenance of protein homeostasis.", + "type": "PubMed ID", + "volume": "6", + "year": 2011 + }, + "publicationId": "BIOMD0000000344", + "submissionId": "MODEL1005280000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000345": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Mathematical modeling and analysis of insulin clearance in vivo.
Koschorreck M, Gilles ED. BMC Syst Biol. 2008 May 13;2:43. 18477391,
Abstract:
BACKGROUND:Analyzing the dynamics of insulin concentration in the blood is necessary for a comprehensive understanding of the effects of insulin in vivo. Insulin removal from the blood has been addressed in many studies. The results are highly variable with respect to insulin clearance and the relative contributions of hepatic and renal insulin degradation.RESULTS:We present a dynamic mathematical model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. The model describes renal and hepatic insulin degradation, pancreatic insulin secretion and nonspecific insulin binding in the liver. Hepatic insulin receptor activation by insulin binding, receptor internalization and autophosphorylation is explicitly included in the model. We present a detailed mathematical analysis of insulin degradation and insulin clearance. Stationary model analysis shows that degradation rates, relative contributions of the different tissues to total insulin degradation and insulin clearance highly depend on the insulin concentration.CONCLUSION:This study provides a detailed dynamic model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. Experimental data sets from literature are used for the model validation. We show that essential dynamic and stationary characteristics of insulin degradation are nonlinear and depend on the actual insulin concentration.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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systems biology", + "link": "http://identifiers.org/pubmed/18477391", + "month": "5", + "pages": "43", + "synopsis": "

Background

Analyzing the dynamics of insulin concentration in the blood is necessary for a comprehensive understanding of the effects of insulin in vivo. Insulin removal from the blood has been addressed in many studies. The results are highly variable with respect to insulin clearance and the relative contributions of hepatic and renal insulin degradation.

Results

We present a dynamic mathematical model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. The model describes renal and hepatic insulin degradation, pancreatic insulin secretion and nonspecific insulin binding in the liver. Hepatic insulin receptor activation by insulin binding, receptor internalization and autophosphorylation is explicitly included in the model. We present a detailed mathematical analysis of insulin degradation and insulin clearance. Stationary model analysis shows that degradation rates, relative contributions of the different tissues to total insulin degradation and insulin clearance highly depend on the insulin concentration.

Conclusion

This study provides a detailed dynamic model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. Experimental data sets from literature are used for the model validation. We show that essential dynamic and stationary characteristics of insulin degradation are nonlinear and depend on the actual insulin concentration.", + "title": "Mathematical modeling and analysis of insulin clearance in vivo.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000345", + "submissionId": "MODEL1108040000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000346": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the original model from Richard FitzHugh, which led the famous FitzHugh\u2013Nagumo model, still used for instance in computational neurosciences.
Impulses and Physiological States in Theoretical Models of Nerve Membrane
FitzHugh R Biophysical Journal, 1961 July:1(6):445-466 doi:10.1016/S0006-3495(61)86902-6 ,
Abstract:
Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting BVP model has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a physiological state diagram, with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.
Bachmann J, Raue A, Schilling M, B\u00f6hm ME, Kreutz C, Kaschek D, Busch H, Gretz N, Lehmann WD, Timmer J, Klingm\u00fcller U. Mol Syst Biol. 2011 Jul 19;7:516. 21772264 ,
Abstract:
Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.

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Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.", + "title": "Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000347", + "submissionId": "MODEL1107220000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000348": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Glucose sensing in the pancreatic beta cell: a computational systems analysis.
Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556,
Abstract:
BACKGROUND:Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.METHODS:We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.RESULTS:The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.CONCLUSION:This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.

This model was taken from the Vcell MathModel directory and was converted to SBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"link": "http://identifiers.org/pubmed/20497556", + "month": "5", + "pages": "15", + "synopsis": "

Background

Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.

Methods

We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.

Results

The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.

Conclusion

This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.", + "title": "Glucose sensing in the pancreatic beta cell: a computational systems analysis.", + "type": "PubMed ID", + "volume": "7", + "year": 2010 + }, + "publicationId": "BIOMD0000000348", + "submissionId": "MODEL1108090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000349": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Glucose sensing in the pancreatic beta cell: a computational systems analysis.
Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556,
Abstract:
BACKGROUND:Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.METHODS:We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.RESULTS:The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.CONCLUSION:This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.

This model was taken from the Vcell MathModel directory and was converted to SBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ "link": "http://identifiers.org/pubmed/20497556", + "month": "5", + "pages": "15", + "synopsis": "

Background

Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.

Methods

We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.

Results

The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.

Conclusion

This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.", + "title": "Glucose sensing in the pancreatic beta cell: a computational systems analysis.", + "type": "PubMed ID", + "volume": "7", + "year": 2010 + }, + "publicationId": "BIOMD0000000349", + "submissionId": "MODEL1108090001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000350": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institute of Molecular Plant Sciences, University of Edinburgh", + "email": "andrew.millar@ed.ac.uk", + "external": false, + "name": "Andrew J Millar" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Multiple light inputs to a simple clock circuit allow complex biological rhythms
Troein C, Corellou F, Dixon LE, van Ooijen G, O'Neill JS, Bouget FY, Millar AJ. Plant J.2011 Apr;66(2):375-85. 21219507,
Abstract:
Circadian clocks are biological timekeepers that allow living cells to time their activity in anticipation of predictable environmental changes. Detailed understanding of the circadian network of higher plants, such as Arabidopsis thaliana, is hampered by the high number of partially redundant genes. However, the picoeukaryotic alga Ostreococcus tauri, which was recently shown to possess a small number of non-redundant clock genes, presents an attractive alternative target for detailed modelling of circadian clocks in the green lineage. Based on extensive time-series data from in vivo reporter gene assays, we developed a model of the Ostreococcus clock as a feedback loop between the genes TOC1 and CCA1. The model reproduces the dynamics of the transcriptional and translational reporters over a range of photoperiods. Surprisingly, the model is also able to predict the transient behaviour of the clock when the light conditions are altered. Despite the apparent simplicity of the clock circuit, it displays considerable complexity in its response to changing light conditions. Systematic screening of the effects of altered day length revealed a complex relationship between phase and photoperiod, which is also captured by the model. The complex light response is shown to stem from circadian gating of light-dependent mechanisms. This study provides insights into the contributions of light inputs to the Ostreococcus clock. The model suggests that a high number of light-dependent reactions are important for flexible timing in a circadian clock with only one feedback loop.

Note: Two-gene model of the Ostreococcus circadian clock

This is a model of the circadian clock of Ostreococcus tauri, with a negative feedback loop between TOC1 and CCA1 (a.k.a. LHY) and multiple light inputs. It was used and described in Troein et al., Plant Journal (2011).

The model incorporates luciferase reporters, and in this SBML model the four different versions of the model for transcriptional and translational reporter lines (pTOC1::LUC, pCCA1::LUC, TOC1-LUC and CCA1-LUC) are all accessible by setting one of the rep_X parameters to 1 and the others to 0. You can also set all four to 0 to only simulate the non-reporter core of the system.

Input to the system should be provided by modifying the \"light\" function. An implementation of LD 12:12 is provided as an example, but the model was also used with more complicated light regimes that vary between data sets and are not convenient to express directly in SBML.

The functions \"ox_cca1\" and \"ox_toc1\" can be altered to add overexpression of CCA1 and TOC1. Setting either to x gives additional, constitutive transcription at x times the maximal (and typically not realizable) transcription rate of the native gene. The overexpression mutant fits in Figure 7 of Troein et al. (2011) used ox_cca1 = 0.115 and oc_toc1 = 0.0584, respectively.

The functions \"copies_toc1\" and \"copies_cca1\" are normally 1 but can be lowered to simulate knockdown experiments. The functions \"transcription\", \"translation\" and \"proteasome\" can be modified to simulate the effects of altering the overall rate of transcription, translation and protein degradation.

The parameters were fitted specifically to data from transgenic reporter lines TOC8, pTOC3, LHY7 and pLHY7 (Corellou et al., Plant Cell 2009). Parameters that begin with \"effcopies\" describe the effective number of copies of CCA1 or TOC1 in the respective translational fusion lines, with anything above 1 due to the fusion proteins.

For the model fitting, the initial values were fitted to the data in the various time courses. The initial values given here correspond to the limit cycle of the system in LD 12:12. The system converges to the limit cycle in just a few days under most light conditions, so these initial values are biologically meaningful.

The species cca1luc_c and cca1luc_n have been merged into cca1luc (which corresponds to the observable luminescence signal), because Copasi refused to run the system otherwise. For TOC1-LUC, the predicted output signal is the sum of toc1luc_1 and toc1luc_2.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
The auxin signalling network translates dynamic input into robust patterning at the shoot apex.
Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Gu\u00e9don Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 ,
Abstract:
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong bufferingcapacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Note:

Figure 3 of the supplementary material of the reference article has been reproduced here. Time evolution of all the variables in the model are plotted, under the influence of a step input of auxin level (auxin=5, when time>1000; 0.11, otherwise). pi_A is varied between 0 and 2 by steps of 0.1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
The auxin signalling network translates dynamic input into robust patterning at the shoot apex.
Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Gu\u00e9don Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 ,
Abstract:
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Note:

Figure 4 of the supplementary material of the reference article has been reproduced here. In this model, the fluctuations of auxin level is represented using sinux function. Time evolution of the variables AUX/IAA (I) and mRNA (R) are plotted, under the influence of fluctuations of auxin level. pi_A is varied between 0 and 2 by steps of 0.1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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teva.vernoux@ens-lyon.fr", + "name": "Teva Vernoux", + "orcid": "0000-0002-8257-4088" + }, + { + "name": "G\u00e9raldine Brunoud" + }, + { + "name": "Etienne Farcot", + "orcid": "0000-0002-0742-3028" + }, + { + "name": "Val\u00e9rie Morin" + }, + { + "name": "Hilde Van den Daele", + "orcid": "0000-0002-4271-6694" + }, + { + "name": "Jonathan Legrand" + }, + { + "name": "Marina Oliva", + "orcid": "0000-0001-8096-6857" + }, + { + "name": "Pradeep Das" + }, + { + "name": "Antoine Larrieu", + "orcid": "0000-0002-9024-5348" + }, + { + "name": "Darren Wells", + "orcid": "0000-0002-4246-4909" + }, + { + "name": "Yann Gu\u00e9don" + }, + { + "name": "Lynne Armitage" + }, + { + "name": "Franck Picard", + "orcid": "0000-0001-8084-5481" + }, + { + "name": "Soazig Guyomarc'h", + "orcid": "0000-0001-9327-4737" + }, + { + "name": "Coralie Cellier" + }, + { + "name": "Geraint Parry" + }, + { + "name": "Rachil Koumproglou" + }, + { + "name": "John H Doonan", + "orcid": "0000-0001-6027-1919" + }, + { + "name": "Mark Estelle" + }, + { + "name": "Christophe Godin" + }, + { + "name": "Stefan Kepinski", + "orcid": "0000-0001-9819-5034" + }, + { + "name": "Malcolm J Bennett", + "orcid": "0000-0003-0475-390X" + }, + { + "name": "Lieven De Veylder", + "orcid": "0000-0003-1150-4426" + }, + { + "name": "Jan Traas" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/21734647", + "month": "7", + "pages": "508", + "synopsis": "The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.", + "title": "The auxin signalling network translates dynamic input into robust patterning at the shoot apex.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000352", + "submissionId": "MODEL1108110000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000353": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Renewable Energy Laboratory", + "email": "ambarish.nag@nrel.gov", + "external": false, + "name": "Ambarish Nag" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.
Nag A, Lunacek M, Graf PA, Chang CH. BMC Syst Biol.2011 Jun 18;5:94. 21682905,
Abstract:
BACKGROUND:Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.RESULTS:Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing \u03b2-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter kcat and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and \u03b2-amylase kcat and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.CONCLUSIONS:We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Background

Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.

Results

Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing \u03b2-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter k(cat) and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and \u03b2-amylase k(cat) and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.

Conclusions

We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.", + "title": "Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.", + "type": "PubMed ID", + "volume": "5", + "year": 2011 + }, + "publicationId": "BIOMD0000000353", + "submissionId": "MODEL1106030000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000354": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Stanford University", + "email": "sbandara@stanford.edu", + "external": false, + "name": "Samuel Bandara" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.
Abell E, Ahrends R, Bandara S, Park BO, Teruel MN. Proc Natl Acad Sci U S A. 2011 Aug 15. 21844332 ,
Abstract:
Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.

Note:

There are two models described in the paper to simulate basal and receptor stimulated Ca 2+ signaling. 1) No adaptive feedback (this model: MODEL1108050000) and 2) with three slow adaptive feedback loops (MODEL1108050001).

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of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.", + "title": "Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.", + "type": "PubMed ID", + "volume": "108", + "year": 2011 + }, + "publicationId": "BIOMD0000000354", + "submissionId": "MODEL1108050000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000355": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Stanford University", + "email": "sbandara@stanford.edu", + "external": false, + "name": "Samuel Bandara" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.
Abell E, Ahrends R, Bandara S, Park BO, Teruel MN. Proc Natl Acad Sci U S A. 2011 Aug 15. 21844332 ,
Abstract:
Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.

Note:

There are two models described in the paper to simulate basal and receptor stimulated Ca 2+ signaling. 1) No adaptive feedback (MODEL1108050000) and 2) with three slow adaptive feedback loops (this model: MODEL1108050001).

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This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.", + "title": "Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.", + "type": "PubMed ID", + "volume": "108", + "year": 2011 + }, + "publicationId": "BIOMD0000000355", + "submissionId": "MODEL1108050001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000356": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis.
Nyman E, Brannmark C, Palmer R, Brugard J, Nystrom FH, Stralfors P, Cedersund G.J Biol Chem.2011 Jul 22;286(29):26028-41. 21572040,
Abstract:
Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. 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Stortelder1997 - Thrombin Generation Amidolytic Activity

Mathematical modelling of a part of the blood coagulation mechanism.

This model is described in the article:

Stortelder W.J.H., Hemker P.W., Hemker, H.C.
CWI. Modelling, Analysis and Simulation, No. R 9720, p.1-11.

Abstract:

This paper describes the mathematical modelling of a part of the blood coagulation mechanism. The model includes the activation of factor X by a purified enzyme from Russel's Viper Venom (RVV), factor V and prothrombin, and also comprises the inactivation of the products formed. In this study we assume that in principle the mechanism of the process is known. However, the exact structure of the mechanism is unknown, and the process still can be described by different mathematical models. These models are put to test by measuring their capacity to explain the course of thrombin generation as observed in plasma after recalcification in presence of RVV. The mechanism studied is mathematically modelled as a system of differential-algebraic equations (DAEs). Each candidate model contains some freedom, which is expressed in the model equations by the presence of unknown parameters. For example, reaction constants or initial concentrations are unknown. The goal of parameter estimation is to determine these unknown parameters in such a way that the theoretical (i.e., computed) results fit the experimental data within measurement accuracy and to judge which modifications of the chemical reaction scheme allow the best fit. We present results on model discrimination and estimation of reaction constants, which are hard to obtain in another way.

This model is hosted on BioModels Database and identified by: BIOMD0000000358 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Thrombin Generation Amidolytic Activity", + "publication": { + "accession": "http://www.narcis.nl/publication/RecordID/oai:cwi.nl:4725", + "authors": [ + { + "name": "Stortelder W.J.H." + }, + { + "name": "Hemker P.W." + }, + { + "name": "Hemker H.C." + } + ], + "journal": "CWI. Modelling, Analysis and Simulation [MAS], No. R 9720, p.1-11", + "link": "http://www.narcis.nl/publication/RecordID/oai:cwi.nl:4725", + "synopsis": "This paper describes the mathematical modelling of a part of the blood coagulation mechanism. The model includes the activation of factor X by a purified enzyme from Russel's Viper Venom (RVV), factor V and prothrombin, and also comprises the inactivation of the products formed. In this study we assume that in principle the mechanism of the process is known. However, the exact structure of the mechanism is unknown, and the process still can be described by different mathematical models. These models are put to test by measuring their capacity to explain the course of thrombin generation as observed in plasma after recalcification in presence of RVV. The mechanism studied is mathematically modelled as a system of differential-algebraic equations (DAEs). Each candidate model contains some freedom, which is expressed in the model equations by the presence of unknown parameters. For example, reaction constants or initial concentrations are unknown. The goal of parameter estimation is to determine these unknown parameters in such a way that the theoretical (i.e., computed) results fit the experimental data within measurement accuracy and to judge which modifications of the chemical reaction scheme allow the best fit. We present results on model discrimination and estimation of reaction constants, which are hard to obtain in another way.", + "title": "Mathematical modelling in blood coagulation ; Simulation and parameter estimation", + "type": "Other Link (URL)", + "year": 1997 + }, + "publicationId": "BIOMD0000000358", + "submissionId": "MODEL1108260009", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000359": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Panteleev2002_TFPImechanism_schmema1", + "publication": { + "accession": "11985578", + "affiliation": "National Research Center for Hematology, Russian Academy of Medical Sciences, Moscow, Russia.", + "authors": [ + { + "name": "Mikhail A Panteleev", + "orcid": "0000-0002-8128-7757" + }, + { + "name": "Veronica I Zarnitsina", + "orcid": "0000-0003-3096-3695" + }, + { + "name": "Fazoil I Ataullakhanov" + } + ], + "issue": "8", + "journal": "European journal of biochemistry", + "link": "http://identifiers.org/pubmed/11985578", + "month": "4", + "pages": "2016-2031", + "synopsis": "We have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested.", + "title": "Tissue factor pathway inhibitor: a possible mechanism of action.", + "type": "PubMed ID", + "volume": "269", + "year": 2002 + }, + "publicationId": "BIOMD0000000361", + "submissionId": "MODEL1108260006", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000362": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"uri": "http://identifiers.org/biomodels.db/MODEL1108260005" + }, + { + "accession": "BIOMD0000000362", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000362" + }, + { + "accession": "15039440", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/15039440" + }, + { + "accession": "9020158", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/9020158" + }, + { + "accession": "8083241", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/8083241" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Butenas2004_BloodCoagulation", + "publication": { + "accession": "15039440", + "affiliation": "Department of Biochemistry, University of Vermont, Burlington, Vermont 05405-0068, USA.", + "authors": [ + { + "institution": "Department of Biochemistry, University of Vermont, Burlington, Vermont 05405-0068, USA.", + "name": "Saulius Butenas" + }, + { + "name": "Thomas Orfeo" + }, + { + "name": "Matthew T Gissel" + }, + { + "name": "Kathleen E Brummel" + }, + { + "name": "Kenneth G Mann" + } + ], + "issue": "22", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/15039440", + "month": "5", + "pages": "22875-22882", + "synopsis": "The presence of activation peptides (AP) of the vitamin K-dependent proteins in the phlebotomy blood of human subjects suggests that active serine proteases may circulate in blood as well. The goal of the current study was to evaluate the influence of trace amounts of key coagulation proteases on tissue factor-independent thrombin generation using three models of coagulation. With procoagulants and select coagulation inhibitors at mean physiological concentrations, concentrations of factor IXa, factor Xa, and thrombin were set either equal to those of their AP or to values that would result based upon the rates of AP/enzyme generation and steady state enzyme inhibition. In the latter case, numerical simulation predicts that sufficient thrombin to produce a solid clot would be generated in approximately 2 min. Empirical data from the synthetic plasma suggest clotting times of 3-5 min, which are similar to that observed in contact pathway-inhibited whole blood (4.3 min) initiated with the same concentrations of factors IXa and Xa and thrombin. Numerical simulations performed with the concentrations of two of the enzymes held constant and one varied suggest that the presence of any pair of enzymes is sufficient to yield rapid clot formation. Modeling of states (numerical simulation and whole blood) where only one circulating protease is present at steady state concentration shows significant thrombin generation only for factor IXa. The addition of factor Xa and thrombin has little effect (if any) on thrombin generation induced by factor IXa alone. These data indicate that 1) concentrations of active coagulation enzymes circulating in vivo are significantly lower than can be predicted from the concentrations of their AP, and 2) expected trace amounts of factor IXa can trigger thrombin generation in the absence of tissue factor.", + "title": "The significance of circulating factor IXa in blood.", + "type": "PubMed ID", + "volume": "279", + "year": 2004 + }, + "publicationId": "BIOMD0000000362", + "submissionId": "MODEL1108260005", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000363": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. 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A model describing the inactivation of factor Va by APC: bond cleavage, fragment dissociation, and product inhibition. Biochemistry 38, 21 (1999).

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inactivation of factor Va is a complex process which includes bond cleavage (at three sites) and dissociation of the A2N.A2C peptides, with intermediate activity in each species. Quantitation of the functional consequences of each step in the reaction has allowed for understanding of the presentation of disease in individuals possessing the factor V polymorphism factor VLEIDEN. APC cleavage of membrane-bound bovine factor Va (Arg306, Arg505, Arg662) leads to the dissociation of fragments of the A2 domain, residues 307-713 (A2N.A2C + A2C-peptide), leaving behind the membrane-bound A1.LC species. Evaluation of the dissociation process by light scattering yields invariant mass loss estimates as a function of APC concentration. The rate constant for A2 fragment dissociation varies with [APC], reaching a maximal value of k = 0.028 s-1, the unimolecular rate constant for A2 domain fragment dissociation. The APC binding site resides in the factor Va light chain (LC) (Kd = 7 nM), suggesting that the membrane-bound LC.A1 product would act to sequester APC. This inhibitory interaction (LC.A1.APC) is demonstrated to exist with either purified factor Va LC or the products of factor Va inactivation. Utilizing these experimental data and the reported rates of bond cleavage, binding constants, and product activity values for factor Va partial inactivation products, a model is developed which describes factor Va inactivation and accounts for the defect in factor VLEIDEN. The model accurately predicts the rates of inactivation of factor Va and factor VaLEIDEN, and the effect of product inhibition. Modeled reaction progress diagrams and activity profiles (from either factor Va or factor VaLEIDEN) are coincident with experimentally derived data, providing a mechanistic and kinetic explanation for all steps in the inactivation of normal factor Va and the pathology associated with factor VLEIDEN.", + "title": "A model describing the inactivation of factor Va by APC: bond cleavage, fragment dissociation, and product inhibition.", + "type": "PubMed ID", + "volume": "38", + "year": 1999 + }, + "publicationId": "BIOMD0000000365", + "submissionId": "MODEL1108260001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000366": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "S. Cunha Orfao, G. Jank, K. Mottaghy, S. Walcher & E. Zerz. Qualitative properties and stabilizability of a model for blood thrombin formation. Journal of Mathematical Analysis and Applications 346, 1 (2008).

We perform a qualitative analysis of a differential equation that was originally introduced by Stortelder, Hemker and Hemker to model the formation of thrombin, and discuss issues of controllability and stabilizability. 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Mathematical Analysis and Applications 2008; 346(1): 218\u2013226", + "link": "http://identifiers.org/doi/10.1016/j.jmaa.2008.05.060", + "synopsis": "We perform a qualitative analysis of a differential equation that was originally introduced by Stortelder, Hemker and Hemker to model the formation of thrombin, and discuss issues of controllability and stabilizability. Results include a general proof of convergence to equilibrium, and of local exponential stabilizability.", + "title": "Qualitative properties and stabilizability of a model for blood thrombin formation", + "type": "DOI", + "year": 2008 + }, + "publicationId": "BIOMD0000000366", + "submissionId": "MODEL1108260000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000367": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"submitter": "Michael Schubert", + "version": 2 + }, + { + "comment": "replaced by model in make framework but no changes to it", + "submitted": 1530610161, + "submitter": "administrator", + "version": 3 + }, + { + "comment": "replaced by model in make framework but no changes to it", + "submitted": 1530610161, + "submitter": "administrator", + "version": 4 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724271204, + "submitter": "Lucian Smith", + "version": 5 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "2759", + "name": "Eukaryota", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/2759" + }, + { + "accession": "MODEL1108260013", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1108260013" + }, + { + "accession": "BIOMD0000000367", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": 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"Mueller2008_ThrombinGeneration_minimal", + "publication": { + "accession": "17936855", + "affiliation": "Technical University Munich, Centre for Mathematical Sciences, Boltzmannstr. 3, D-85748 Garching/Munich, Germany. johannes.mueller@gsf.de", + "authors": [ + { + "institution": "Technical University Munich, Centre for Mathematical Sciences, Boltzmannstr. 3, D-85748 Garching/Munich, Germany. johannes.mueller@gsf.de", + "name": "Johannes M\u00fcller" + }, + { + "name": "Stefan Brandt" + }, + { + "name": "Katrin Mayerhofer" + }, + { + "name": "Thorsten Tjardes" + }, + { + "name": "Marc Maegele" + } + ], + "issue": "2", + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/17936855", + "month": "2", + "pages": "226-254", + "synopsis": "This paper focus on the quest for mechanisms that are able to create tolerance and an activation threshold in the extrinsic coagulation cascade. We propose that the interplay of coagulation inhibitor and blood flow creates threshold behavior. First we test this hypothesis in a minimal, four dimensional model. This model can be analysed by means of time scale analysis. We find indeed that only the interplay of blood flow and inhibition together are able to produce threshold behavior. The mechanism relays on a combination of raw substance supply and wash-out effect by the blood flow and a stabilization of the resting state by the inhibition. We use the insight into this minimal model to interpret the simulation results of a large model. Here, we find that the initiating steps (TF that produces together with fVII(a) factor Xa) does not exhibit threshold behavior, but the overall system does. Hence, the threshold behavior appears via the feedback loop (in that fIIa produces indirectly fXa that in turn produces fIIa again) inhibited by ATIII and blood flow.", + "title": "Tolerance and threshold in the extrinsic coagulation system.", + "type": "PubMed ID", + "volume": "211", + "year": 2008 + }, + "publicationId": "BIOMD0000000367", + "submissionId": "MODEL1108260013", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000368": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBI", + "email": "schubert@ebi.ac.uk", + "external": false, + "name": "Michael Schubert" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc14 endocycles
Vinod PK, Freire P, Rattani A, Ciliberto A, Uhlmann F, Novak B. J R Soc Interface. 2011 Aug 7;8(61):1128-41. Epub 2011 Feb 2. 21288956 ,
Abstract:
The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20-APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Channel sharing in pancreatic beta -cells revisited: enhancement of emergentbursting by noise.
De Vries G, Sherman A. J Theor Biol2000 Dec 21;207(4):513-30 11093836,
Abstract:
Secretion of insulin by electrically coupled populations of pancreatic beta-cells is governed by bursting electrical activity. Isolated beta -cells,however, exhibit atypical bursting or continuous spike activity. We studybursting as an emergent property of the population, focussing on interactionsamong the subclass of spiking cells. These are modelled by equipping the fastsubsystem with a saddle-node-loop bifurcation, which makes it monostable. Suchcells can only spike tonically or remain silent when isolated, but can beinduced to burst with weak diffusive coupling. With stronger coupling, the cellsrevert to tonic spiking. We demonstrate that the addition of noise dramaticallyincreases, via a phenomenon like stochastic resonance, the coupling range overwhich bursting is seen. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: De Vries G, Sherman A. (2000) - version01

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modeling the insulin-glucose feedback system: the significance of pulsatileinsulin secretion.
Tolic IM, Mosekilde E, Sturis J. J Theor Biol2000 Dec 7;207(3):361-75 11082306,
Abstract:
A mathematical model of the insulin-glucose feedback regulation in man is usedto examine the effects of an oscillatory supply of insulin compared to aconstant supply at the same average rate. We show that interactions between theoscillatory insulin supply and the receptor dynamics can be of minutesignificance only. It is possible, however, to interpret seemingly conflictingresults of clinical studies in terms of their different experimental conditionswith respect to the hepatic glucose release. If this release is operating nearan upper limit, an oscillatory insulin supply will be more efficient in loweringthe blood glucose level than a constant supply. If the insulin level is highenough for the hepatic release of glucose to nearly vanish, the opposite effectis observed. For insulin concentrations close to the point of inflection of theinsulin-glucose dose-response curve an oscillatory and a constant insulininfusion produce similar effects. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Tolic IM, Mosekilde E, Sturis J. (2000) - version=1.0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"institution": "The Rugjer Boskovic Institute, Zagreb, Croatia. itolic@hsph.harvard.edu", + "name": "I M Toli\u0107", + "orcid": "0000-0003-1305-7922" + }, + { + "name": "E Mosekilde", + "orcid": "0000-0001-7575-8543" + }, + { + "name": "J Sturis", + "orcid": "0000-0001-9938-5278" + } + ], + "issue": "3", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/11082306", + "month": "12", + "pages": "361-375", + "synopsis": "A mathematical model of the insulin-glucose feedback regulation in man is used to examine the effects of an oscillatory supply of insulin compared to a constant supply at the same average rate. 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For insulin concentrations close to the point of inflection of the insulin-glucose dose-response curve an oscillatory and a constant insulin infusion produce similar effects.", + "title": "Modeling the insulin-glucose feedback system: the significance of pulsatile insulin secretion.", + "type": "PubMed ID", + "volume": "207", + "year": 2000 + }, + "publicationId": "BIOMD0000000372", + "submissionId": "MODEL1006230109", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000373": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Calcium and glycolysis mediate multiple bursting modes in pancreatic islets.
Bertram R, Satin L, Zhang M, Smolen P, Sherman A. Biophys J2004 Nov;87(5):3074-87 15347584,
Abstract:
Pancreatic islets of Langerhans produce bursts of electrical activity whenexposed to stimulatory glucose levels. These bursts often have a regularrepeating pattern, with a period of 10-60 s. In some cases, however, the burstsare episodic, clustered into bursts of bursts, which we call compound bursting.Consistent with this are recordings of free Ca2+ concentration, oxygenconsumption, mitochondrial membrane potential, and intraislet glucose levelsthat exhibit very slow oscillations, with faster oscillations superimposed. Wedescribe a new mathematical model of the pancreatic beta-cell that can accountfor these multimodal patterns. The model includes the feedback of cytosolic Ca2+onto ion channels that can account for bursting, and a metabolic subsystem thatis capable of producing slow oscillations driven by oscillations in glycolysis.This slow rhythm is responsible for the slow mode of compound bursting in themodel. We also show that it is possible for glycolytic oscillations alone todrive a very slow form of bursting, which we call \"glycolytic bursting.\"Finally, the model predicts that there is bistability between stationary andoscillatory glycolysis for a range of parameter values. We provide experimentalsupport for this model prediction. Overall, the model can account for adiversity of islet behaviors described in the literature over the past 20 years.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Satin L, Zhang M, Smolen P, Sherman A. (2004) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"Department of Mathematics and Programs in Neuroscience and Molecular Biophysics, Florida State University, Tallahassee, Florida, USA. bertram@math.fsu.edu", + "name": "Richard Bertram", + "orcid": "0000-0001-8577-2592" + }, + { + "name": "Leslie Satin" + }, + { + "name": "Min Zhang" + }, + { + "name": "Paul Smolen" + }, + { + "name": "Arthur Sherman", + "orcid": "0000-0002-8372-5941" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/15347584", + "month": "11", + "pages": "3074-3087", + "synopsis": "Pancreatic islets of Langerhans produce bursts of electrical activity when exposed to stimulatory glucose levels. These bursts often have a regular repeating pattern, with a period of 10-60 s. In some cases, however, the bursts are episodic, clustered into bursts of bursts, which we call compound bursting. Consistent with this are recordings of free Ca2+ concentration, oxygen consumption, mitochondrial membrane potential, and intraislet glucose levels that exhibit very slow oscillations, with faster oscillations superimposed. We describe a new mathematical model of the pancreatic beta-cell that can account for these multimodal patterns. The model includes the feedback of cytosolic Ca2+ onto ion channels that can account for bursting, and a metabolic subsystem that is capable of producing slow oscillations driven by oscillations in glycolysis. This slow rhythm is responsible for the slow mode of compound bursting in the model. We also show that it is possible for glycolytic oscillations alone to drive a very slow form of bursting, which we call \"glycolytic bursting.\" Finally, the model predicts that there is bistability between stationary and oscillatory glycolysis for a range of parameter values. We provide experimental support for this model prediction. Overall, the model can account for a diversity of islet behaviors described in the literature over the past 20 years.", + "title": "Calcium and glycolysis mediate multiple bursting modes in pancreatic islets.", + "type": "PubMed ID", + "volume": "87", + "year": 2004 + }, + "publicationId": "BIOMD0000000373", + "submissionId": "MODEL1006230042", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000374": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A role for calcium release-activated current (CRAC) in cholinergic modulation ofelectrical activity in pancreatic beta-cells.
Bertram R, Smolen P, Sherman A, Mears D, Atwater I, Martin F, Soria B. Biophys J1995 Jun;68(6):2323-32 7647236,
Abstract:
S. Bordin and colleagues have proposed that the depolarizing effects ofacetylcholine and other muscarinic agonists on pancreatic beta-cells aremediated by a calcium release-activated current (CRAC). We support thishypothesis with additional data, and present a theoretical model which accountsfor most known data on muscarinic effects. Additional phenomena, such as thebiphasic responses of beta-cells to changes in glucose concentration and thedepolarizing effects of the sarco-endoplasmic reticulum calcium ATPase pumppoison thapsigargin, are also accounted for by our model. The ability of thissingle hypothesis, that CRAC is present in beta-cells, to explain so manyphenomena motivates a more complete characterization of this current.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Smolen P, Sherman A, Mears D, Atwater I, Martin F, Soria B. (1995) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Bordin and colleagues have proposed that the depolarizing effects of acetylcholine and other muscarinic agonists on pancreatic beta-cells are mediated by a calcium release-activated current (CRAC). We support this hypothesis with additional data, and present a theoretical model which accounts for most known data on muscarinic effects. Additional phenomena, such as the biphasic responses of beta-cells to changes in glucose concentration and the depolarizing effects of the sarco-endoplasmic reticulum calcium ATPase pump poison thapsigargin, are also accounted for by our model. The ability of this single hypothesis, that CRAC is present in beta-cells, to explain so many phenomena motivates a more complete characterization of this current.", + "title": "A role for calcium release-activated current (CRAC) in cholinergic modulation of electrical activity in pancreatic beta-cells.", + "type": "PubMed ID", + "volume": "68", + "year": 1995 + }, + "publicationId": "BIOMD0000000374", + "submissionId": "MODEL1006230051", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000375": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Evidence that calcium release-activated current mediates the biphasic electricalactivity of mouse pancreatic beta-cells.
Mears D, Sheppard NF Jr, Atwater I, Rojas E, Bertram R, Sherman A. J Membr Biol1997 Jan 1;155(1):47-59 9002424,
Abstract:
The electrical response of pancreatic beta-cells to step increases in glucoseconcentration is biphasic, consisting of a prolonged depolarization with actionpotentials (Phase 1) followed by membrane potential oscillations known asbursts. We have proposed that the Phase 1 response results from the combineddepolarizing influences of potassium channel closure and an inward, nonselectivecation current (ICRAN) that activates as intracellular calcium stores emptyduring exposure to basal glucose (Bertram et al., 1995). The stores refillduring Phase 1, deactivating ICRAN and allowing steady-state bursting tocommence. We support this hypothesis with additional simulations andexperimental results indicating that Phase 1 duration is sensitive to thefilling state of intracellular calcium stores. First, the duration of the Phase1 transient increases with duration of prior exposure to basal (2.8 mM) glucose,reflecting the increased time required to fill calcium stores that have beenemptying for longer periods. Second, Phase 1 duration is reduced when islets areexposed to elevated K+ to refill calcium stores in the presence of basalglucose. Third, when extracellular calcium is removed during the basal glucoseexposure to reduce calcium influx into the stores, Phase 1 duration increases.Finally, no Phase 1 is observed following hyperpolarization of the beta-cellmembrane with diazoxide in the continued presence of 11 mm glucose, a conditionin which intracellular calcium stores remain full. Application of carbachol toempty calcium stores during basal glucose exposure did not increase Phase 1duration as the model predicts. Despite this discrepancy, the good agreementbetween most of the experimental results and the model predictions providesevidence that a calcium release-activated current mediates the Phase 1electrical response of the pancreatic beta-cell.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Mears D, Sheppard NF Jr, Atwater I, Rojas E, Bertram R, Sherman A. (1997) - version=1.0
The original CellML model was created by:
Tessa Paris
tpar054@aucklanduni.ac.uk
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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], + "issue": "1", + "journal": "The Journal of membrane biology", + "link": "http://identifiers.org/pubmed/9002424", + "month": "1", + "pages": "47-59", + "synopsis": "The electrical response of pancreatic beta-cells to step increases in glucose concentration is biphasic, consisting of a prolonged depolarization with action potentials (Phase 1) followed by membrane potential oscillations known as bursts. We have proposed that the Phase 1 response results from the combined depolarizing influences of potassium channel closure and an inward, nonselective cation current (ICRAN) that activates as intracellular calcium stores empty during exposure to basal glucose (Bertram et al., 1995). The stores refill during Phase 1, deactivating ICRAN and allowing steady-state bursting to commence. We support this hypothesis with additional simulations and experimental results indicating that Phase 1 duration is sensitive to the filling state of intracellular calcium stores. First, the duration of the Phase 1 transient increases with duration of prior exposure to basal (2.8 mM) glucose, reflecting the increased time required to fill calcium stores that have been emptying for longer periods. Second, Phase 1 duration is reduced when islets are exposed to elevated K+ to refill calcium stores in the presence of basal glucose. Third, when extracellular calcium is removed during the basal glucose exposure to reduce calcium influx into the stores, Phase 1 duration increases. Finally, no Phase 1 is observed following hyperpolarization of the beta-cell membrane with diazoxide in the continued presence of 11 mm glucose, a condition in which intracellular calcium stores remain full. Application of carbachol to empty calcium stores during basal glucose exposure did not increase Phase 1 duration as the model predicts. Despite this discrepancy, the good agreement between most of the experimental results and the model predictions provides evidence that a calcium release-activated current mediates the Phase 1 electrical response of the pancreatic beta-cell.", + "title": "Evidence that calcium release-activated current mediates the biphasic electrical activity of mouse pancreatic beta-cells.", + "type": "PubMed ID", + "volume": "155", + "year": 1997 + }, + "publicationId": "BIOMD0000000375", + "submissionId": "MODEL1006230074", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000376": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the model described in the article:
Interaction of glycolysis and mitochondrial respiration in metabolic oscillations of pancreatic islets.
Bertram R, Satin LS, Pedersen MG, Luciani DS, Sherman A. Biophys J. 2007 Mar 1;92(5):1544-55. Pubmed ID: 17172305, doi: 10.1529/biophysj.106.097154.
Abstract:
Insulin secretion from pancreatic beta-cells is oscillatory, with a typical period of 2-7 min, reflecting oscillations in membrane potential and the cytosolic Ca(2+) concentration. Our central hypothesis is that the slow 2-7 min oscillations are due to glycolytic oscillations, whereas faster oscillations that are superimposed are due to Ca(2+) feedback onto metabolism or ion channels. We extend a previous mathematical model based on this hypothesis to include a more detailed description of mitochondrial metabolism. We demonstrate that this model can account for typical oscillatory patterns of membrane potential and Ca(2+) concentration in islets. It also accounts for temporal data on oxygen consumption in islets. A recent challenge to the notion that glycolytic oscillations drive slow Ca(2+) oscillations in islets are data showing that oscillations in Ca(2+), mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization. We demonstrate that these data are in fact compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity. Finally, we use the model to address the recent finding that the activity of islets from some mice is uniformly fast, whereas that from islets of other mice is slow. We propose a mechanism for this dichotomy.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram, Satin, Pedersen, Luciani, Sherman, 2007 version 02
The original CellML model was created and curated by:
Catherine May Lloyd
c.lloyd(at)auckland.ac.nz
The University of Auckland, Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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bertram@math.fsu.edu", + "name": "Richard Bertram", + "orcid": "0000-0001-8577-2592" + }, + { + "name": "Leslie S Satin" + }, + { + "name": "Morten Gram Pedersen", + "orcid": "0000-0002-2639-2394" + }, + { + "name": "Dan S Luciani", + "orcid": "0000-0002-4318-4159" + }, + { + "name": "Arthur Sherman", + "orcid": "0000-0002-8372-5941" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/17172305", + "month": "3", + "pages": "1544-1555", + "synopsis": "Insulin secretion from pancreatic beta-cells is oscillatory, with a typical period of 2-7 min, reflecting oscillations in membrane potential and the cytosolic Ca(2+) concentration. Our central hypothesis is that the slow 2-7 min oscillations are due to glycolytic oscillations, whereas faster oscillations that are superimposed are due to Ca(2+) feedback onto metabolism or ion channels. We extend a previous mathematical model based on this hypothesis to include a more detailed description of mitochondrial metabolism. We demonstrate that this model can account for typical oscillatory patterns of membrane potential and Ca(2+) concentration in islets. It also accounts for temporal data on oxygen consumption in islets. A recent challenge to the notion that glycolytic oscillations drive slow Ca(2+) oscillations in islets are data showing that oscillations in Ca(2+), mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization. We demonstrate that these data are in fact compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity. Finally, we use the model to address the recent finding that the activity of islets from some mice is uniformly fast, whereas that from islets of other mice is slow. We propose a mechanism for this dichotomy.", + "title": "Interaction of glycolysis and mitochondrial respiration in metabolic oscillations of pancreatic islets.", + "type": "PubMed ID", + "volume": "92", + "year": 2007 + }, + "publicationId": "BIOMD0000000376", + "submissionId": "MODEL7889980156", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000377": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
The phantom burster model for pancreatic beta-cells.
Bertram R, Previte J, Sherman A, Kinard TA, Satin LS. Biophys J2000 Dec;79(6):2880-92 11106596,
Abstract:
Pancreatic beta-cells exhibit bursting oscillations with a wide range ofperiods. Whereas periods in isolated cells are generally either a few seconds ora few minutes, in intact islets of Langerhans they are intermediate (10-60 s).We develop a mathematical model for beta-cell electrical activity capable ofgenerating this wide range of bursting oscillations. Unlike previous models,bursting is driven by the interaction of two slow processes, one with arelatively small time constant (1-5 s) and the other with a much larger timeconstant (1-2 min). Bursting on the intermediate time scale is generated withoutneed for a slow process having an intermediate time constant, hence phantombursting. The model suggests that isolated cells exhibiting a fast pattern maynonetheless possess slower processes that can be brought out by injectingsuitable exogenous currents. Guided by this, we devise an experimental protocolusing the dynamic clamp technique that reliably elicits islet-like, mediumperiod oscillations from isolated cells. Finally, we show that strong electricalcoupling between a fast burster and a slow burster can produce synchronizedmedium bursting, suggesting that islets may be composed of cells that areintrinsically either fast or slow, with few or none that are intrinsicallymedium.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Previte J, Sherman A, Kinard TA, Satin LS. (2000) - version02

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"11106596", + "affiliation": "School of Science, Pennsylvania State University, Erie, Pennsylvania 16563, USA. bertram@sb.fsu.edu", + "authors": [ + { + "institution": "Department of Mathematics and Programs in Neuroscience and Molecular Biophysics, Florida State University, Tallahassee, Florida, USA. bertram@math.fsu.edu", + "name": "R Bertram", + "orcid": "0000-0001-8577-2592" + }, + { + "name": "J Previte" + }, + { + "name": "A Sherman" + }, + { + "name": "T A Kinard" + }, + { + "name": "L S Satin" + } + ], + "issue": "6", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/11106596", + "month": "12", + "pages": "2880-2892", + "synopsis": "Pancreatic beta-cells exhibit bursting oscillations with a wide range of periods. Whereas periods in isolated cells are generally either a few seconds or a few minutes, in intact islets of Langerhans they are intermediate (10-60 s). We develop a mathematical model for beta-cell electrical activity capable of generating this wide range of bursting oscillations. Unlike previous models, bursting is driven by the interaction of two slow processes, one with a relatively small time constant (1-5 s) and the other with a much larger time constant (1-2 min). Bursting on the intermediate time scale is generated without need for a slow process having an intermediate time constant, hence phantom bursting. The model suggests that isolated cells exhibiting a fast pattern may nonetheless possess slower processes that can be brought out by injecting suitable exogenous currents. Guided by this, we devise an experimental protocol using the dynamic clamp technique that reliably elicits islet-like, medium period oscillations from isolated cells. Finally, we show that strong electrical coupling between a fast burster and a slow burster can produce synchronized medium bursting, suggesting that islets may be composed of cells that are intrinsically either fast or slow, with few or none that are intrinsically medium.", + "title": "The phantom burster model for pancreatic beta-cells.", + "type": "PubMed ID", + "volume": "79", + "year": 2000 + }, + "publicationId": "BIOMD0000000377", + "submissionId": "MODEL0911270001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000378": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Effects of extracellular calcium on electrical bursting and intracellular and luminal calcium oscillations in insulin secreting pancreatic beta-cells.
Chay TR Biophys J.1997 Sep;73(3):1673-88. 9284334,
Abstract:
The extracellular calcium concentration has interesting effects on bursting of pancreatic beta-cells. The mechanism underlying the extracellular Ca2+ effect is not well understood. By incorporating a low-threshold transient inward current to the store-operated bursting model of Chay, this paper elucidates the role of the extracellular Ca2+ concentration in influencing electrical activity, intracellular Ca2+ concentration, and the luminal Ca2+ concentration in the intracellular Ca2+ store. The possibility that this inward current is a carbachol-sensitive and TTX-insensitive Na+ current discovered by others is discussed. In addition, this paper explains how these three variables respond when various pharmacological agents are applied to the store-operated model.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Chay TR (1997) - version05
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The mechanism underlying the extracellular Ca2+ effect is not well understood. By incorporating a low-threshold transient inward current to the store-operated bursting model of Chay, this paper elucidates the role of the extracellular Ca2+ concentration in influencing electrical activity, intracellular Ca2+ concentration, and the luminal Ca2+ concentration in the intracellular Ca2+ store. The possibility that this inward current is a carbachol-sensitive and TTX-insensitive Na+ current discovered by others is discussed. In addition, this paper explains how these three variables respond when various pharmacological agents are applied to the store-operated model.", + "title": "Effects of extracellular calcium on electrical bursting and intracellular and luminal calcium oscillations in insulin secreting pancreatic beta-cells.", + "type": "PubMed ID", + "volume": "73", + "year": 1997 + }, + "publicationId": "BIOMD0000000378", + "submissionId": "MODEL0491199816", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000379": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Meal simulation model of the glucose-insulin system.
Dalla Man C, Rizza RA, Cobelli C.IEEE Trans Biomed Eng.2007 Oct;54(10):1740-9. 17926672,
Abstract:
A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"10", + "pages": "1740-1749", + "synopsis": "A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes.", + "title": "Meal simulation model of the glucose-insulin system.", + "type": "PubMed ID", + "volume": "54", + "year": 2007 + }, + "publicationId": "BIOMD0000000379", + "submissionId": "MODEL1110030000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000380": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae.
Smallbone K, Malys N, Messiha HL, Wishart JA, Simeonidis E. Methods Enzymol. 2011;500:355-70. 21943906 ,
Abstract:
In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed.

This SBML model is made available under the Creative Commons Attribution-Share Alike 3.0 Unported Licence (see www.creativecommons.org ).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modelling the onset of Type 1 diabetes: can impaired macrophage phagocytosis make the difference between health and disease?
Maree AF, Kublik R, Finegood DT, Edelstein-Keshet L.Philos Transact A Math Phys Eng Sci.2006 May 15;364(1842):1267-82. 16608707,
Abstract:
A wave of apoptosis (programmed cell death) occurs normally in pancreatic beta-cells of newborn mice. We previously showed that macrophages from non-obese diabetic (NOD) mice become activated more slowly and engulf apoptotic cells at a lower rate than macrophages from control (Balb/c) mice. It has been hypothesized that this low clearance could result in secondary necrosis, escalating inflammation and self-antigen presentation that later triggers autoimmune, Type 1 diabetes (T1D). We here investigate whether this hypothesis could offer a reasonable and parsimonious explanation for onset of T1D in NOD mice. We quantify variants of the Copenhagen model (Freiesleben De Blasio et al. 1999 Diabetes 48, 1677), based on parameters from NOD and Balb/c experimental data. We show that the original Copenhagen model fails to explain observed phenomena within a reasonable range of parameter values, predicting an unrealistic all-or-none disease occurrence for both strains. However, if we take into account that, in general, activated macrophages produce harmful cytokines only when engulfing necrotic (but not apoptotic) cells, then the revised model becomes qualitatively and quantitatively reasonable. Further, we show that known differences between NOD and Balb/c mouse macrophage kinetics are large enough to account for the fact that an apoptotic wave can trigger escalating inflammatory response in NOD, but not Balb/c mice. In Balb/c mice, macrophages clear the apoptotic wave so efficiently, that chronic inflammation is prevented.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Biology/Bioinformatics, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands. a.f.m.maree@bio.uu.nl", + "authors": [ + { + "institution": "Theoretical Biology/Bioinformatics, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands. a.f.m.maree@bio.uu.nl", + "name": "Athanasius F M Mar\u00e9e", + "orcid": "0000-0003-2689-2484" + }, + { + "name": "Richard Kublik", + "orcid": "0000-0003-2266-5698" + }, + { + "name": "Diane T Finegood" + }, + { + "name": "Leah Edelstein-Keshet" + } + ], + "issue": "1842", + "journal": "Philosophical transactions. Series A, Mathematical, physical, and engineering sciences", + "link": "http://identifiers.org/pubmed/16608707", + "month": "5", + "pages": "1267-1282", + "synopsis": "A wave of apoptosis (programmed cell death) occurs normally in pancreatic beta-cells of newborn mice. We previously showed that macrophages from non-obese diabetic (NOD) mice become activated more slowly and engulf apoptotic cells at a lower rate than macrophages from control (Balb/c) mice. It has been hypothesized that this low clearance could result in secondary necrosis, escalating inflammation and self-antigen presentation that later triggers autoimmune, Type 1 diabetes (T1D). We here investigate whether this hypothesis could offer a reasonable and parsimonious explanation for onset of T1D in NOD mice. We quantify variants of the Copenhagen model (Freiesleben De Blasio et al. 1999 Diabetes 48, 1677), based on parameters from NOD and Balb/c experimental data. We show that the original Copenhagen model fails to explain observed phenomena within a reasonable range of parameter values, predicting an unrealistic all-or-none disease occurrence for both strains. However, if we take into account that, in general, activated macrophages produce harmful cytokines only when engulfing necrotic (but not apoptotic) cells, then the revised model becomes qualitatively and quantitatively reasonable. Further, we show that known differences between NOD and Balb/c mouse macrophage kinetics are large enough to account for the fact that an apoptotic wave can trigger escalating inflammatory response in NOD, but not Balb/c mice. In Balb/c mice, macrophages clear the apoptotic wave so efficiently, that chronic inflammation is prevented.", + "title": "Modelling the onset of Type 1 diabetes: can impaired macrophage phagocytosis make the difference between health and disease?", + "type": "PubMed ID", + "volume": "364", + "year": 2006 + }, + "publicationId": "BIOMD0000000381", + "submissionId": "MODEL1110070000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000382": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "

\t This a model from the article:\t
Computer model for mechanisms underlying ultradian oscillations of insulin and glucose.\t
\t Sturis J, Polonsky KS, Mosekilde E, Van Cauter E.\t Am J Physiol.1991 May;260(5 Pt 1):E801-9.\t 2035636,\t
Abstract:
Oscillations in human insulin secretion have been observed in two distinct period ranges, 10-15 min (i.e. rapid) and 100-150 min (i.e., ultradian). The cause of the ultradian oscillations remains to be elucidated. To determine whether the oscillations could result from the feedback loops between insulin and glucose, a parsimonious mathematical model including the major mechanisms involved in glucose regulation was developed. This model comprises two major negative feedback loops describing the effects of insulin on glucose utilization and glucose production, respectively, and both loops include the stimulatory effect of glucose on insulin secretion. Model formulations and parameters are representative of results from published clinical investigations. The occurrence of sustained insulin and glucose oscillations was found to be dependent on two essential features: 1) a time delay of 30-45 min for the effect of insulin on glucose production and 2) a sluggish effect of insulin on glucose utilization, because insulin acts from a compartment remote from plasma. When these characteristics were incorporated in the model, numerical simulations mimicked all experimental findings so far observed for these ultradian oscillations, including 1) self-sustained oscillations during constant glucose infusion at various rates; 2) damped oscillations after meal or oral glucose ingestion; 3) increased amplitude of oscillation after increased stimulation of insulin secretion, without change in frequency; and 4) slight advance of the glucose oscillation compared with the insulin oscillation.(ABSTRACT TRUNCATED AT 250 WORDS)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"institution": "Department of Medicine, University of Chicago, Pritzker School of Medicine, Illinois 60637.", + "name": "J Sturis" + }, + { + "name": "K S Polonsky" + }, + { + "name": "E Mosekilde" + }, + { + "name": "E Van Cauter" + } + ], + "issue": "5 Pt 1", + "journal": "The American journal of physiology", + "link": "http://identifiers.org/pubmed/2035636", + "month": "5", + "pages": "E801-9", + "synopsis": "Oscillations in human insulin secretion have been observed in two distinct period ranges, 10-15 min (i.e. rapid) and 100-150 min (i.e., ultradian). The cause of the ultradian oscillations remains to be elucidated. To determine whether the oscillations could result from the feedback loops between insulin and glucose, a parsimonious mathematical model including the major mechanisms involved in glucose regulation was developed. This model comprises two major negative feedback loops describing the effects of insulin on glucose utilization and glucose production, respectively, and both loops include the stimulatory effect of glucose on insulin secretion. Model formulations and parameters are representative of results from published clinical investigations. The occurrence of sustained insulin and glucose oscillations was found to be dependent on two essential features: 1) a time delay of 30-45 min for the effect of insulin on glucose production and 2) a sluggish effect of insulin on glucose utilization, because insulin acts from a compartment remote from plasma. 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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Farquhar et al. (1980, DOI:10.1007/BF00386231 ).

The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ).

The parameter values are widely taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713) . A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Schultz (2003, DOI:10.1071/FP02146 ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Sharkey et al. (2007, DOI:10.1111/j.1365-3040.2007.01710.x ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Damour and Urban (2007, [for PDF click here] ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Urban et al. (2003, DOI:10.1093/treephys/23.5.289 ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle by Zhu et al. (2009, DOI:10.1016/j.nonrwa.2008.01.021 ).

The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ).A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis by Hahn (1986, [click here for abstract] ).

The parameter values are taken from Hahn (1984, [click here for abstract] ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle by Giersch et al. (1990, DOI:10.1007/BF00032595).

The parameter values are taken from Figure 4 and 5. The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathway to starch synthesis by Poolman et al. (2000, DOI:10.1093/jexbot/51.suppl_1.319).

The parameter values are widely taken from Pettersson and Ryde-Pettersson (1988, DOI:10.1111/j.1432-1033.1988.tb14242.x) and Poolman (1999, [click here for PDF]). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis by Laisk et al. (2006, DOI:10.1007/s11120-006-9109-1) and the personally provided implementation to Laisk et al. (2009, DOI:10.1007/978-1-4020-9237-4_13).

A reduced version of the published model is implemented (light-dependent reactions are taken out). The parameter values are widely taken from Laisk et al. (1989, [click here for PDF]). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
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The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.", + "title": "A quantitative comparison of Calvin-Benson cycle models.", + "type": "PubMed ID", + "volume": "16", + "year": 2011 + }, + "publicationId": "BIOMD0000000392", + "submissionId": "MODEL1109270008", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000393": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max-Planck-Institute of Molecular Plant Physiology", + "email": "arnold@mpimp-golm.mpg.de", + "external": false, + "name": "Anne Arnold" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis and photorespiration by Zhu et al. (2007, DOI:10.1104/pp.107.103713) and the personally provided implementation.

The parameter values are partly taken from Pettersson and Ryde-Pettersson (1988, DOI:10.1111/j.1432-1033.1988.tb14242.x). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
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\t Sivakumar2011 - EGF Receptor Signaling Pathway\t
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EGFR belongs to the human epidermal receptor (HER) family of receptor tyrosine kinases, which consists of four closely related receptors (EGFR (HER1, erbB1), HER2 (neu, erbB2), HER3 (erbB3), and HER4 (erbB4)) that mediate cellular signaling pathways involved in growth and proliferation in response to the binding of a variety of growth factor ligands. There are currently six known endogenous ligands for EGFR: EGF, transforming growth factor- (TGF-), amphiregulin, betacellulin, heparin-binding EGF (HB-EGF), and epiregulin.Upon ligand binding, the EGFR forms homo- or heterodimeric complexes (usually with HER2), which leads to activation of the receptor tyrosine kinase, via autophosphorylation.

References:

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This model is described in the article:

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Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
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Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
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Abstract:

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The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

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This model is hosted on BioModels Database and identified by: BIOMD0000000394.

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To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

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Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. 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To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.", + "title": "A systems biology approach to model neural stem cell regulation by notch, shh, wnt, and EGF signaling pathways.", + "type": "PubMed ID", + "volume": "15", + "year": 2011 + }, + "publicationId": "BIOMD0000000394", + "submissionId": "MODEL1101270003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000395": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Rajiv Gandhi Centre for Biotechnology", + "email": "sivakumar.kc@gmail.com", + "external": false, + "name": "KC Sivakumar" + } + ] + }, + "curationStatus": "CURATED", + "description": " \t
\t Sivakumar2011 - Hedgehog Signaling Pathway\t
\t
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This is the current model for the Hedgehog signaling pathway. The best data for mechanism of signaling has been worked out in Drosophila, so this model is based largely on Drosophila data. Hedgehog target genes vary from tissue to tissue, so the identities of individual target genes have not been listed. The main difference between the Drosophila and mammalian Hedgehog signaling pathways is the fact that there are three mammalian homologs of Cubitus interruptus, Gli1 Gli2 and Gli3. Some or all of the mammalian homologs may be proteolytically processed, but the data are controversial. There are two mammalian Ptc genes and three mammalian Hedgehog genes as well. The pathway for Sonic Hedgehog appears to be most similar to the Drosophila hedgehog pathway.

References:

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This model is described in the article:

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Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
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Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
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Abstract:

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The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

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\t
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This model is hosted on BioModels Database and identified by: BIOMD0000000395.

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To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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\t Sivakumar2011 - Notch Signaling Pathway\t
\t
\t

Notch is a transmembrane receptor that mediates local cell-cell communication and coordinates a signaling cascade. It plays a key role in modulating cell fate decisions throughout the development of invertebrate and vertebrate species and the misregulation leads to a number of human diseases.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000396.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Sivakumar2011_WntSignalingPathway
The secreted protein Wnt activates theheptahelical receptor Frizzled on nieghboring cells. Activation ofFrizzled causes the recruitment of additional membrane proteinswhich in turn result in 1) the activation of the proteinDishevelled via phosphorylation and 2) the activation of aheterotrimeric G protein of unknown type. Activation of Dishevelledresults in the down-regulation of the Beta-Catenin destructioncomplex which causes ubiquitination of Beta-Catenin and itsultimate degradation via the proteasome. Inhibition of theBeta-Catenin destruction complex yields a higher cytosolicconcentration of Beta-Catenin, which enters the nucleus, bindsvarious transcriptional regulatory molecules including the TCF/LEFclass of proteins, and results in the transcription of TCF/LEFtarget genes. Activation of the heterotrimeric G-protein pathway inturn activates Phospholipase C which in turn catalyzes thecatalysis of PI(4,5)P2 into DAG and IP3.

Reference:

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
OMICS 2011 Oct; 15(10): 729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000397.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Thiruvananthapuram, Kerala, India.", + "authors": [ + { + "institution": "Bioinformatics Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India.", + "name": "Krishnankutty Chandrika Sivakumar" + }, + { + "name": "Sivadasan Bindu Dhanesh" + }, + { + "name": "Sekar Shobana" + }, + { + "name": "Jackson James", + "orcid": "0000-0002-7038-2687" + }, + { + "name": "Sathish Mundayoor" + } + ], + "issue": "10", + "journal": "Omics : a journal of integrative biology", + "link": "http://identifiers.org/pubmed/21978399", + "month": "10", + "pages": "729-737", + "synopsis": "The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.", + "title": "A systems biology approach to model neural stem cell regulation by notch, shh, wnt, and EGF signaling pathways.", + "type": "PubMed ID", + "volume": "15", + "year": 2011 + }, + "publicationId": "BIOMD0000000397", + "submissionId": "MODEL1101270002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000398": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sivakumar2011_NeuralStemCellDifferentiation_Crosstalk
This model is generated by integrating BIOMD0000000394(EGFR), BIOMD0000000395(Hedgehog), BIOMD0000000396(Notch) and BIOMD0000000397(Wnt), to investigate the signalling crosstalk between the fourpathways.

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
OMICS 2011 Oct; 15(10): 729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000398.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"publication": { + "accession": "21978399", + "affiliation": "Bioinformatics Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India.", + "authors": [ + { + "institution": "Bioinformatics Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, India.", + "name": "Krishnankutty Chandrika Sivakumar" + }, + { + "name": "Sivadasan Bindu Dhanesh" + }, + { + "name": "Sekar Shobana" + }, + { + "name": "Jackson James", + "orcid": "0000-0002-7038-2687" + }, + { + "name": "Sathish Mundayoor" + } + ], + "issue": "10", + "journal": "Omics : a journal of integrative biology", + "link": "http://identifiers.org/pubmed/21978399", + "month": "10", + "pages": "729-737", + "synopsis": "The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.", + "title": "A systems biology approach to model neural stem cell regulation by notch, shh, wnt, and EGF signaling pathways.", + "type": "PubMed ID", + "volume": "15", + "year": 2011 + }, + "publicationId": "BIOMD0000000398", + "submissionId": "MODEL1111020000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000399": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA", + "email": "jenkinson@jhu.edu", + "external": false, + "name": "Garrett Jenkinson" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is a model described in the article:
Thermodynamically Consistent Model Calibration in Chemical Kinetics.
Garrett Jenkinson and John Goutsias, BMC Systems Biology 2011 May 6;5(1):64.; PMID:21548948.

ABSTRACT:
BACKGROUND:
The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function.
RESULTS:
We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database.
CONCLUSIONS:
TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.

This model is a thermodynamically feasible version of a previous modelin the BioModels database,BIOMD0000000019, described in Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Schoeberl et al (2002), PMID:11923843.
The only difference between the present model and the model listed under BIOMD0000000019 are the values of the parameters.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. goutsias@jhu.edu", + "authors": [ + { + "institution": "Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. goutsias@jhu.edu", + "name": "Garrett Jenkinson" + }, + { + "name": "John Goutsias" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/21548948", + "month": "5", + "pages": "64", + "synopsis": "

Background

The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function.

Results

We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from http://www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database.

Conclusions

TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.", + "title": "Thermodynamically consistent model calibration in chemical kinetics.", + "type": "PubMed ID", + "volume": "5", + "year": 2011 + }, + "publicationId": "BIOMD0000000399", + "submissionId": "MODEL1105060003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000400": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Modeling hypertrophic IP3 transients in the cardiac myocyte.
Cooling M, Hunter P, Crampin EJ. Biophys J2007 Nov 15;93(10):3421-33 17693463,
Abstract:
Cardiac hypertrophy is a known risk factor for heart disease, and at thecellular level is caused by a complex interaction of signal transductionpathways. The IP3-calcineurin pathway plays an important role in stimulating thetranscription factor NFAT which binds to DNA cooperatively with otherhypertrophic transcription factors. Using available kinetic data, we construct amathematical model of the IP3 signal production system after stimulation by ahypertrophic alpha-adrenergic agonist (endothelin-1) in the mouse atrial cardiacmyocyte. We use a global sensitivity analysis to identify key controllingparameters with respect to the resultant IP3 transient, including thephosphorylation of cell-membrane receptors, the ligand strength and bindingkinetics to precoupled (with G(alpha)GDP) receptor, and the kinetics associatedwith precoupling the receptors. We show that the kinetics associated with thereceptor system contribute to the behavior of the system to a great extent, withprecoupled receptors driving the response to extracellular ligand. Finally, byreparameterizing for a second hypertrophic alpha-adrenergic agonist,angiotensin-II, we show that differences in key receptor kinetic and membranedensity parameters are sufficient to explain different observed IP3 transientsin essentially the same pathway.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Cooling M, Hunter P, Crampin EJ. (2007) - version02
The original CellML model was created by:
Cooling, Mike,
m.cooling@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model without Tumour.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

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Background

Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.

Results

Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.

Conclusions

The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Reviewers

This article was reviewed by Ariosto Silva and Mark P. Little.", + "title": "A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease.", + "type": "PubMed ID", + "volume": "5", + "year": 2010 + }, + "publicationId": "BIOMD0000000401", + "submissionId": "MODEL1112060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000402": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

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Background

Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.

Results

Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.

Conclusions

The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Reviewers

This article was reviewed by Ariosto Silva and Mark P. Little.", + "title": "A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease.", + "type": "PubMed ID", + "volume": "5", + "year": 2010 + }, + "publicationId": "BIOMD0000000402", + "submissionId": "MODEL1112060001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000403": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour and Drug Treatment.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

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ayati@math.uiowa.edu", + "authors": [ + { + "institution": "Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA. ayati@math.uiowa.edu", + "name": "Bruce P Ayati", + "orcid": "0000-0002-4472-6271" + }, + { + "name": "Claire M Edwards", + "orcid": "0000-0002-1257-5659" + }, + { + "name": "Glenn F Webb" + }, + { + "name": "John P Wikswo" + } + ], + "journal": "Biology direct", + "link": "http://identifiers.org/pubmed/20406449", + "month": "4", + "pages": "28", + "synopsis": "

Background

Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.

Results

Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.

Conclusions

The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Reviewers

This article was reviewed by Ariosto Silva and Mark P. Little.", + "title": "A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease.", + "type": "PubMed ID", + "volume": "5", + "year": 2010 + }, + "publicationId": "BIOMD0000000403", + "submissionId": "MODEL1112060002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000404": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This version of the model is very close to the version described in the paper with one exception: the binding of aspartate to the various receptor complexes, as well as the formation of the different complexes are modeled using chemical kinetics (mass action law), rather than instant equilibrium. The qualitative behaviour of the model is unchanged. Note that in order to quantitatively replicate the figure 8b, and in particular to have a basal bias of 0.7, we have to change the rate constant of the aspartate-triggered dephosphorylation of CheY from 59000 to 70000. The peaks have then slightly different values.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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chemotactic response of Escherichia coli bacteria to the attractant aspartate and the repellent Ni2+ ions. The model is built from modular units representing the molecular components involved, which are each assigned a known value of intracellular concentration and enzymatic rate constant wherever possible. The components are linked into a network of coupled biochemical reactions based on a compilation of widely accepted mechanisms but incorporating several novel features. The computer motor shows the same pattern of runs, tumbles and pauses seen in actual bacteria and responds in the same way as living bacteria to sudden changes in concentration of aspartate or Ni2+. The simulated network accurately reproduces the phenotype of more than 30 mutants in which components of the chemotactic pathway are deleted and/or expressed in excess amounts and shows a rapidity of response to a step change in aspartate concentration similar to living bacteria. Discrepancies between the simulation and real bacteria in the phenotype of certain mutants and in the gain of the chemotactic response to aspartate suggest the existence of additional as yet unidentified interactions in the in vivo signal processing pathway.", + "title": "Computer simulation of the phosphorylation cascade controlling bacterial chemotaxis.", + "type": "PubMed ID", + "volume": "4", + "year": 1993 + }, + "publicationId": "BIOMD0000000404", + "submissionId": "MODEL6929313478", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000405": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Hasty Lab, UCSD Biology", + "email": "wmather@ucsd.edu", + "external": false, + "name": "William Mather" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Queueing up for enzymatic processing: correlated signaling through coupled degradation.
Natalie A Cookson, William H Mather, Tal Danino, Octavio Mondrag\u00f3n-Palomino, Ruth J Williams, Lev S Tsimring, & Jeff Hasty Molecular Systems Biology2011; 7:561; DOI:10.1038/msb.2011.94
Abstract:
High-throughput technologies have led to the generation of complex wiring diagrams as a post-sequencing paradigm for depicting the interactions between vast and diverse cellular species. While these diagrams are useful for analyzing biological systems on a large scale, a detailed understanding of the molecular mechanisms that underlie the observed network connections is critical for the further development of systems and synthetic biology. Here, we use queueing theory to investigate how \u2018waiting lines\u2019 can lead to correlations between protein \u2018customers\u2019 that are coupled solely through a downstream set of enzymatic \u2018servers\u2019. Using the E. coli ClpXP degradation machine as a model processing system, we observe significant cross-talk between two networks that are indirectly coupled through a common set of processors. We further illustrate the implications of enzymatic queueing using a synthetic biology application, in which two independent synthetic networks demonstrate synchronized behavior when common ClpXP machinery is overburdened. Our results demonstrate that such post-translational processes can lead to dynamic connections in cellular networks and may provide a mechanistic understanding of existing but currently inexplicable links.

Note:
Individual stochastic trajectories for a queueing system in three different conditions, 1) Underloaded, 2) Balanced and 3) Overloaded, demonstrate correlation resonance. The parameter values in this model correspond to the Balanced Condition.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Overexpression limits of fission yeast cell-cycle regulators in vivo and in silico.
Moriya H, Chino A, Kapuy O, Csik\u00e1sz-Nagy A, Nov\u00e1k B. Mol Syst Biol. 2011 Dec 6;7:556. 22146300 ,
Abstract:
Cellular systems are generally robust against fluctuations of intracellular parameters such as gene expression level. However, little is known about expression limits of genes required to halt cellular systems. In this study, using the fission yeast Schizosaccharomyces pombe, we developed a genetic 'tug-of-war' (gTOW) method to assess the overexpression limit of certain genes. Using gTOW, we determined copy number limits for 31 cell-cycle regulators; the limits varied from 1 to >100. Comparison with orthologs of the budding yeast Saccharomyces cerevisiae suggested the presence of a conserved fragile core in the eukaryotic cell cycle. Robustness profiles of networks regulating cytokinesis in both yeasts (septation-initiation network (SIN) and mitotic exit network (MEN)) were quite different, probably reflecting differences in their physiologic functions. Fragility in the regulation of GTPase spg1 was due to dosage imbalance against GTPase-activating protein (GAP) byr4. Using the gTOW data, we modified a mathematical model and successfully reproduced the robustness of the S. pombe cell cycle with the model.


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Heterogeneity Reduces Sensitivity of Cell Death for TNF-Stimuli
Schliemann M, Bullinger E, Borchers S, Allgower F, Findeisen R, Scheurich P. BMC Syst Biol. 2011 Dec 28;5(1):204. 22204418 ,
Abstract:
BACKGROUND:Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor kappaB (NF-kappaB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner.RESULTS:This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-kappaB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability.CONCLUSIONS:Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-kappaB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity.

Note: SBML model generated from Matlab system description on 12-July-2011 21:08:15 by exportSBML Copyright Eric Bullinger 2007-2011

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Schliemann", + "orcid": "0000-0002-3714-1523" + }, + { + "name": "Eric Bullinger" + }, + { + "name": "Steffen Borchers" + }, + { + "name": "Frank Allg\u00f6wer", + "orcid": "0000-0002-3702-3658" + }, + { + "name": "Rolf Findeisen", + "orcid": "0000-0002-9112-5946" + }, + { + "name": "Peter Scheurich" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/22204418", + "month": "12", + "pages": "204", + "synopsis": "

Background

Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor \u03baB (NF-\u03baB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner.

Results

This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-\u03baB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability.

Conclusions

Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-\u03baB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity.", + "title": "Heterogeneity reduces sensitivity of cell death for TNF-stimuli.", + "type": "PubMed ID", + "volume": "5", + "year": 2011 + }, + "publicationId": "BIOMD0000000407", + "submissionId": "MODEL1112210000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000408": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "VU University Amsterdam", + "email": "j.hettling@vu.nl", + "external": false, + "name": "Hannes Hettling" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
\tAnalyzing the functional properties of the creatine kinase system with multiscale 'sloppy' modeling.
Hettling H, van Beek JH PLoS Comput Biol.2011 Aug;7(8):e1002130. PMEDID,
Abstract:
In this study the function of the two isoforms of creatine kinase (CK; EC 2.7.3.2) in myocardium is investigated. The 'phosphocreatine shuttle' hypothesis states that mitochondrial and cytosolic CK plays a pivotal role in the transport of high-energy phosphate (HEP) groups from mitochondria to myofibrils in contracting muscle. Temporal buffering of changes in ATP and ADP is another potential role of CK. With a mathematical model, we analyzed energy transport and damping of high peaks of ATP hydrolysis during the cardiac cycle. The analysis was based on multiscale data measured at the level of isolated enzymes, isolated mitochondria and on dynamic response times of oxidative phosphorylation measured at the whole heart level. Using 'sloppy modeling' ensemble simulations, we derived confidence intervals for predictions of the contributions by phosphocreatine (PCr) and ATP to the transfer of HEP from mitochondria to sites of ATP hydrolysis. Our calculations indicate that only 15\u00b18% (mean\u00b1SD) of transcytosolic energy transport is carried by PCr, contradicting the PCr shuttle hypothesis. We also predicted temporal buffering capabilities of the CK isoforms protecting against high peaks of ATP hydrolysis (3750 \u00b5M*s(-1)) in myofibrils. CK inhibition by 98% in silico leads to an increase in amplitude of mitochondrial ATP synthesis pulsation from 215\u00b123 to 566\u00b131 \u00b5M*s(-1), while amplitudes of oscillations in cytosolic ADP concentration double from 77\u00b111 to 146\u00b11 \u00b5M. Our findings indicate that CK acts as a large bandwidth high-capacity temporal energy buffer maintaining cellular ATP homeostasis and reducing oscillations in mitochondrial metabolism. However, the contribution of CK to the transport of high-energy phosphate groups appears limited. Mitochondrial CK activity lowers cytosolic inorganic phosphate levels while cytosolic CK has the opposite effect.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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VU University Amsterdam, Amsterdam, The Netherlands. hettling@few.vu.nl", + "name": "Hannes Hettling" + }, + { + "name": "Johannes H G M van Beek" + } + ], + "issue": "8", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/21912519", + "month": "8", + "pages": "e1002130", + "synopsis": "In this study the function of the two isoforms of creatine kinase (CK; EC 2.7.3.2) in myocardium is investigated. The 'phosphocreatine shuttle' hypothesis states that mitochondrial and cytosolic CK plays a pivotal role in the transport of high-energy phosphate (HEP) groups from mitochondria to myofibrils in contracting muscle. Temporal buffering of changes in ATP and ADP is another potential role of CK. With a mathematical model, we analyzed energy transport and damping of high peaks of ATP hydrolysis during the cardiac cycle. The analysis was based on multiscale data measured at the level of isolated enzymes, isolated mitochondria and on dynamic response times of oxidative phosphorylation measured at the whole heart level. Using 'sloppy modeling' ensemble simulations, we derived confidence intervals for predictions of the contributions by phosphocreatine (PCr) and ATP to the transfer of HEP from mitochondria to sites of ATP hydrolysis. Our calculations indicate that only 15\u00b18% (mean\u00b1SD) of transcytosolic energy transport is carried by PCr, contradicting the PCr shuttle hypothesis. 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Mitochondrial CK activity lowers cytosolic inorganic phosphate levels while cytosolic CK has the opposite effect.", + "title": "Analyzing the functional properties of the creatine kinase system with multiscale 'sloppy' modeling.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000408", + "submissionId": "MODEL1201250000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000409": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in budding yeast.
Queralt E, Lehane C, Novak B, Uhlmann F. Cell. 2006 May 19;125(4):719-32. 16713564 ,
Abstract:
After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the \"mitotic exit network\" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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"month": "5", + "pages": "719-732", + "synopsis": "After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the \"mitotic exit network\" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast.", + "title": "Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in budding yeast.", + "type": "PubMed ID", + "volume": "125", + "year": 2006 + }, + "publicationId": "BIOMD0000000409", + "submissionId": "MODEL5951818256", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000410": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Cooperative State University Baden-Wuerttemberg, Erzberger Str. 121, D-76133 Karlsruhe, Germany", + "email": "wengler@dhbw-karlsruhe.de", + "external": false, + "name": "Katja Wengler (ne\u00e9 Wegner)" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Dynamics and feedback loops in the transforming growth factor \u03b2 signaling pathway.
Wegner K, Bachmann A, Schad JU, Lucarelli P, Sahle S, Nickel P, Meyer C, Klingm\u00fcller U, Dooley S, Kummer U. Biophys Chem. 2012 Jan 5. 22284904 ,
Abstract:
Transforming growth factor \u03b2 (TGF-\u03b2) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-\u03b2 target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-\u03b2 target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.", + "title": "Dynamics and feedback loops in the transforming growth factor \u03b2 signaling pathway.", + "type": "PubMed ID", + "volume": "162", + "year": 2012 + }, + "publicationId": "BIOMD0000000410", + "submissionId": "MODEL1202090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000411": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Dept. of Bioinformatics Friedrich Schiller University Jena", + "email": "heiland.ines@uni-jena.de", + "external": false, + "name": "Ines Heiland" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Modeling temperature entrainment of circadian clocks using the Arrhenius equation and a reconstructed model from Chlamydomonas reinhardtii
Ines Heiland, Christian Bodenstein, Thomas Hinze, Olga Weisheit, Oliver Ebenhoeh, Maria Mittag and Stefan Schuster Journal of Biological Physics 4 March 2012; pp 1-16; doi: 10.1007/s10867-012-9264-x ,
Abstract:
Endogenous circadian rhythms allow living organisms to anticipate daily variations in their natural environment. Temperature regulation and entrainment mechanisms of circadian clocks are still poorly understood. To better understand the molecular basis of these processes, we built a mathematical model based on experimental data examining temperature regulation of the circadian RNA-binding protein CHLAMY1 from the unicellular green alga Chlamydomonas reinhardtii , simulating the effect of temperature on the rates by applying the Arrhenius equation. Using numerical simulations, we demonstrate that our model is temperature-compensated and can be entrained to temperature cycles of various length and amplitude. The range of periods that allow entrainment of the model depends on the shape of the temperature cycles and is larger for sinusoidal compared to rectangular temperature curves. We show that the response to temperature of protein (de)phosphorylation rates play a key role in facilitating temperature entrainment of the oscillator in Chlamydomonas reinhardtii . We systematically investigated the response of our model to single temperature pulses to explain experimentally observed phase response curves.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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University of Norway, Troms\u00f8, Norway ines.heiland@uit.no.", + "name": "Ines Heiland", + "orcid": "0000-0002-9124-5112" + }, + { + "name": "Christian Bodenstein" + }, + { + "name": "Thomas Hinze" + }, + { + "name": "Olga Weisheit" + }, + { + "name": "Oliver Ebenhoeh" + }, + { + "name": "Maria Mittag" + }, + { + "name": "Stefan Schuster" + } + ], + "issue": "3", + "journal": "Journal of biological physics", + "link": "http://identifiers.org/pubmed/23729908", + "month": "6", + "pages": "449-464", + "synopsis": "Endogenous circadian rhythms allow living organisms to anticipate daily variations in their natural environment. Temperature regulation and entrainment mechanisms of circadian clocks are still poorly understood. To better understand the molecular basis of these processes, we built a mathematical model based on experimental data examining temperature regulation of the circadian RNA-binding protein CHLAMY1 from the unicellular green alga Chlamydomonas reinhardtii, simulating the effect of temperature on the rates by applying the Arrhenius equation. Using numerical simulations, we demonstrate that our model is temperature-compensated and can be entrained to temperature cycles of various length and amplitude. The range of periods that allow entrainment of the model depends on the shape of the temperature cycles and is larger for sinusoidal compared to rectangular temperature curves. We show that the response to temperature of protein (de)phosphorylation rates play a key role in facilitating temperature entrainment of the oscillator in Chlamydomonas reinhardtii. We systematically investigated the response of our model to single temperature pulses to explain experimentally observed phase response curves.", + "title": "Modeling temperature entrainment of circadian clocks using the Arrhenius equation and a reconstructed model from Chlamydomonas reinhardtii.", + "type": "PubMed ID", + "volume": "38", + "year": 2012 + }, + "publicationId": "BIOMD0000000411", + "submissionId": "MODEL1201130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000412": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Edinburgh University", + "email": "apokhilk@staffmail.ed.ac.uk", + "external": false, + "name": "Alexandra Pokhilko" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops
Pokhilko A, Fern\u00e1ndez AP, Edwards KD, Southern MM, Halliday KJ, Millar AJ. Mol Syst Biol.2012 Mar 6;8:574. 22395476,
Abstract:
Circadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene PSEUDO-RESPONSE REGULATOR 9 was repressed by an evening gene, previously identified with TIMING OF CAB EXPRESSION1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes LATE ELONGATED HYPOCOTYL and CIRCADIAN CLOCK ASSOCIATED1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure.

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Waddington Building, Edinburgh EH9 3JD, UK karen.halliday@ed.ac.uk.", + "name": "Karen J Halliday", + "orcid": "0000-0003-0467-104X" + }, + { + "name": "Andrew J Millar", + "orcid": "0000-0003-1756-3654" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/22395476", + "month": "3", + "pages": "574", + "synopsis": "Circadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene Pseudo-Response Regulator 9 was repressed by an evening gene, previously identified with Timing Of CAB Expression1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes Late Elongated Hypocotyl and Circadian Clock Associated1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure.", + "title": "The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000412", + "submissionId": "MODEL1109200000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000413": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Centre for Plant Integrative Biology, University of Nottingham", + "email": "leah.band@cpib.ac.uk", + "external": false, + "name": "Leah Band" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.
Band LR, Wells DM, Larrieu A, Sun J, Middleton AM, French AP, Brunoud G, Sato EM, Wilson MH, P\u00e9ret B, Oliva M, Swarup R, Sairanen I, Parry G, Ljung K, Beeckman T, Garibaldi JM, Estelle M, Owen MR, Vissenberg K, Hodgman TC, Pridmore TP, King JR, Vernoux T, Bennett MJ. Proc Natl Acad Sci U S A.2012 Mar 20;109(12):4668-73 22393022,
Abstract:
Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

This model corresponds to the full model described in the article.

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"0000-0002-8257-4088" + }, + { + "name": "Malcolm J Bennett", + "orcid": "0000-0003-0475-390X" + } + ], + "issue": "12", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/22393022", + "month": "3", + "pages": "4668-4673", + "synopsis": "Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.", + "title": "Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.", + "type": "PubMed ID", + "volume": "109", + "year": 2012 + }, + "publicationId": "BIOMD0000000413", + "submissionId": "MODEL1203230002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000414": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Centre for Plant Integrative Biology, University of Nottingham", + "email": "leah.band@cpib.ac.uk", + "external": false, + "name": "Leah Band" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.
Band LR, Wells DM, Larrieu A, Sun J, Middleton AM, French AP, Brunoud G, Sato EM, Wilson MH, P\u00e9ret B, Oliva M, Swarup R, Sairanen I, Parry G, Ljung K, Beeckman T, Garibaldi JM, Estelle M, Owen MR, Vissenberg K, Hodgman TC, Pridmore TP, King JR, Vernoux T, Bennett MJ. Proc Natl Acad Sci U S A.2012 Mar 20;109(12):4668-73 22393022,
Abstract:
Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

This model corresponds to the simplified model described in the article. It is assumed that, on the timescale of DII-VENUS degradation, the concentrations of auxin, TIR1/AFB, and their complexes can be approximated by quasi-steady-state expressions. This reduced the full model to a single ODE that describes how the DII-VENUS dynamics depend on the auxin influx and four parameter groupings.

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Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.", + "title": "Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.", + "type": "PubMed ID", + "volume": "109", + "year": 2012 + }, + "publicationId": "BIOMD0000000414", + "submissionId": "MODEL1203230001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000415": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "CPIB, The University of Nottingham", + "email": "sbxnm2@nottingham.ac.uk", + "external": false, + "name": "Nathan Mellor" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Reduction of off-flavor generation in soybean homogenates: a mathematical model.
Mellor N , Bligh F , Chandler I , Hodgman C \tJ. Food Sci.2010 Sep; 75(7): R131-8; PMID: 2153556,
Abstract:
The generation of off-flavors in soybean homogenates such as n-hexanal via the lipoxygenase (LOX) pathway can be a problem in the processed food industry. Previous studies have examined the effect of using soybean varieties missing one or more of the 3 LOX isozymes on n-hexanal generation. A dynamic mathematical model of the soybean LOX pathway using ordinary differential equations was constructed using parameters estimated from existing data with the aim of predicting how n-hexanal generation could be reduced. Time-course simulations of LOX-null beans were run and compared with experimental results. Model L(2), L(3), and L(12) beans were within the range relative to the wild type found experimentally, with L(13) and L(23) beans close to the experimental range. Model L(1) beans produced much more n-hexanal relative to the wild type than those in experiments. Sensitivity analysis indicates that reducing the estimated K(m) parameter for LOX isozyme 3 (L-3) would improve the fit between model predictions and experimental results found in the literature. The model also predicts that increasing L-3 or reducing L-2 levels within beans may reduce n-hexanal generation. PRACTICAL APPLICATION: This work describes the use of mathematics to attempt to quantify the enzyme-catalyzed conversions of compounds in soybean homogenates into undesirable flavors, primarily from the compound n-hexanal. The effect of different soybean genotypes and enzyme kinetic constants was also studied, leading to recommendations on which combinations might minimize off-flavor levels and what further work might be carried out to substantiate these conclusions.

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Unlabelled

The generation of off-flavors in soybean homogenates such as n-hexanal via the lipoxygenase (LOX) pathway can be a problem in the processed food industry. Previous studies have examined the effect of using soybean varieties missing one or more of the 3 LOX isozymes on n-hexanal generation. A dynamic mathematical model of the soybean LOX pathway using ordinary differential equations was constructed using parameters estimated from existing data with the aim of predicting how n-hexanal generation could be reduced. Time-course simulations of LOX-null beans were run and compared with experimental results. Model L(2), L(3), and L(12) beans were within the range relative to the wild type found experimentally, with L(13) and L(23) beans close to the experimental range. Model L(1) beans produced much more n-hexanal relative to the wild type than those in experiments. Sensitivity analysis indicates that reducing the estimated K(m) parameter for LOX isozyme 3 (L-3) would improve the fit between model predictions and experimental results found in the literature. The model also predicts that increasing L-3 or reducing L-2 levels within beans may reduce n-hexanal generation.

Practical application

This work describes the use of mathematics to attempt to quantify the enzyme-catalyzed conversions of compounds in soybean homogenates into undesirable flavors, primarily from the compound n-hexanal. The effect of different soybean genotypes and enzyme kinetic constants was also studied, leading to recommendations on which combinations might minimize off-flavor levels and what further work might be carried out to substantiate these conclusions.", + "title": "Reduction of off-flavor generation in soybean homogenates: a mathematical model.", + "type": "PubMed ID", + "volume": "75", + "year": 2010 + }, + "publicationId": "BIOMD0000000415", + "submissionId": "MODEL1203230000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000416": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "Daniele.Muraro@nottingham.ac.uk", + "external": false, + "name": "Daniele Muraro" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
The influence of cytokinin-auxin cross-regulation on cell-fate determination in Arabidopsis thaliana root development
Muraro D, Byrne H, King J, Voss U, Kieber J, Bennett M. J Theor Biol.2011 Aug 21;283(1):152-67.PMID: 21640126,
Abstract:
Root growth and development in Arabidopsis thaliana are sustained by a specialised zone termed the meristem, which contains a population of dividing and differentiating cells that are functionally analogous to a stem cell niche in animals. The hormones auxin and cytokinin control meristem size antagonistically. Local accumulation of auxin promotes cell division and the initiation of a lateral root primordium. By contrast, high cytokinin concentrations disrupt the regular pattern of divisions that characterises lateral root development, and promote differentiation. The way in which the hormones interact is controlled by a genetic regulatory network. In this paper, we propose a deterministic mathematical model to describe this network and present model simulations that reproduce the experimentally observed effects of cytokinin on the expression of auxin regulated genes. We show how auxin response genes and auxin efflux transporters may be affected by the presence of cytokinin. We also analyse and compare the responses of the hormones auxin and cytokinin to changes in their supply with the responses obtained by genetic mutations of SHY2, which encodes a protein that plays a key role in balancing cytokinin and auxin regulation of meristem size. We show that although shy2 mutations can qualitatively reproduce the effect of varying auxin and cytokinin supply on their response genes, some elements of the network respond differently to changes in hormonal supply and to genetic mutations, implying a different, general response of the network. We conclude that an analysis based on the ratio between these two hormones may be misleading and that a mathematical model can serve as a useful tool for stimulate further experimental work by predicting the response of the network to changes in hormone levels and to other genetic mutations.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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+ "institution": "Institute for Systems Biology, Seattle, WA, USA.", + "name": "Alexander V Ratushny" + }, + { + "name": "Ramsey A Saleem" + }, + { + "name": "Katherine Sitko" + }, + { + "name": "Stephen A Ramsey" + }, + { + "name": "John D Aitchison" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/22531117", + "month": "4", + "pages": "577", + "synopsis": "Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.", + "title": "Asymmetric positive feedback loops reliably control biological responses.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000419", + "submissionId": "MODEL1203010004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000420": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institute for Systems Biology (ISB) and Seattle BioMed", + "email": "alexander.ratushny@systemsbiology.org", + "external": false, + "name": "Alexander Ratushny" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.", + "title": "Asymmetric positive feedback loops reliably control biological responses.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000420", + "submissionId": "MODEL1203010001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000421": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institute for Systems Biology (ISB) and Seattle BioMed", + "email": "alexander.ratushny@systemsbiology.org", + "external": false, + "name": "Alexander Ratushny" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Mathematical modeling elucidates the role of transcriptional feedback in gibberellin signaling.
Middleton AM , \u00dabeda-Tom\u00e1s S , Griffiths J , Holman T , Hedden P , Thomas SG , Phillips AL , Holdsworth MJ , Bennett MJ , King JR, Owen MR Proc. Natl. Acad. Sci. U.S.A. 2012 May; 109(19): 7571-6 22523240 ,
Abstract:
The hormone gibberellin (GA) is a key regulator of plant growth. Many of the components of the gibberellin signal transduction [e.g., GIBBERELLIN INSENSITIVE DWARF 1 (GID1) and DELLA], biosynthesis [e.g., GA 20-oxidase (GA20ox) and GA3ox], and deactivation pathways have been identified. Gibberellin binds its receptor, GID1, to form a complex that mediates the degradation of DELLA proteins. In this way, gibberellin relieves DELLA-dependent growth repression. However, gibberellin regulates expression of GID1, GA20ox, and GA3ox, and there is also evidence that it regulates DELLA expression. In this paper, we use integrated mathematical modeling and experiments to understand how these feedback loops interact to control gibberellin signaling. Model simulations are in good agreement with in vitro data on the signal transduction and biosynthesis pathways and in vivo data on the expression levels of gibberellin-responsive genes. We find that GA-GID1 interactions are characterized by two timescales (because of a lid on GID1 that can open and close slowly relative to GA-GID1 binding and dissociation). Furthermore, the model accurately predicts the response to exogenous gibberellin after a number of chemical and genetic perturbations. Finally, we investigate the role of the various feedback loops in gibberellin signaling. We find that regulation of GA20ox transcription plays a significant role in both modulating the level of endogenous gibberellin and generating overshoots after the removal of exogenous gibberellin. Moreover, although the contribution of other individual feedback loops seems relatively small, GID1 and DELLA transcriptional regulation acts synergistically with GA20ox feedback.

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We find that GA-GID1 interactions are characterized by two timescales (because of a lid on GID1 that can open and close slowly relative to GA-GID1 binding and dissociation). Furthermore, the model accurately predicts the response to exogenous gibberellin after a number of chemical and genetic perturbations. Finally, we investigate the role of the various feedback loops in gibberellin signaling. We find that regulation of GA20ox transcription plays a significant role in both modulating the level of endogenous gibberellin and generating overshoots after the removal of exogenous gibberellin. Moreover, although the contribution of other individual feedback loops seems relatively small, GID1 and DELLA transcriptional regulation acts synergistically with GA20ox feedback.", + "title": "Mathematical modeling elucidates the role of transcriptional feedback in gibberellin signaling.", + "type": "PubMed ID", + "volume": "109", + "year": 2012 + }, + "publicationId": "BIOMD0000000422", + "submissionId": "MODEL1206190000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000423": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Linkoping University", + "email": "elin.nyman@gmail.com", + "external": false, + "name": "Elin Nyman" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes.
Nyman E, Fagerholm S, Jullesson D, Str\u00e5lfors P, Cedersund G. FEBS J. 2012 Jan 16. 22248283 ,
Abstract:
Insulin signaling through insulin receptor (IR) and insulin receptor substrate-1 (IRS1) is important for insulin control of target cells. We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling. Database The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/nyman/index.html free of charge.

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We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling.", + "title": "Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes.", + "type": "PubMed ID", + "volume": "279", + "year": 2012 + }, + "publicationId": "BIOMD0000000423", + "submissionId": "MODEL1207270000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000424": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Edinburgh", + "email": "galina.lebedeva@ed.ac.uk", + "external": false, + "name": "Galina Lebedeva" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Faratian2009 - Role of PTEN in Trastuzumabresistance

This model is described in the article:

Faratian D, Goltsov A, Lebedeva G, Sorokin A, Moodie S, Mullen P, Kay C, Um IH, Langdon S, Goryanin I, Harrison DJ.
Cancer Res. 2009 Aug; 69(16): 6713-6720

Abstract:

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000424.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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d.faratian@ed.ac.uk", + "authors": [ + { + "institution": "University of Edinburgh, Scotland, United Kingdom. d.faratian@ed.ac.uk", + "name": "Dana Faratian" + }, + { + "name": "Alexey Goltsov", + "orcid": "0000-0001-6725-189X" + }, + { + "name": "Galina Lebedeva" + }, + { + "name": "Anatoly Sorokin", + "orcid": "0000-0002-0047-0606" + }, + { + "name": "Stuart Moodie" + }, + { + "name": "Peter Mullen", + "orcid": "0000-0002-0841-609X" + }, + { + "name": "Charlene Kay" + }, + { + "name": "In Hwa Um" + }, + { + "name": "Simon Langdon" + }, + { + "name": "Igor Goryanin", + "orcid": "0000-0002-8293-774X" + }, + { + "name": "David J Harrison", + "orcid": "0000-0001-9041-9988" + } + ], + "issue": "16", + "journal": "Cancer research", + "link": "http://identifiers.org/pubmed/19638581", + "month": "8", + "pages": "6713-6720", + "synopsis": "Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.", + "title": "Systems biology reveals new strategies for personalizing cancer medicine and confirms the role of PTEN in resistance to trastuzumab.", + "type": "PubMed ID", + "volume": "69", + "year": 2009 + }, + "publicationId": "BIOMD0000000424", + "submissionId": "MODEL1108180000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000425": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Carnegie Mellon University", + "email": "cheemeng@gmail.com", + "external": false, + "name": "Cheemeng Tan" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Tan2012 - Antibiotic Treatment, Inoculum Effect

The efficacy of many antibiotics decreases with increasing bacterial density, a phenomenon called the \u2018inoculum effect\u2019 (IE). This study reveals that, for ribosome-targeting antibiotics, IE is due to bistable inhibition of bacterial growth, which reduces the efficacy of certain treatment frequencies.

This model is described in the article:

Tan C, Phillip Smith R, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L.
Mol Syst Biol. 2012 Oct 9; 8:617

Abstract:

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.

This model is hosted on BioModels Database and identified by: MODEL1208300000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. 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Mosca2012 - Central Carbon Metabolism Regulated by AKT

The role of the PI3K/Akt/PKB signalling pathway in oncogenesis has been extensively investigated and altered expression or mutations of many components of this pathway have been implicated in human cancers. Indeed, expression of constitutively active forms of Akt/PKB can prevent cell death upon growth factor withdrawal. PI3K/Akt/mTOR-mediated survival relies on a profound metabolic adaptation, including aerobic glycolysis. Here, the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis has been modelled, considering two states - high and low PI3K/Akt/mTOR activity. The high PI3K/Akt/mTOR activity represents cancer cell line where PI3K/Akt/mTOR promotes a high rate of glucose metabolism (condition H) and the low PI3K/Akt/mTOR activity is characterised by a lower glycolytic rate due to a reduced PI3K/Akt/mTOR signal (condition L). This model corresponds to the high PI3K/Akt/mTOR signal (condition H).

This model is described in the article:

Mosca E, Alfieri R, Maj C, Bevilacqua A, Canti G, Milanesi L.
Frontiers in Systems Biology. 2012 Oct 13

Abstract:

Signal transduction pathways and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modelled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis. We used a computational model in order to compare two metabolic states generated by the specific variation of the metabolic fluxes regulated by the activity of the PI3K/Akt/mTOR pathway. One of the two states represented the metabolism of a growing cancer cell characterised by aerobic glycolysis and cellular biosynthesis, while the other state represented the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism, as reported in literature in relation to the activity of the PI3K/Akt/mTOR. Some steps that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.

This model is hosted on BioModels Database and identified by: MODEL1210150000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modeled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production, and nucleotide biosynthesis. We used a computational model to compare two metabolic states generated by two different levels of signaling through the PI3K/Akt/mTOR pathway: one of the two states represents the metabolism of a growing cancer cell characterized by aerobic glycolysis and cellular biosynthesis, while the other state represents the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism. Biochemical reactions that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.", + "title": "Computational modeling of the metabolic States regulated by the kinase akt.", + "type": "PubMed ID", + "volume": "3", + "year": 2012 + }, + "publicationId": "BIOMD0000000426", + "submissionId": "MODEL1210150000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000427": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Univeristy of Perugia", + "email": "fortunato.bianconi@gmail.com", + "external": false, + "name": "Fortunato Bianconi" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Bianconi2012 - EGFR and IGF1R pathway in lung cancer

EGFR and IGF1R pathways play a key role in various human cancers and are crucial for tumour transformation and survival of malignant cells. High EGFR and IGF1R expression and activity has been associated with multiple aspects of cancer progression including tumourigenesis, metastasis, resistance to chemotherapeutics and other molecularly targeted drugs. Here, the biological relationship between the proteins involved in EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks has been modelled to study the time behaviour of the overall system, and the functional interdependencies among the receptors, the proteins and kinases involved.

This model is described in the article:

Bianconi F, Baldelli E, Ludovini V, Crin\u00f2 L, Flacco A, Valigi P.
Biotechnol Adv. 2012 Jan-Feb;30(1):142-53.

Abstract:

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome.

This model is hosted on BioModels Database and identified by: MODEL1209230000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"21620944", + "affiliation": "Department of Electronic and Information Engineering, Perugia University, Italy. fortunato.bianconi@diei.unipg.it", + "authors": [ + { + "institution": "Department of Electronic and Information Engineering, Perugia University, Italy. fortunato.bianconi@diei.unipg.it", + "name": "Fortunato Bianconi", + "orcid": "0000-0002-3132-0061" + }, + { + "name": "Elisa Baldelli" + }, + { + "name": "Vienna Ludovini" + }, + { + "name": "Lucio Crin\u00f2" + }, + { + "name": "Antonella Flacco" + }, + { + "name": "Paolo Valigi", + "orcid": "0000-0002-0486-7678" + } + ], + "issue": "1", + "journal": "Biotechnology advances", + "link": "http://identifiers.org/pubmed/21620944", + "month": "0", + "pages": "142-153", + "synopsis": "In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome.", + "title": "Computational model of EGFR and IGF1R pathways in lung cancer: a Systems Biology approach for Translational Oncology.", + "type": "PubMed ID", + "volume": "30", + "year": 2012 + }, + "publicationId": "BIOMD0000000427", + "submissionId": "MODEL1209230000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000428": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "TBI", + "email": "lukas.endler@gmx.at", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Achcar2012 - Glycolysis in bloodstream form T. brucei

Kinetic models of metabolism require quantitative knowledge of detailed kinetic parameters. However, the knowledge about these parameters is often uncertain. An analysis of the effect of parameter uncertainties on a particularly well defined example of a quantitative metablic model, the model of glycolysis in bloodstream form Trypanosoma brucei , has been presented here.

This model is described in the article:

Achcar F, Kerkhoven EJ; SilicoTryp Consortium, Bakker BM, Barrett MP, Breitling R.
PLoS Comput Biol. 2012 Jan; 8(1):e1002352.

Abstract:

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

This model is hosted on BioModels Database and identified by: MODEL1209130000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Glycolysis in bloodstream form T. brucei", + "publication": { + "accession": "22379410", + "affiliation": "Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.", + "authors": [ + { + "name": "Achcar F" + }, + { + "name": "Kerkhoven EJ" + }, + { + "name": "Bakker BM" + }, + { + "name": "Barrett MP" + }, + { + "name": "Breitling R" + } + ], + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/22379410", + "month": "1", + "pages": "e1002352", + "synopsis": "Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.", + "title": "Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000428", + "submissionId": "MODEL1209130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000429": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "OvGU", + "email": "schaber@med.ovgu.de", + "external": false, + "name": "Joerg Schaber" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Schaber2012 - Hog pathway in yeast

The high osmolarity glycerol (HOG) pathway in the yeast Saccharomyces cerevisiae is one of the best-studied mitogen-activated protein kinase (MAPK) pathways and serves as a prototype signalling system for eukaryotes. This pathway is necessary and sufficient to adapt to high external osmolarity. A key component of this pathway is the stress-activated protein kinase (SAPK) Hog1, which is rapidly phosphorylated by the SAPK kinase Pbs2 upon hyper-osmotic shock, and which is the terminal kinase of two parallel signalling pathways, subsequently called the Sho1 branch and the Sln1 branch, respectively. Ensemble modelling (192 models) is used to study the yeast HOG pathway, a prototype for eukaryotic mitogen-activated kinase signalling systems. The best fit model (Model Nr.22: described here) provides new insights into the function of this system, some of which are then experimentally validated.

This model is described in the article:

Schaber J, Baltanas R, Bush A, Klipp E, Colman-Lerner A.
Mol Syst Biol. 2012 Nov 13;8:622.

Abstract:

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.

This model is hosted on BioModels Database and identified by: MODEL1209110001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Hog pathway in yeast", + "publication": { + "accession": "23149687", + "affiliation": "Institute for Experimental Internal Medicine, Medical Faculty, Otto von Guericke University, Magdeburg, Germany. schaber@med.ovgu.de", + "authors": [ + { + "institution": "Max Planck Institute for Molecular Genetics, Berlin, Germany.", + "name": "J\u00f6rg Schaber", + "orcid": "0000-0001-6971-2530" + }, + { + "name": "Rodrigo Baltanas" + }, + { + "name": "Alan Bush" + }, + { + "name": "Edda Klipp" + }, + { + "institution": "Department of Physiology, Molecular and Cellular Biology, University of Buenos Aires, Buenos Aires, Argentina colman-lerner@fbmc.fcen.uba.ar.Institute of Physiology, Molecular Biology and Neurosciences, National Research Council (CONICET), Buenos Aires, Argentina.", + "name": "Alejandro Colman-Lerner", + "orcid": "0000-0002-2557-8883" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/23149687", + "month": "0", + "pages": "622", + "synopsis": "The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.", + "title": "Modelling reveals novel roles of two parallel signalling pathways and homeostatic feedbacks in yeast.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000429", + "submissionId": "MODEL1209110001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000430": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipans@gmail.com", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-40). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280020 - M4_K2_USEQ] correspond to type M4 with mass-action kinetics K2, in USEQ (Unsequestrated ) condition.

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280020 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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+ "name": "Uddipan Sarma" + }, + { + "name": "Indira Ghosh" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/22748295", + "month": "7", + "pages": "82", + "synopsis": "

Background

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

Results

We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

Conclusions

Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.", + "title": "Different designs of kinase-phosphatase interactions and phosphatase sequestration shapes the robustness and signal flow in the MAPK cascade.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000430", + "submissionId": "MODEL1204280020", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000431": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipans@gmail.com", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280024 - M4_K2_PSEQ] correspond to type M4 with mass-action kinetics K2, in PSEQ (sequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280024 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

Results

We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

Conclusions

Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.", + "title": "Different designs of kinase-phosphatase interactions and phosphatase sequestration shapes the robustness and signal flow in the MAPK cascade.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000431", + "submissionId": "MODEL1204280024", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000432": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipans@gmail.com", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280036 - M4_K2_QSS_USEQ] correspond to type M4 with mass-action kinetics K2, in QSS (quasi steady state) and USEQ (Unsequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280036 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

Results

We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

Conclusions

Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.", + "title": "Different designs of kinase-phosphatase interactions and phosphatase sequestration shapes the robustness and signal flow in the MAPK cascade.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000432", + "submissionId": "MODEL1204280036", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000433": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipans@gmail.com", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280040 - M4_K2_QSS_PSEQ] correspond to type M4 with mass-action kinetics K2, in QSS (quasi steady state) and USEQ (Unsequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280040 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

Results

We have built four architecturally distinct types of models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

Conclusions

Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.", + "title": "Different designs of kinase-phosphatase interactions and phosphatase sequestration shapes the robustness and signal flow in the MAPK cascade.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000433", + "submissionId": "MODEL1204280040", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000434": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Liverpool Hope University", + "email": "mcaulem@hope.ac.uk", + "external": false, + "name": "Mark Mc Auley" + } + ] + }, + "curationStatus": "CURATED", + "description": "
McAuley2012 - Whole-body Cholesterol Metabolism

Lipid metabolism has a key role to play in human longevity and healthy aging. A whole-body mathematical model of cholesterol metabolism that explores the changes in both the rate of intestinal cholesterol absorption and the hepatic rate of clearance of LDL-C from the plasma, has been presented here. The model showed that of these two mechanisms, changes to the rate of LDL-C removal from the plasma with age had the most significant effect on cholesterol metabolism.

The original SBML model file was generated using MathSBML 2.5.1.

This model is described in the article:

Mc Auley MM, Wilkinson DJ, Jones JJ, Kirkwood TT.
BMC Syst Biol. 2012 Oct 10;6(1):130.

Abstract:

BACKGROUND: Global demographic changes have stimulated marked interest in the process of ageing. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.

RESULTS: The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116mg/dL.

CONCLUSIONS: Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism.

This model is hosted on BioModels Database and identified by: MODEL1206010000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Whole-body Cholesterol Metabolism", + "publication": { + "accession": "23046614", + "affiliation": "Campus for Ageing and Vitality, Newcastle University, Henry Wellcome Biogerontology Building, Newcastle upon Tyne NE4 5PL, United Kingdom. mcaulem@hope.ac.uk", + "authors": [ + { + "institution": "Campus for Ageing and Vitality, Newcastle University, Henry Wellcome Biogerontology Building, Newcastle upon Tyne NE4 5PL, United Kingdom. mcaulem@hope.ac.uk", + "name": "Mark T Mc Auley" + }, + { + "name": "Darren J Wilkinson", + "orcid": "0000-0003-0736-802X" + }, + { + "name": "Janette J L Jones" + }, + { + "name": "Thomas B L Kirkwood" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/23046614", + "month": "10", + "pages": "130", + "synopsis": "

Background

Global demographic changes have stimulated marked interest in the process of aging. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.

Results

The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34 mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116 mg/dL.

Conclusions

Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism.", + "title": "A whole-body mathematical model of cholesterol metabolism and its age-associated dysregulation.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000434", + "submissionId": "MODEL1206010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000435": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Center for Information Services and High Performance Computing (ZIH), TUD Dresden University of Technology", + "email": "lutz.brusch@tu-dresden.de", + "external": false, + "name": "Lutz Brusch", + "orcid": "0000-0003-0137-5106" + } + ] + }, + "curationStatus": "CURATED", + "description": "
deBack2012 - Lineage Specification in Pancreas Development

This model of two neighbouring pancreas precursor cells, describes the exocrine versus endocrine lineage specification process. To account for the tissue scale patterns, this couplet model has been extended to hundreds of coupled cells.

This model is described in the article:

de Back W., Zhou JX, Brusch L
J. R. Soc. Interface 6 February 2013 vol. 10 no. 79 20120766

Abstract:

The cell fate decision of multi-potent pancreatic progenitor cells between the exocrine and endocrine lineages is regulated by Notch signalling, mediated by cell\u2013cell interactions. However, canonical models of Notch-mediated lateral inhibition cannot explain the scattered spatial distribution of endocrine cells and the cell-type ratio in the developing pancreas. Based on evidence from acinar-to-islet cell transdifferentiation in vitro, we propose that lateral stabilization, i.e. positive feedback between adjacent progenitor cells, acts in parallel with lateral inhibition to regulate pattern formation in the pancreas. A simple mathematical model of transcriptional regulation and cell\u2013cell interaction reveals the existence of multi-stability of spatial patterns whose simultaneous occurrence causes scattering of endocrine cells in the presence of noise. The scattering pattern allows for control of the endocrine-to-exocrine cell-type ratio by modulation of lateral stabilization strength. These theoretical results suggest a previously unrecognized role for lateral stabilization in lineage specification, spatial patterning and cell-type ratio control in organ development.

This model is hosted on BioModels Database and identified by: MODEL1211010000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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for Information Services and High Performance Computing, Technische Universit\u00e4t Dresden, Dresden, Germany.", + "name": "Walter de Back", + "orcid": "0000-0003-4641-8472" + }, + { + "name": "Joseph Xu Zhou" + }, + { + "institution": "Center for Information Services and High Performance Computing (ZIH), TUD Dresden University of Technology", + "name": "Lutz Brusch", + "orcid": "0000-0003-0137-5106" + } + ], + "issue": "79", + "journal": "Journal of the Royal Society, Interface", + "link": "http://identifiers.org/pubmed/23193107", + "month": "2", + "pages": "20120766", + "synopsis": "The cell fate decision of multi-potent pancreatic progenitor cells between the exocrine and endocrine lineages is regulated by Notch signalling, mediated by cell-cell interactions. However, canonical models of Notch-mediated lateral inhibition cannot explain the scattered spatial distribution of endocrine cells and the cell-type ratio in the developing pancreas. Based on evidence from acinar-to-islet cell transdifferentiation in vitro, we propose that lateral stabilization, i.e. positive feedback between adjacent progenitor cells, acts in parallel with lateral inhibition to regulate pattern formation in the pancreas. A simple mathematical model of transcriptional regulation and cell-cell interaction reveals the existence of multi-stability of spatial patterns whose simultaneous occurrence causes scattering of endocrine cells in the presence of noise. The scattering pattern allows for control of the endocrine-to-exocrine cell-type ratio by modulation of lateral stabilization strength. These theoretical results suggest a previously unrecognized role for lateral stabilization in lineage specification, spatial patterning and cell-type ratio control in organ development.", + "title": "On the role of lateral stabilization during early patterning in the pancreas.", + "type": "PubMed ID", + "volume": "10", + "year": 2013 + }, + "publicationId": "BIOMD0000000435", + "submissionId": "MODEL1211010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000436": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "San Diego Supercomputer Center and Department of Bioengineering, UCSD", + "email": "rbyrnes@sdsc.edu", + "external": false, + "name": "Byrnes Robert" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Gupta2009 - Eicosanoid Metabolism

Integrated model of eicosanoid metabolism and signaling based on lipidomics flux analysis.

This model is described in the article:

Gupta S, Maurya MR, Stephens DL, Dennis EA, Subramaniam S.
Biophys. J. 2009 Jun; 96(11):4542-51.

Abstract:

There is increasing evidence for a major and critical involvement of lipids in signal transduction and cellular trafficking, and this has motivated large-scale studies on lipid pathways. The Lipid Metabolites and Pathways Strategy consortium is actively investigating lipid metabolism in mammalian cells and has made available time-course data on various lipids in response to treatment with KDO(2)-lipid A (a lipopolysaccharide analog) of macrophage RAW 264.7 cells. The lipids known as eicosanoids play an important role in inflammation. We have reconstructed an integrated network of eicosanoid metabolism and signaling based on the KEGG pathway database and the literature and have developed a kinetic model. A matrix-based approach was used to estimate the rate constants from experimental data and these were further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data well for all species, and simulated enzyme activities were similar to their literature values. The quantitative model for eicosanoid metabolism that we have developed can be used to design experimental studies utilizing genetic and pharmacological perturbations to probe fluxes in lipid pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000436 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Eicosanoid Metabolism", + "publication": { + "accession": "19486676", + "affiliation": "Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.", + "authors": [ + { + "institution": "Department of Bioengineering, University of California, San Diego, La Jolla, California, USA.", + "name": "Shakti Gupta" + }, + { + "name": "Mano Ram Maurya" + }, + { + "name": "Daren L Stephens" + }, + { + "name": "Edward A Dennis", + "orcid": "0000-0003-3738-3140" + }, + { + "name": "Shankar Subramaniam" + } + ], + "issue": "11", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/19486676", + "month": "6", + "pages": "4542-4551", + "synopsis": "There is increasing evidence for a major and critical involvement of lipids in signal transduction and cellular trafficking, and this has motivated large-scale studies on lipid pathways. 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The quantitative model for eicosanoid metabolism that we have developed can be used to design experimental studies utilizing genetic and pharmacological perturbations to probe fluxes in lipid pathways.", + "title": "An integrated model of eicosanoid metabolism and signaling based on lipidomics flux analysis.", + "type": "PubMed ID", + "volume": "96", + "year": 2009 + }, + "publicationId": "BIOMD0000000436", + "submissionId": "MODEL1204240001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000437": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Circadian Clocks & Metabolism Quantitative Modeling Lab, Shih Chien University, Taiwan", + "email": "Yu-yao.Tseng@postgrad.manchester.ac.uk", + "external": false, + "name": "Yu-yao Tseng" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Tseng2012 - Circadian clock of N.crassa

A comprehensive model of the circardian clock of fungal Neurospora crassa , which encompasses existing knowledge of the biochemistry of Neurospora clock, is described by Tseng et al. (2012). The model is validated against a wide range of experimental phenotypes and has been used to investigate possible molecular explanations of temperature compensation.

This model is described in the article:

Tseng YY, Hunt SM, Heintzen C, Crosthwaite SK, Schwartz JM
PLoS Comput. Biol. [2012 ; Volume: 8 (Issue: 3 )] Page info: e1002437

Abstract:

Circadian clocks provide an internal measure of external time allowing organisms to anticipate and exploit predictable daily changes in the environment. Rhythms driven by circadian clocks have a temperature compensated periodicity of approximately 24 hours that persists in constant conditions and can be reset by environmental time cues. Computational modelling has aided our understanding of the molecular mechanisms of circadian clocks, nevertheless it remains a major challenge to integrate the large number of clock components and their interactions into a single, comprehensive model that is able to account for the full breadth of clock phenotypes. Here we present a comprehensive dynamic model of the Neurospora crassa circadian clock that incorporates its key components and their transcriptional and post-transcriptional regulation. The model accounts for a wide range of clock characteristics including: a periodicity of 21.6 hours, persistent oscillation in constant conditions, arrhythmicity in constant light, resetting by brief light pulses, and entrainment to full photoperiods. Crucial components influencing the period and amplitude of oscillations were identified by control analysis. Furthermore, simulations enabled us to propose a mechanism for temperature compensation, which is achieved by simultaneously increasing the translation of frq RNA and decreasing the nuclear import of FRQ protein.

Figure 3 of the reference publication has been reproduced using Copasi 4.8 (Build 35).

This model is hosted on BioModels Database and identifiedby: MODEL1212150000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Rhythms driven by circadian clocks have a temperature compensated periodicity of approximately 24 hours that persists in constant conditions and can be reset by environmental time cues. Computational modelling has aided our understanding of the molecular mechanisms of circadian clocks, nevertheless it remains a major challenge to integrate the large number of clock components and their interactions into a single, comprehensive model that is able to account for the full breadth of clock phenotypes. Here we present a comprehensive dynamic model of the Neurospora crassa circadian clock that incorporates its key components and their transcriptional and post-transcriptional regulation. The model accounts for a wide range of clock characteristics including: a periodicity of 21.6 hours, persistent oscillation in constant conditions, arrhythmicity in constant light, resetting by brief light pulses, and entrainment to full photoperiods. Crucial components influencing the period and amplitude of oscillations were identified by control analysis. Furthermore, simulations enabled us to propose a mechanism for temperature compensation, which is achieved by simultaneously increasing the translation of frq RNA and decreasing the nuclear import of FRQ protein.", + "title": "Comprehensive modelling of the Neurospora circadian clock and its temperature compensation.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000437", + "submissionId": "MODEL1212150000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000438": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "NTU", + "email": "nazaninsaeidi@ntu.edu.sg", + "external": false, + "name": "Nazanin Saeidi" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Saeidi2012 - Quorum sensing device that produces GFP

Saeidi et al. (2012) has modelled a quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL).

This model is described in the article:

Nazanin Saeidi, Mohamed Arshath, Matthew Wook Chang, Chueh Loo Poh
Chemical Engineering Science. December 2012.

Abstract:

Modeling of biological parts is of crucial importance as it enables the in silico study of synthetic biological systems prior to the actual construction of genetic circuits, which can be time consuming and costly. Because standard biological parts are utilized to build the synthetic systems, it is important that each of these standard parts is well characterized and has a corresponding mathematical model that could simulate the characteristics of the part. These models could be used in computer aided design (CAD) tools during the design stage to facilitate the building of the model of biological systems. This paper describes the development of a mathematical model that is able to simulate both the dynamic and static performance of a biological device created using standard parts. We modeled an example quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL). The parameters of the model were estimated using experimental results. The simulation results show that the model was able to simulate behavior similar to experimental results. Since it is important that these models and the content in the models can be searchable and readable by machines, standard SBML (system biology markup language) format was used to store the models. All parts and reactions are fully annotated to enable easy searching, and the models follow the Minimum Information Requested In the Annotation of Models (MIRIAM) compliance as well as the Minimum Information About a Simulation Experiment (MIASE).

Figure 4a of the reference publication has been reproduced as curation figure. The plot shows the performance of the model at different concentrations of the inducer (3OC12HSL=5E-10, 5E-07, 5E-07).

This model is hosted on BioModels Database and identifiedby: MODEL1301110000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Quorum sensing device that produces GFP", + "publication": { + "accession": "10.1016/j.ces.2012.12.016", + "affiliation": "School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459", + "authors": [ + { + "name": "Nazanin Saeidi" + }, + { + "name": "Mohamed Arshath" + }, + { + "name": "Matthew Wook Chang" + }, + { + "name": "and Chueh Loo Poh" + } + ], + "journal": "Chemical Engineering Science 2012; 0009-2509", + "link": "http://identifiers.org/doi/10.1016/j.ces.2012.12.016", + "synopsis": "Modeling of biological parts is of crucial importance as it enables the in silico study of synthetic biological systems prior to the actual construction of genetic circuits, which can be time consuming and costly. Because standard biological parts are utilized to build the synthetic systems, it is important that each of these standard parts is well characterized and has a corresponding mathematical model that could simulate the characteristics of the part. These models could be used in computer aided design (CAD) tools during the design stage to facilitate the building of the model of biological systems. This paper describes the development of a mathematical model that is able to simulate both the dynamic and static performance of a biological device created using standard parts. We modeled an example quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL). The parameters of the model were estimated using experimental results. The simulation results show that the model was able to simulate behavior similar to experimental results. Since it is important that these models and the content in the models can be searchable and readable by machines, standard SBML (system biology markup language) format was used to store the models. All parts and reactions are fully annotated to enable easy searching, and the models follow the Minimum Information Requested In the Annotation of Models (MIRIAM) compliance as well as the Minimum Information About a Simulation Experiment (MIASE).", + "title": "Characterization of a quorum sensing device for synthetic biology design: Experimental and modeling validation", + "type": "DOI", + "year": 2012 + }, + "publicationId": "BIOMD0000000438", + "submissionId": "MODEL1301110000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000439": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Warwick", + "email": "M.A.Esparza-Franco@warwick.ac.uk", + "external": false, + "name": "Manuel Esparza-Franco" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Smith2009 - RGS mediated GTP hydrolysis

This model is described in the article:

Smith B, Hill C, Godfrey EL, Rand D, van den Berg H, Thornton S, Hodgkin M, Davey J, Ladds G.
Cell Signal. 2009 Jul;21(7):1151-60.

Abstract:

G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increases the intrinsic GTPase activity of Galpha-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where Galpha(GTP) enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the Galpha produces this unimodal relationship.

To reproduce dose-response plots in the publication, the model is simulated with 12 different concentrations (see parameter Ligand_conc). For each concentration, a single value must be obtained from the integral of the trajectory of species z3 from time=0 to time=30. These values are then used to build a dose-response plot (authors used GraphPad Prism). Mutant strains are simulated with alternative parameter values or initial conditions in Table S3.

This model is hosted on BioModels Database and identified by: MODEL1212040001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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RGS mediated GTP hydrolysis", + "publication": { + "accession": "19285552", + "affiliation": "Molecular Organization and Assembly of Cells Centre, University of Warwick, Coventry, UK.", + "authors": [ + { + "institution": "Molecular Organization and Assembly of Cells Centre, University of Warwick, Coventry, UK.", + "name": "Benjamin Smith" + }, + { + "name": "Claire Hill" + }, + { + "name": "Emma L Godfrey" + }, + { + "name": "David A Rand", + "orcid": "0000-0002-2217-3274" + }, + { + "name": "Hugo van den Berg", + "orcid": "0000-0002-8230-6227" + }, + { + "name": "Steven Thornton" + }, + { + "name": "Matthew Hodgkin" + }, + { + "name": "John Davey" + }, + { + "institution": "Univeristy of Cambridge", + "name": "Graham Ladds", + "orcid": "0000-0001-7320-9612" + } + ], + "issue": "7", + "journal": "Cellular signalling", + "link": "http://identifiers.org/pubmed/19285552", + "month": "7", + "pages": "1151-1160", + "synopsis": "G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increases the intrinsic GTPase activity of Galpha-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where Galpha(GTP) enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the Galpha produces this unimodal relationship.", + "title": "Dual positive and negative regulation of GPCR signaling by GTP hydrolysis.", + "type": "PubMed ID", + "volume": "21", + "year": 2009 + }, + "publicationId": "BIOMD0000000439", + "submissionId": "MODEL1212040001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000440": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipan@nccs.res.in", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Oscillations in MAPK cascade (S1)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S1 that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Background

Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

Results

We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

Conclusion

The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.", + "title": "Oscillations in MAPK cascade triggered by two distinct designs of coupled positive and negative feedback loops.", + "type": "PubMed ID", + "volume": "5", + "year": 2012 + }, + "publicationId": "BIOMD0000000440", + "submissionId": "MODEL1112190003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000441": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipan@nccs.res.in", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Oscillations in MAPK cascade (S2)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S2 that comprises negative feedback from MK-PP to MKK_PP layer coupled to positive feedback from MK-PP to MKKK-P layer.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190004 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Background

Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

Results

We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

Conclusion

The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.", + "title": "Oscillations in MAPK cascade triggered by two distinct designs of coupled positive and negative feedback loops.", + "type": "PubMed ID", + "volume": "5", + "year": 2012 + }, + "publicationId": "BIOMD0000000441", + "submissionId": "MODEL1112190004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000442": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipan@nccs.res.in", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling module

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S2 that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer. In addition, a positive feedback loop from S2 to the phosphorylation step of Kinase X has been introduced.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190005 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Oscillations in MAPK cascade (S2), inclusion of external signalling module", + "publication": { + "accession": "22694947", + "affiliation": "National Centre for Cell Science, Ganeshkhind, Pune, India. uddipans@gmail.com", + "authors": [ + { + "institution": "National Centre for Cell Science, Ganeshkhind, Pune, India. uddipans@gmail.com", + "name": "Uddipan Sarma" + }, + { + "name": "Indira Ghosh" + } + ], + "journal": "BMC research notes", + "link": "http://identifiers.org/pubmed/22694947", + "month": "6", + "pages": "287", + "synopsis": "

Background

Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

Results

We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

Conclusion

The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.", + "title": "Oscillations in MAPK cascade triggered by two distinct designs of coupled positive and negative feedback loops.", + "type": "PubMed ID", + "volume": "5", + "year": 2012 + }, + "publicationId": "BIOMD0000000442", + "submissionId": "MODEL1112190005", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000443": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipan@nccs.res.in", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Oscillations in MAPK cascade (S1n)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. Further these models were extended to S1n and S2n, to incorporate the nuclear-cytoplasmic translocation of the MK layer components of the cascade. This model corresponds to model S1n that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer, with the inclusion of nuclear-cytoplasmic translocation.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190006 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Oscillations in MAPK cascade (S1n)", + "publication": { + "accession": "22694947", + "affiliation": "National Centre for Cell Science, Ganeshkhind, Pune, India. uddipans@gmail.com", + "authors": [ + { + "institution": "National Centre for Cell Science, Ganeshkhind, Pune, India. uddipans@gmail.com", + "name": "Uddipan Sarma" + }, + { + "name": "Indira Ghosh" + } + ], + "journal": "BMC research notes", + "link": "http://identifiers.org/pubmed/22694947", + "month": "6", + "pages": "287", + "synopsis": "

Background

Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

Results

We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

Conclusion

The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.", + "title": "Oscillations in MAPK cascade triggered by two distinct designs of coupled positive and negative feedback loops.", + "type": "PubMed ID", + "volume": "5", + "year": 2012 + }, + "publicationId": "BIOMD0000000443", + "submissionId": "MODEL1112190006", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000444": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science", + "email": "uddipan@nccs.res.in", + "external": false, + "name": "Uddipan Sarma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sarma2012 - Oscillations in MAPK cascade (S2n)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. Further these models were extended to S1n and S2n, to incorporate the nuclear-cytoplasmic translocation of the MK layer components of the cascade. This model corresponds to model S2n that comprises negative feedback from MK-PP to MKK-PP layer coupled to positive feedback from MK-PP to MKKK-PP layer, with the inclusion of nuclear-cytoplasmic translocation.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190007 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Oscillations in MAPK cascade (S2n)", + "publication": { + "accession": "22694947", + "affiliation": "National Centre for Cell Science, Ganeshkhind, Pune, India. uddipans@gmail.com", + "authors": [ + { + "institution": "National Centre for Cell Science, Ganeshkhind, Pune, India. uddipans@gmail.com", + "name": "Uddipan Sarma" + }, + { + "name": "Indira Ghosh" + } + ], + "journal": "BMC research notes", + "link": "http://identifiers.org/pubmed/22694947", + "month": "6", + "pages": "287", + "synopsis": "

Background

Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

Results

We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

Conclusion

The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.", + "title": "Oscillations in MAPK cascade triggered by two distinct designs of coupled positive and negative feedback loops.", + "type": "PubMed ID", + "volume": "5", + "year": 2012 + }, + "publicationId": "BIOMD0000000444", + "submissionId": "MODEL1112190007", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000445": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Edinburgh University", + "email": "SashaPokh@gmail.com", + "external": false, + "name": "Alexandra Pokhilko" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Pokhilko2013 - TOC1 signalling in Arabidopsiscircadian clock

In this model, Pokhilko et al. has incorporated the negative transcriptional regulations of the core clock genes by TOC1 and the up-regulation of TOC1 expression by ABA signalling, to their previous model BIOMD0000000412

This model is described in the article:

Pokhilko A, Mas P, Millar AJ.
BMC Syst Biol 2013; 7: 23

Abstract:

BACKGROUND: 24-hour biological clocks are intimately connected to the cellular signalling network, which complicates the analysis of clock mechanisms. The transcriptional regulator TOC1 (TIMING OF CAB EXPRESSION 1) is a founding component of the gene circuit in the plant circadian clock. Recent results show that TOC1 suppresses transcription of multiple target genes within the clock circuit, far beyond its previously-described regulation of the morning transcription factors LHY (LATE ELONGATED HYPOCOTYL) and CCA1 (CIRCADIAN CLOCK ASSOCIATED 1). It is unclear how this pervasive effect of TOC1 affects the dynamics of the clock and its outputs. TOC1 also appears to function in a nested feedback loop that includes signalling by the plant hormone Abscisic Acid (ABA), which is upregulated by abiotic stresses, such as drought. ABA treatments both alter TOC1 levels and affect the clock's timing behaviour. Conversely, the clock rhythmically modulates physiological processes induced by ABA, such as the closing of stomata in the leaf epidermis. In order to understand the dynamics of the clock and its outputs under changing environmental conditions, the reciprocal interactions between the clock and other signalling pathways must be integrated. RESULTS: We extended the mathematical model of the plant clock gene circuit by incorporating the repression of multiple clock genes by TOC1, observed experimentally. The revised model more accurately matches the data on the clock's molecular profiles and timing behaviour, explaining the clock's responses in TOC1 over-expression and toc1 mutant plants. A simplified representation of ABA signalling allowed us to investigate the interactions of ABA and circadian pathways. Increased ABA levels lengthen the free-running period of the clock, consistent with the experimental data. Adding stomatal closure to the model, as a key ABA- and clock-regulated downstream process allowed to describe TOC1 effects on the rhythmic gating of stomatal closure. CONCLUSIONS: The integrated model of the circadian clock circuit and ABA-regulated environmental sensing allowed us to explain multiple experimental observations on the timing and stomatal responses to genetic and environmental perturbations. These results crystallise a new role of TOC1 as an environmental sensor, which both affects the pace of the central oscillator and modulates the kinetics of downstream processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000445.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

24-hour biological clocks are intimately connected to the cellular signalling network, which complicates the analysis of clock mechanisms. The transcriptional regulator TOC1 (TIMING OF CAB EXPRESSION 1) is a founding component of the gene circuit in the plant circadian clock. Recent results show that TOC1 suppresses transcription of multiple target genes within the clock circuit, far beyond its previously-described regulation of the morning transcription factors LHY (LATE ELONGATED HYPOCOTYL) and CCA1 (CIRCADIAN CLOCK ASSOCIATED 1). It is unclear how this pervasive effect of TOC1 affects the dynamics of the clock and its outputs. TOC1 also appears to function in a nested feedback loop that includes signalling by the plant hormone Abscisic Acid (ABA), which is upregulated by abiotic stresses, such as drought. ABA treatments both alter TOC1 levels and affect the clock's timing behaviour. Conversely, the clock rhythmically modulates physiological processes induced by ABA, such as the closing of stomata in the leaf epidermis. In order to understand the dynamics of the clock and its outputs under changing environmental conditions, the reciprocal interactions between the clock and other signalling pathways must be integrated.

Results

We extended the mathematical model of the plant clock gene circuit by incorporating the repression of multiple clock genes by TOC1, observed experimentally. The revised model more accurately matches the data on the clock's molecular profiles and timing behaviour, explaining the clock's responses in TOC1 over-expression and toc1 mutant plants. A simplified representation of ABA signalling allowed us to investigate the interactions of ABA and circadian pathways. Increased ABA levels lengthen the free-running period of the clock, consistent with the experimental data. Adding stomatal closure to the model, as a key ABA- and clock-regulated downstream process allowed to describe TOC1 effects on the rhythmic gating of stomatal closure.

Conclusions

The integrated model of the circadian clock circuit and ABA-regulated environmental sensing allowed us to explain multiple experimental observations on the timing and stomatal responses to genetic and environmental perturbations. These results crystallise a new role of TOC1 as an environmental sensor, which both affects the pace of the central oscillator and modulates the kinetics of downstream processes.", + "title": "Modelling the widespread effects of TOC1 signalling on the plant circadian clock and its outputs.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000445", + "submissionId": "MODEL1209110002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000446": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cyprus", + "email": "erguler.kamil@ucy.ac.cy", + "external": false, + "name": "Kamil Erguler" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Erguler2013 - Unfolded protein stress response

The model investigates the mechanism by which UPR (unfolded protein response) outcome switches between survival and death.

This model is described in the article:

Erguler K, Pieri M, Deltas C.
BMC Syst Biol. 2013 Feb 21;7(1):16.

Abstract:

BACKGROUND: The unfolded protein response (UPR) is a major signalling cascade acting in the quality control ofprotein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory rolein a range of genetic and environmental disorders including neurodegenerative and cardiovasculardiseases, diabetes and kidney diseases. The three major receptors of the ER stress involved withthe UPR, i.e. IRE1a, PERK and ATF6, signal through a complex web of pathways to convey anappropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on theseverity of unfolded protein accumulation in the ER; however, the decision mechanism for the switchand its timing have so far been poorly understood.

RESULTS:Here, we propose a mechanism by which the UPR outcome switches between survival and death.We compose a mathematical model integrating the three signalling branches, and perform a comprehensivebifurcation analysis to investigate possible responses to stimuli. The analysis reveals threedistinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance,and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understoodas a dynamic process where the balance between the stress and the folding capacity of the ER playsa pivotal role in managing the delivery of the most appropriate response. The model demonstratesfor the first time that the UPR is capable of generating oscillations in translation attenuation and theapoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set ofparameters controlling this behaviour.

CONCLUSIONS:This work contributes largely to the understanding of one of the most ubiquitous signalling pathwaysinvolved in protein folding quality control in the metazoan ER. The insights gained have direct consequenceson the management of many UPR-related diseases, revealing, in addition, an extended listof candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioningmight be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the wayto novel approaches for the treatment of many UPR-related conditions.

In the paper, PERKA refers to the amount of phosphorylated PERK monomer. However, it refers to the active complex in the model. The complex with the model parameterization is formed of 4 monomers (n=4). So, the value of PERKA should be multiplied by 4, in order to generate the figures in the paper (eg. Figure 12).

An additional parameter (tmr=10)) is used in the model. This parameter is not mentioned in the paper. The model values of kf(=10) and kr(=1) are not consistent with that of the paper (kf=100, kr=10, in the paper). However, this is corrected by the introduction of \"tmr\" in the model, which is multiplied with kf and kr to get the resulting values.

The term \"tmr\" was missing in the kinetic laws of the reactions reu7 and reu8, in the original model. This has been corrected as per the author's request.

This model is hosted on BioModels Database and identifiedby: MODEL1302180000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Unfolded protein stress response", + "publication": { + "accession": "23433609", + "affiliation": "Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus. erguler.kamil@ucy.ac.cy", + "authors": [ + { + "institution": "Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological Sciences, University of Cyprus, Kallipoleos 75, 1678 Nicosia, Cyprus. erguler.kamil@ucy.ac.cy", + "name": "Kamil Erguler", + "orcid": "0000-0002-2674-592X" + }, + { + "name": "Myrtani Pieri", + "orcid": "0000-0002-8883-4434" + }, + { + "name": "Constantinos Deltas", + "orcid": "0000-0001-5549-9169" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/23433609", + "month": "2", + "pages": "16", + "synopsis": "

Background

The unfolded protein response (UPR) is a major signalling cascade acting in the quality control of protein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory role in a range of genetic and environmental disorders including neurodegenerative and cardiovascular diseases, diabetes and kidney diseases. The three major receptors of the ER stress involved with the UPR, i.e. IRE1 \u03b1, PERK and ATF6, signal through a complex web of pathways to convey an appropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on the severity of unfolded protein accumulation in the ER; however, the decision mechanism for the switch and its timing have so far been poorly understood.

Results

Here, we propose a mechanism by which the UPR outcome switches between survival and death. We compose a mathematical model integrating the three signalling branches, and perform a comprehensive bifurcation analysis to investigate possible responses to stimuli. The analysis reveals three distinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance, and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understood as a dynamic process where the balance between the stress and the folding capacity of the ER plays a pivotal role in managing the delivery of the most appropriate response. The model demonstrates for the first time that the UPR is capable of generating oscillations in translation attenuation and the apoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set of parameters controlling this behaviour.

Conclusions

This work contributes largely to the understanding of one of the most ubiquitous signalling pathways involved in protein folding quality control in the metazoan ER. The insights gained have direct consequences on the management of many UPR-related diseases, revealing, in addition, an extended list of candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioning might be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the way to novel approaches for the treatment of many UPR-related conditions.", + "title": "A mathematical model of the unfolded protein stress response reveals the decision mechanism for recovery, adaptation and apoptosis.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000446", + "submissionId": "MODEL1302180000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000447": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Singapore MIT Alliance, Computational and Systems Biology", + "email": "lihuipengsmacsb@gmail.com", + "external": false, + "name": "Huipeng Li" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Venkatraman2012 - Interplay between PLS and TSP1 in TGF-\u03b21 activation

The interplay between PLS (Plasmin) and TSP1 (Thrombospondin-1) in TGF-\u03b21 (Transforming growth factor-\u03b21)is shown using mathematical modelling and in vitro experimentents.

This model is described in the article:

Venkatraman L, Chia SM, Narmada BC, White JK, Bhowmick SS, Forbes Dewey C Jr, So PT, Tucker-Kellogg L, Yu H.
Biophys J. 2012 Sep 5;103(5):1060-8.

Abstract:

Transforming growth factor-\u03b21 (TGF-\u03b21) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-\u03b21 from its latent form provides spatiotemporal control over TGF-\u03b21 signaling, but the current understanding of TGF-\u03b21 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-\u03b21, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-\u03b21 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-\u03b21, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-\u03b21 positive feedback loop and causes an unexpected net decrease in TGF-\u03b21 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-\u03b21. The TGF-\u03b21 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-\u03b21 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-\u03b21 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-\u03b21 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-\u03b2 dysregulation.

This model is hosted on BioModels Database and identifiedby: MODEL1303130000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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in TGF-\u03b21 activation", + "submitted": 1396838557, + "submitter": "Huipeng Li", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724273651, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL1303130000", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1303130000" + }, + { + "accession": "BIOMD0000000447", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000447" + }, + { + "accession": "23009856", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/23009856" + }, + { + "accession": "MODEL1303130001", + "qualifier": "bqmodel:isDerivedFrom", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1303130001" + }, + { + "accession": "GO:0070053", + "name": 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TGF-\u03b21 activation", + "publication": { + "accession": "23009856", + "affiliation": "Singapore-MIT Alliance, Computational Systems Biology Programme, Singapore.", + "authors": [ + { + "institution": "Singapore-MIT Alliance, Computational Systems Biology Programme, Singapore.", + "name": "Lakshmi Venkatraman" + }, + { + "name": "Ser-Mien Chia" + }, + { + "name": "Balakrishnan Chakrapani Narmada", + "orcid": "0000-0002-9536-5015" + }, + { + "name": "Jacob K White" + }, + { + "name": "Sourav S Bhowmick" + }, + { + "name": "C Forbes Dewey" + }, + { + "name": "Peter T So" + }, + { + "name": "Lisa Tucker-Kellogg", + "orcid": "0000-0002-1301-7069" + }, + { + "name": "Hanry Yu", + "orcid": "0000-0002-0339-3685" + } + ], + "issue": "5", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/23009856", + "month": "9", + "pages": "1060-1068", + "synopsis": "Transforming growth factor-\u03b21 (TGF-\u03b21) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-\u03b21 from its latent form provides spatiotemporal control over TGF-\u03b21 signaling, but the current understanding of TGF-\u03b21 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-\u03b21, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-\u03b21 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-\u03b21, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-\u03b21 positive feedback loop and causes an unexpected net decrease in TGF-\u03b21 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-\u03b21. The TGF-\u03b21 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-\u03b21 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-\u03b21 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-\u03b21 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-\u03b2 dysregulation.", + "title": "Plasmin triggers a switch-like decrease in thrombospondin-dependent activation of TGF-\u03b21.", + "type": "PubMed ID", + "volume": "103", + "year": 2012 + }, + "publicationId": "BIOMD0000000447", + "submissionId": "MODEL1303130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000448": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Link\u00f6ping University", + "email": "elin.nyman@liu.se", + "external": false, + "name": "Elin Nyman" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (normal condition)

The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under normal condtion

This model is described in the article:

Br\u00e4nnmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Str\u00e5lfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.

Abstract:

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

This model is hosted on BioModels Database and identifiedby: MODEL1304190000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (diabetic condition)

The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under diabetic condtion

This model is described in the article:

Br\u00e4nnmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Str\u00e5lfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.

Abstract:

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

This model is hosted on BioModels Database and identifiedby: MODEL1304160000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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}, + { + "institution": "Department of Clinical and Experimental Medicine, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden.", + "name": "Siri Fagerholm" + }, + { + "institution": "Department of Clinical and Experimental Medicine, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden.", + "name": "Linn\u00e9a Bergenholm" + }, + { + "institution": "Department of Clinical and Experimental Medicine, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden.", + "name": "Eva-Maria Ekstrand", + "orcid": "0000-0001-5260-1826" + }, + { + "institution": "Department of Clinical and Experimental Medicine, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden; Department of Biomedical Engineering, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden.", + "name": "Gunnar Cedersund" + }, + { + "institution": "Department of Clinical and Experimental Medicine, Link\u00f6ping University, SE58185 Link\u00f6ping, Sweden. Electronic address: peter.stralfors@liu.se.", + "name": "Peter Str\u00e5lfors" + } + ], + "issue": "14", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/23400783", + "month": "4", + "pages": "9867-9880", + "synopsis": "Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.", + "title": "Insulin signaling in type 2 diabetes: experimental and modeling analyses reveal mechanisms of insulin resistance in human adipocytes.", + "type": "PubMed ID", + "volume": "288", + "year": 2013 + }, + "publicationId": "BIOMD0000000449", + "submissionId": "MODEL1304160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000450": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of M?laga. CIBERER (Enfermedades Raras)", + "email": "armando@uma.es", + "external": false, + "name": "Armando Reyes-Palomares" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version2

Mammalian polyamine metabolism consists of a bi-cycle with two required entrances, omithine and S-adenosyl methionine (SAM), and several alternative exists. The relevant regulatory roles of the short half-life enzymes ornithine decarboxylase (ODC), S-adenosyl methione decarboxylase (SAMDC) and spermindine/spermine acetyl transferase (SSAT) in polyamine metabolism are well studied, and has been modelled here.

This model is described in the article:

Reyes-Palomares A, Monta\u00f1ez R, S\u00e1nchez-Jim\u00e9nez F, Medina MA
Amino Acids, February 2012, Volume 42, Issue 2-3, pp 597-610

Abstract:

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

Notes by the author:

This model combines BIOMD0000000190 and BIOMD0000000268 from BioModels Database, both models include corrections respect to their originals publications.

To simulate a MATI/MATIII switch to MATII in proliferating liver:

We set to 0 the Vmax parameters of MATI and MATIII

We included MATII reaction equation.

We add a regulation factor dependent of SAM levels in ODC and SAMDCe rates of synthesis (66.5/[SAM]).

H2O2 was increased in a 50 % according to an initial state ofproliferating and regenerating liver.

This model is hosted on BioModels Database and identifiedby: MODEL1305060001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.", + "title": "A combined model of hepatic polyamine and sulfur amino acid metabolism to analyze S-adenosyl methionine availability.", + "type": "PubMed ID", + "volume": "42", + "year": 2012 + }, + "publicationId": "BIOMD0000000450", + "submissionId": "MODEL1305060001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000451": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Center for Modeling Immunity to Enteric Pathogens", + "email": "acarbo@vbi.vt.edu", + "external": false, + "name": "Adria Carbo" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity

CD4+ T cells can differentiate into different phenotypes depending on the cytokine milieu. Here a computational and mathematical model with sixty ordinary differential equations representing a CD4+ T cell differentiating into either Th1, Th2, Th17 or iTreg cells, has been constructed. The model includes cytokines, nuclear receptors and transcription factors that define fate and function of CD4+ T cells. Computational simulations illustrate how a proinflammatory Th17 cell can undergo reprogramming into an anti-inflammatory iTreg phenotype following PPARc activation.

This model is described in the article:

Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J
PLoS Computational Biology [2013, 9(4):e1003027]

Abstract:

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPAR\u03b3) in modulating plasticity between Th17 and iTreg cells. PPAR\u03b3 regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPAR\u03b3 activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPAR\u03b3. Deletion of PPAR\u03b3 in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPAR\u03b3 in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

Author's comment: CD4+ T cell computational model (Version 1.4)Steady state corrected. There was a problem in the internalization of IL-17 in its mathematical function.

This model is hosted on BioModels Database and identifiedby: MODEL1304230001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPAR\u03b3) in modulating plasticity between Th17 and iTreg cells. PPAR\u03b3 regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPAR\u03b3 activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPAR\u03b3. Deletion of PPAR\u03b3 in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPAR\u03b3 in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.", + "title": "Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000451", + "submissionId": "MODEL1304230001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000452": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "university of tehran", + "email": "bidkhori@ibb.ut.ac.ir", + "external": false, + "name": "Gholamreza Bidkhori" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Bidkhori2012 - normal EGFR signalling

The paper describes and compares two models on EGFR signalling between normal and NSCLC cells. Moreover, it is shown that ERK (MAPK), STAT and Akt factor's activation pattern are different between normal and NSCLA models. This model corresponds to EGFR signalling in normal cells.

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

This model is described in the article:

Bidkhori G, Moeini A, Masoudi-Nejad A
PloS one [2012, 7(10):e48004]

Abstract:

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

This model is hosted on BioModels Database and identifiedby: MODEL1304020000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Bidkhori2012 - EGFR signalling in NSCLC

The paper describes and compares two models on EGFR signalling between normal and NSCLC cells. Moreover, it is shown that ERK (MAPK), STAT and Akt factor's activation pattern are different between normal and NSCLA models. This model corresponds to EGFR signalling in NSCLA cells.

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

This model is described in the article:

Bidkhori G, Moeini A, Masoudi-Nejad A
PloS one [2012, 7(10):e48004]

Abstract:

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

This model is hosted on BioModels Database and identifiedby: MODEL1304020001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Smallbone2013 - Metabolic Control Analysis - Example 1

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Smallbone2013 - Metabolic Control Analysis - Example 2

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Smallbone2013 - Metabolic Control Analysis - Example 3

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Metabolic Control Analysis - Example 3", + "publication": { + "accession": "10.48550/arXiv.1305.6449", + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "authors": [ + { + "name": "Kieran Smallbone" + } + ], + "journal": "Quantitative Methods", + "link": "http://identifiers.org/doi/10.48550/arXiv.1305.6449", + "month": "10", + "synopsis": "Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.", + "title": "Metabolic Control Analysis: Rereading Reder", + "type": "DOI", + "year": 2013 + }, + "publicationId": "BIOMD0000000456", + "submissionId": "MODEL1305030002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000457": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Manchester", + "email": "pedro.mendes@manchester.ac.uk", + "external": false, + "name": "Pedro Mendes" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Firczuk2013 - Eukaryotic mRNA translation machinery

This is a model of Saccharomyces cerevisiae mRNA translation which includes the initiation, elongation and termination phases. The model is for 20 condon mRNAs. The building of a multi-factor complex in initiation and also the different processes in elongation and termination are modelled in detail. The model takes into account that ribosomes cover more than one codon of mRNA so that the movement of ribosomes are effectively blocked by other ribosomes several codons downstream. It is assumed that 15 codons are occupied by each ribosome. This blocking effect is considered in reaction R18 in initiation and also reaction R26, the reaction where translocation of ribosomes takes place in elongation. The kinetic functions of these two reactions are based on MacDonald et al. 1968 and Heinrich & Rapaport 1980. All other kinetic functions follow mass-action kinetics. The concentrations of transfer RNA species (Met-tRNA, aa-tRNA and tRNA in the model) are kept constant, while the other species' concentrations can change in the course of the simulation. The model describes the translation of a short mRNA with 20 codons. Therefore, all reactions in the elongation cycle (R22, R23, R25, R26, R28 and R29) and the corresponding species are replicated accordingly to model the species with ribosomes bound at different positions. In summary, the model contains 165 different species and 141 reactions.

The value of the 56 rate constant parameters were estimated by fitting the model against a series of experimental data consisting of modulation of the various translation factors (Figures 2, 3 and S3). Overall the parameter estimation was carried out over 212 different data points (steady states).

This model is described in the article:

Helena Firczuk, Shichina Kannambath, J\u00fcrgen Pahle, Amy Claydon, Robert Beynon, John Duncan, Hans Westerhoff, Pedro Mendes and John EG McCarthy
Molecular Systems Biology. 9:635

Abstract:

Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNAi to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5\u2032untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5\u2032UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.

This model is hosted on BioModels Database and identified by: BIOMD0000000457 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Eukaryotic mRNA translation machinery", + "publication": { + "accession": "23340841", + "affiliation": "School of Life Sciences, University of Warwick, Coventry, UK.", + "authors": [ + { + "institution": "School of Life Sciences, University of Warwick, Coventry, UK.", + "name": "Helena Firczuk" + }, + { + "name": "Shichina Kannambath" + }, + { + "name": "J\u00fcrgen Pahle", + "orcid": "0000-0002-7637-7547" + }, + { + "name": "Amy Claydon" + }, + { + "name": "Robert Beynon", + "orcid": "0000-0003-0857-495X" + }, + { + "name": "John Duncan" + }, + { + "institution": "Infrastructure for Systems Biology Europe - The Netherlands (ISBE.NL), Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "Hans Westerhoff", + "orcid": "0000-0002-0443-6114" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.", + "name": "Pedro Mendes", + "orcid": "0000-0001-6507-9168" + }, + { + "name": "John Eg McCarthy" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/23340841", + "month": "0", + "pages": "635", + "synopsis": "Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. 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Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.", + "title": "An in vivo control map for the eukaryotic mRNA translation machinery.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000457", + "submissionId": "MODEL1205190000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000458": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Smallbone2013 - Serine biosynthesis

Kinetic modelling of metabolic pathways in application to Serine biosynthesis.

This model is described in the article:

Kieran Smallbone, Natalie J. Stanford
Methods in Molecular Biology. 2013; 985:113-121

Abstract:

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway using kinetic data from both experimental measurements and literature review. Our methodology is presented by using the example of serine biosynthesis in E. coli.

As there are no plots to be reproduced as curation figure, table 6 and 7 that corresponds to steady state concentration of metabolite and steady state fluxes of reactions has been reproduced.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000458 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Our methodology is presented by using the example of serine biosynthesis in E. coli.", + "title": "Kinetic modeling of metabolic pathways: application to serine biosynthesis.", + "type": "PubMed ID", + "volume": "985", + "year": 2013 + }, + "publicationId": "BIOMD0000000458", + "submissionId": "MODEL1203210000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000459": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Liebal2012 - B.subtilis post-transcription instability model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the post-transcription instability model (MODEL1302080001).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1302080001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Liebal2012 - B.subtilis sigB proteolysis model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the sigma B proteolysis model (MODEL1302080000).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1302080000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Liebal2012 - B.subtilis transcription inhibition model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models are constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the Transcription inhibition model (MODEL1212180000).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1212180000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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B.subtilis transcription inhibition model", + "publication": { + "accession": "22511268", + "affiliation": "Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany. ulf.liebal@uni-rostock.de", + "authors": [ + { + "institution": "Department of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany. ulf.liebal@uni-rostock.de", + "name": "Ulf W Liebal", + "orcid": "0000-0001-5172-7339" + }, + { + "name": "Praveen K Sappa" + }, + { + "name": "Thomas Millat", + "orcid": "0000-0002-5023-4142" + }, + { + "name": "Leif Steil", + "orcid": "0000-0002-0733-8421" + }, + { + "name": "Georg Homuth" + }, + { + "name": "Uwe V\u00f6lker", + "orcid": "0000-0002-5689-3448" + }, + { + "name": "Olaf Wolkenhauer", + "orcid": "0000-0001-6105-2937" + } + ], + "issue": "6", + "journal": "Molecular bioSystems", + "link": "http://identifiers.org/pubmed/22511268", + "month": "6", + "pages": "1806-1814", + "synopsis": "In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. 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The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.", + "title": "Proteolysis of beta-galactosidase following SigmaB activation in Bacillus subtilis.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000461", + "submissionId": "MODEL1212180000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000462": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2012 - Amyloid-beta aggregation

This model supports the current thinking that levels of dimers are important in initiating the aggregation process.

This model is described in the article:

Proctor CJ, Pienaar IS, Elson JL, Kirkwood TB
Molecular Neurodegeneration. 2012; 7:32

Abstract:

BACKGROUND: Alzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (A\u00df), particularly A\u00df\u2084\u2082, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting A\u00df\u2084\u2082 immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine A\u00df\u2084\u2082 turnover and its aggregation in detail and to test the effect of immunization against A\u00df dimers.

RESULTS: Our model demonstrates for the first time that even a slight decrease in the clearance rate of A\u00df\u2084\u2082 monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of A\u03b2 are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.

CONCLUSION: Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies.

This model is hosted on BioModels Database and identified by: BIOMD0000000462 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

Alzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (A\u00df), particularly A\u00df\u2084\u2082, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting A\u00df\u2084\u2082 immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine A\u00df\u2084\u2082 turnover and its aggregation in detail and to test the effect of immunization against A\u00df dimers.

Results

Our model demonstrates for the first time that even a slight decrease in the clearance rate of A\u00df\u2084\u2082 monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of A\u03b2 are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.

Conclusion

Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies.", + "title": "Aggregation, impaired degradation and immunization targeting of amyloid-beta dimers in Alzheimer's disease: a stochastic modelling approach.", + "type": "PubMed ID", + "volume": "7", + "year": 2012 + }, + "publicationId": "BIOMD0000000462", + "submissionId": "MODEL1202290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000463": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Bioprocess Engineering Group, Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany", + "email": "heldt@mpi-magdeburg.mpg.de", + "external": false, + "name": "Frank Stefan Heldt" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Heldt2012 - Influenza Virus Replication

The model describes the life cycle of influenza A virus in a mammalian cell including the following steps: attachment of parental virions to the cell membrane, receptor-mediated endocytosis, fusion of the virus envelope with the endosomal membrane, nuclear import of vRNPs, viral transcription and replication, translation of the structural viral proteins, nuclear export of progeny vRNPs and budding of new virions. It also explicitly accounts for the stabilization of cRNA by viral polymerases and NP and the inhibition of vRNP activity by M1 protein binding. In short, the model focuses on the molecular mechanism that controls viral transcription and replication.

This model is described in the article:

Heldt FS, Frensing T, Reichl U.
J Virol.

Abstract:

Influenza viruses transcribe and replicate their negative-sense RNA genome inside the nucleus of host cells via three viral RNA species. In the course of an infection, these RNAs show distinct dynamics, suggesting that differential regulation takes place. To investigate this regulation in a systematic way, we developed a mathematical model of influenza virus infection at the level of a single mammalian cell. It accounts for key steps of the viral life cycle, from virus entry to progeny virion release, while focusing in particular on the molecular mechanisms that control viral transcription and replication. We therefore explicitly consider the nuclear export of viral genome copies (vRNPs) and a recent hypothesis proposing that replicative intermediates (cRNA) are stabilized by the viral polymerase complex and the nucleoprotein (NP). Together, both mechanisms allow the model to capture a variety of published data sets at an unprecedented level of detail. Our findings provide theoretical support for an early regulation of replication by cRNA stabilization. However, they also suggest that the matrix protein 1 (M1) controls viral RNA levels in the late phase of infection as part of its role during the nuclear export of viral genome copies. Moreover, simulations show an accumulation of viral proteins and RNA toward the end of infection, indicating that transport processes or budding limits virion release. Thus, our mathematical model provides an ideal platform for a systematic and quantitative evaluation of influenza virus replication and its complex regulation.

With the current parameter set, the model reproduces an infection at a multiplicity of infection (MOI) of 10. Figure 2A of the paper is reproduced here, with parameters kDegRnp and kSynP changed to zeros.

Initial conditions and parameter changes that were used to obtain specific figures in the article can be found in Table A2.

The model has the correct value for kAttLo as 4.55e-04. The value of this parameter mentioned as 4.55e-02 in Table 1 of the paper is incorrect. This is checked with the author.

This model is hosted on BioModels Database and identifiedby: MODEL1307270000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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"institution": "Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.", + "name": "Andrew Koo" + }, + { + "name": "David Nordsletten", + "orcid": "0000-0002-5363-4715" + }, + { + "name": "Renato Umeton", + "orcid": "0000-0002-5561-6932" + }, + { + "name": "Beracah Yankama" + }, + { + "name": "Shiva Ayyadurai" + }, + { + "name": "Guillermo Garc\u00eda-Carde\u00f1a" + }, + { + "name": "C Forbes Dewey" + } + ], + "issue": "10", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/23708369", + "month": "5", + "pages": "2295-2306", + "synopsis": "Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. 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Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "title": "In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology.", + "type": "PubMed ID", + "volume": "104", + "year": 2013 + }, + "publicationId": "BIOMD0000000467", + "submissionId": "MODEL1302180006", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000468": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. 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Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. 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Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

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Industrial Biotechnology 9, 4 (2013).

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They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "title": "Large-Scale Metabolic Models: From Reconstruction to Differential Equations", + "type": "DOI", + "year": 2013 + }, + "publicationId": "BIOMD0000000473", + "submissionId": "MODEL1302140005", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000474": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "Newcastle University", + "email": "graham.smith3@ncl.ac.uk", + "external": false, + "name": "Graham Smith" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Smith2013 - Regulation of Insulin Signalling by Oxidative Stress

The model describes insulin signalling (in rodent adipocytes), which includes in addition to the core pathway, the transcriptional feedback through the Forkhead box type O (FOXO) transcription factor and interaction with oxidative stress.

This model is described in the article:

Smith GR, Shanley DP.
BMC Syst Biol. 2013 May 24;7:41.

Abstract:

BACKGROUND: Existing models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO.

RESULTS: Insulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear.

CONCLUSION: Our model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level.A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000.Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012_insulin_signalling.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000474 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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"institution": "Centre for Integrated Systems Biology of Ageing & Nutrition (CISBAN), Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK.", + "name": "Graham R Smith" + }, + { + "name": "Daryl P Shanley", + "orcid": "0000-0003-3096-6386" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/23705851", + "month": "5", + "pages": "41", + "synopsis": "

Background

Existing models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO.

Results

Insulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear.

Conclusion

Our model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level.A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000.Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012_insulin_signalling.", + "title": "Computational modelling of the regulation of Insulin signalling by oxidative stress.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000474", + "submissionId": "MODEL1212210000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000475": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Bergamo", + "email": "paolo.cazzaniga@unibg.it", + "external": false, + "name": "Paolo Cazzaniga" + } + ] + }, + "curationStatus": "CURATED", + "description": " Mechanistic model of the Post-Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. 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PCNA ubiquitylation in the activation of PRR pathway", + "publication": { + "accession": "23514624", + "affiliation": "Dipartimento di Bioscienze, Universit\u00e0 degli Studi di Milano, Milano, Italy.", + "authors": [ + { + "institution": "Dipartimento di Bioscienze, Universit\u00e0 degli Studi di Milano, Milano, Italy.", + "name": "Flavio Amara" + }, + { + "name": "Riccardo Colombo", + "orcid": "0000-0002-9499-0126" + }, + { + "name": "Paolo Cazzaniga" + }, + { + "name": "Dario Pescini", + "orcid": "0000-0002-3090-4823" + }, + { + "name": "Attila Csik\u00e1sz-Nagy", + "orcid": "0000-0002-2919-5601" + }, + { + "name": "Marco Muzi Falconi" + }, + { + "name": "Daniela Besozzi", + "orcid": "0000-0001-5532-3059" + }, + { + "name": "Paolo Plevani", + "orcid": "0000-0003-1869-2626" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/23514624", + "month": "3", + "pages": "24", + "synopsis": "

Background

The genome of living organisms is constantly exposed to several damaging agents that induce different types of DNA lesions, leading to cellular malfunctioning and onset of many diseases. To maintain genome stability, cells developed various repair and tolerance systems to counteract the effects of DNA damage. Here we focus on Post Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA).

Results

We developed a novel protocol to measure the time-course ratios between mono-, di- and tri-ubiquitylated PCNA isoforms on a single western blot, which were used as the wet readout for PRR events in wild type and mutant S. cerevisiae cells exposed to acute UV radiation doses. Stochastic simulations of PCNA ubiquitylation dynamics, performed by exploiting a novel mechanistic model of PRR, well fitted the experimental data at low UV doses, but evidenced divergent behaviors at high UV doses, thus driving the design of further experiments to verify new hypothesis on the functioning of PRR. The model predicted the existence of a UV dose threshold for the proper functioning of the PRR model, and highlighted an overlapping effect of Nucleotide Excision Repair (the pathway effectively responsible to clean the genome from UV lesions) on the dynamics of PCNA ubiquitylation in different phases of the cell cycle. In addition, we showed that ubiquitin concentration can affect the rate of PCNA ubiquitylation in PRR, offering a possible explanation to the DNA damage sensitivity of yeast strains lacking deubiquitylating enzymes.

Conclusions

We exploited an in vivo and in silico combinational approach to analyze for the first time in a Systems Biology context the events of PCNA ubiquitylation occurring in PRR in budding yeast cells. Our findings highlighted an intricate functional crosstalk between PRR and other events controlling genome stability, and evidenced that PRR is more complicated and still far less characterized than previously thought.", + "title": "In vivo and in silico analysis of PCNA ubiquitylation in the activation of the Post Replication Repair pathway in S. cerevisiae.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000475", + "submissionId": "MODEL1211260000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000476": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

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However, publicly available models encoded in standard representations such as the Systems Biology Markup Language (SBML) either do not encode this input or use different mechanisms to do so, which hinders reproducibility of published results as well as model reuse. The authors describe here a numerically continuous function suitable for use in SBML for models of circadian rhythms forced by periodic light-dark cycles. The Input Signal Step Function (ISSF) is broadly applicable to encoding experimental manipulations, such as drug treatments, temperature changes, or inducible transgene expression, which may be transient, periodic, or mixed. It is highly configurable and is able to reproduce a wide range of waveforms. The authors have implemented this function in SBML and demonstrated its ability to modify the behavior of publicly available models to accurately reproduce published results. The implementation of ISSF allows standard simulation software to reproduce specialized circadian protocols, such as the phase-response curve. To facilitate the reuse of this function in public models, the authors have developed software to configure its behavior without any specialist knowledge of SBML. A community-standard approach to represent the inputs that entrain circadian clock models could particularly facilitate research in chronobiology.", + "title": "The Input Signal Step Function (ISSF), a standard method to encode input signals in SBML models with software support, applied to circadian clock models.", + "type": "PubMed ID", + "volume": "27", + "year": 2012 + }, + "publicationId": "BIOMD0000000476", + "submissionId": "MODEL1001130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000477": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "National Centre for Cell Science, Pune", + "email": "singhs@nccs.res.in", + "external": false, + "name": "Shailza Singh" + } + ] + }, + "curationStatus": "CURATED", + "description": "

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"DOID:9065", + "name": "leishmaniasis", + "qualifier": "bqbiol:hasProperty", + "resource": "Human Disease Ontology", + "uri": "http://identifiers.org/doid/DOID:9065" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Mol2013 - Immune Signal Transduction in Leishmaniasis", + "publication": { + "accession": "23994140", + "affiliation": "National Centre for Cell Science, NCCS Complex, Ganeshkhind, Pune University Campus, Pune 411007, India.", + "authors": [ + { + "institution": "National Centre for Cell Science, NCCS Complex, Ganeshkhind, Pune University Campus, Pune 411007, India.", + "name": "Milsee Mol" + }, + { + "name": "Milind S Patole" + }, + { + "name": "Shailza Singh" + } + ], + "issue": "1", + "journal": "Biochimica et biophysica acta", + "link": "http://identifiers.org/pubmed/23994140", + "month": "1", + "pages": "71-79", + "synopsis": "

Background

Modulated immune signal (CD14-TLR and TNF) in leishmaniasis can be linked to EGFR pathway involved in wound healing, through crosstalk points. This signaling network can be further linked to a synthetic gene circuit acting as a positive feedback loop to elicit a synchronized intercellular communication among the immune cells which may contribute to a better understanding of signaling dynamics in leishmaniasis.

Methods

Network reconstruction with positive feedback loop, simulation (ODE 15s solver) and sensitivity analysis of CD14-TLR, TNF and EGFR was done in SimBiology (MATLAB 7.11.1). Cytoscape and adjacency matrix were used to calculate network topology. PCA was extracted by using sensitivity coefficient in MATLAB. Model reduction was done using time, flux and sensitivity score.

Results

Network has five crosstalk points: NIK, I\u03baB-NF\u03baB and MKK (4/7, 3/6, 1/2) which show high flux and sensitivity. PI3K in EGFR pathway shows high flux and sensitivity. PCA score was high for cytoplasmic ERK1/2, PI3K, Atk, STAT1/3 and nuclear JNK. Of the 125 parameters, 20% are crucial as deduced by model reduction.

Conclusions

EGFR can be linked to CD14-TLR and TNF through the MAPK crosstalk points. These pathways may be controlled through Ras and Raf that lie upstream of signaling components ERK \u00bd (c) and JNK (n) that have a high PCA score via a synthetic gene circuit for activating cell-cell communication to elicit an inflammatory response. Also a disease resolving effect may be achieved through PI3K in the EGFR pathway.

General significance

The reconstructed signaling network can be linked to a gene circuit with a positive feedback loop, for cell-cell communication resulting in synchronized response in the immune cell population, for disease resolving effect in leishmaniasis.", + "title": "Immune signal transduction in leishmaniasis from natural to artificial systems: role of feedback loop insertion.", + "type": "PubMed ID", + "volume": "1840", + "year": 2014 + }, + "publicationId": "BIOMD0000000477", + "submissionId": "MODEL1308080000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000478": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Bergamo", + "email": "paolo.cazzaniga@unibg.it", + "external": false, + "name": "Paolo Cazzaniga" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Besozzi2012 - Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiae

Mechanistic model of the Ras/cAMP/PKA in yeast S.cerevisiae. The Ras/cAMP/PKA pathway plays a major role in the regulation of metabolism, stress resistance and cell cycle progress and is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA). This model investigates the dynamics of the second messenger cAMP on Ras/cAMP/PKA pathway, to determine the effects of the feedback mechanisms on establising stable oscillatory regimes.

The model has been defined according to the stochastic formulation of chemical kinetics [Gillespie DT, 1977] , which requires to specify the set of molecular species occurring in the pathway and their respective interactions, formally described as a set of biochemical reactions.

The volume considered for this system is 30fL; this value can be used to convert the model into the deterministic formulation.

This model is described in the article:

Besozzi D, Cazzaniga P, Pescini D, Mauri G, Colombo S, Martegani E.
EURASIP J Bioinform Syst Biol. 2012 Jul 20;2012(1):10.

Abstract:

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. This multi-level regulation of the amplitude and frequency of oscillations in the Ras/cAMP/PKA pathway might act as a fine tuning mechanism for the downstream targets of PKA, as also recently evidenced by some experimental investigations on the nucleocytoplasmic shuttling of the transcription factor Msn2 in yeast cells.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000478 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiae", + "publication": { + "accession": "22818197", + "affiliation": "Universit\u00e0 degli Studi di Milano, Dipartimento di Informatica, Via Comelico 39, 20135 Milano, Italy. besozzi@di.unimi.it.", + "authors": [ + { + "name": "Daniela Besozzi", + "orcid": "0000-0001-5532-3059" + }, + { + "name": "Paolo Cazzaniga" + }, + { + "name": "Dario Pescini", + "orcid": "0000-0002-3090-4823" + }, + { + "name": "Giancarlo Mauri", + "orcid": "0000-0003-3520-4022" + }, + { + "name": "Sonia Colombo", + "orcid": "0000-0001-5104-508X" + }, + { + "name": "Enzo Martegani", + "orcid": "0000-0001-9196-4224" + } + ], + "issue": "1", + "journal": "EURASIP journal on bioinformatics & systems biology", + "link": "http://identifiers.org/pubmed/22818197", + "month": "7", + "pages": "10", + "synopsis": ": In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. 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Croft2013 - GPCR-RGS interaction that compartmentalizes RGS activity

Through modelling studies, the classic quaternary complex (ligand-GPCR-G-RGS) has been extended to include an additional layer of regulation through GPCR-RGS interactions, which facilitate the compartmentalization of RGS activity into the plasma membrane and non-plasma compartments.

This model is described in the article:

Croft W, Hill C, McCann E, Bond M, Esparza-Franco M, Bennett J, Rand D, Davey J, Ladds G.
J Biol Chem. 2013 Sep 20;288(38):27327-42.

Abstract:

G protein-coupled receptors (GPCRs) can interact with regulator of G protein signaling (RGS) proteins. However, the effects of such interactions on signal transduction and their physiological relevance have been largely undetermined. Ligand-bound GPCRs initiate by promoting exchange of GDP for GTP on the G\u03b1 subunit of heterotrimeric G proteins. Signaling is terminated by hydrolysis of GTP to GDP through intrinsic GTPase activity of the G\u03b1 subunit, a reaction catalyzed by RGS proteins. Using yeast as a tool to study GPCR signaling in isolation, we define an interaction between the cognate GPCR (Mam2) and RGS (Rgs1), mapping the interaction domains. This reaction tethers Rgs1 at the plasma membrane and is essential for physiological signaling response. In vivo quantitative data inform the development of a kinetic model of the GTPase cycle, which extends previous attempts by including GPCR-RGS interactions. In vivo and in silico data confirm that GPCR-RGS interactions can impose an additional layer of regulation through mediating RGS subcellular localization to compartmentalize RGS activity within a cell, thus highlighting their importance as potential targets to modulate GPCR signaling pathways.

Author's comment on reproducing the plots:
To reproduce dose-response plots in the publication, the model is simulated with 12 different ligand concentrations (see parameter Ligand_conc).
For each ligand concentration, a single value corresponding to total amount of output must be obtained, by calculating the area under the curve of the trajectory of species z3, from time=0 to time=30.
These total output values are then used to build a dose-response plot (authors used GraphPad Prism).
Mutant strains are simulated with alternative parameter values or initial conditions specified in the Supplementary Material.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000479 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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However, the effects of such interactions on signal transduction and their physiological relevance have been largely undetermined. Ligand-bound GPCRs initiate by promoting exchange of GDP for GTP on the G\u03b1 subunit of heterotrimeric G proteins. Signaling is terminated by hydrolysis of GTP to GDP through intrinsic GTPase activity of the G\u03b1 subunit, a reaction catalyzed by RGS proteins. Using yeast as a tool to study GPCR signaling in isolation, we define an interaction between the cognate GPCR (Mam2) and RGS (Rgs1), mapping the interaction domains. This reaction tethers Rgs1 at the plasma membrane and is essential for physiological signaling response. In vivo quantitative data inform the development of a kinetic model of the GTPase cycle, which extends previous attempts by including GPCR-RGS interactions. In vivo and in silico data confirm that GPCR-RGS interactions can impose an additional layer of regulation through mediating RGS subcellular localization to compartmentalize RGS activity within a cell, thus highlighting their importance as potential targets to modulate GPCR signaling pathways.", + "title": "A physiologically required G protein-coupled receptor (GPCR)-regulator of G protein signaling (RGS) interaction that compartmentalizes RGS activity.", + "type": "PubMed ID", + "volume": "288", + "year": 2013 + }, + "publicationId": "BIOMD0000000479", + "submissionId": "MODEL1305280000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000480": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Center for Modeling Immunity to Enteric Pathogens", + "email": "acarbo@vbi.vt.edu", + "external": false, + 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"name": "Pedragosa M" + }, + { + "name": "Viladomiu M" + }, + { + "name": "Marathe M" + }, + { + "name": "Eubank S" + }, + { + "name": "Wendelsdorf K" + }, + { + "name": "Bisset K" + }, + { + "name": "Hoops S" + }, + { + "name": "Deng X" + }, + { + "name": "Alam M" + }, + { + "name": "Kronsteiner B" + }, + { + "name": "Mei Y" + }, + { + "name": "Hontecillas R." + } + ], + "journal": "PLoS ONE 2013; 8(9): e73365", + "link": "http://identifiers.org/doi/10.1371/journal.pone.0073365", + "synopsis": "T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levels of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor \u03b3 (PPAR\u03b3) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPAR\u03b3 knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. 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It describes the growth and decay of the follicles and the corpus luteum, as well as the change of the key reproductive hormones, enzymes and processes over time. In this work we describe recent developments of this model towards the administration of prostaglandin F2\u03b1. We validate our model by showing that the simulations agree with observations from synchronization studies and with measured progesterone data after single dose administrations of synthetic prostaglandin F2\u03b1.", + "title": "Advances in modeling of the bovine estrous cycle: synchronization with PGF2\u03b1.", + "type": "PubMed ID", + "volume": "78", + "year": 2012 + }, + "publicationId": "BIOMD0000000481", + "submissionId": "MODEL1308100000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000482": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department of Computational Biology, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan", + "email": "noguchi@bi.s.u-tokyo.ac.jp", + "external": false, + "name": "Rei Noguchi" + } + ] + }, + "curationStatus": "CURATED", + "description": "Rei Noguchi, Hiroyuki Kubota, Katsuyuki Yugi, Yu Toyoshima, Yasunori Komori, Tomoyoshi Soga & Shinya Kuroda. The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns. Molecular Systems Biology 9 (2013).

Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin-signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLC(ex)). Based on the experimental results, we constructed a simple computational model that characterises response of insulin-signalling-dependent glucose metabolism. 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Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability. BMC Systems Biology 2 (2008).

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Background

Stochasticity plays important roles in many molecular networks when molecular concentrations are in the range of 0.1 muM to 10nM (about 100 to 10 copies in a cell). The chemical master equation provides a fundamental framework for studying these networks, and the time-varying landscape probability distribution over the full microstates, i.e., the combination of copy numbers of molecular species, provide a full characterization of the network dynamics. A complete characterization of the space of the microstates is a prerequisite for obtaining the full landscape probability distribution of a network. However, there are neither closed-form solutions nor algorithms fully describing all microstates for a given molecular network.

Results

We have developed an algorithm that can exhaustively enumerate the microstates of a molecular network of small copy numbers under the condition that the net gain in newly synthesized molecules is smaller than a predefined limit. We also describe a simple method for computing the exact mean or steady state landscape probability distribution over microstates. We show how the full landscape probability for the gene networks of the self-regulating gene and the toggle-switch in the steady state can be fully characterized. We also give an example using the MAPK cascade network. Data and server will be available at URL: http://scsb.sjtu.edu.cn/statespace.

Conclusion

Our algorithm works for networks of small copy numbers buffered with a finite copy number of net molecules that can be synthesized, regardless of the reaction stoichiometry, and is optimal in both storage and time complexity. The algorithm can also be used to calculate the rates of all transitions between microstates from given reactions and reaction rates. The buffer size is limited by the available memory or disk storage. Our algorithm is applicable to a class of biological networks when the copy numbers of molecules are small and the network is closed, or the network is open but the net gain in newly synthesized molecules does not exceed a predefined buffer capacity. For these networks, our method allows full stochastic characterization of the mean landscape probability distribution, and the steady state when it exists.", + "title": "Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000483", + "submissionId": "MODEL1308080008", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000484": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Illinois at Chicago", + "email": "youfang@uic.edu", + "external": false, + "name": "Youfang Cao" + } + ] + }, + "curationStatus": "CURATED", + "description": "Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. 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For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. 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"synopsis": "Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. 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For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "title": "Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.", + "type": "PubMed ID", + "volume": "139", + "year": 2013 + }, + "publicationId": "BIOMD0000000484", + "submissionId": "MODEL1308080004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000485": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Illinois at Chicago", + "email": "youfang@uic.edu", + "external": false, + "name": "Youfang Cao" + } + ] + }, + "curationStatus": "CURATED", + "description": "F. Schl\ufffdgl. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift f\ufffdr Physik 253, 2 (1972).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. 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Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).

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Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proceedings of the National Academy of Sciences 102, 7 (2005).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. 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The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "title": "Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.", + "type": "PubMed ID", + "volume": "139", + "year": 2013 + }, + "publicationId": "BIOMD0000000487", + "submissionId": "MODEL1308080007", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000488": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2013 - Effect of A\u03b2 immunisationin Alzheimer's disease (deterministic version)

Extension of a previously published stochastic model (designed to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau ( BIOMD0000000286, BIOMD0000000462)) to include the main processes involved in passive and active immunisation against A\u03b2 and then to demonstrate the effects of this intervention on soluble A\u03b2. This is the deterministic version of the model, the stochastic version is BIOMD0000000634.

This model is described in the article:

Proctor CJ, Boche D, Gray DA, Nicoll JA.
PLoS ONE 2013; 8(9): e73631

Abstract:

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000488.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Effect of A\u03b2 immunisation in Alzheimer's disease (deterministic version)", + "publication": { + "accession": "24098635", + "affiliation": "Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom.", + "authors": [ + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + }, + { + "name": "Delphine Boche", + "orcid": "0000-0002-5884-130X" + }, + { + "name": "Douglas A Gray", + "orcid": "0000-0002-8634-4984" + }, + { + "name": "James A R Nicoll", + "orcid": "0000-0002-9444-7246" + } + ], + "issue": "9", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/24098635", + "month": "0", + "pages": "e73631", + "synopsis": "Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.", + "title": "Investigating interventions in Alzheimer's disease with computer simulation models.", + "type": "PubMed ID", + "volume": "8", + "year": 2013 + }, + "publicationId": "BIOMD0000000488", + "submissionId": "MODEL1212030000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000489": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Edinburgh", + "email": "gemma.sharp@ed.ac.uk", + "external": false, + "name": "Gemma Sharp" + } + ] + }, + "curationStatus": "CURATED", + "description": "Markus W. Covert, Thomas H. Leung, Jahlionais E. Gaston & David Baltimore. Achieving stability of lipopolysaccharide-induced NF-kappaB activation. Science 309, 5742 (2005).

Preterm birth is the single biggest cause of significant neonatal morbidity and mortality, and the incidence is rising. Development of new therapies to treat and prevent preterm labour is seriously hampered by incomplete understanding of the molecular mechanisms that initiate labour at term and preterm. Computational modelling provides a new opportunity to improve this understanding. It is a useful tool in (i) identifying gaps in knowledge and informing future research, and (ii) providing the basis for an in silico model of parturition in which novel drugs to prevent or treat preterm labour can be \"tested\". Despite their merits, computational models are rarely used to study the molecular events initiating labour. Here, we present the first attempt to generate a dynamic kinetic model that has relevance to the molecular mechanisms of preterm labour. Using published data, we model an important candidate signalling pathway in infection-induced preterm labour: that of lipopolysaccharide (LPS) -induced activation of Nuclear Factor kappa B. This is the first model of this pathway to explicitly include molecular interactions upstream of Nuclear Factor kappa B activation. We produced a formalised graphical depiction of the pathway and built a kinetic model based on ordinary differential equations. The kinetic model accurately reproduced published in vitro time course plots of Lipopolysaccharide-induced Nuclear Factor kappa B activation in mouse embryo fibroblasts. In this preliminary work we have provided proof of concept that it is possible to build computational models of signalling pathways that are relevant to the regulation of labour, and suggest that models that are validated with wet-lab experiments have the potential to greatly benefit the field.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2)", + "fileSize": "113758", + "md5sum": "d32437d8026c696c02270edf5c6ba629", + "mimeType": "application/rdf+xml", + "name": "BIOMD0000000489-biopax2.owl", + "sha1sum": "df16bfb941c119c8264fc614c68c4b85b9a0262c", + "sha256sum": "e26a257988628dc780cfc5f2cc4f31698223f380936f5dd81d7928d39b2422b7" + }, + { + "description": "Auto-generated BioPAX (Level 3)", + "fileSize": "189834", + "md5sum": "6c96dd2b11675a769284d790c368536f", + "mimeType": "application/rdf+xml", + "name": "BIOMD0000000489-biopax3.owl", + "sha1sum": "7d4af6aaa19b8d8f4ca5ac2c594d0615ce07a3db", + "sha256sum": 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Gemma.sharp@ed.ac.uk", + "authors": [ + { + "institution": "Medical Research Council (MRC) Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom. Gemma.sharp@ed.ac.uk", + "name": "Gemma C Sharp", + "orcid": "0000-0003-2906-4035" + }, + { + "name": "Hongwu Ma" + }, + { + "name": "Philippa T K Saunders", + "orcid": "0000-0001-9051-9380" + }, + { + "name": "Jane E Norman" + } + ], + "issue": "7", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/23936158", + "month": "0", + "pages": "e70180", + "synopsis": "Preterm birth is the single biggest cause of significant neonatal morbidity and mortality, and the incidence is rising. Development of new therapies to treat and prevent preterm labour is seriously hampered by incomplete understanding of the molecular mechanisms that initiate labour at term and preterm. Computational modelling provides a new opportunity to improve this understanding. It is a useful tool in (i) identifying gaps in knowledge and informing future research, and (ii) providing the basis for an in silico model of parturition in which novel drugs to prevent or treat preterm labour can be \"tested\". Despite their merits, computational models are rarely used to study the molecular events initiating labour. Here, we present the first attempt to generate a dynamic kinetic model that has relevance to the molecular mechanisms of preterm labour. Using published data, we model an important candidate signalling pathway in infection-induced preterm labour: that of lipopolysaccharide (LPS) -induced activation of Nuclear Factor kappa B. This is the first model of this pathway to explicitly include molecular interactions upstream of Nuclear Factor kappa B activation. We produced a formalised graphical depiction of the pathway and built a kinetic model based on ordinary differential equations. The kinetic model accurately reproduced published in vitro time course plots of Lipopolysaccharide-induced Nuclear Factor kappa B activation in mouse embryo fibroblasts. In this preliminary work we have provided proof of concept that it is possible to build computational models of signalling pathways that are relevant to the regulation of labour, and suggest that models that are validated with wet-lab experiments have the potential to greatly benefit the field.", + "title": "A computational model of lipopolysaccharide-induced nuclear factor kappa B activation: a key signalling pathway in infection-induced preterm labour.", + "type": "PubMed ID", + "volume": "8", + "year": 2013 + }, + "publicationId": "BIOMD0000000489", + "submissionId": "MODEL1303230000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000490": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Demin2013 - PKPD behaviour - 5-Lipoxygenaseinhibitors

This model is described in the article:

Demin O, Karelina T, Svetlichniy D, Metelkin E, Speshilov G, Demin O Jr, Fairman D, van der Graaf PH, Agoram BM.
CPT Pharmacometrics Syst Pharmacol 2013; 2: e74

Abstract:

Zileuton, a 5-lipoxygenase (5LO) inhibitor, displays complex pharmaokinetic (PK)-pharmacodynamic (PD) behavior. Available clinical data indicate a lack of dose-bronchodilatory response during initial treatment, with a dose response developing after ~1-2 weeks. We developed a quantitative systems pharmacology (QSP) model to understand the mechanism behind this phenomenon. The model described the release, maturation, and trafficking of eosinophils into the airways, leukotriene synthesis by the 5LO enzyme, leukotriene signaling and bronchodilation, and the PK of zileuton. The model provided a plausible explanation for the two-phase bronchodilatory effect of zileuton-the short-term bronchodilation was due to leukotriene inhibition and the long-term bronchodilation was due to inflammatory cell infiltration blockade. The model also indicated that the theoretical maximum bronchodilation of both 5LO inhibition and leukotriene receptor blockade is likely similar. QSP modeling provided interesting insights into the effects of leukotriene modulation.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e74; doi:10.1038/psp.2013.49; advance online publication 11 September 2013.

This model is hosted on BioModels Database and identified by: BIOMD0000000490.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Demin2013 - PKPD behaviour - 5-Lipoxygenase inhibitors", + "publication": { + "accession": "24026253", + "affiliation": "Institute for Systems Biology SPb, Moscow, Russia.", + "authors": [ + { + "institution": "Institute for Systems Biology SPb, Moscow, Russia.", + "name": "O Demin" + }, + { + "name": "T Karelina" + }, + { + "name": "D Svetlichniy" + }, + { + "institution": "Institute for Systems Biology, Moscow, Russia.", + "name": "E Metelkin", + "orcid": "0000-0001-6612-5373" + }, + { + "name": "G Speshilov", + "orcid": "0000-0002-6539-6494" + }, + { + "name": "O Demin" + }, + { + "name": "D Fairman" + }, + { + "name": "P H van der Graaf" + }, + { + "name": "B M Agoram" + } + ], + "journal": "CPT: pharmacometrics & systems pharmacology", + "link": "http://identifiers.org/pubmed/24026253", + "month": "9", + "pages": "e74", + "synopsis": "Zileuton, a 5-lipoxygenase (5LO) inhibitor, displays complex pharmaokinetic (PK)-pharmacodynamic (PD) behavior. Available clinical data indicate a lack of dose-bronchodilatory response during initial treatment, with a dose response developing after ~1-2 weeks. We developed a quantitative systems pharmacology (QSP) model to understand the mechanism behind this phenomenon. The model described the release, maturation, and trafficking of eosinophils into the airways, leukotriene synthesis by the 5LO enzyme, leukotriene signaling and bronchodilation, and the PK of zileuton. The model provided a plausible explanation for the two-phase bronchodilatory effect of zileuton-the short-term bronchodilation was due to leukotriene inhibition and the long-term bronchodilation was due to inflammatory cell infiltration blockade. The model also indicated that the theoretical maximum bronchodilation of both 5LO inhibition and leukotriene receptor blockade is likely similar. QSP modeling provided interesting insights into the effects of leukotriene modulation.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e74; doi:10.1038/psp.2013.49; advance online publication 11 September 2013.", + "title": "Systems pharmacology models can be used to understand complex pharmacokinetic-pharmacodynamic behavior: an example using 5-lipoxygenase inhibitors.", + "type": "PubMed ID", + "volume": "2", + "year": 2013 + }, + "publicationId": "BIOMD0000000490", + "submissionId": "MODEL1309160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000491": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "G. B. Pant University of Agriculture and Technology", + "email": "ak_gupta2k@rediffmail.com", + "external": false, + "name": "Anil Kumar" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Pathak2013 - MAPK activation in response to various abiotic stresses

MAPK activation mechanism in response to various abiotic stress conditions, such as cold, salt, drought, H2O2, heavy metal and ethylene, in plants

This model is described in the article:

Pathak RK, Taj G, Pandey D, Arora S, Kumar A.
Bioinformation 2013; 9(9): 443-449

Abstract:

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000491 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Pathak2013 - MAPK activation in response to various biotic stresses

MAPK activation mechanism in response to various biotic (fungal and bacterial pathogens) stress conditions in plants

This model is described in the article:

Pathak RK, Taj G, Pandey D, Arora S, Kumar A.
Bioinformation 2013; 9(9): 443-449

Abstract:

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000492 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.", + "title": "Modeling of the MAPK machinery activation in response to various abiotic and biotic stresses in plants by a system biology approach.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000492", + "submissionId": "MODEL1308220000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000493": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institute for Automation Engineering, Otto von Guericke University Magdeburg", + "email": "steffen.waldherr@ovgu.de", + "external": false, + "name": "Steffen Waldherr" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes

Mathematical model describing the mechanism of differentiation of mesenchymal stem cells to bone (osteoblasts) or cartilage (chondrocytes) cells.

This model is described in the article:

Schittler D, Hasenauer J, Allg\u00f6wer F, Waldherr S.
Chaos 2010 Dec; 20(4): 045121

Abstract:

Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000493 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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"http://identifiers.org/pubmed/21198133", + "month": "12", + "pages": "045121", + "synopsis": "Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation.", + "title": "Cell differentiation modeled via a coupled two-switch regulatory network.", + "type": "PubMed ID", + "volume": "20", + "year": 2010 + }, + "publicationId": "BIOMD0000000493", + "submissionId": "MODEL1310150000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000494": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Zuse Institute Berlin", + "email": "susanna.roeblitz@zib.de", + "external": false, + "name": "Susanna R\u00f6blitz" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Roblitz2013 - Menstrual Cycle following GnRH analogue administration

The model describes the menstrual cycle feedback mechanisms. GnRH, FSH, LH, E2, P4, inbibins A and B, and follicular development are modelled. The model predicts hormonal changes following GnRH analogue administration. Simulation results agree with measurements of hormone blood concentrations. The model gives insight into mechanisms underlying gonadotropin supression.

This model is described in the article:

R\u00f6blitz S, St\u00f6tzel C, Deuflhard P, Jones HM, Azulay DO, van der Graaf PH, Martin SW.
J. Theor. Biol. 2013 Mar; 321: 8-27

Abstract:

The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000494 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Menstrual Cycle following GnRH analogue administration", + "publication": { + "accession": "23206386", + "affiliation": "Computational Systems Biology Group, Zuse Institute Berlin (ZIB), Berlin, Germany. susanna.roeblitz@zib.de", + "authors": [ + { + "institution": "Zuse Institute Berlin, Berlin 14195, Germany.", + "name": "Susanna R\u00f6blitz", + "orcid": "0000-0002-2735-0030" + }, + { + "name": "Claudia St\u00f6tzel" + }, + { + "name": "Peter Deuflhard" + }, + { + "name": "Hannah M Jones" + }, + { + "name": "David-Olivier Azulay" + }, + { + "name": "Piet H van der Graaf" + }, + { + "name": "Steven W Martin" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/23206386", + "month": "3", + "pages": "8-27", + "synopsis": "The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.", + "title": "A mathematical model of the human menstrual cycle for the administration of GnRH analogues.", + "type": "PubMed ID", + "volume": "321", + "year": 2013 + }, + "publicationId": "BIOMD0000000494", + "submissionId": "MODEL1311220000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000495": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Montpellier 2", + "email": "ovidiu.radulescu@univ-montp2.fr", + "external": false, + "name": "Ovidiu Radulescu" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sen2013 - Phospholipid Synthesis in P.knowlesi

The model describes the multiple phospholipid synthetic pathways in Plasmodium knowlesi.

This model is described in the article:

Sen P, Vial HJ, Radulescu O.
BMC Syst Biol 2013 Nov; 7(1): 123

Abstract:

BACKGROUND: Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species. RESULTS: Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step.CONCLUSION: We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000495 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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"submitter": "Ovidiu Radulescu", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724275612, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL1310130000", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1310130000" + }, + { + "accession": "BIOMD0000000495", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000495" + }, + { + "accession": "24209716", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/24209716" + }, + { + "accession": "GO:0008654", + "name": "phospholipid biosynthetic process", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0008654" + }, + { + "accession": "5850", + "name": "Plasmodium knowlesi", + "qualifier": 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702 81 \u00d6rebro, Sweden. Electronic address: partho.sen@utu.fi.", + "name": "Partho Sen", + "orcid": "0000-0003-0475-2763" + }, + { + "name": "Henri J Vial" + }, + { + "name": "Ovidiu Radulescu", + "orcid": "0000-0001-6453-5707" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/24209716", + "month": "11", + "pages": "123", + "synopsis": "

Background

Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species.

Results

Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step.

Conclusion

We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance.", + "title": "Kinetic modelling of phospholipid synthesis in Plasmodium knowlesi unravels crucial steps and relative importance of multiple pathways.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000495", + "submissionId": "MODEL1310130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000496": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Humboldt University Berlin", + "email": "natalie.stanford-2@postgrad.manchester.ac.uk", + "external": false, + "name": "Natalie Stanford" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Stanford2013 - Kinetic model of yeast metabolic network (standard)

Large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants.

This model is described in the article:

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W.
PLoS ONE 2013; 8(11): e79195

Abstract:

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

[corrections made to the model compared to the paper, optional]

This model is hosted on BioModels Database and identifiedby: BIOMD0000000496 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Stanford2013 - Kinetic model of yeast metabolic network (standard)

Large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. This model is built with regulatory information.

This model is described in the article:

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W.
PLoS ONE 2013; 8(11): e79195

Abstract:

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000497 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Mitchell2013 - Liver Iron Metabolism

The model includes the core regulatory components of human liver iron metabolism.

This model is described in the article:

Mitchell S, Mendes P.
PLoS Comput. Biol. 2013 Nov; 9(11): e1003299

Abstract:

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000498 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Liver Iron Metabolism", + "publication": { + "accession": "24244122", + "affiliation": "School of Computer Science and Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom.", + "authors": [ + { + "institution": "University of California, Los Angeles", + "name": "Simon Mitchell", + "orcid": "0000-0003-1091-6349" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.", + "name": "Pedro Mendes", + "orcid": "0000-0001-6507-9168" + } + ], + "issue": "11", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/24244122", + "month": "0", + "pages": "e1003299", + "synopsis": "Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.", + "title": "A computational model of liver iron metabolism.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000498", + "submissionId": "MODEL1302260000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000499": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Developmental Signalling Lab, Cancer Research UK London Research Institute", + "email": "Bernhard.Schmierer@ymail.com", + "external": false, + "name": "Bernhard Schmierer" + } + ] + }, + "curationStatus": "CURATED", + "description": "Pedro Viz\u00e1n, Daniel S. J. Miller, Ilaria Gori, Debipriya Das, Bernhard Schmierer & Caroline S. Hill. Controlling long-term signaling: receptor dynamics determine attenuation and refractory behavior of the TGF-\u03b2 pathway. Science Signaling 6, 305 (2013).

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Although signaling dynamics have been studied for pathways downstream of receptor tyrosine kinases and G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors, they have not been investigated for the transforming growth factor-\u03b2 (TGF-\u03b2) superfamily pathways. Using an integrative experimental and mathematical modeling approach, we dissected the dynamic behavior of the TGF-\u03b2 to Smad pathway, which is mediated by type I and type II receptor serine/threonine kinases, in response to acute, chronic, and repeated ligand stimulations. TGF-\u03b2 exposure produced a transient response that attenuated over time, resulting in desensitized cells that were refractory to further acute stimulation. This loss of signaling competence depended on ligand binding, but not on receptor activity, and was restored only after the ligand had been depleted. 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Thus, we have shown that the long-term signaling behavior of the TGF-\u03b2 pathway is determined by receptor dynamics, does not require TGF-\u03b2-induced gene expression, and influences context-dependent responses in vivo.", + "title": "Controlling long-term signaling: receptor dynamics determine attenuation and refractory behavior of the TGF-\u03b2 pathway.", + "type": "PubMed ID", + "volume": "6", + "year": 2013 + }, + "publicationId": "BIOMD0000000499", + "submissionId": "MODEL1203120000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000500": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "pmxmb2@nottingham.ac.uk", + "external": false, + "name": "Michelle Baker" + }, + { + "affiliation": "University of Nottingham", + "email": "michelle.baker@nottingham.ac.uk", + "external": false, + "name": "Michelle Baker" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer model

The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely \"single GAS polymer model\" and \"double GAS polymer model\" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the \"single GAS polymer model\".

This model is described in the article:

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U
Nat Immunol. 2014 Feb;15(2):168-76.

Abstract:

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000500 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.", + "title": "STAT1-cooperative DNA binding distinguishes type 1 from type 2 interferon signaling.", + "type": "PubMed ID", + "volume": "15", + "year": 2014 + }, + "publicationId": "BIOMD0000000500", + "submissionId": "MODEL1311130001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000501": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "pmxmb2@nottingham.ac.uk", + "external": false, + "name": "Michelle Baker" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer model

The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely \"single GAS polymer model\" and \"double GAS polymer model\" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the \"double GAS polymer model\".

This model is described in the article:

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U
Nat Immunol. 2014 Feb;15(2):168-76.

Abstract:

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000501 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Messiha2013 - Pentose phosphate pathway model

This model describes the dynamic behaviour of the pentose phosphate pathway with the inclusion of various enzymes involved in the pathway. The model's predictions are compared with experimental observations of transient metabolite concentrations following a glucose pulse.

This model is described in the article:

Hanan L. Messiha, Edward Kent, Naglis Malys, Kathleen M. Carroll, Pedro Mendes, Kieran Smallbone
PeerJ PrePrints 1:e146v2

Abstract:

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000502 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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University of Manchester, Manchester, United Kingdom", + "authors": [ + { + "name": "Messiha HL" + }, + { + "name": "Kent E" + }, + { + "name": "Malys N" + }, + { + "name": "Carroll KM" + }, + { + "name": "Mendes P" + }, + { + "name": "Smallbone K." + } + ], + "journal": "PeerJ PrePrints", + "link": "http://identifiers.org/doi/10.7287/peerj.preprints.146v2", + "synopsis": "We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.", + "title": "Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast", + "type": "DOI", + "year": 2013 + }, + "publicationId": "BIOMD0000000502", + "submissionId": "MODEL1311290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000503": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Messiha2013 - combined glycolysis and pentose phosphate pathway model

BIOMD0000000502 and MODEL1303260018 are combined to examine the response to oxidative stress.

This model is described in the article:

Hanan L. Messiha, Edward Kent, Naglis Malys, Kathleen M. Carroll, Pedro Mendes, Kieran Smallbone
PeerJ PrePrints 1:e146v2

Abstract:

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000503 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.", + "title": "Enzyme characterisation and kinetic modelling of the pentose phosphate pathway in yeast", + "type": "DOI", + "year": 2013 + }, + "publicationId": "BIOMD0000000503", + "submissionId": "MODEL1311290001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000504": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2013 - Cartilage breakdown, interventions to reduce collagen release

The molecular pathways involved in cartilage breakdown is studied using this model to examine possible interventions to reduce cartilage collagen release. The model contains three separate submodels, one which describes the IL-1/JNK signalling pathway, secondly the OSM/STAT3 signalling pathway, and lastly a module which includes proMMP (Matrix matalloproteinase) activation, and aggrecan and collagen release.

This model is described in the article:

Proctor CJ, Macdonald C, Milner JM, Rowan AD, Cawston TE.
Arthritis Rheum. 2013 Nov 27.

Abstract:

Objective. To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions to reduce collagen release. Methods. We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language (SBML), a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein and activity and initiate cartilage collagen breakdown. Simulations were performed in the COPASI software package. Results. The model predicted that simulated inhibition of c-Jun N-terminal kinase (JNK) or p38 mitogen-activated protein kinase, and over-expression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Over-expression of TIMP-1 was much less effective than TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of Janus kinase 1 (JAK1), the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets which are downstream, such as the JNK pathway, rather than close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion. Our study shows the value of computer modelling as a tool for examining possible interventions to reduce cartilage collagen breakdown. The model predicts interventions that either prevent transcription or inhibit activity of collagenases are promising strategies and should be investigated further in an experimental setting. \u00a9 2013 American College of Rheumatology.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000504 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Cartilage breakdown, interventions to reduce collagen release", + "publication": { + "accession": "24285357", + "affiliation": "MRC-Arthritis Research UK Centre for Integrated research into Musculoskeletal Ageing; Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, UK.", + "authors": [ + { + "name": "Proctor CJ" + }, + { + "name": "Macdonald C" + }, + { + "name": "Milner JM" + }, + { + "name": "Rowan AD" + }, + { + "name": "Cawston TE." + } + ], + "journal": "Arthritis Rheum. 2013 Nov; :", + "link": "http://identifiers.org/pubmed/24285357", + "synopsis": "Objective. To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions to reduce collagen release. Methods. We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language (SBML), a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein and activity and initiate cartilage collagen breakdown. Simulations were performed in the COPASI software package. Results. The model predicted that simulated inhibition of c-Jun N-terminal kinase (JNK) or p38 mitogen-activated protein kinase, and over-expression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Over-expression of TIMP-1 was much less effective than TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of Janus kinase 1 (JAK1), the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets which are downstream, such as the JNK pathway, rather than close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion. Our study shows the value of computer modelling as a tool for examining possible interventions to reduce cartilage collagen breakdown. The model predicts interventions that either prevent transcription or inhibit activity of collagenases are promising strategies and should be investigated further in an experimental setting. \u00a9 2013 American College of Rheumatology.", + "title": "A computer simulation approach for assessing therapeutic intervention points to prevent cytokine-induced cartilage breakdown.", + "type": "PubMed ID", + "year": 2013 + }, + "publicationId": "BIOMD0000000504", + "submissionId": "MODEL1305280001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000505": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "
vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (steady-state model)

Lipid metabolism plays an important role in the development of metabolic syndrome, a major risk factor for cardiovascular disease and diabetes. This model gives insights into the response of lipid oxidation to dietart and medical interventions. The model predicts the rate of lipid oxidation and the time course of most acyl carnitines. There are two models described in the paper, (i) steady-state model [ BIOMD0000000505 ], (ii) time-course model [ BIOMD0000000506 ]. This model corresponds to the steady-state model.

This model is described in the article:

van Eunen K, Simons SM, Gerding A, Bleeker A, den Besten G, Touw CM, Houten SM, Groen BK, Krab K, Reijngoud DJ, Bakker BM.
PLoS Comput Biol. 2013;9(8):e1003186.

Abstract:

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000505 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.", + "title": "Biochemical competition makes fatty-acid \u03b2-oxidation vulnerable to substrate overload.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000505", + "submissionId": "MODEL1312040000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000506": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "
vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (time-course model)

Lipid metabolism plays an important role in the development of metabolic syndrome, a major risk factor for cardiovascular disease and diabetes. This model gives insights into the response of lipid oxidation to dietart and medical interventions. The model predicts the rate of lipid oxidation and the time course of most acyl carnitines. There are two models described in the paper, (i) steady-state model [ BIOMD0000000505 ], (ii) time-course model [ BIOMD0000000506 ]. This model corresponds to the time-course model.

This model is described in the article:

van Eunen K, Simons SM, Gerding A, Bleeker A, den Besten G, Touw CM, Houten SM, Groen BK, Krab K, Reijngoud DJ, Bakker BM.
PLoS Comput Biol. 2013;9(8):e1003186.

Abstract:

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000506 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Gardner2000 - genetic toggle switch in E.coli

The behaviour of the genetic toggle switch and the conditions for bistability has been studies using a synthetic, bistable gene circuit.

This model is described in the article:

Gardner TS, Cantor CR, Collins JJ
Nature. 2000 Jan 20;403(6767):339-42.

Abstract:

It has been proposed' that gene-regulatory circuits with virtually any desired property can be constructed from networks of simple regulatory elements. These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000507 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.", + "title": "Construction of a genetic toggle switch in Escherichia coli.", + "type": "PubMed ID", + "volume": "403", + "year": 2000 + }, + "publicationId": "BIOMD0000000507", + "submissionId": "MODEL1401190000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000508": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "duncan.barrack@nottingham.ac.uk", + "external": false, + "name": "Duncan Barrack" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Barrack2014 - Calcium/cell cycle coupling - Cyclin D dependent ATP release

This model is designed based on the hypothesis that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescents cells onto the cell cycle. The model describes the ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell.

This model is described in the article:

Barrack DS, Thul R, Owen MR.
J Theor Biol. 2014 Jan 13;347C:17-32.

Abstract:

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000508 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Calcium/cell cycle coupling - Cyclin D dependent ATP release", + "publication": { + "accession": "24434742", + "affiliation": "Horizon Digital Economy Research Institute, University of Nottingham, Nottingham NG7 2TU, UK; School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK. Electronic address: duncan.barrack@nottingham.ac.uk.", + "authors": [ + { + "institution": "Horizon Digital Economy Research Institute, University of Nottingham, Nottingham NG7 2TU, UK; School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK. Electronic address: duncan.barrack@nottingham.ac.uk.", + "name": "Duncan S Barrack" + }, + { + "institution": "School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK.", + "name": "R\u00fcdiger Thul" + }, + { + "institution": "School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK.", + "name": "Markus R Owen", + "orcid": "0000-0002-3028-9138" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/24434742", + "month": "4", + "pages": "17-32", + "synopsis": "Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.", + "title": "Modelling the coupling between intracellular calcium release and the cell cycle during cortical brain development.", + "type": "PubMed ID", + "volume": "347", + "year": 2014 + }, + "publicationId": "BIOMD0000000508", + "submissionId": "MODEL1401200000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000509": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "duncan.barrack@nottingham.ac.uk", + "external": false, + "name": "Duncan Barrack" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Barrack2014 - Calcium/cell cycle coupling - Rs dependent ATP release

This model is designed based on the hypothesis that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescents cells onto the cell cycle. The model describes the ATP mediated calcium-cell cycle coupling via Rs (retinoblastoma tumour suppressor protein bound to the E2F transcription factor) in a single radial glial cell.

This model is described in the article:

Barrack DS, Thul R, Owen MR.
J Theor Biol. 2014 Jan 13;347C:17-32.

Abstract:

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000509 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Calcium/cell cycle coupling - Rs dependent ATP release", + "publication": { + "accession": "24434742", + "affiliation": "Horizon Digital Economy Research Institute, University of Nottingham, Nottingham NG7 2TU, UK; School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK. Electronic address: duncan.barrack@nottingham.ac.uk.", + "authors": [ + { + "institution": "Horizon Digital Economy Research Institute, University of Nottingham, Nottingham NG7 2TU, UK; School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK. Electronic address: duncan.barrack@nottingham.ac.uk.", + "name": "Duncan S Barrack" + }, + { + "institution": "School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK.", + "name": "R\u00fcdiger Thul" + }, + { + "institution": "School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK.", + "name": "Markus R Owen", + "orcid": "0000-0002-3028-9138" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/24434742", + "month": "4", + "pages": "17-32", + "synopsis": "Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.", + "title": "Modelling the coupling between intracellular calcium release and the cell cycle during cortical brain development.", + "type": "PubMed ID", + "volume": "347", + "year": 2014 + }, + "publicationId": "BIOMD0000000509", + "submissionId": "MODEL1401200001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000510": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Chalmers University of Technology", + "email": "eduardk@chalmers.se", + "external": false, + "name": "Eduard Kerkhoven" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei -MODEL C (with glucosomal ribokinase)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model C of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000510 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C (with glucosomal ribokinase)", + "publication": { + "accession": "24339766", + "affiliation": "Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation and Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom ; Systems and Synthetic Biology Group, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.", + "authors": [ + { + "institution": "Department of Biology and Biological Engineering, Chalmers University of Technology, Kemiv\u00e4gen 10, SE412 96, Gothenburg, Sweden.", + "name": "Eduard J Kerkhoven", + "orcid": "0000-0002-3593-5792" + }, + { + "name": "Fiona Achcar", + "orcid": "0000-0001-8792-7615" + }, + { + "name": "Vincent P Alibu" + }, + { + "name": "Richard J Burchmore", + "orcid": "0000-0003-1663-9004" + }, + { + "name": "Ian H Gilbert" + }, + { + "name": "Maciej Trybi\u0142o" + }, + { + "name": "Nicole N Driessen" + }, + { + "name": "David Gilbert" + }, + { + "name": "Rainer Breitling", + "orcid": "0000-0001-7173-0922" + }, + { + "institution": "Microbial Physiology, BioCentrum Amsterdam, Vrije Universiteit, De Boelelaan 1087, NL-1081 HV Amsterdam, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12, NL-1018 TV Amsterdam, The Netherlands.", + "name": "Barbara M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "name": "Michael P Barrett", + "orcid": "0000-0001-9447-3519" + } + ], + "issue": "12", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/24339766", + "month": "0", + "pages": "e1003371", + "synopsis": "Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.", + "title": "Handling uncertainty in dynamic models: the pentose phosphate pathway in Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000510", + "submissionId": "MODEL1401070000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000511": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Chalmers University of Technology", + "email": "eduardk@chalmers.se", + "external": false, + "name": "Eduard Kerkhoven" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D (with ATP:ADP antiporter)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model D of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000511 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D (with ATP:ADP antiporter)", + "publication": { + "accession": "24339766", + "affiliation": "Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation and Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom ; Systems and Synthetic Biology Group, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.", + "authors": [ + { + "institution": "Department of Biology and Biological Engineering, Chalmers University of Technology, Kemiv\u00e4gen 10, SE412 96, Gothenburg, Sweden.", + "name": "Eduard J Kerkhoven", + "orcid": "0000-0002-3593-5792" + }, + { + "name": "Fiona Achcar", + "orcid": "0000-0001-8792-7615" + }, + { + "name": "Vincent P Alibu" + }, + { + "name": "Richard J Burchmore", + "orcid": "0000-0003-1663-9004" + }, + { + "name": "Ian H Gilbert" + }, + { + "name": "Maciej Trybi\u0142o" + }, + { + "name": "Nicole N Driessen" + }, + { + "name": "David Gilbert" + }, + { + "name": "Rainer Breitling", + "orcid": "0000-0001-7173-0922" + }, + { + "institution": "Microbial Physiology, BioCentrum Amsterdam, Vrije Universiteit, De Boelelaan 1087, NL-1081 HV Amsterdam, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12, NL-1018 TV Amsterdam, The Netherlands.", + "name": "Barbara M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "name": "Michael P Barrett", + "orcid": "0000-0001-9447-3519" + } + ], + "issue": "12", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/24339766", + "month": "0", + "pages": "e1003371", + "synopsis": "Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.", + "title": "Handling uncertainty in dynamic models: the pentose phosphate pathway in Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000511", + "submissionId": "MODEL1401070001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000512": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Benson2014 - FAAH inhibitors for the treatment of osteoarthritic pain

Evaluation of fatty acid amide hydrolase (FAAH) as a target for osteoarthritic pain in humans, using an integrated systems pharmacology model.

The SBML version of the model is obtained from the supplementary material of the corresponding paper (see below).

This model is described in the article:

Benson N, Metelkin E, Demin O, Li GL, Nichols D, van der Graaf PH.
CPT Pharmacometrics Syst Pharmacol. 2014 Jan 15;3:e91.

Abstract:

The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). In 2011, PF-04457845, an irreversible inhibitor of FAAH, was progressed to phase II clinical trials for osteoarthritic pain. This article discusses a prospective, integrated systems pharmacology model evaluation of FAAH as a target for pain in humans, using physiologically based pharmacokinetic and systems biology approaches. The model integrated physiological compartments; endocannabinoid production, degradation, and disposition data; PF-04457845 pharmacokinetics and pharmacodynamics, and cannabinoid receptor CB1-binding kinetics. The modeling identified clear gaps in our understanding and highlighted key risks going forward, in particular relating to whether methods are in place to demonstrate target engagement and pharmacological effect. The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.

This model is hosted on BioModels Database and identifiedby: MODEL1402030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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FAAH inhibitors for the treatment of osteoarthritic pain", + "publication": { + "accession": "24429592", + "affiliation": "Xenologiq, Canterbury, Kent, UK.", + "authors": [ + { + "institution": "Xenologiq, Canterbury, Kent, UK.", + "name": "N Benson" + }, + { + "institution": "Institute for Systems Biology, Moscow, Russia.", + "name": "E Metelkin", + "orcid": "0000-0001-6612-5373" + }, + { + "institution": "Institute for Systems Biology, Moscow, Russia.", + "name": "O Demin" + }, + { + "institution": "Johnson and Johnson, Beijing, China.", + "name": "G L Li" + }, + { + "institution": "Pfizer Clinical Pharmacology, Cambridge, Massachusetts, USA.", + "name": "D Nichols" + }, + { + "institution": "Leiden Academic Centre for Drug Research, Leiden, The Netherlands.", + "name": "P H van der Graaf" + } + ], + "journal": "CPT: pharmacometrics & systems pharmacology", + "link": "http://identifiers.org/pubmed/24429592", + "month": "1", + "pages": "e91", + "synopsis": "The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). 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The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.CPT: Pharmacometrics Systems Pharmacology (2014) 3, e91; doi:10.1038/psp.2013.72; published online 15 January 2014.", + "title": "A systems pharmacology perspective on the clinical development of Fatty Acid amide hydrolase inhibitors for pain.", + "type": "PubMed ID", + "volume": "3", + "year": 2014 + }, + "publicationId": "BIOMD0000000512", + "submissionId": "MODEL1402030000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000513": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kerkhoven2013 - Glycolysis in T.brucei - MODEL A

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model A of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000513 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. 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Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL B

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model B of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000514 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL B", + "publication": { + "accession": "24339766", + "affiliation": "Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation and Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom ; Systems and Synthetic Biology Group, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.", + "authors": [ + { + "institution": "Department of Biology and Biological Engineering, Chalmers University of Technology, Kemiv\u00e4gen 10, SE412 96, Gothenburg, Sweden.", + "name": "Eduard J Kerkhoven", + "orcid": "0000-0002-3593-5792" + }, + { + "name": "Fiona Achcar", + "orcid": "0000-0001-8792-7615" + }, + { + "name": "Vincent P Alibu" + }, + { + "name": "Richard J Burchmore", + "orcid": "0000-0003-1663-9004" + }, + { + "name": "Ian H Gilbert" + }, + { + "name": "Maciej Trybi\u0142o" + }, + { + "name": "Nicole N Driessen" + }, + { + "name": "David Gilbert" + }, + { + "name": "Rainer Breitling", + "orcid": "0000-0001-7173-0922" + }, + { + "institution": "Microbial Physiology, BioCentrum Amsterdam, Vrije Universiteit, De Boelelaan 1087, NL-1081 HV Amsterdam, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12, NL-1018 TV Amsterdam, The Netherlands.", + "name": "Barbara M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "name": "Michael P Barrett", + "orcid": "0000-0001-9447-3519" + } + ], + "issue": "12", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/24339766", + "month": "0", + "pages": "e1003371", + "synopsis": "Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.", + "title": "Handling uncertainty in dynamic models: the pentose phosphate pathway in Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000514", + "submissionId": "MODEL1401310001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000515": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C in fructose medium (with glucosomal ribokinase)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model C-fruc of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000515 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.", + "title": "Handling uncertainty in dynamic models: the pentose phosphate pathway in Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000515", + "submissionId": "MODEL1401310002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000516": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D in fructose medium (with ATP:ADP antiporter)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model D-fruc of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000516 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D in fructose medium (with ATP:ADP antiporter)", + "publication": { + "accession": "24339766", + "affiliation": "Wellcome Trust Centre for Molecular Parasitology, Institute of Infection, Immunity and Inflammation and Glasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom ; Systems and Synthetic Biology Group, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.", + "authors": [ + { + "institution": "Department of Biology and Biological Engineering, Chalmers University of Technology, Kemiv\u00e4gen 10, SE412 96, Gothenburg, Sweden.", + "name": "Eduard J Kerkhoven", + "orcid": "0000-0002-3593-5792" + }, + { + "name": "Fiona Achcar", + "orcid": "0000-0001-8792-7615" + }, + { + "name": "Vincent P Alibu" + }, + { + "name": "Richard J Burchmore", + "orcid": "0000-0003-1663-9004" + }, + { + "name": "Ian H Gilbert" + }, + { + "name": "Maciej Trybi\u0142o" + }, + { + "name": "Nicole N Driessen" + }, + { + "name": "David Gilbert" + }, + { + "name": "Rainer Breitling", + "orcid": "0000-0001-7173-0922" + }, + { + "institution": "Microbial Physiology, BioCentrum Amsterdam, Vrije Universiteit, De Boelelaan 1087, NL-1081 HV Amsterdam, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12, NL-1018 TV Amsterdam, The Netherlands.", + "name": "Barbara M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "name": "Michael P Barrett", + "orcid": "0000-0001-9447-3519" + } + ], + "issue": "12", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/24339766", + "month": "0", + "pages": "e1003371", + "synopsis": "Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.", + "title": "Handling uncertainty in dynamic models: the pentose phosphate pathway in Trypanosoma brucei.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000516", + "submissionId": "MODEL1401310003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000517": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK.", + "email": "kieran.smallbone@ncl.ac.uk", + "external": false, + "name": "Kieran Smallbone", + "orcid": "0000-0003-2815-7045" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Smallbone2013 - Colon Crypt cycle - Version 3

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Smallbone2013 - Colon Crypt cycle - Version 2

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190002 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Smallbone2013 - Colon Crypt cycle - Version 1

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Smallbone2013 - Colon Crypt cycle - Version 0

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Ribba2012 - Low-grade gliomas, tumour growth inhibition model

Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy.

This model is described in the article:

Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F.
Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080

Abstract:

PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.

EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.

RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.

CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000521 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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69007 Lyon Cedex 07, France. benjamin.ribba@inria.fr", + "authors": [ + { + "institution": "Ribba, INRIA, Project-team NUMED, Ecole Normale Superieure de Lyon, 46 allee d0Italie, 69007 Lyon Cedex 07, France. benjamin.ribba@inria.fr", + "name": "Benjamin Ribba" + }, + { + "name": "Gentian Kaloshi" + }, + { + "name": "Mathieu Peyre", + "orcid": "0000-0001-8470-9516" + }, + { + "name": "Damien Ricard" + }, + { + "name": "Vincent Calvez", + "orcid": "0000-0002-3674-1965" + }, + { + "name": "Vincent Calvez", + "orcid": "0009-0002-6855-3472" + }, + { + "name": "Michel Tod" + }, + { + "name": "Branka Cajavec-Bernard" + }, + { + "name": "Ahmed Idbaih", + "orcid": "0000-0001-5290-1204" + }, + { + "name": "Dimitri Psimaras", + "orcid": "0000-0002-6641-288X" + }, + { + "name": "Linda Dainese" + }, + { + "name": "Johan Pallud" + }, + { + "name": "St\u00e9phanie Cartalat-Carel" + }, + { + "name": "Jean-Yves Delattre" + }, + { + "name": "J\u00e9r\u00f4me Honnorat" + }, + { + "name": "Emmanuel Grenier" + }, + { + "name": "Fran\u00e7ois Ducray", + "orcid": "0000-0002-8150-5785" + } + ], + "issue": "18", + "journal": "Clinical cancer research : an official journal of the American Association for Cancer Research", + "link": "http://identifiers.org/pubmed/22761472", + "month": "9", + "pages": "5071-5080", + "synopsis": "

Purpose

To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.

Experimental design

Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.

Results

The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.

Conclusions

Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.", + "title": "A tumor growth inhibition model for low-grade glioma treated with chemotherapy or radiotherapy.", + "type": "PubMed ID", + "volume": "18", + "year": 2012 + }, + "publicationId": "BIOMD0000000521", + "submissionId": "MODEL1402250000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000522": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Muraro2014 - Vascular patterning in Arabidopsis roots

Using a multicellular model, maintanence of vascular patterning in Arabidopsis roots has been studied. The model that is provided here is the single-cell version of the model. The two-cell and multicellular models described in the paper can be downloaded as python scripts (follow the curation tab to get these files).

This model is described in the article:

Muraro D, Mellor N, Pound MP, Help H, Lucas M, Chopard J, Byrne HM, Godin C, Hodgman TC, King JR, Pridmore TP, Helariutta Y, Bennett MJ, Bishopp A.
Proc Natl Acad Sci U S A. 2014 Jan 14;111(2):857-62.

Abstract:

As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000522 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. 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Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000523 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000524 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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"affiliation": "1Department for Bioinformatics and Functional Genomics, Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg 69120, Germany.", + "authors": [ + { + "institution": "1Department for Bioinformatics and Functional Genomics, Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant, Heidelberg University, Heidelberg 69120, Germany.", + "name": "Stefan M Kallenberger" + }, + { + "name": "Jo\u00ebl Beaudouin" + }, + { + "name": "Juliane Claus" + }, + { + "name": "Carmen Fischer" + }, + { + "name": "Peter K Sorger", + "orcid": "0000-0002-3364-1838" + }, + { + "name": "Stefan Legewie", + "orcid": "0000-0003-4111-0567" + }, + { + "name": "Roland Eils", + "orcid": "0000-0002-0034-4036" + } + ], + "issue": "316", + "journal": "Science signaling", + "link": "http://identifiers.org/pubmed/24619646", + "month": "3", + "pages": "ra23", + "synopsis": "Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.", + "title": "Intra- and interdimeric caspase-8 self-cleavage controls strength and timing of CD95-induced apoptosis.", + "type": "PubMed ID", + "volume": "7", + "year": 2014 + }, + "publicationId": "BIOMD0000000524", + "submissionId": "MODEL1403050001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000525": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center", + "email": "s.kallenberger@dkfz.de", + "external": false, + "name": "Stefan Kallenberger" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans-cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000525 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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signaling", + "link": "http://identifiers.org/pubmed/24619646", + "month": "3", + "pages": "ra23", + "synopsis": "Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.", + "title": "Intra- and interdimeric caspase-8 self-cleavage controls strength and timing of CD95-induced apoptosis.", + "type": "PubMed ID", + "volume": "7", + "year": 2014 + }, + "publicationId": "BIOMD0000000525", + "submissionId": "MODEL1403050002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000526": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center", + "email": "s.kallenberger@dkfz.de", + "external": false, + "name": "Stefan Kallenberger" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans-cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000526 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Kaiser2014 - Salmonella persistence after ciprofloxacin treatment

The model describes the bacterial tolerance to antibiotics. Using a mouse model for Salmonella diarrhea, the authors have found that bacterial persistence occurs in the presence of the antibiotic ciprofloxacin because Salmonella can exist in two different states. One, the fast-growing population that spreads in the host's tissues and the other, slow-growing \"persister\" population that hide out inside dendritic cells of the host's immune system and cannot be attacked by the antibiotics. However, this can be killed by adding agents that directly stimulate the host's immune defense.

This model is described in the article:

Kaiser P, Regoes RR, Dolowschiak T, Wotzka SY, Lengefeld J, Slack E, Grant AJ, Ackermann M, Hardt WD.
PLoS Biol. 2014 Feb 18;12(2):e1001793.

Abstract:

In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103\u207aCX\u2083CR1\u207bCD11c\u207a dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

This model is hosted on BioModels Database and identifiedby: MODEL1312170001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103\u207aCX\u2083CR1\u207bCD11c\u207a dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.", + "title": "Cecum lymph node dendritic cells harbor slow-growing bacteria phenotypically tolerant to antibiotic treatment.", + "type": "PubMed ID", + "volume": "12", + "year": 2014 + }, + "publicationId": "BIOMD0000000527", + "submissionId": "MODEL1312170001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000528": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Icahn School of Medicine at Mount Sinai", + "email": "miguel.fribourg@mssm.edu", + "external": false, + "name": "Miguel Fribourg" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - Cal/09)

The dynamics of the interplay between the viral antagonism and the innate immune response has been studied using modelling approaches. The responses of human monocyte-derived dendritic cells infected by two influenza A H1N1 strains (the pandemic swine-origin A/California/4/2009 (Cal/09) and the seasonal A/New Caledonia/20/1999 (NC/99)) that have different clinical outcomes have been modelled. From the time course gene expression measurements of a set of selected genes, the dynamic features of viral antagonism and innate immune response are extracted. It is found that the strength and the time scale of action of viral antagonism is significantly different between the two viruses. This model describes the viral infection by seasonal Cal/09.

This model is described in the article:

Fribourg M, Hartmann B, Schmolke M, Marjanovic N, Albrecht RA, Garc\u00eda-Sastre A, Sealfon SC, Jayaprakash C, Hayot F.
J Theor Biol. 2014 Mar 2;351C:47-57.

Abstract:

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

This model is hosted on BioModels Database and identifiedby: MODEL1403310002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Electronic address: fernand.hayot@mssm.edu.", + "name": "F Hayot" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/24594370", + "month": "6", + "pages": "47-57", + "synopsis": "Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.", + "title": "Model of influenza A virus infection: dynamics of viral antagonism and innate immune response.", + "type": "PubMed ID", + "volume": "351", + "year": 2014 + }, + "publicationId": "BIOMD0000000528", + "submissionId": "MODEL1403310002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000529": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Icahn School of Medicine at Mount Sinai", + "email": "miguel.fribourg@mssm.edu", + "external": false, + "name": "Miguel Fribourg" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - NC/99)

The dynamics of the interplay between the viral antagonism and the innate immune response has been studied using modelling approaches. The responses of human monocyte-derived dendritic cells infected by two influenza A H1N1 strains (the pandemic swine-origin A/California/4/2009 (Cal/09) and the seasonal A/New Caledonia/20/1999 (NC/99)) that have different clinical outcomes have been modelled. From the time course gene expression measurements of a set of selected genes, the dynamic features of viral antagonism and innate immune response are extracted. It is found that the strength and the time scale of action of viral antagonism is significantly different between the two viruses. This model describes the viral infection by seasonal NC/99.

This model is described in the article:

Fribourg M, Hartmann B, Schmolke M, Marjanovic N, Albrecht RA, Garc\u00eda-Sastre A, Sealfon SC, Jayaprakash C, Hayot F.
J Theor Biol. 2014 Mar 2;351C:47-57.

Abstract:

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

This model is hosted on BioModels Database and identifiedby: MODEL1403310001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Electronic address: fernand.hayot@mssm.edu.", + "name": "F Hayot" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/24594370", + "month": "6", + "pages": "47-57", + "synopsis": "Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.", + "title": "Model of influenza A virus infection: dynamics of viral antagonism and innate immune response.", + "type": "PubMed ID", + "volume": "351", + "year": 2014 + }, + "publicationId": "BIOMD0000000529", + "submissionId": "MODEL1403310001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000530": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Rostock", + "email": "winter@kapora.de", + "external": false, + "name": "Felix Winter" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Schmitz2014 - RNA triplex formation
The model is parameterized using theparameters for gene CCDC3 from Supplementary Table S1. The twomiRNAs which form the triplex together with CCDC3 are miR-551b andmiR-138.

This model is described in the article:

Schmitz U, Lai X, Winter F, Wolkenhauer O, Vera J, Gupta SK.
Nucleic Acids Res. 2014 Jul; 42(12): 7539-7552

Abstract:

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.

This model is hosted on BioModels Database and identified by: BIOMD0000000530.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. 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Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.", + "title": "Cooperative gene regulation by microRNA pairs and their identification using a computational workflow.", + "type": "PubMed ID", + "volume": "42", + "year": 2014 + }, + "publicationId": "BIOMD0000000530", + "submissionId": "MODEL1402210000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000531": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Crespo2012 - Kinetics of Amyloid FibrilFormation

This model is described in the article:

Crespo R, Rocha FA, Damas AM, Martins PM.
J. Biol. Chem. 2012 Aug; 287(36): 30585-30594

Abstract:

Associated with neurodegenerative disorders such as Alzheimer, Parkinson, or prion diseases, the conversion of soluble proteins into amyloid fibrils remains poorly understood. Extensive \"in vitro\" measurements of protein aggregation kinetics have been reported, but no consensus mechanism has emerged until now. This contribution aims at overcoming this gap by proposing a theoretically consistent crystallization-like model (CLM) that is able to describe the classic types of amyloid fibrillization kinetics identified in our literature survey. Amyloid conversion represented as a function of time is shown to follow different curve shapes, ranging from sigmoidal to hyperbolic, according to the relative importance of the nucleation and growth steps. Using the CLM, apparently unrelated data are deconvoluted into generic mechanistic information integrating the combined influence of seeding, nucleation, growth, and fibril breakage events. It is notable that this complex assembly of interdependent events is ultimately reduced to a mathematically simple model, whose two parameters can be determined by little more than visual inspection. The good fitting results obtained for all cases confirm the CLM as a good approximation to the generalized underlying principle governing amyloid fibrillization. A perspective is presented on possible applications of the CLM during the development of new targets for amyloid disease therapeutics.

This model is hosted on BioModels Database and identified by: BIOMD0000000531.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"Rosa Crespo" + }, + { + "name": "Fernando A Rocha", + "orcid": "0000-0002-2778-8374" + }, + { + "name": "Ana M Damas" + }, + { + "name": "Pedro M Martins", + "orcid": "0000-0002-6908-1828" + } + ], + "issue": "36", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/22767606", + "month": "8", + "pages": "30585-30594", + "synopsis": "Associated with neurodegenerative disorders such as Alzheimer, Parkinson, or prion diseases, the conversion of soluble proteins into amyloid fibrils remains poorly understood. Extensive \"in vitro\" measurements of protein aggregation kinetics have been reported, but no consensus mechanism has emerged until now. This contribution aims at overcoming this gap by proposing a theoretically consistent crystallization-like model (CLM) that is able to describe the classic types of amyloid fibrillization kinetics identified in our literature survey. Amyloid conversion represented as a function of time is shown to follow different curve shapes, ranging from sigmoidal to hyperbolic, according to the relative importance of the nucleation and growth steps. Using the CLM, apparently unrelated data are deconvoluted into generic mechanistic information integrating the combined influence of seeding, nucleation, growth, and fibril breakage events. It is notable that this complex assembly of interdependent events is ultimately reduced to a mathematically simple model, whose two parameters can be determined by little more than visual inspection. The good fitting results obtained for all cases confirm the CLM as a good approximation to the generalized underlying principle governing amyloid fibrillization. A perspective is presented on possible applications of the CLM during the development of new targets for amyloid disease therapeutics.", + "title": "A generic crystallization-like model that describes the kinetics of amyloid fibril formation.", + "type": "PubMed ID", + "volume": "287", + "year": 2012 + }, + "publicationId": "BIOMD0000000531", + "submissionId": "MODEL1407170000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000532": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Vazquez2014 - Chemical inhibition fromamyloid protein aggregation kinetics

This model is described in the article:

V\u00e1zquez JA.
BMC Pharmacol Toxicol 2014; 15(1): 9

Abstract:

BACKGROUNDS: The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. RESULTS: In this manuscript, experimental data of insulin, A?42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH?=?7.4/T?=?37\u00b0C) and taiwaniaflavone for A?42 were the compounds studied that shown the greatest inhibition capacity. CONCLUSIONS: An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

This model is hosted on BioModels Database and identified by: BIOMD0000000532.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Investigaci\u00f3ns Mari\u00f1as (IIM-CSIC), C/ Eduardo Cabello 6, CP36208 Vigo, Spain. jvazquez@iim.csic.es.", + "name": "Jos\u00e9 Antonio V\u00e1zquez", + "orcid": "0000-0002-1122-4726" + } + ], + "journal": "BMC pharmacology & toxicology", + "link": "http://identifiers.org/pubmed/24572069", + "month": "2", + "pages": "9", + "synopsis": "

Backgrounds

The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation.

Results

In this manuscript, experimental data of insulin, A\u03b242 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH\u2009=\u20097.4/T\u2009=\u200937\u00b0C) and taiwaniaflavone for A\u03b242 were the compounds studied that shown the greatest inhibition capacity.

Conclusions

An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.", + "title": "Modeling of chemical inhibition from amyloid protein aggregation kinetics.", + "type": "PubMed ID", + "volume": "15", + "year": 2014 + }, + "publicationId": "BIOMD0000000532", + "submissionId": "MODEL1407300000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000533": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Steckmann2012 - Amyloid beta-proteinfibrillogenesis (kinetics of secondary structure conversion)

This model is described in the article:

Steckmann T, Awan Z, Gerstman BS, Chapagain PP.
J. Theor. Biol. 2012 May; 301: 95-102

Abstract:

Amyloid fibrils are a common component in many debilitating human neurological diseases such as Alzheimer's (AD), Parkinson's, and Creutzfeldt-Jakob, and in animal diseases such as BSE. The role of fibrillar ?? proteins in AD has stimulated interest in the kinetics of ?? fibril formation. Kinetic models that include reaction pathways and rate parameters for the various stages of the process can be helpful towards understanding the dynamics on a molecular level. Based upon experimental data, we have developed a mathematical model for the reaction pathways and determined rate parameters for peptide secondary structural conversion and aggregation during the entire fibrillogenesis process from random coil to mature fibrils, including the molecular species that accelerate the conversions. The model and the rate parameters include different molecular structural stages in the nucleation and polymerization processes and the numerical solutions yield graphs of concentrations of different molecular species versus time that are in close agreement with experimental results. The model also allows for the calculation of the time-dependent increase in aggregate size. The calculated results agree well with experimental results, and allow differences in experimental conditions to be included in the calculations. The specific steps of the model and the rate constants that are determined by fitting to experimental data provide insight on the molecular species involved in the fibril formation process.

This model is hosted on BioModels Database and identified by: BIOMD0000000533.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dwivedi2014 - Healthy Volunteer IL6Model
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Healthy Volunteer model are 2c and2d.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000534.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dwivedi2014 - Crohns IL6 Disease model -Anti-IL6 Antibody
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Crohn's Disease model are 4a, 4b,4c and 5a.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000535.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dwivedi2014 - Crohns IL6 Disease model -sgp130 activity
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
The figure referring to this Crohn's Disease model is 6b.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000536.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.", + "authors": [ + { + "institution": "The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA.", + "name": "G Dwivedi", + "orcid": "0000-0003-0681-0795" + }, + { + "institution": "Biotherapeutics Clinical Research, Pfizer Inc, Cambridge, Massachusetts, USA.", + "name": "L Fitz" + }, + { + "institution": "Immunoscience, Pfizer Inc, Cambridge, Massachusetts, USA.", + "name": "M Hegen" + }, + { + "institution": "Global Clinical Pharmacology, Pfizer Inc, Cambridge, Massachusetts, USA.", + "name": "S W Martin" + }, + { + "institution": "Pharmacokinetics, Dynamics & Metabolism, Pfizer Inc, Cambridge, Massachusetts, USA.", + "name": "J Harrold" + }, + { + "institution": "Quantitative Clinical Sciences, Biotherapeutics, Pfizer Inc, Cambridge, Massachusetts, USA.", + "name": "A Heatherington" + }, + { + "institution": "Quantitative Clinical Sciences, Biotherapeutics, Pfizer Inc, Cambridge, Massachusetts, USA.", + "name": "C Li" + } + ], + "journal": "CPT: pharmacometrics & systems pharmacology", + "link": "http://identifiers.org/pubmed/24402116", + "month": "1", + "pages": "e89", + "synopsis": "In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R\u03b1, or the IL-6/sIL-6R\u03b1 complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R\u03b1 complex in addition to IL-6 or IL-6R\u03b1. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.", + "title": "A multiscale model of interleukin-6-mediated immune regulation in Crohn's disease and its application in drug discovery and development.", + "type": "PubMed ID", + "volume": "3", + "year": 2014 + }, + "publicationId": "BIOMD0000000536", + "submissionId": "MODEL1408050003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000537": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "vknight@ebi.ac.uk", + "external": false, + "name": "Vincent Knight-Schrijver" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Dwivedi2014 - Crohns IL6 Disease model -Anti-IL6R Antibody
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Crohn's Disease model are 3a, 4d,4e, 4f and 5b.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000537.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Clarke2000 - One-hit model of cell death inneuronal degenerations
This one-hit model fits differentneuronal-death associated diseases for different animalmodels.\u00a0

This model is described in the article:

Clarke G, Collins RA, Leavitt BR, Andrews DF, Hayden MR, Lumsden CJ, McInnes RR.
Nature 2000 Jul; 406(6792): 195-199

Abstract:

In genetic disorders associated with premature neuronal death, symptoms may not appear for years or decades. This delay in clinical onset is often assumed to reflect the occurrence of age-dependent cumulative damage. For example, it has been suggested that oxidative stress disrupts metabolism in neurological degenerative disorders by the cumulative damage of essential macromolecules. A prediction of the cumulative damage hypothesis is that the probability of cell death will increase over time. Here we show in contrast that the kinetics of neuronal death in 12 models of photoreceptor degeneration, hippocampal neurons undergoing excitotoxic cell death, a mouse model of cerebellar degeneration and Parkinson's and Huntington's diseases are all exponential and better explained by mathematical models in which the risk of cell death remains constant or decreases exponentially with age. These kinetics argue against the cumulative damage hypothesis; instead, the time of death of any neuron is random. Our findings are most simply accommodated by a 'one-hit' biochemical model in which mutation imposes a mutant steady state on the neuron and a single event randomly initiates cell death. This model appears to be common to many forms of neurodegeneration and has implications for therapeutic strategies.

This model is hosted on BioModels Database and identified by: BIOMD0000000538.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This delay in clinical onset is often assumed to reflect the occurrence of age-dependent cumulative damage. For example, it has been suggested that oxidative stress disrupts metabolism in neurological degenerative disorders by the cumulative damage of essential macromolecules. A prediction of the cumulative damage hypothesis is that the probability of cell death will increase over time. Here we show in contrast that the kinetics of neuronal death in 12 models of photoreceptor degeneration, hippocampal neurons undergoing excitotoxic cell death, a mouse model of cerebellar degeneration and Parkinson's and Huntington's diseases are all exponential and better explained by mathematical models in which the risk of cell death remains constant or decreases exponentially with age. These kinetics argue against the cumulative damage hypothesis; instead, the time of death of any neuron is random. Our findings are most simply accommodated by a 'one-hit' biochemical model in which mutation imposes a mutant steady state on the neuron and a single event randomly initiates cell death. 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Yugi2014 - Insulin induced signalling (PFKLphosphorylation) - model 1

Insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, which thereby controls a key reaction in glycolysis. This mechanism is revealed using the mathematical model. In this model, the PFKL phosphorylation time courses are obtained from experimental data.

Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio University, Japan) for his kind advice in converting the model from MATLAB to SBML.

This model is described in the article:

Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4): 1171-1183

Abstract:

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

This model is hosted on BioModels Database and identified by: BIOMD0000000540.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Insulin induced signalling (PFKL phosphorylation) - model 1", + "publication": { + "accession": "25131207", + "affiliation": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "authors": [ + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Katsuyuki Yugi", + "orcid": "0000-0002-2046-4289" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Division of integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan; PRESTO, Japan Science and Technology Corporation, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan.", + "name": "Hiroyuki Kubota" + }, + { + "name": "Yu Toyoshima", + "orcid": "0000-0001-5239-2235" + }, + { + "institution": "Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Rei Noguchi", + "orcid": "0000-0002-6898-948X" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Kentaro Kawata" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Yasunori Komori" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Division of integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan.", + "name": "Shinsuke Uda" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Katsuyuki Kunida" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan.", + "name": "Yoko Tomizawa", + "orcid": "0000-0003-1730-7300" + }, + { + "institution": "Department of Cellular Regulation, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan.", + "name": "Yosuke Funato" + }, + { + "institution": "Department of Cellular Regulation, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka 565-0871, Japan.", + "name": "Hiroaki Miki" + }, + { + "institution": "Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan.", + "name": "Masaki Matsumoto", + "orcid": "0000-0002-6987-0288" + }, + { + "institution": "Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka 812-8582, Japan.", + "name": "Keiichi I Nakayama" + }, + { + "institution": "Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan.", + "name": "Kasumi Kashikura" + }, + { + "institution": "Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan.", + "name": "Keiko Endo" + }, + { + "institution": "Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan.", + "name": "Kazutaka Ikeda" + }, + { + "institution": "Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata 997-0052, Japan.", + "name": "Tomoyoshi Soga" + }, + { + "institution": "Department of Biological Sciences, Graduate School of Science, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Computational Biology, Graduate School of Frontier Sciences, University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan; CREST, Japan Science and Technology Corporation, Bunkyo-ku, Tokyo 113-0033, Japan. Electronic address: skuroda@bs.s.u-tokyo.ac.jp.", + "name": "Shinya Kuroda", + "orcid": "0000-0001-5059-8299" + } + ], + "issue": "4", + "journal": "Cell reports", + "link": "http://identifiers.org/pubmed/25131207", + "month": "8", + "pages": "1171-1183", + "synopsis": "Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.", + "title": "Reconstruction of insulin signal flow from phosphoproteome and metabolome data.", + "type": "PubMed ID", + "volume": "8", + "year": 2014 + }, + "publicationId": "BIOMD0000000540", + "submissionId": "MODEL1406130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000541": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Tokyo", + "email": "yugi@bs.s.u-tokyo.ac.jp", + "external": false, + "name": "Katsuyuki Yugi" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Yugi2014 - Insulin induced signalling (PFKLphosphorylation) - model 2

Insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, which thereby controls a key reaction in glycolysis. This mechanism is revealed using the mathematical model. In this model, the PFKL phosphorylation time courses are calculation from the signalling pathway model developed by Kubata et al. (2012) ( MODEL1204060000 - Kubota2012_InsulinAction_AKTpathway).

Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio University, Japan) for his kind advice in converting the model from MATLAB to SBML.

This model is described in the article:

Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4): 1171-1183

Abstract:

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

This model is hosted on BioModels Database and identified by: BIOMD0000000541.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Yuraszeck2010 - Vulnerabilities in the TauNetwork in Tau Pathophysiology

This model is described in the article:

Yuraszeck TM, Neveu P, Rodriguez-Fernandez M, Robinson A, Kosik KS, Doyle FJ 3rd.
PLoS Comput. Biol. 2010; 6(11): e1000997

Abstract:

The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's.

This model is hosted on BioModels Database and identified by: BIOMD0000000542.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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+ "pages": "e1000997", + "synopsis": "The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's.", + "title": "Vulnerabilities in the tau network and the role of ultrasensitive points in tau pathophysiology.", + "type": "PubMed ID", + "volume": "6", + "year": 2010 + }, + "publicationId": "BIOMD0000000542", + "submissionId": "MODEL1408150000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000543": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "vknight@ebi.ac.uk", + "external": false, + "name": "Vincent Knight-Schrijver" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Qi2013 - IL-6 and IFN crosstalk model(non-competitive)
This model [BIOMD0000000543]describes the crosstalk between IFN-gamma and IL-6 inducedsignalling; it aims to outline mechanisms and factors that maycontrol the interaction between both signalling pathways,discussing a role of heterodimer formation in signallingdysfunction.

To account for the possibility of different IFNR and gp130binding sites for STAT1 and STAT3, model 1 [BIOMD0000000543]assumes that there is no competition between STAT1 and STAT3 forthe receptor complexes (includes two extra reactions).
The reverse of this is true in model 2 [BIOMD0000000544]where it generally is assumed that there is competition betweenSTAT1 and STAT3 for the receptor complexes.

This model is described in the article:

Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX.
BMC Bioinformatics 2013; 14: 41

Abstract:

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000543.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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IL-6 and IFN crosstalk model (non-competitive)", + "publication": { + "accession": "23384097", + "affiliation": "National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.", + "authors": [ + { + "institution": "National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.", + "name": "Yun-feng Qi" + }, + { + "name": "Yan-xin Huang" + }, + { + "name": "Hong-yan Wang" + }, + { + "name": "Yu Zhang" + }, + { + "name": "Yong-li Bao" + }, + { + "name": "Lu-guo Sun" + }, + { + "name": "Yin Wu" + }, + { + "name": "Chun-lei Yu" + }, + { + "name": "Zhen-bo Song" + }, + { + "name": "Li-hua Zheng" + }, + { + "name": "Ying Sun" + }, + { + "name": "Guan-nan Wang" + }, + { + "name": "Yu-xin Li" + } + ], + "journal": "BMC bioinformatics", + "link": "http://identifiers.org/pubmed/23384097", + "month": "2", + "pages": "41", + "synopsis": "

Background

Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear.

Results

Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model.

Conclusion

In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.", + "title": "Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling.", + "type": "PubMed ID", + "volume": "14", + "year": 2013 + }, + "publicationId": "BIOMD0000000543", + "submissionId": "MODEL1408190003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000544": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "vknight@ebi.ac.uk", + "external": false, + "name": "Vincent Knight-Schrijver" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Qi2013 - IL-6 and IFN crosstalk model
This model [BIOMD0000000544]describes the crosstalk between IFN-gamma and IL-6 inducedsignalling; it aims to outline mechanisms and factors that maycontrol the interaction between both signalling pathways,discussing a role of heterodimer formation in signallingdysfunction.

To account for the possibility of different IFNR and gp130binding sites for STAT1 and STAT3, model 1
[BIOMD0000000543]assumes that there is no competition between STAT1 and STAT3 forthe receptor complexes (includes two extra reactions).
The reverse of this is true in model 2 [BIOMD0000000544]where it generally is assumed that there is competition betweenSTAT1 and STAT3 for the receptor complexes.

This model is described in the article:

Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX.
BMC Bioinformatics 2013; 14: 41

Abstract:

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000544.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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IL-6 and IFN crosstalk model", + "publication": { + "accession": "23384097", + "affiliation": "National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.", + "authors": [ + { + "institution": "National Engineering Laboratory for Druggable Gene and Protein Screening, Northeast Normal University, Changchun 130024, China.", + "name": "Yun-feng Qi" + }, + { + "name": "Yan-xin Huang" + }, + { + "name": "Hong-yan Wang" + }, + { + "name": "Yu Zhang" + }, + { + "name": "Yong-li Bao" + }, + { + "name": "Lu-guo Sun" + }, + { + "name": "Yin Wu" + }, + { + "name": "Chun-lei Yu" + }, + { + "name": "Zhen-bo Song" + }, + { + "name": "Li-hua Zheng" + }, + { + "name": "Ying Sun" + }, + { + "name": "Guan-nan Wang" + }, + { + "name": "Yu-xin Li" + } + ], + "journal": "BMC bioinformatics", + "link": "http://identifiers.org/pubmed/23384097", + "month": "2", + "pages": "41", + "synopsis": "

Background

Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear.

Results

Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model.

Conclusion

In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.", + "title": "Elucidating the crosstalk mechanism between IFN-gamma and IL-6 via mathematical modelling.", + "type": "PubMed ID", + "volume": "14", + "year": 2013 + }, + "publicationId": "BIOMD0000000544", + "submissionId": "MODEL1408190002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000545": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Pkeing University", + "email": "oyxh1983@126.com", + "external": false, + "name": "Xinhao Ouyang" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ouyang2014 - photomorphogenic UV-B signallingnetwork

This model is described in the article:

Ouyang X, Huang X, Jin X, Chen Z, Yang P, Ge H, Li S, Deng XW.
Proc. Natl. Acad. Sci. U.S.A. 2014 Aug; 111(31): 11539-11544

Abstract:

Long-wavelength and low-fluence UV-B light is an informational signal known to induce photomorphogenic development in plants. Using the model plant Arabidopsis thaliana, a variety of factors involved in UV-B-specific signaling have been experimentally characterized over the past decade, including the UV-B light receptor UV resistance locus 8; the positive regulators constitutive photomorphogenesis 1 and elongated hypocotyl 5; and the negative regulators cullin4, repressor of UV-B photomorphogenesis 1 (RUP1), and RUP2. Individual genetic and molecular studies have revealed that these proteins function in either positive or negative regulatory capacities for the sufficient and balanced transduction of photomorphogenic UV-B signal. Less is known, however, regarding how these signaling events are systematically linked. In our study, we use a systems biology approach to investigate the dynamic behaviors and correlations of multiple signaling components involved in Arabidopsis UV-B-induced photomorphogenesis. We define a mathematical representation of photomorphogenic UV-B signaling at a temporal scale. Supplemented with experimental validation, our computational modeling demonstrates the functional interaction that occurs among different protein complexes in early and prolonged response to photomorphogenic UV-B.

This model is hosted on BioModels Database and identified by: BIOMD0000000545.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"institution": "Peking-Yale Joint Center for Plant Molecular Genetics and Agro-Biotechnology, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing 100871, China;Rice Research Institute, Sichuan Agricultural University, Chengdu, Sichuan 611130, China;", + "name": "Xinhao Ouyang" + }, + { + "institution": "Peking-Yale Joint Center for Plant Molecular Genetics and Agro-Biotechnology, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing 100871, China;", + "name": "Xi Huang" + }, + { + "institution": "Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China;", + "name": "Xiao Jin" + }, + { + "institution": "Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63105; and.", + "name": "Zheng Chen" + }, + { + "institution": "Peking-Yale Joint Center for Plant Molecular Genetics and Agro-Biotechnology, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing 100871, China;", + "name": "Panyu Yang" + }, + { + "institution": "Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China;", + "name": "Hao Ge" + }, + { + "institution": "Rice Research Institute, Sichuan Agricultural University, Chengdu, Sichuan 611130, China;", + "name": "Shigui Li" + }, + { + "institution": "Peking-Yale Joint Center for Plant Molecular Genetics and Agro-Biotechnology, State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing 100871, China;Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520-8104 xingwang.deng@yale.edu.", + "name": "Xing Wang Deng" + } + ], + "issue": "31", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/25049395", + "month": "8", + "pages": "11539-11544", + "synopsis": "Long-wavelength and low-fluence UV-B light is an informational signal known to induce photomorphogenic development in plants. Using the model plant Arabidopsis thaliana, a variety of factors involved in UV-B-specific signaling have been experimentally characterized over the past decade, including the UV-B light receptor UV resistance locus 8; the positive regulators constitutive photomorphogenesis 1 and elongated hypocotyl 5; and the negative regulators cullin4, repressor of UV-B photomorphogenesis 1 (RUP1), and RUP2. Individual genetic and molecular studies have revealed that these proteins function in either positive or negative regulatory capacities for the sufficient and balanced transduction of photomorphogenic UV-B signal. Less is known, however, regarding how these signaling events are systematically linked. In our study, we use a systems biology approach to investigate the dynamic behaviors and correlations of multiple signaling components involved in Arabidopsis UV-B-induced photomorphogenesis. We define a mathematical representation of photomorphogenic UV-B signaling at a temporal scale. Supplemented with experimental validation, our computational modeling demonstrates the functional interaction that occurs among different protein complexes in early and prolonged response to photomorphogenic UV-B.", + "title": "Coordinated photomorphogenic UV-B signaling network captured by mathematical modeling.", + "type": "PubMed ID", + "volume": "111", + "year": 2014 + }, + "publicationId": "BIOMD0000000545", + "submissionId": "MODEL1407230000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000546": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Rochester Medical Center", + "email": "alain_leblanc@urmc.rochester.edu", + "external": false, + "name": "Alain Leblanc" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Miao2010 - Innate and adaptive immuneresponses to primary Influenza A Virus infection

This model is described in the article:

Miao H, Hollenbaugh JA, Zand MS, Holden-Wiltse J, Mosmann TR, Perelson AS, Wu H, Topham DJ.
J. Virol. 2010 Jul; 84(13): 6687-6698

Abstract:

Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is approximately 0.5 days, and the average half-life of free infectious virus is approximately 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity.

This model is hosted on BioModels Database and identified by: BIOMD0000000546.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"link": "http://identifiers.org/pubmed/20410284", + "month": "7", + "pages": "6687-6698", + "synopsis": "Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. 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This study provides a basis to better understand and predict influenza virus immunity.", + "title": "Quantifying the early immune response and adaptive immune response kinetics in mice infected with influenza A virus.", + "type": "PubMed ID", + "volume": "84", + "year": 2010 + }, + "publicationId": "BIOMD0000000546", + "submissionId": "MODEL1405150000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000547": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Otto von Guericke University", + "email": "soheil.rastgou@med.ovgu.de", + "external": false, + "name": "Soheil Rastgou Talemi" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Talemi2014 - Arsenic toxicity anddetoxification mechanisms in yeast
The model implements arsenite (AsIII)transport regulation, its distribution within main cellular AsIIIpools and detoxification. The intracellular As pools considered arefree AsIII (AsIIIin), protein-bound AsIII (AsIIIprot), glutathioneconjugated AsIII (AsGS3) and vacuolar sequestered AsIII (vAsGS3).

This model is described in the article:

Talemi SR, Jacobson T, Garla V, Navarrete C, Wagner A, Tam\u00e1s MJ, Schaber J.
Mol. Microbiol. 2014 Jun; 92(6): 1343-1356

Abstract:

Arsenic has a dual role as causative and curative agent of human disease. Therefore, there is considerable interest in elucidating arsenic toxicity and detoxification mechanisms. By an ensemble modelling approach, we identified a best parsimonious mathematical model which recapitulates and predicts intracellular arsenic dynamics for different conditions and mutants, thereby providing novel insights into arsenic toxicity and detoxification mechanisms in yeast, which could partly be confirmed experimentally by dedicated experiments. Specifically, our analyses suggest that: (i) arsenic is mainly protein-bound during short-term (acute) exposure, whereas glutathione-conjugated arsenic dominates during long-term (chronic) exposure, (ii) arsenic is not stably retained, but can leave the vacuole via an export mechanism, and (iii) Fps1 is controlled by Hog1-dependent and Hog1-independent mechanisms during arsenite stress. Our results challenge glutathione depletion as a key mechanism for arsenic toxicity and instead suggest that (iv) increased glutathione biosynthesis protects the proteome against the damaging effects of arsenic and that (v) widespread protein inactivation contributes to the toxicity of this metalloid. Our work in yeast may prove useful to elucidate similar mechanisms in higher eukaryotes and have implications for the use of arsenic in medical therapy.

This model is hosted on BioModels Database and identified by: BIOMD0000000547.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Sneppen2009 - Modeling proteasome dynamics inParkinson's disease

This model is described in the article:

Sneppen K, Lizana L, Jensen MH, Pigolotti S, Otzen D.
Phys Biol 2009; 6(3): 036005

Abstract:

In Parkinson's disease (PD), there is evidence that alpha-synuclein (alphaSN) aggregation is coupled to dysfunctional or overburdened protein quality control systems, in particular the ubiquitin-proteasome system. Here, we develop a simple dynamical model for the on-going conflict between alphaSN aggregation and the maintenance of a functional proteasome in the healthy cell, based on the premise that proteasomal activity can be titrated out by mature alphaSN fibrils and their protofilament precursors. In the presence of excess proteasomes the cell easily maintains homeostasis. However, when the ratio between the available proteasome and the alphaSN protofilaments is reduced below a threshold level, we predict a collapse of homeostasis and onset of oscillations in the proteasome concentration. Depleted proteasome opens for accumulation of oligomers. Our analysis suggests that the onset of PD is associated with a proteasome population that becomes occupied in periodic degradation of aggregates. This behavior is found to be the general state of a proteasome/chaperone system under pressure, and suggests new interpretations of other diseases where protein aggregation could stress elements of the protein quality control system.

This model is hosted on BioModels Database and identified by: BIOMD0000000548.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Here, we develop a simple dynamical model for the on-going conflict between alphaSN aggregation and the maintenance of a functional proteasome in the healthy cell, based on the premise that proteasomal activity can be titrated out by mature alphaSN fibrils and their protofilament precursors. In the presence of excess proteasomes the cell easily maintains homeostasis. However, when the ratio between the available proteasome and the alphaSN protofilaments is reduced below a threshold level, we predict a collapse of homeostasis and onset of oscillations in the proteasome concentration. Depleted proteasome opens for accumulation of oligomers. Our analysis suggests that the onset of PD is associated with a proteasome population that becomes occupied in periodic degradation of aggregates. This behavior is found to be the general state of a proteasome/chaperone system under pressure, and suggests new interpretations of other diseases where protein aggregation could stress elements of the protein quality control system.", + "title": "Modeling proteasome dynamics in Parkinson's disease.", + "type": "PubMed ID", + "volume": "6", + "year": 2009 + }, + "publicationId": "BIOMD0000000548", + "submissionId": "MODEL1409100000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000549": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "vknight@ebi.ac.uk", + "external": false, + "name": "Vincent Knight-Schrijver" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Baker2013 - Cytokine Mediated Inflammation inRheumatoid Arthritis - Age Dependant
This model by Baker M. 2013, describesthe interaction between pro and anti-inflammatory cytokinesignalling in rheumatoid arthritis.

Using two ordinary differential equations, the first model [BIOMD0000000550]analyses bifurcation and describes different pathological states byaltering inflammatory regulation parameters.
The second model [BIOMD0000000549]includes the effect that ageing has on pro-inflammatory signalling,allowing for time-dependant properties and disease progression tobe observed. The author also describes potential dosing forreversal of the disease state.

This model is described in the article:

Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR.
Math Med Biol 2013 Dec; 30(4): 311-337

Abstract:

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.

This model is hosted on BioModels Database and identified by: BIOMD0000000549.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cytokine Mediated Inflammation in Rheumatoid Arthritis - Age Dependent", + "publication": { + "accession": "23002057", + "affiliation": "Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK.", + "authors": [ + { + "institution": "School of Computer Science, University of Nottingham, Nottingham, UK. michelle.baker@nottingham.ac.uk.", + "name": "Michelle Baker", + "orcid": "0000-0002-8926-7689" + }, + { + "name": "Sarah Denman-Johnson" + }, + { + "name": "Bindi S Brook" + }, + { + "name": "Ian Gaywood" + }, + { + "institution": "School of Mathematical Sciences and Centre for Mathematical Medicine and Biology, University of Nottingham, Nottingham, UK.", + "name": "Markus R Owen", + "orcid": "0000-0002-3028-9138" + } + ], + "issue": "4", + "journal": "Mathematical medicine and biology : a journal of the IMA", + "link": "http://identifiers.org/pubmed/23002057", + "month": "12", + "pages": "311-337", + "synopsis": "Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.", + "title": "Mathematical modelling of cytokine-mediated inflammation in rheumatoid arthritis.", + "type": "PubMed ID", + "volume": "30", + "year": 2013 + }, + "publicationId": "BIOMD0000000549", + "submissionId": "MODEL1409250001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000550": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "vknight@ebi.ac.uk", + "external": false, + "name": "Vincent Knight-Schrijver" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Baker2013 - Cytokine Mediated Inflammation inRheumatoid Arthritis
This model by Baker M. 2013, describesthe interaction between pro and anti-inflammatory cytokinesignalling in rheumatoid arthritis.

Using two ordinary differential equations, the first model [BIOMD0000000550]analyses bifurcation and describes different pathological states byaltering inflammatory regulation parameters.
The second model [BIOMD0000000549]includes the effect that ageing has on pro-inflammatory signalling,allowing for time-dependant properties and disease progression tobe observed. The author also describes potential dosing forreversal of the disease state.

This model is described in the article:

Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR.
Math Med Biol 2013 Dec; 30(4): 311-337

Abstract:

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.

This model is hosted on BioModels Database and identified by: BIOMD0000000550.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. 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Das2010 - Effect of a gamma-secretaseinhibitor on Amyloid-beta dynamics

This model is described in the article:

Das R, Nachbar RB, Edelstein-Keshet L, Saltzman JS, Wiener MC, Bagchi A, Bailey J, Coombs D, Simon AJ, Hargreaves RJ, Cook JJ.
Bull. Math. Biol. 2011 Jan; 73(1): 230-247

Abstract:

Aggregation of the small peptide amyloid beta (A?) into oligomers and fibrils in the brain is believed to be a precursor to Alzheimer's disease. A? is produced via multiple proteolytic cleavages of amyloid precursor protein (APP), mediated by the enzymes ?- and ?-secretase. In this study, we examine the temporal dynamics of soluble (unaggregated) A? in the plasma and cerebral-spinal fluid (CSF) of rhesus monkeys treated with different oral doses of a ?-secretase inhibitor. A dose-dependent reduction of A? concentration was observed within hours of drug ingestion, for all doses tested. A? concentration in the CSF returned to its predrug level over the monitoring period. In contrast, A? concentration in the plasma exhibited an unexpected overshoot to as high as 200% of the predrug concentration, and this overshoot persisted as late as 72 hours post-drug ingestion. To account for these observations, we proposed and analyzed a minimal physiological model for A? dynamics that could fit the data. Our analysis suggests that the overshoot arises from the attenuation of an A? clearance mechanism, possibly due to the inhibitor. Our model predicts that the efficacy of A? clearance recovers to its basal (pretreatment) value with a characteristic time of >48 hours, matching the time-scale of the overshoot. These results point to the need for a more detailed investigation of soluble A? clearance mechanisms and their interaction with A?-reducing drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000551.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Ehrenstein2000 - Positive-Feedback model forthe loss of acetylcholine in Alzheimer's disease
Curated model derived from

BIOMD0000000553

This model is described in the article:

Ehrenstein G, Galdzicki Z, Lange GD.
Ann. N. Y. Acad. Sci. 2000; 899: 283-291

Abstract:

We describe a two-component positive-feedback system that could account for the large reduction of acetylcholine that is characteristic of patients with Alzheimer's disease (AD). One component is beta-amyloid-induced apoptosis of cholinergic cells, leading to a decrease in acetylcholine. The other component is an increase in the concentration of beta-amyloid in response to a decrease in acetylcholine. We describe each mechanism with a differential equation, and then solve the two equations numerically. The solution provides a description of the time course of the reduction of acetylcholine in AD patients that is consistent with epidemiological data. This model may also provide an explanation for the significant, but lesser, decrease of other neurotransmitters that is characteristic of AD.

This model is hosted on BioModels Database and identified by: BIOMD0000000552.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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and Stroke, Bethesda, Maryland 20892, USA. gerry@helix.nih.gov", + "authors": [ + { + "institution": "Biophysics Section, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland 20892, USA. gerry@helix.nih.gov", + "name": "G Ehrenstein" + }, + { + "name": "Z Galdzicki" + }, + { + "name": "G D Lange" + } + ], + "journal": "Annals of the New York Academy of Sciences", + "link": "http://identifiers.org/pubmed/10863547", + "month": "0", + "pages": "283-291", + "synopsis": "We describe a two-component positive-feedback system that could account for the large reduction of acetylcholine that is characteristic of patients with Alzheimer's disease (AD). One component is beta-amyloid-induced apoptosis of cholinergic cells, leading to a decrease in acetylcholine. The other component is an increase in the concentration of beta-amyloid in response to a decrease in acetylcholine. We describe each mechanism with a differential equation, and then solve the two equations numerically. The solution provides a description of the time course of the reduction of acetylcholine in AD patients that is consistent with epidemiological data. This model may also provide an explanation for the significant, but lesser, decrease of other neurotransmitters that is characteristic of AD.", + "title": "A positive-feedback model for the loss of acetylcholine in Alzheimer's disease.", + "type": "PubMed ID", + "volume": "899", + "year": 2000 + }, + "publicationId": "BIOMD0000000552", + "submissionId": "MODEL1410200001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000553": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ehrenstein1997 - The choline-leakagehypothesis in Alzheimer's disease

This model is described in the article:

Ehrenstein G, Galdzicki Z, Lange GD.
Biophys. J. 1997 Sep; 73(3): 1276-1280

Abstract:

We present a hypothesis for the loss of acetylcholine in Alzheimer's disease that is based on two recent experimental results: that beta-amyloid causes leakage of choline across cell membranes and that decreased production of acetylcholine increases the production of beta-amyloid. According to the hypothesis, an increase in beta-amyloid concentration caused by proteolysis of the amyloid precursor protein results in an increase in the leakage of choline out of cells. This leads to a reduction in intracellular choline concentration and hence a reduction in acetylcholine production. The reduction in acetylcholine production, in turn, causes an increase in the concentration of beta-amyloid. The resultant positive feedback between decreased acetylcholine and increased beta-amyloid accelerates the loss of acetylcholine. We compare the predictions of the choline-leakage hypothesis with a number of experimental observations. We also approximate it with a pair of ordinary differential equations. The solutions of these equations indicate that the loss of acetylcholine is very sensitive to the initial rate of beta-amyloid production.

This model is hosted on BioModels Database and identified by: BIOMD0000000553.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cloutier2009 - Brain Energy Metabolism


This model was taken from the\u00a0 CellMLrepository \u00a0andautomatically converted to SBML.\u00a0\u00a0Following thesubmission the parameters are manually encoded and annotated asspices and global quantities by BioModels curators.\u00a0
\u00a0
Theoriginal model was:\u00a0 Cloutier M, Bolger FB, Lowry JP, Wellstead P. (2009) -version=1.0\u00a0
Theoriginal CellML model was created by:\u00a0
CatherineLloyd \u00a0
c.lloyd@auckland.ac.nz\u00a0
TheUniversity of Auckland\u00a0

This model is described in the article:

Cloutier M, Bolger FB, Lowry JP, Wellstead P.
J Comput Neurosci 2009 Dec; 27(3): 391-414

Abstract:

An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50 microM propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model's predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue.

This model is hosted on BioModels Database and identified by: BIOMD0000000554.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue.", + "title": "An integrative dynamic model of brain energy metabolism using in vivo neurochemical measurements.", + "type": "PubMed ID", + "volume": "27", + "year": 2009 + }, + "publicationId": "BIOMD0000000554", + "submissionId": "MODEL1006230041", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000555": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Auer2010 - Correlation between lag time andaggregation rate in protein aggregation

This model is described in the article:

Auer S, Kashchiev D.
Proteins 2010 Aug; 78(11): 2412-2416

Abstract:

Under favorable conditions, many proteins can assemble into macroscopically large aggregates such as the amyloid fibrils that are associated with Alzheimer's, Parkinson's, and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation.

This model is hosted on BioModels Database and identified by: BIOMD0000000555.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Kingdom. s.auer@leeds.ac.uk", + "name": "Stefan Auer", + "orcid": "0000-0003-0418-1745" + }, + { + "name": "Dimo Kashchiev" + } + ], + "issue": "11", + "journal": "Proteins", + "link": "http://identifiers.org/pubmed/20602358", + "month": "8", + "pages": "2412-2416", + "synopsis": "Under favorable conditions, many proteins can assemble into macroscopically large aggregates such as the amyloid fibrils that are associated with Alzheimer's, Parkinson's, and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation.", + "title": "Insight into the correlation between lag time and aggregation rate in the kinetics of protein aggregation.", + "type": "PubMed ID", + "volume": "78", + "year": 2010 + }, + "publicationId": "BIOMD0000000555", + "submissionId": "MODEL1411100000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000556": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ortega2013 - Interplay between secretasesdetermines biphasic amyloid-beta level

This model is described in the article:

Ortega F, Stott J, Visser SA, Bendtsen C.
J. Biol. Chem. 2013 Jan; 288(2): 785-792

Abstract:

Amyloid-? (A?) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by ?-secretase, generating C99, and then by ?-secretase. APP is also cleaved by ?-secretase. It is hypothesized that reducing the production of A? in the brain may slow the progression of Alzheimer disease. Therefore, different ?-secretase inhibitors have been developed to reduce A? production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in A? production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the A? rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the A? dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in A? production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by ?-secretase, leading to an additional increase in C99 that overcomes the loss in ?-secretase activity. With a minor extension, the model also describes plasma A? profiles observed in humans upon dosing with a ?-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting A? production in Alzheimer disease.

This model is hosted on BioModels Database and identified by: BIOMD0000000556.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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\u03b3-secretase. APP is also cleaved by \u03b1-secretase. It is hypothesized that reducing the production of A\u03b2 in the brain may slow the progression of Alzheimer disease. Therefore, different \u03b3-secretase inhibitors have been developed to reduce A\u03b2 production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in A\u03b2 production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the A\u03b2 rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the A\u03b2 dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in A\u03b2 production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by \u03b2-secretase, leading to an additional increase in C99 that overcomes the loss in \u03b3-secretase activity. With a minor extension, the model also describes plasma A\u03b2 profiles observed in humans upon dosing with a \u03b3-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting A\u03b2 production in Alzheimer disease.", + "title": "Interplay between \u03b1-, \u03b2-, and \u03b3-secretases determines biphasic amyloid-\u03b2 protein level in the presence of a \u03b3-secretase inhibitor.", + "type": "PubMed ID", + "volume": "288", + "year": 2013 + }, + "publicationId": "BIOMD0000000556", + "submissionId": "MODEL1409240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000557": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University College Dublin", + "email": "dirk.fey@ucd.ie", + "external": false, + "name": "Dirk Fey" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Reiterer2013 - pseudophosphatase STYX role inERK signalling

This model is described in the article:

Reiterer V, Fey D, Kolch W, Kholodenko BN, Farhan H.
Proc. Natl. Acad. Sci. U.S.A. 2013 Jul; 110(31): E2934-43

Abstract:

Serine/threonine/tyrosine-interacting protein (STYX) is a catalytically inactive member of the dual-specificity phosphatases (DUSPs) family. Whereas the role of DUSPs in cellular signaling is well explored, the function of STYX is still unknown. Here, we identify STYX as a spatial regulator of ERK signaling. We used predictive-model simulation to test several hypotheses for possible modes of STYX action. We show that STYX localizes to the nucleus, competes with nuclear DUSP4 for binding to ERK, and acts as a nuclear anchor that regulates ERK nuclear export. Depletion of STYX increases ERK activity in both cytosol and nucleus. Importantly, depletion of STYX causes an ERK-dependent fragmentation of the Golgi apparatus and inhibits Golgi polarization and directional cell migration. Finally, we show that overexpression of STYX reduces ERK1/2 activation, thereby blocking PC12 cell differentiation. Overall, our results identify STYX as an important regulator of ERK1/2 signaling critical for cell migration and PC12 cell differentiation.

This model is hosted on BioModels Database and identified by: BIOMD0000000557.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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pseudophosphatase STYX role in ERK signalling", + "publication": { + "accession": "23847209", + "affiliation": "Biotechnology Institute Thurgau, University of Konstanz, Kreuzlingen, Switzerland.", + "authors": [ + { + "institution": "Biotechnology Institute Thurgau, University of Konstanz, Kreuzlingen, Switzerland.", + "name": "Veronika Reiterer" + }, + { + "name": "Dirk Fey", + "orcid": "0000-0002-5558-0167" + }, + { + "institution": "1] Systems Biology Ireland, University College Dublin, Dublin 4, Ireland [2] Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin 4, Ireland [3] School of Medicine and Medical Sciences, University College Dublin, Dublin 4, Ireland [4].", + "name": "Walter Kolch", + "orcid": "0000-0001-5777-5016" + }, + { + "name": "Boris N Kholodenko", + "orcid": "0000-0002-9483-4975" + }, + { + "name": "Hesso Farhan", + "orcid": "0000-0002-0889-8463" + } + ], + "issue": "31", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/23847209", + "month": "7", + "pages": "E2934-43", + "synopsis": "Serine/threonine/tyrosine-interacting protein (STYX) is a catalytically inactive member of the dual-specificity phosphatases (DUSPs) family. 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Overall, our results identify STYX as an important regulator of ERK1/2 signaling critical for cell migration and PC12 cell differentiation.", + "title": "Pseudophosphatase STYX modulates cell-fate decisions and cell migration by spatiotemporal regulation of ERK1/2.", + "type": "PubMed ID", + "volume": "110", + "year": 2013 + }, + "publicationId": "BIOMD0000000557", + "submissionId": "MODEL1410300001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000558": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Cloutier2012 - Feedback motif for Parkinson'sdisease

This model is described in the article:

Cloutier M, Middleton R, Wellstead P.
IET Syst Biol 2012 Jun; 6(3): 86-93

Abstract:

Previous article on the integrative modelling of Parkinson's disease (PD) described a mathematical model with properties suggesting that PD pathogenesis is associated with a feedback-induced biochemical bistability. In this article, the authors show that the dynamics of the mathematical model can be extracted and distilled into an equivalent two-state feedback motif whose stability properties are controlled by multi-factorial combinations of risk factors and genetic mutations associated with PD. Based on this finding, the authors propose a principle for PD pathogenesis in the form of the switch-like transition of a bistable feedback process from 'healthy' homeostatic levels of reactive oxygen species and the protein ?-synuclein, to an alternative 'disease' state in which concentrations of both molecules are stable at the damagingly high-levels associated with PD. The bistability is analysed using the rate curves and steady-state response characteristics of the feedback motif. In particular, the authors show how a bifurcation in the feedback motif marks the pathogenic moment at which the 'healthy' state is lost and the 'disease' state is initiated. Further analysis shows how known risks (such as: age, toxins and genetic predisposition) modify the stability characteristics of the feedback motif in a way that is compatible with known features of PD, and which explain properties such as: multi-factorial causality, variability in susceptibility and severity, multi-timescale progression and the special cases of familial Parkinson's and Parkinsonian symptoms induced purely by toxic stress.

Stress of 2.6 obtained by optimization(parameter S1) was imposed between days 10 and 150 in order toreproduce the Figure.

This model is hosted on BioModels Database and identified by: BIOMD0000000558.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"http://identifiers.org/pubmed/22757587", + "month": "6", + "pages": "86-93", + "synopsis": "Previous article on the integrative modelling of Parkinson's disease (PD) described a mathematical model with properties suggesting that PD pathogenesis is associated with a feedback-induced biochemical bistability. In this article, the authors show that the dynamics of the mathematical model can be extracted and distilled into an equivalent two-state feedback motif whose stability properties are controlled by multi-factorial combinations of risk factors and genetic mutations associated with PD. Based on this finding, the authors propose a principle for PD pathogenesis in the form of the switch-like transition of a bistable feedback process from 'healthy' homeostatic levels of reactive oxygen species and the protein \u03b1-synuclein, to an alternative 'disease' state in which concentrations of both molecules are stable at the damagingly high-levels associated with PD. The bistability is analysed using the rate curves and steady-state response characteristics of the feedback motif. In particular, the authors show how a bifurcation in the feedback motif marks the pathogenic moment at which the 'healthy' state is lost and the 'disease' state is initiated. Further analysis shows how known risks (such as: age, toxins and genetic predisposition) modify the stability characteristics of the feedback motif in a way that is compatible with known features of PD, and which explain properties such as: multi-factorial causality, variability in susceptibility and severity, multi-timescale progression and the special cases of familial Parkinson's and Parkinsonian symptoms induced purely by toxic stress.", + "title": "Feedback motif for the pathogenesis of Parkinson's disease.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000558", + "submissionId": "MODEL1410300000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000559": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "Department of Informatics and Telecommunications, Graduate Program \"Information Technologies in Medicine and Biology\", National and Kapodistrian University of Athens", + "email": "elouzou@central.ntua.gr", + "external": false, + "name": "Eleftherios Ouzounoglou" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ouzounoglou2014 - Modeling of alpha-synucleineffects on neuronal homeostasis

This model is described in the article:

Ouzounoglou E, Kalamatianos D, Emmanouilidou E, Xilouri M, Stefanis L, Vekrellis K, Manolakos ES.
BMC Syst Biol 2014; 8: 54

Abstract:

BACKGROUND: Alpha-synuclein (ASYN) is central in Parkinson's disease (PD) pathogenesis. Converging pieces of evidence suggest that the levels of ASYN expression play a critical role in both familial and sporadic Parkinson's disease. ASYN fibrils are the main component of inclusions called Lewy Bodies (LBs) which are found mainly in the surviving neurons of the substantia nigra. Despite the accumulated knowledge regarding the involvement of ASYN in molecular mechanisms underlying the development of PD, there is much information missing which prevents understanding the causes of the disease and how to stop its progression. RESULTS: Using a Systems Biology approach, we develop a biomolecular reactions model that describes the intracellular ASYN dynamics in relation to overexpression, post-translational modification, oligomerization and degradation of the protein. Especially for the proteolysis of ASYN, the model takes into account the biological knowledge regarding the contribution of Chaperone Mediated Autophagy (CMA), macro-autophagic and proteasome pathways in the protein's degradation. Importantly, inhibitory phenomena, caused by ASYN, concerning CMA (more specifically the lysosomal-associated membrane protein 2a, abbreviated as Lamp2a receptor, which is the rate limiting step of CMA) and the proteasome are carefully modeled. The model is validated by simulation studies of known experimental overexpression data from SH-SY5Y cells and the unknown model parameters are estimated either computationally or by experimental fitting. The calibrated model is then tested under three hypothetical intervention scenarios and in all cases predicts increased cell viability that agrees with experimental evidence. The biomodel has been annotated and is made available in SBML format. CONCLUSIONS: The mathematical model presented here successfully simulates the dynamic phenomena of ASYN overexpression and oligomerization and predicts the biological system's behavior in a number of scenarios not used for model calibration. It allows, for the first time, to qualitatively estimate the protein levels that are capable of deregulating proteolytic homeostasis. In addition, it can help form new hypotheses for intervention that could be tested experimentally.


Note: The model contains reactions of species located in different compartments. If the model is applied using volume sizes unequal to one, an extension of the model might be reasonable to guarantee mass conservation.


This model is hosted on BioModels Database and identified by: BIOMD0000000559.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

Alpha-synuclein (ASYN) is central in Parkinson's disease (PD) pathogenesis. Converging pieces of evidence suggest that the levels of ASYN expression play a critical role in both familial and sporadic Parkinson's disease. ASYN fibrils are the main component of inclusions called Lewy Bodies (LBs) which are found mainly in the surviving neurons of the substantia nigra. Despite the accumulated knowledge regarding the involvement of ASYN in molecular mechanisms underlying the development of PD, there is much information missing which prevents understanding the causes of the disease and how to stop its progression.

Results

Using a Systems Biology approach, we develop a biomolecular reactions model that describes the intracellular ASYN dynamics in relation to overexpression, post-translational modification, oligomerization and degradation of the protein. Especially for the proteolysis of ASYN, the model takes into account the biological knowledge regarding the contribution of Chaperone Mediated Autophagy (CMA), macro-autophagic and proteasome pathways in the protein's degradation. Importantly, inhibitory phenomena, caused by ASYN, concerning CMA (more specifically the lysosomal-associated membrane protein 2a, abbreviated as Lamp2a receptor, which is the rate limiting step of CMA) and the proteasome are carefully modeled. The model is validated by simulation studies of known experimental overexpression data from SH-SY5Y cells and the unknown model parameters are estimated either computationally or by experimental fitting. The calibrated model is then tested under three hypothetical intervention scenarios and in all cases predicts increased cell viability that agrees with experimental evidence. The biomodel has been annotated and is made available in SBML format.

Conclusions

The mathematical model presented here successfully simulates the dynamic phenomena of ASYN overexpression and oligomerization and predicts the biological system's behavior in a number of scenarios not used for model calibration. It allows, for the first time, to qualitatively estimate the protein levels that are capable of deregulating proteolytic homeostasis. In addition, it can help form new hypotheses for intervention that could be tested experimentally.", + "title": "In silico modeling of the effects of alpha-synuclein oligomerization on dopaminergic neuronal homeostasis.", + "type": "PubMed ID", + "volume": "8", + "year": 2014 + }, + "publicationId": "BIOMD0000000559", + "submissionId": "MODEL1412010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000560": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Hui2014 - Age-related changes in articularcartilage

This model is described in the article:

Wang Hui1, David A Young1, Andrew D Rowan1, Xin Xu2, Tim E Cawston1, Carole J Proctor1,3
Annals of the Rheumatic Diseases

Abstract:

Objective: To use a computational approach to investigate the cellular and extracellular matrix changes that occur with age in the knee joints of mice. Methods: Knee joints from an inbred C57/BL1/6 (ICRFa) mouse colony were harvested at 3\u201330?months of age. Sections were stained with H&E, Safranin-O, Picro-sirius red and antibodies to matrix metalloproteinase-13 (MMP-13), nitrotyrosine, LC-3B, Bcl-2, and cleaved type II collagen used for immunohistochemistry. Based on this and other data from the literature, a computer simulation model was built using the Systems Biology Markup Language using an iterative approach of data analysis and modelling. Individual parameters were subsequently altered to assess their effect on the model. Results: A progressive loss of cartilage matrix occurred with age. Nitrotyrosine, MMP-13 and anaplastic lymphoma kinase (ALK1) staining in cartilage increased with age with a concomitant decrease in LC-3B and Bcl-2. Stochastic simulations from the computational model showed a good agreement with these data, once transforming growth factor-? signalling via ALK1/ALK5 receptors was included. Oxidative stress and the interleukin 1 pathway were identified as key factors in driving the cartilage breakdown associated with ageing. Conclusions: A progressive loss of cartilage matrix and cellularity occurs with age. This is accompanied with increased levels of oxidative stress, apoptosis and MMP-13 and a decrease in chondrocyte autophagy. These changes explain the marked predisposition of joints to develop osteoarthritis with age. Computational modelling provides useful insights into the underlying mechanisms involved in age-related changes in musculoskeletal tissues.

This model is hosted on BioModels Database and identified by: BIOMD0000000560.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Hui2016 - Age-related changes in articular cartilage", + "publication": { + "accession": "25475114", + "affiliation": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "authors": [ + { + "institution": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "name": "Wang Hui" + }, + { + "institution": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "name": "David A Young", + "orcid": "0000-0002-7078-6745" + }, + { + "institution": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "name": "Andrew D Rowan" + }, + { + "institution": "Biomedicine Biobank, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "name": "Xin Xu" + }, + { + "institution": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "name": "Tim E Cawston" + }, + { + "institution": "Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, United Kingdom.MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.", + "name": "Carole J Proctor", + "orcid": "0000-0002-1366-1399" + } + ], + "issue": "2", + "journal": "Annals of the rheumatic diseases", + "link": "http://identifiers.org/pubmed/25475114", + "month": "2", + "pages": "449-458", + "synopsis": "

Objective

To use a computational approach to investigate the cellular and extracellular matrix changes that occur with age in the knee joints of mice.

Methods

Knee joints from an inbred C57/BL1/6 (ICRFa) mouse colony were harvested at 3-30\u2005months of age. Sections were stained with H&E, Safranin-O, Picro-sirius red and antibodies to matrix metalloproteinase-13 (MMP-13), nitrotyrosine, LC-3B, Bcl-2, and cleaved type II collagen used for immunohistochemistry. Based on this and other data from the literature, a computer simulation model was built using the Systems Biology Markup Language using an iterative approach of data analysis and modelling. Individual parameters were subsequently altered to assess their effect on the model.

Results

A progressive loss of cartilage matrix occurred with age. Nitrotyrosine, MMP-13 and activin receptor-like kinase-1 (ALK1) staining in cartilage increased with age with a concomitant decrease in LC-3B and Bcl-2. Stochastic simulations from the computational model showed a good agreement with these data, once transforming growth factor-\u03b2 signalling via ALK1/ALK5 receptors was included. Oxidative stress and the interleukin 1 pathway were identified as key factors in driving the cartilage breakdown associated with ageing.

Conclusions

A progressive loss of cartilage matrix and cellularity occurs with age. This is accompanied with increased levels of oxidative stress, apoptosis and MMP-13 and a decrease in chondrocyte autophagy. These changes explain the marked predisposition of joints to develop osteoarthritis with age. Computational modelling provides useful insights into the underlying mechanisms involved in age-related changes in musculoskeletal tissues.", + "title": "Oxidative changes and signalling pathways are pivotal in initiating age-related changes in articular cartilage.", + "type": "PubMed ID", + "volume": "75", + "year": 2016 + }, + "publicationId": "BIOMD0000000560", + "submissionId": "MODEL1402200004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000561": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Martins2013 - True and apparent inhibition ofamyloid fribril formation

This model is described in the article:

Martins PM.
Prion 2013 Mar-Apr; 7(2): 136-139

Abstract:

A possible therapeutic strategy for amyloid diseases involves the use of small molecule compounds to inhibit protein assembly into insoluble aggregates. According to the recently proposed Crystallization-Like Model, the kinetics of amyloid fibrillization can be retarded by decreasing the frequency of new fibril formation or by decreasing the elongation rate of existing fibrils. To the compounds that affect the nucleation and/or the growth steps we call true inhibitors. An apparent inhibition mechanism may however result from the alteration of thermodynamic properties such as the solubility of the amyloidogenic protein. Apparent inhibitors markedly influence protein aggregation kinetics measured in vitro, yet they are likely to lead to disappointing results when tested in vivo. This is because cells and tissues media are in general much more buffered against small variations in composition than the solutions prepared in lab. Here we show how to discriminate between true and apparent inhibition mechanisms from experimental data on protein aggregation kinetics. The goal is to be able to identify false positives much earlier during the drug development process.

This model is hosted on BioModels Database and identified by: BIOMD0000000561.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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According to the recently proposed Crystallization-Like Model, the kinetics of amyloid fibrillization can be retarded by decreasing the frequency of new fibril formation or by decreasing the elongation rate of existing fibrils. To the compounds that affect the nucleation and/or the growth steps we call true inhibitors. An apparent inhibition mechanism may however result from the alteration of thermodynamic properties such as the solubility of the amyloidogenic protein. Apparent inhibitors markedly influence protein aggregation kinetics measured in vitro, yet they are likely to lead to disappointing results when tested in vivo. This is because cells and tissues media are in general much more buffered against small variations in composition than the solutions prepared in lab. Here we show how to discriminate between true and apparent inhibition mechanisms from experimental data on protein aggregation kinetics. The goal is to be able to identify false positives much earlier during the drug development process.", + "title": "True and apparent inhibition of amyloid fibril formation.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000561", + "submissionId": "MODEL1412110000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000562": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "cokelaer@ebi.ac.uk", + "external": false, + "name": "Thomas Cokelaer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Chaouiya2013 - EGF and TNFalpha mediated signalling pathway

This model is described in the article:

Chaouiya C, B\u00e9renguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, Dr\u00e4ger A, B\u00fcchel F, Cokelaer T, Kowal B, Wicks B, Gon\u00e7alves E, Dorier J, Page M, Monteiro PT, von Kamp A, Xenarios I, de Jong H, Hucka M, Klamt S, Thieffry D, Le Nov\u00e8re N, Saez-Rodriguez J, Helikar T.
BMC Syst Biol 2013; 7: 135

Abstract:

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (\"qual\"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000562.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

Results

We present the Systems Biology Markup Language (SBML) Qualitative Models Package (\"qual\"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.

Conclusions

SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.", + "title": "SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000562", + "submissionId": "MODEL1411240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000563": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "The James Hutton Institute", + "email": "leighton.pritchard@hutton.ac.uk", + "external": false, + "name": "Leighton Pritchard" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Pritchard2014 - plant-microbeinteraction
Thismodel is an abstraction of a generic interaction between microbes,and a plant host. The reactions are generally intended to berepresentative of processes, not specific molecular mechanisms(except where indicated, eg. for activation of receptors). Themodel is intended to be of a similar level of abstraction as theZig-Zag model proposed in Jones and Dangl (2006) [PMID: 17108957], but to represent a dynamic system. Jones and Dangl (2006) model is used here to illustrate the advantages of dynamic representations of systems over expository models such as the Zig-Zag model.

This model is described in the article:

Pritchard L, Birch PR.
Mol. Plant Pathol. 2014 Dec; 15(9): 865-870

Abstract:


This model is hosted on BioModels Database and identified by: BIOMD0000000563.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Gould2011 - Temperature Sensitive CircadianClock
This model is a temperature sensitiveversion of Pokhilko et al.\u00a0 2010 (PMID:20865009),which is BIOMD0000000273in BioModels.

This model is described in the article:

Gould PD, Ugarte N, Domijan M, Costa M, Foreman J, Macgregor D, Rose K, Griffiths J, Millar AJ, Finkenst\u00e4dt B, Penfield S, Rand DA, Halliday KJ, Hall AJ.
Mol. Syst. Biol. 2013; 9: 650

Abstract:

Circadian clocks exhibit 'temperature compensation', meaning that they show only small changes in period over a broad temperature range. Several clock genes have been implicated in the temperature-dependent control of period in Arabidopsis. We show that blue light is essential for this, suggesting that the effects of light and temperature interact or converge upon common targets in the circadian clock. Our data demonstrate that two cryptochrome photoreceptors differentially control circadian period and sustain rhythmicity across the physiological temperature range. In order to test the hypothesis that the targets of light regulation are sufficient to mediate temperature compensation, we constructed a temperature-compensated clock model by adding passive temperature effects into only the light-sensitive processes in the model. Remarkably, this model was not only capable of full temperature compensation and consistent with mRNA profiles across a temperature range, but also predicted the temperature-dependent change in the level of LATE ELONGATED HYPOCOTYL, a key clock protein. Our analysis provides a systems-level understanding of period control in the plant circadian oscillator.

This model is hosted on BioModels Database and identified by: BIOMD0000000564.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Our analysis provides a systems-level understanding of period control in the plant circadian oscillator.", + "title": "Network balance via CRY signalling controls the Arabidopsis circadian clock over ambient temperatures.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000564", + "submissionId": "MODEL1410030000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000565": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Minho", + "email": "dmachado@deb.uminho.pt", + "external": false, + "name": "Daniel Machado" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Machado2014 - Curcumin production pathway inEscherichia coli

This model is described in the article:

Machado D, Rodrigues LR, Rocha I.
BioSystems 2014 Nov; 125: 16-21

Abstract:

Curcumin is a natural compound obtained from turmeric, and is well known for its pharmacological effects. In this work, we design a heterologous pathway for industrial production of curcumin in Escherichia coli. A kinetic model of the pathway is then developed and connected to a kinetic model of the central carbon metabolism of E. coli. This model is used for optimization of the mutant strain through a rational design approach, and two manipulation targets are identified for overexpression. Dynamic simulations are then performed to compare the curcumin production profiles of the different mutant strains. Our results show that it is possible to obtain a significant improvement in the curcumin production rates with the proposed mutants. The kinetic model here developed can be an important framework to optimize curcumin production at an industrial scale and add value to its biomedical potential.

This model is hosted on BioModels Database and identified by: BIOMD0000000565.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Curcumin production pathway in Escherichia coli", + "publication": { + "accession": "25218090", + "affiliation": "Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. Electronic address: dmachado@deb.uminho.pt.", + "authors": [ + { + "institution": "Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. Electronic address: dmachado@deb.uminho.pt.", + "name": "Daniel Machado", + "orcid": "0000-0002-2063-5383" + }, + { + "institution": "Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal. Electronic address: lrmr@deb.uminho.pt.", + "name": "L\u00edgia R Rodrigues", + "orcid": "0000-0001-9265-0630" + }, + { + "institution": "Centre of Biological Engineering, University of Minho, Braga, Portugal.Instituto de Tecnologia Qu\u00edmica e Biol\u00f3gica Ant\u00f3nio Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal.", + "name": "Rocha I", + "orcid": "0000-0001-9494-3410" + } + ], + "journal": "Bio Systems", + "link": "http://identifiers.org/pubmed/25218090", + "month": "11", + "pages": "16-21", + "synopsis": "Curcumin is a natural compound obtained from turmeric, and is well known for its pharmacological effects. In this work, we design a heterologous pathway for industrial production of curcumin in Escherichia coli. A kinetic model of the pathway is then developed and connected to a kinetic model of the central carbon metabolism of E. coli. This model is used for optimization of the mutant strain through a rational design approach, and two manipulation targets are identified for overexpression. Dynamic simulations are then performed to compare the curcumin production profiles of the different mutant strains. Our results show that it is possible to obtain a significant improvement in the curcumin production rates with the proposed mutants. The kinetic model here developed can be an important framework to optimize curcumin production at an industrial scale and add value to its biomedical potential.", + "title": "A kinetic model for curcumin production in Escherichia coli.", + "type": "PubMed ID", + "volume": "125", + "year": 2014 + }, + "publicationId": "BIOMD0000000565", + "submissionId": "MODEL1406060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000566": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Morris2009 - \u03b1-Synuclein aggregationvariable temperature and pH

This model is described in the article:

Morris AM, Finke RG.
Biophys. Chem. 2009 Mar; 140(1-3): 9-15

Abstract:

The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an \"Ockham's razor\"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)x[A](0) (where k(2) is the rate constant for autocatalytic growth and [A](0) is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.

This model is hosted on BioModels Database and identified by: BIOMD0000000566.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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of Chemistry, Colorado State University, Fort Collins, CO 80523, USA.", + "authors": [ + { + "institution": "Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, USA.", + "name": "Aimee M Morris", + "orcid": "0000-0002-2442-126X" + }, + { + "name": "Richard G Finke" + } + ], + "issue": "1-3", + "journal": "Biophysical chemistry", + "link": "http://identifiers.org/pubmed/19101068", + "month": "3", + "pages": "9-15", + "synopsis": "The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an \"Ockham's razor\"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)x[A](0) (where k(2) is the rate constant for autocatalytic growth and [A](0) is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.", + "title": "Alpha-synuclein aggregation variable temperature and variable pH kinetic data: a re-analysis using the Finke-Watzky 2-step model of nucleation and autocatalytic growth.", + "type": "PubMed ID", + "volume": "140", + "year": 2009 + }, + "publicationId": "BIOMD0000000566", + "submissionId": "MODEL1501160001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000567": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Morris2008 - Fitting protein aggregation datavia F-W 2-step mechanism

This model is described in the article:

Morris AM, Watzky MA, Agar JN, Finke RG.
Biochemistry 2008 Feb; 47(8): 2413-2427

Abstract:

The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/\"Ockham's razor\" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Sait\u00f4 and co-workers. In particular, the kinetic equation for Sait\u00f4's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Sait\u00f4 and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.

This model is hosted on BioModels Database and identified by: BIOMD0000000567.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"Murielle A Watzky", + "orcid": "0000-0002-7127-8507" + }, + { + "name": "Jeffrey N Agar", + "orcid": "0000-0003-2645-1873" + }, + { + "name": "Richard G Finke" + } + ], + "issue": "8", + "journal": "Biochemistry", + "link": "http://identifiers.org/pubmed/18247636", + "month": "2", + "pages": "2413-2427", + "synopsis": "The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/\"Ockham's razor\" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Sait\u00f4 and co-workers. In particular, the kinetic equation for Sait\u00f4's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Sait\u00f4 and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.", + "title": "Fitting neurological protein aggregation kinetic data via a 2-step, minimal/\"Ockham's razor\" model: the Finke-Watzky mechanism of nucleation followed by autocatalytic surface growth.", + "type": "PubMed ID", + "volume": "47", + "year": 2008 + }, + "publicationId": "BIOMD0000000567", + "submissionId": "MODEL1501160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000568": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "German Cancer Research Center", + "email": "m.schilling@dkfz.de", + "external": false, + "name": "Marcel Schilling" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Mueller2015 - Hepatocyte proliferation, T160phosphorylation of CDK2

This model is described in the article:

Mueller S, Huard J, Waldow K, Huang X, D'Alessandro LA, Bohl S, B\u00f6rner K, Grimm D, Klamt S, Klingm\u00fcller U, Schilling M.
Mol. Syst. Biol. 2015; 11(3): 795

Abstract:

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

This model is hosted on BioModels Database and identified by: BIOMD0000000568.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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+ "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0072574" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Mueller2015 - Hepatocyte proliferation, T160 phosphorylation of CDK2", + "publication": { + "accession": "25771250", + "affiliation": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "authors": [ + { + "name": "Mueller S" + }, + { + "name": "Huard J" + }, + { + "name": "Waldow K" + }, + { + "name": "Huang X" + }, + { + "name": "D'Alessandro LA" + }, + { + "name": "Bohl S" + }, + { + "name": "B\u00f6rner K" + }, + { + "name": "Grimm D" + }, + { + "name": "Klamt S" + }, + { + "name": "Klingm\u00fcller U" + }, + { + "name": "Schilling M." + } + ], + "journal": "Mol. Syst. Biol. 2015 Jan; 11(1): 795", + "link": "http://identifiers.org/pubmed/25771250", + "synopsis": "Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.", + "title": "T160-phosphorylated CDK2 defines threshold for HGF-dependent proliferation in primary hepatocytes.", + "type": "PubMed ID", + "year": 2015 + }, + "publicationId": "BIOMD0000000568", + "submissionId": "MODEL1502090000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000569": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "The Babraham Institute", + "email": "n.lenovere@gmail.com", + "external": false, + "name": "Nicolas Le Nov\u00e8re" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Dutta-Roy2015 - Opening of the multiple AMPAreceptor conductance states

This model is described in the article:

Dutta-Roy R, Rosenmund C, Edelstein SJ, Le Nov\u00e8re N.
PLoS ONE 2015; 10(1): e0116616

Abstract:

Modulation of the properties of AMPA receptors at the post-synaptic membrane is one of the main suggested mechanisms underlying fast synaptic transmission in the central nervous system of vertebrates. Electrophysiological recordings of single channels stimulated with agonists showed that both recombinant and native AMPA receptors visit multiple conductance states in an agonist concentration dependent manner. We propose an allosteric model of the multiple conductance states based on concerted conformational transitions of the four subunits, as an iris diaphragm. Our model predicts that the thermodynamic behaviour of the conductance states upon full and partial agonist stimulations can be described with increased affinity of receptors as they progress to higher conductance states. The model also predicts the existence of AMPA receptors in non-liganded conductive substates. However, the probability of spontaneous openings decreases with increasing conductances. Finally, we predict that the large conductance states are stabilized within the rise phase of a whole-cell EPSC in glutamatergic hippocampal neurons. Our model provides a mechanistic link between ligand concentration and conductance states that can explain thermodynamic and kinetic features of AMPA receptor gating.

This model is hosted on BioModels Database and identified by: BIOMD0000000569.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Aubert2002 - Coupling between Brainelectrical activity, Metabolism and Hemodynamics
Felix Winter encoded this model in SBMLas part of his work at ASD GmbH

This model is described in the article:

Aubert A, Costalat R.
Neuroimage 2002 Nov; 17(3): 1162-1181

Abstract:

In order to improve the interpretation of functional neuroimaging data, we implemented a mathematical model of the coupling between membrane ionic currents, energy metabolism (i.e., ATP regeneration via phosphocreatine buffer effect, glycolysis, and mitochondrial respiration), blood-brain barrier exchanges, and hemodynamics. Various hypotheses were tested for the variation of the cerebral metabolic rate of oxygen (CMRO(2)): (H1) the CMRO(2) remains at its baseline level; (H2) the CMRO(2) is enhanced as soon as the cerebral blood flow (CBF) increases; (H3) the CMRO(2) increase depends on intracellular oxygen and pyruvate concentrations, and intracellular ATP/ADP ratio; (H4) in addition to hypothesis H3, the CMRO(2) progressively increases, due to the action of a second messenger. A good agreement with experimental data from magnetic resonance imaging and spectroscopy (MRI and MRS) was obtained when we simulated sustained and repetitive activation protocols using hypotheses (H3) or (H4), rather than hypotheses (H1) or (H2). Furthermore, by studying the effect of the variation of some physiologically important parameters on the time course of the modeled blood-oxygenation-level-dependent (BOLD) signal, we were able to formulate hypotheses about the physiological or biochemical significance of functional magnetic resonance data, especially the poststimulus undershoot and the baseline drift.

This model is hosted on BioModels Database and identified by: BIOMD0000000570.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Nishio2008 - Design of the phosphotransferasesystem for enhanced glucose uptake in E. coli.

This model is described in the article:

Nishio Y, Usuda Y, Matsui K, Kurata H.
Mol. Syst. Biol. 2008; 4: 160

Abstract:

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate.

This model is hosted on BioModels Database and identified by: BIOMD0000000571.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Costa2014 - Computational Model of L. lactisMetabolism

This model is described in the article:

Costa RS, Hartmann A, Gaspar P, Neves AR, Vinga S.
Mol Biosyst 2014 Mar; 10(3): 628-639

Abstract:

Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising possibilities to elucidate the effect of alterations in the main metabolism of L. lactis, with application in strain optimization.

This model is hosted on BioModels Database and identified by: BIOMD0000000572.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Aguilera 2014 - HIV latency. Interactionbetween HIV proteins and immune response

This model is described in the article:

Aguilera LU, Rodr\u00edguez-Gonz\u00e1lez J.
J. Theor. Biol. 2014 Nov; 360: 67-77

Abstract:

HIV infection leads to two cell fates, the viral productive state or viral latency (a reversible non-productive state). HIV latency is relevant because infected active CD4+ T-lymphocytes can reach a resting memory state in which the provirus remains silent for long periods of time. Despite experimental and theoretical efforts, the causal molecular mechanisms responsible for HIV latency are only partially understood. Studies have determined that HIV latency is influenced by the innate immune response carried out by cell restriction factors that inhibit the postintegration steps in the virus replication cycle. In this study, we present a mathematical study that combines deterministic and stochastic approaches to analyze the interactions between HIV proteins and the innate immune response. Using wide ranges of parameter values, we observed the following: (1) a phenomenological description of the viral productive and latent cell phenotypes is obtained by bistable and bimodal dynamics, (2) biochemical noise reduces the probability that an infected cell adopts the latent state, (3) the effects of the innate immune response enhance the HIV latency state, (4) the conditions of the cell before infection affect the latent phenotype, i.e., the existing expression of cell restriction factors propitiates HIV latency, and existing expression of HIV proteins reduces HIV latency.

This model is hosted on BioModels Database and identified by: BIOMD0000000573.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Electronic address: jrodriguez@cinvestav.mx.", + "name": "Jes\u00fas Rodr\u00edguez-Gonz\u00e1lez" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/24997239", + "month": "11", + "pages": "67-77", + "synopsis": "HIV infection leads to two cell fates, the viral productive state or viral latency (a reversible non-productive state). HIV latency is relevant because infected active CD4+ T-lymphocytes can reach a resting memory state in which the provirus remains silent for long periods of time. Despite experimental and theoretical efforts, the causal molecular mechanisms responsible for HIV latency are only partially understood. Studies have determined that HIV latency is influenced by the innate immune response carried out by cell restriction factors that inhibit the postintegration steps in the virus replication cycle. In this study, we present a mathematical study that combines deterministic and stochastic approaches to analyze the interactions between HIV proteins and the innate immune response. 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Lai2014 - Hemiconcerted MWC model of intactcalmodulin with two targets

This model is described in the article:

Lai M, Brun D, Edelstein SJ, Le Nov\u00e8re N.
PLoS Comput. Biol. 2015 Jan; 11(1): e1004063

Abstract:

Calmodulin is a calcium-binding protein ubiquitous in eukaryotic cells, involved in numerous calcium-regulated biological phenomena, such as synaptic plasticity, muscle contraction, cell cycle, and circadian rhythms. It exibits a characteristic dumbell shape, with two globular domains (N- and C-terminal lobe) joined by a linker region. Each lobe can take alternative conformations, affected by the binding of calcium and target proteins. Calmodulin displays considerable functional flexibility due to its capability to bind different targets, often in a tissue-specific fashion. In various specific physiological environments (e.g. skeletal muscle, neuron dendritic spines) several targets compete for the same calmodulin pool, regulating its availability and affinity for calcium. In this work, we sought to understand the general principles underlying calmodulin modulation by different target proteins, and to account for simultaneous effects of multiple competing targets, thus enabling a more realistic simulation of calmodulin-dependent pathways. We built a mechanistic allosteric model of calmodulin, based on an hemiconcerted framework: each calmodulin lobe can exist in two conformations in thermodynamic equilibrium, with different affinities for calcium and different affinities for each target. Each lobe was allowed to switch conformation on its own. The model was parameterised and validated against experimental data from the literature. In spite of its simplicity, a two-state allosteric model was able to satisfactorily represent several sets of experiments, in particular the binding of calcium on intact and truncated calmodulin and the effect of different skMLCK peptides on calmodulin's saturation curve. The model can also be readily extended to include multiple targets. We show that some targets stabilise the low calcium affinity T state while others stabilise the high affinity R state. Most of the effects produced by calmodulin targets can be explained as modulation of a pre-existing dynamic equilibrium between different conformations of calmodulin's lobes, in agreement with linkage theory and MWC-type models.

This model is hosted on BioModels Database and identified by: BIOMD0000000574.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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We built a mechanistic allosteric model of calmodulin, based on an hemiconcerted framework: each calmodulin lobe can exist in two conformations in thermodynamic equilibrium, with different affinities for calcium and different affinities for each target. Each lobe was allowed to switch conformation on its own. The model was parameterised and validated against experimental data from the literature. In spite of its simplicity, a two-state allosteric model was able to satisfactorily represent several sets of experiments, in particular the binding of calcium on intact and truncated calmodulin and the effect of different skMLCK peptides on calmodulin's saturation curve. The model can also be readily extended to include multiple targets. We show that some targets stabilise the low calcium affinity T state while others stabilise the high affinity R state. Most of the effects produced by calmodulin targets can be explained as modulation of a pre-existing dynamic equilibrium between different conformations of calmodulin's lobes, in agreement with linkage theory and MWC-type models.", + "title": "Modulation of calmodulin lobes by different targets: an allosteric model with hemiconcerted conformational transitions.", + "type": "PubMed ID", + "volume": "11", + "year": 2015 + }, + "publicationId": "BIOMD0000000574", + "submissionId": "MODEL1405060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000575": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lloret@ebi.ac.uk", + "external": false, + "name": "Audald Lloret i Villas" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sass2009 - Approach to an\u03b1-synuclein-based BST model of Parkinson's disease

This model is described in the article:

Sass MB, Lorenz AN, Green RL, Coleman RA.
J. Neurosci. Methods 2009 Apr; 178(2): 366-377

Abstract:

This paper presents a detailed systems model of Parkinson's disease (PD), developed utilizing a pragmatic application of biochemical systems theory (BST) intended to assist experimentalists in the study of system behavior. This approach utilizes relative values as a reasonable initial estimate for BST and provides a theoretical means of applying numerical solutions to qualitative and semi-quantitative understandings of cellular pathways and mechanisms. The approach allows for the simulation of human disease through its ability to organize and integrate existing information about metabolic pathways without having a full quantitative description of those pathways, so that hypotheses about individual processes may be tested in a systems environment. Incorporating this method, the PD model describes alpha-synuclein aggregation as mediated by dopamine metabolism, the ubiquitin-proteasome system, and lysosomal degradation, allowing for the examination of dynamic pathway interactions and the evaluation of possible toxic mechanisms in the aggregation process. Four system perturbations: elevated alpha-synuclein aggregation, impaired dopamine packaging, increased neurotoxins, and alpha-synuclein overexpression, were analyzed for correlation to qualitative PD system hypotheses present in the literature, with the model demonstrating a high level of agreement with these hypotheses. Additionally, various PD treatment methods, including levadopa and monoamine oxidase inhibition (MAOI) therapy, were applied to the disease models to examine their effects on the system. Future additions and refinements to the model may further the understanding of the emergent behaviors of the disease, helping in the identification of system sensitivities and possible therapeutic targets.

This model is hosted on BioModels Database and identified by: BIOMD0000000575.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Kolodkin2013 - Nuclear receptor-mediatedcortisol signalling network

This model is described in the article:

Kolodkin A, Sahin N, Phillips A, Hood SR, Bruggeman FJ, Westerhoff HV, Plant N.
Nat Commun 2013; 4: 1792

Abstract:

It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body's response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress.

This model is hosted on BioModels Database and identified by: BIOMD0000000576.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "Hans V Westerhoff", + "orcid": "0000-0002-0443-6114" + }, + { + "institution": "School of Molecular and Cellular Biology, Faculty of Biological Sciences, University of Leeds, Leeds, UK.", + "name": "Nick Plant", + "orcid": "0000-0003-4332-8308" + } + ], + "journal": "Nature communications", + "link": "http://identifiers.org/pubmed/23653204", + "month": "0", + "pages": "1792", + "synopsis": "It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body's response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress.", + "title": "Optimization of stress response through the nuclear receptor-mediated cortisol signalling network.", + "type": "PubMed ID", + "volume": "4", + "year": 2013 + }, + "publicationId": "BIOMD0000000576", + "submissionId": "MODEL1506290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000577": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "Duke University", + "email": "sk197@duke.edu", + "external": false, + "name": "Sargis Karapetyan" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Zhou2015 - Circadian clock with immuneregulator NPR1
Arabidopsis clock model modified fromP2012 (Pokhilko et al., 2013 - BIOMD0000000445)model to include the master immune regulator NPR1 coupling to LHY,TOC1 and PRR7.
Triggers: The Global Quantities contain triggers that allowone to change coupling settings, Salicyclic acid (SA) treatment andnpr1 mutants.
LHY_on: true->NPR1 couples to LHY
PRR7_on: true->NPR1 couples to PRR7
WT: true->WT plants, false->npr1 mutant plants
SA: true->SA treated plants, false->no treatment
This model has L=1, i.e. operates only under constant lightconditions and is not aiming to make preditions under diurnalconditions. Due to period overshoot only time points after 28h arerelevant.

This model is described in the article:

Zhou M, Wang W, Karapetyan S, Mwimba M, Marqu\u00e9s J, Buchler NE, Dong X.
Nature 2015 Jun;

Abstract:

Recent studies have shown that in addition to the transcriptional circadian clock, many organisms, including Arabidopsis, have a circadian redox rhythm driven by the organism's metabolic activities. It has been hypothesized that the redox rhythm is linked to the circadian clock, but the mechanism and the biological significance of this link have only begun to be investigated. Here we report that the master immune regulator NPR1 (non-expressor of pathogenesis-related gene 1) of Arabidopsis is a sensor of the plant's redox state and regulates transcription of core circadian clock genes even in the absence of pathogen challenge. Surprisingly, acute perturbation in the redox status triggered by the immune signal salicylic acid does not compromise the circadian clock but rather leads to its reinforcement. Mathematical modelling and subsequent experiments show that NPR1 reinforces the circadian clock without changing the period by regulating both the morning and the evening clock genes. This balanced network architecture helps plants gate their immune responses towards the morning and minimize costs on growth at night. Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.

This model is hosted on BioModels Database and identified by: BIOMD0000000577.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.", + "title": "Redox rhythm reinforces the circadian clock to gate immune response.", + "type": "PubMed ID", + "volume": "523", + "year": 2015 + }, + "publicationId": "BIOMD0000000577", + "submissionId": "MODEL1506010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000578": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "invergo@ebi.ac.uk", + "external": false, + "name": "Brandon Invergo" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Invergo2014 - Phototransduction cascade inmouse rod cells

This model is described in the article:

Invergo BM, Dell'Orco D, Montanucci L, Koch KW, Bertranpetit J.
Mol Biosyst 2014 Jun; 10(6): 1481-1489

Abstract:

Vertebrate visual phototransduction is perhaps the most well-studied G-protein signaling pathway. A wealth of available biochemical and electrophysiological data has resulted in a rich history of mathematical modeling of the system. However, while the most comprehensive models have relied upon amphibian biochemical and electrophysiological data, modern research typically employs mammalian species, particularly mice, which exhibit significantly faster signaling dynamics. In this work, we present an adaptation of a previously published, comprehensive model of amphibian phototransduction that can produce quantitatively accurate simulations of the murine photoresponse. We demonstrate the ability of the model to predict responses to a wide range of stimuli and under a variety of mutant conditions. Finally, we employ the model to highlight a likely unknown mechanism related to the interaction between rhodopsin and rhodopsin kinase.

This model is hosted on BioModels Database and identified by: BIOMD0000000578.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Sengupta2015 - Knowledge base model of humanenergy pool network (HEPNet)

This model is described in the article:

Sengupta A, Grover M, Chakraborty A, Saxena S.
PLoS ONE 2015; 10(6): e0127918

Abstract:

HEPNet is an electronic representation of metabolic reactions occurring within human cellular organization focusing on inflow and outflow of the energy currency ATP, GTP and other energy associated moieties. The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. The results also highlight that energy currencies play a minor role. However, the moiety creatine phosphate has a unique character, since it is a ready-made source of phosphoryl groups for the rapid synthesis of ATP from ADP. HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders.

This model is hosted on BioModels Database and identified by: BIOMD0000000579.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. 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HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders.", + "title": "HEPNet: A Knowledge Base Model of Human Energy Pool Network for Predicting the Energy Availability Status of an Individual.", + "type": "PubMed ID", + "volume": "10", + "year": 2015 + }, + "publicationId": "BIOMD0000000579", + "submissionId": "MODEL1507210000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000580": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "piero.dallepezze@gmail.com", + "external": false, + "name": "Piero Dalle Pezze" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sonntag2012 - mTOR model - IRS dependent regulation of AMPK by insulin
TSC1-TSC2 complex has two states: 1)active (TSC1_TSC2_pS1387), regulated by AMPK_pT172; 2) inactive(TSC1_TSC2_pT1462) regulated by Akt_pT308. Particularly, mTORC1 isinhibited by TSC1_TSC2 in active state. AMPK is activated at T172by the species IRS1_p activated by the insulin receptor uponinsulin stimulation. Consequently, AMPK_pT172 is inhibited bymTORC1_pS2448 indirectly by the p70-S6K-negative feedback loop.

This model is described in the article:

Sonntag AG, Dalle Pezze P, Shanley DP, Thedieck K.
FEBS J. 2012 Sep; 279(18): 3314-3328

Abstract:

Mammalian target of rapamycin (mTOR) kinase responds to growth factors, nutrients and cellular energy status and is a central controller of cellular growth. mTOR exists in two multiprotein complexes that are embedded into a complex signalling network. Adenosine monophosphate-dependent kinase (AMPK) is activated by energy deprivation and shuts off adenosine 5'-triphosphate (ATP)-consuming anabolic processes, in part via the inactivation of mTORC1. Surprisingly, we observed that AMPK not only responds to energy deprivation but can also be activated by insulin, and is further induced in mTORC1-deficient cells. We have recently modelled the mTOR network, covering both mTOR complexes and their insulin and nutrient inputs. In the present study we extended the network by an AMPK module to generate the to date most comprehensive data-driven dynamic AMPK-mTOR network model. In order to define the intersection via which AMPK is activated by the insulin network, we compared simulations for six different hypothetical model structures to our observed AMPK dynamics. Hypotheses ranking suggested that the most probable intersection between insulin and AMPK was the insulin receptor substrate (IRS) and that the effects of canonical IRS downstream cues on AMPK would be mediated via an mTORC1-driven negative-feedback loop. We tested these predictions experimentally in multiple set-ups, where we inhibited or induced players along the insulin-mTORC1 signalling axis and observed AMPK induction or inhibition. We confirmed the identified model and therefore report a novel connection within the insulin-mTOR-AMPK network: we conclude that AMPK is positively regulated by IRS and can be inhibited via the negative-feedback loop.

This model is hosted on BioModels Database and identified by: BIOMD0000000580.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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mTOR model - IRS dependent regulation of AMPK by insulin", + "publication": { + "accession": "22452783", + "affiliation": "Bioinformatics and Molecular Genetics (Faculty of Biology), Institute for Biology 3, Albert-Ludwigs-Universit\u00e4t Freiburg, Germany.", + "authors": [ + { + "institution": "Bioinformatics and Molecular Genetics (Faculty of Biology), Institute for Biology 3, Albert-Ludwigs-Universit\u00e4t Freiburg, Germany.", + "name": "Annika G Sonntag" + }, + { + "institution": "Institute for Cell and Molecular Biosciences (ICaMB), Newcastle University, Newcastle upon Tyne NE2 4HH, UK.Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University Institute for Ageing, Newcastle upon Tyne NE4 5PL, UK.Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK.", + "name": "Piero Dalle Pezze", + "orcid": "0000-0003-1695-6763" + }, + { + "name": "Daryl P Shanley", + "orcid": "0000-0003-3096-6386" + }, + { + "institution": "Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands kathrin.thedieck@uibk.ac.at.Department for Neuroscience, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.Institute of Biochemistry and Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria.", + "name": "Kathrin Thedieck", + "orcid": "0000-0002-9069-2930" + } + ], + "issue": "18", + "journal": "The FEBS journal", + "link": "http://identifiers.org/pubmed/22452783", + "month": "9", + "pages": "3314-3328", + "synopsis": "Mammalian target of rapamycin (mTOR) kinase responds to growth factors, nutrients and cellular energy status and is a central controller of cellular growth. mTOR exists in two multiprotein complexes that are embedded into a complex signalling network. Adenosine monophosphate-dependent kinase (AMPK) is activated by energy deprivation and shuts off adenosine 5'-triphosphate (ATP)-consuming anabolic processes, in part via the inactivation of mTORC1. Surprisingly, we observed that AMPK not only responds to energy deprivation but can also be activated by insulin, and is further induced in mTORC1-deficient cells. We have recently modelled the mTOR network, covering both mTOR complexes and their insulin and nutrient inputs. In the present study we extended the network by an AMPK module to generate the to date most comprehensive data-driven dynamic AMPK-mTOR network model. In order to define the intersection via which AMPK is activated by the insulin network, we compared simulations for six different hypothetical model structures to our observed AMPK dynamics. 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We confirmed the identified model and therefore report a novel connection within the insulin-mTOR-AMPK network: we conclude that AMPK is positively regulated by IRS and can be inhibited via the negative-feedback loop.", + "title": "A modelling-experimental approach reveals insulin receptor substrate (IRS)-dependent regulation of adenosine monosphosphate-dependent kinase (AMPK) by insulin.", + "type": "PubMed ID", + "volume": "279", + "year": 2012 + }, + "publicationId": "BIOMD0000000580", + "submissionId": "MODEL1506230002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000581": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "piero.dallepezze@gmail.com", + "external": false, + "name": "Piero Dalle Pezze" + } + ] + }, + "curationStatus": "CURATED", + "description": "
DallePezze2012 - TSC-independent mTORC2regulation

This model is described in the article:

Dalle Pezze P, Sonntag AG, Thien A, Prentzell MT, G\u00f6del M, Fischer S, Neumann-Haefelin E, Huber TB, Baumeister R, Shanley DP, Thedieck K.
Sci Signal 2012 Mar; 5(217): ra25

Abstract:

The kinase mammalian target of rapamycin (mTOR) exists in two multiprotein complexes (mTORC1 and mTORC2) and is a central regulator of growth and metabolism. Insulin activation of mTORC1, mediated by phosphoinositide 3-kinase (PI3K), Akt, and the inhibitory tuberous sclerosis complex 1/2 (TSC1-TSC2), initiates a negative feedback loop that ultimately inhibits PI3K. We present a data-driven dynamic insulin-mTOR network model that integrates the entire core network and used this model to investigate the less well understood mechanisms by which insulin regulates mTORC2. By analyzing the effects of perturbations targeting several levels within the network in silico and experimentally, we found that, in contrast to current hypotheses, the TSC1-TSC2 complex was not a direct or indirect (acting through the negative feedback loop) regulator of mTORC2. Although mTORC2 activation required active PI3K, this was not affected by the negative feedback loop. Therefore, we propose an mTORC2 activation pathway through a PI3K variant that is insensitive to the negative feedback loop that regulates mTORC1. This putative pathway predicts that mTORC2 would be refractory to Akt, which inhibits TSC1-TSC2, and, indeed, we found that mTORC2 was insensitive to constitutive Akt activation in several cell types. Our results suggest that a previously unknown network structure connects mTORC2 to its upstream cues and clarifies which molecular connectors contribute to mTORC2 activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000581.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Insulin activation of mTORC1, mediated by phosphoinositide 3-kinase (PI3K), Akt, and the inhibitory tuberous sclerosis complex 1/2 (TSC1-TSC2), initiates a negative feedback loop that ultimately inhibits PI3K. We present a data-driven dynamic insulin-mTOR network model that integrates the entire core network and used this model to investigate the less well understood mechanisms by which insulin regulates mTORC2. By analyzing the effects of perturbations targeting several levels within the network in silico and experimentally, we found that, in contrast to current hypotheses, the TSC1-TSC2 complex was not a direct or indirect (acting through the negative feedback loop) regulator of mTORC2. Although mTORC2 activation required active PI3K, this was not affected by the negative feedback loop. Therefore, we propose an mTORC2 activation pathway through a PI3K variant that is insensitive to the negative feedback loop that regulates mTORC1. 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DallePazze2014 - Cellular senescene-inducedmitochondrial dysfunction

This model is described in the article:

Dalle Pezze P, Nelson G, Otten EG, Korolchuk VI, Kirkwood TB, von Zglinicki T, Shanley DP.
PLoS Comput. Biol. 2014 Aug; 10(8): e1003728

Abstract:

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset.

This model is hosted on BioModels Database and identified by: BIOMD0000000582.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset.", + "title": "Dynamic modelling of pathways to cellular senescence reveals strategies for targeted interventions.", + "type": "PubMed ID", + "volume": "10", + "year": 2014 + }, + "publicationId": "BIOMD0000000582", + "submissionId": "MODEL1506230000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000583": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Virginia Tech", + "email": "ajleber@vbi.vt.edu", + "external": false, + "name": "Andrew Leber" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Leber2015 - Mucosal immunity and gutmicrobiome interaction during C. difficile infection

This model is described in the article:

Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J.
PLoS ONE 2015; 10(7): e0134849

Abstract:

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000583.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.", + "title": "Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection.", + "type": "PubMed ID", + "volume": "10", + "year": 2015 + }, + "publicationId": "BIOMD0000000583", + "submissionId": "MODEL1507200000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000584": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "National Centre for Cell Science, Pune", + "email": "singhs@nccs.res.in", + "external": false, + "name": "Shailza Singh" + } + ] + }, + "curationStatus": "CURATED", + "description": "Vineetha Mandlik, Mayuri Gurav & Shailza Singh. Regulatory dynamics of network architecture and function in tristable genetic circuit of Leishmania: a mathematical biology approach. Journal of Biomolecular Structure and Dynamics 33, 12 (2015).

The emerging field of synthetic biology has led to the design of tailor-made synthetic circuits for several therapeutic applications. Biological networks can be reprogramed by designing synthetic circuits that modulate the expression of target proteins. IPCS (inositol phosphorylceramide synthase) has been an attractive target in the sphingolipid metabolism of the parasite Leishmania. In this study, we have constructed a tristable circuit for the IPCS protein. The circuit has been validated and its long-term behavior has been assessed. The robustness and evolvability of the circuit has been estimated using evolutionary algorithms. The tristable synthetic circuit has been specifically designed to improve the rate of production of phosphatidylcholine: ceramide cholinephosphotransferase 4 (SLS4 protein). 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Rateitschak2012 - Interferon-gamma (IFN\u03b3) induced STAT1 signalling (PC_IFNg100)

This model is described in the article:

Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O.
PLoS Comput. Biol. 2012; 8(12): e1002815

Abstract:

The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFN\u03b3) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFN\u03b3 inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFN\u03b3 induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types.

This model is hosted on BioModels Database and identified by: BIOMD0000000585.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Karapetyan2016 - Genetic oscillatory network - Activator Titration Circuit (ATC)

This model is described in the article:

Karapetyan S, Buchler NE.
Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712

Abstract:

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

This model is hosted on BioModels Database and identified by: BIOMD0000000586.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC)

This model is described in the article:

Karapetyan S, Buchler NE.
Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712

Abstract:

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

This model is hosted on BioModels Database and identified by: BIOMD0000000587.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.", + "title": "Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators.", + "type": "PubMed ID", + "volume": "92", + "year": 2015 + }, + "publicationId": "BIOMD0000000587", + "submissionId": "MODEL1512100001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000588": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Benson2013 - Identification of key drug targets in nerve growth factor pathway

This model is described in the article:

Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P, van der Graaf PH.
Interface Focus 2013 Apr; 3(2): 20120071

Abstract:

The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000588.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Identification of key drug targets in nerve growth factor pathway", + "publication": { + "accession": "24427523", + "affiliation": "Xenologiq Ltd, Unit 7 , Denne Hill Business Park, Canterbury CT4 6HD , UK ; Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA.", + "authors": [ + { + "institution": "Xenologiq Ltd, Unit 7 , Denne Hill Business Park, Canterbury CT4 6HD , UK ; Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA.", + "name": "Neil Benson" + }, + { + "institution": "Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA ; Astellas , 21 Miyukigaoka, Tsukuba, Ibaraki 305-8585 , Japan.", + "name": "Tomomi Matsuura" + }, + { + "institution": "Institute for Systems Biology SPb, Leninskie Gory 1/75G, Moscow 119992 , Russia.", + "name": "Sergey Smirnov" + }, + { + "institution": "Institute for Systems Biology SPb, Leninskie Gory 1/75G, Moscow 119992 , Russia.", + "name": "Oleg Demin" + }, + { + "institution": "Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA.", + "name": "Hannah M Jones" + }, + { + "institution": "Pharmatherapeutics Clinical Pharmacology, Pfizer Neusentis , The Portway Building, Granta Park, Cambridge CB21 6GS , UK ; Neusentis, Pfizer Global Clinical Pharmacology , The Portway Building, Granta Park, Cambridge CB21 6GS , UK.", + "name": "Pinky Dua" + }, + { + "institution": "Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA ; Neusentis, Pfizer Global Clinical Pharmacology , The Portway Building, Granta Park, Cambridge CB21 6GS , UK ; Leiden Academic Centre for Drug Research (LACDR) , Leiden RA 2300 , The Netherlands.", + "name": "Piet H van der Graaf" + } + ], + "issue": "2", + "journal": "Interface focus", + "link": "http://identifiers.org/pubmed/24427523", + "month": "4", + "pages": "20120071", + "synopsis": "The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.", + "title": "Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology.", + "type": "PubMed ID", + "volume": "3", + "year": 2013 + }, + "publicationId": "BIOMD0000000588", + "submissionId": "MODEL1601290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000589": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Castilla-La Mancha, 02071 Albaceta, Spain.", + "email": "Edelmira.Valero@uclm.es", + "external": false, + "name": "Edelmira Valero" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Valero2016 - Ascorbate-Glutathione cycle in chloroplasts under light/dark conditions

This model is described in the article:

Valero E, Maci\u00e0 H, De la Fuente IM, Hern\u00e1ndez JA, Gonz\u00e1lez-S\u00e1nchez MI, Garc\u00eda-Carmona F.
BMC Syst Biol 2016; 10(1): 11

Abstract:

Light/dark cycles are probably the most important environmental signals that regulate plant development. Light is essential for photosynthesis, but an excess, in combination with the unavoidable presence of atmospheric oxygen inside the chloroplast, leads to excessive reactive oxygen species production. Among the defense mechanisms that activate plants to cope with environmental stress situations, it is worth noting the ascorbate-glutathione cycle, a complex metabolic pathway in which a variety of photochemical, chemical and enzymatic steps are involved.We herein studied the dynamic behavior of this pathway under light/dark conditions and for several consecutive days. For this purpose, a mathematical model was developed including a variable electron source with a rate law proportional to the intensity of solar irradiance during the photoperiod, and which is continuously turned off at night and on again the next day. The model is defined by a nonlinear system of ordinary differential equations with an on/off time-dependent input, including a parameter to simulate the fact that the photoperiod length is not constant throughout the year, and which takes into account the particular experimental kinetics of each enzyme involved in the pathway. Unlike previous models, which have only provided steady-state solutions, the present model is able to simulate diurnal fluctuations in the metabolite concentrations, fluxes and enzymatic rates involved in the network.The obtained results are broadly consistent with experimental observations and highlight the key role played by ascorbate recycling for plants to adapt to their surrounding environment. This approach provides a new strategy to in vivo studies to analyze plant defense mechanisms against oxidative stress induced by external changes, which can also be extrapolated to other complex metabolic pathways to constitute a useful tool to the scientific community in general.

This model is hosted on BioModels Database and identified by: BIOMD0000000580.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Edelmira.Valero@uclm.es.", + "authors": [ + { + "name": "Edelmira Valero", + "orcid": "0000-0001-8636-4574" + }, + { + "institution": "Department of Mathematics, School of Computer Science, University of Castilla-La Mancha, Campus Universitario, s/n, Albacete, E-02071, Spain. Hermenegilda.Macia@uclm.es.", + "name": "Hermenegilda Maci\u00e0", + "orcid": "0000-0003-1462-5274" + }, + { + "institution": "Institute of Parasitology and Biomedicine \"L\u00f3pez-Neyra\", CSIC, Granada, Spain. mtpmadei@ehu.es.Department of Mathematics, University of the Basque Country, UPV/EHU, Leioa, Spain. mtpmadei@ehu.es.", + "name": "Ildefonso M De la Fuente", + "orcid": "0000-0001-6548-7583" + }, + { + "institution": "Department of Plant Breeding, CEBAS, CSIC, Group Fruit Trees Biotechnology, Murcia, Spain. jahernan@cebas.csic.es.", + "name": "Jos\u00e9-Antonio Hern\u00e1ndez", + "orcid": "0000-0001-7211-5147" + }, + { + "institution": "Department of Physical Chemistry, School of Industrial Engineering, University of Castilla-La Mancha, Campus Universitario, s/n, Albacete, E-02071, Spain. MIsabel.Gonzalez@uclm.es.", + "name": "Mar\u00eda-Isabel Gonz\u00e1lez-S\u00e1nchez", + "orcid": "0000-0002-7390-5420" + }, + { + "institution": "Department of Biochemistry and Molecular Biology A, Biology Faculty, University of Murcia, Murcia, E-30100, Spain. gcarmona@um.es.", + "name": "Francisco Garc\u00eda-Carmona", + "orcid": "0000-0002-1318-7690" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/26797294", + "month": "1", + "pages": "11", + "synopsis": "

Background

Light/dark cycles are probably the most important environmental signals that regulate plant development. Light is essential for photosynthesis, but an excess, in combination with the unavoidable presence of atmospheric oxygen inside the chloroplast, leads to excessive reactive oxygen species production. Among the defense mechanisms that activate plants to cope with environmental stress situations, it is worth noting the ascorbate-glutathione cycle, a complex metabolic pathway in which a variety of photochemical, chemical and enzymatic steps are involved.

Results

We herein studied the dynamic behavior of this pathway under light/dark conditions and for several consecutive days. For this purpose, a mathematical model was developed including a variable electron source with a rate law proportional to the intensity of solar irradiance during the photoperiod, and which is continuously turned off at night and on again the next day. The model is defined by a nonlinear system of ordinary differential equations with an on/off time-dependent input, including a parameter to simulate the fact that the photoperiod length is not constant throughout the year, and which takes into account the particular experimental kinetics of each enzyme involved in the pathway. Unlike previous models, which have only provided steady-state solutions, the present model is able to simulate diurnal fluctuations in the metabolite concentrations, fluxes and enzymatic rates involved in the network.

Conclusions

The obtained results are broadly consistent with experimental observations and highlight the key role played by ascorbate recycling for plants to adapt to their surrounding environment. This approach provides a new strategy to in vivo studies to analyze plant defense mechanisms against oxidative stress induced by external changes, which can also be extrapolated to other complex metabolic pathways to constitute a useful tool to the scientific community in general.", + "title": "Modeling the ascorbate-glutathione cycle in chloroplasts under light/dark conditions.", + "type": "PubMed ID", + "volume": "10", + "year": 2016 + }, + "publicationId": "BIOMD0000000589", + "submissionId": "MODEL1503250002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000590": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Gutenkunst Group - University of Arizona", + "email": "mannakee@email.arizona.edu", + "external": false, + "name": "Brian Mannakee" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Hermansen2015 - denovo biosynthesis of pyrimidines in yeast

This model is described in the article:

Hermansen RA , Mannakee BK , Knecht W , Liberles DA , Gutenkunst RN
BMC Evolutionary Biology. 2015, 15:232

Abstract:

Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes.Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint.We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

This model is hosted on BioModels Database and identified by: MODEL1512160000.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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denovo biosynthesis of pyrimidines in yeast", + "publication": { + "accession": "26511837", + "affiliation": "Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA. rhermans@uwyo.edu.", + "authors": [ + { + "institution": "Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA. rhermans@uwyo.edu.Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. rhermans@uwyo.edu.", + "name": "Russell A Hermansen" + }, + { + "institution": "Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85721, USA. mannakee@email.arizona.edu.", + "name": "Brian K Mannakee" + }, + { + "institution": "Department of Biology and Lund Protein Production Platform, Lund University, 22362, Lund, Sweden. wolfgang.knecht@biol.lu.se.", + "name": "Wolfgang Knecht" + }, + { + "institution": "Department of Molecular Biology, University of Wyoming, Laramie, WY, 82071, USA. daliberles@temple.edu.Department of Biology and Center for Computational Genetics and Genomics, Temple University, Philadelphia, PA, 19122, USA. daliberles@temple.edu.", + "name": "David A Liberles" + }, + { + "institution": "Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, 85721, USA. rgutenk@email.arizona.edu.", + "name": "Ryan N Gutenkunst" + } + ], + "journal": "BMC evolutionary biology", + "link": "http://identifiers.org/pubmed/26511837", + "month": "10", + "pages": "232", + "synopsis": "

Background

Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes.

Results

Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint.

Conclusions

We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.", + "title": "Characterizing selective pressures on the pathway for de novo biosynthesis of pyrimidines in yeast.", + "type": "PubMed ID", + "volume": "15", + "year": 2015 + }, + "publicationId": "BIOMD0000000590", + "submissionId": "MODEL1512160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000591": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "German Cancer Research Center (DKFZ) and Heidelberg University", + "email": "l.adlung@dkfz-heidelberg.de", + "external": false, + "name": "Lorenz Adlung" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Boehm2014 - isoform-specific dimerization of pSTAT5A and pSTAT5B
To study STAT5 activation, the authors build a dynamic model of pSTAT5 isoform dimerization. Combinatorial binding of pSTAT5A and pSTAT5B is analysed using model hypotheses and concurrent experiments. Model parameters are derived from the experiments on Ba/F3 cells. Results show that pSTAT5 heterodimerization hypothesis for STAT5 activation favours experimental results.

This model is described in the article:

Boehm ME, Adlung L, Schilling M, Roth S, Klingm\u00fcller U, Lehmann WD
J Proteome Res. 2014 Dec 5;13(12):5685-94

Abstract:

STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5.

This model is hosted on BioModels Database and identified by: BIOMD0000000591.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality, and Use..

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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isoform-specific dimerization of pSTAT5A and pSTAT5B", + "publication": { + "accession": "25333863", + "affiliation": "Molecular Structure Analysis, \u2021Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ) , Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.", + "authors": [ + { + "institution": "Molecular Structure Analysis, \u2021Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ) , Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.", + "name": "Martin E Boehm" + }, + { + "name": "Lorenz Adlung", + "orcid": "0000-0002-0971-2309" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "name": "Susanne Roth" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + }, + { + "name": "Wolf D Lehmann" + } + ], + "issue": "12", + "journal": "Journal of proteome research", + "link": "http://identifiers.org/pubmed/25333863", + "month": "12", + "pages": "5685-5694", + "synopsis": "STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5.", + "title": "Identification of isoform-specific dynamics in phosphorylation-dependent STAT5 dimerization by quantitative mass spectrometry and mathematical modeling.", + "type": "PubMed ID", + "volume": "13", + "year": 2014 + }, + "publicationId": "BIOMD0000000591", + "submissionId": "MODEL1501220000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000592": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Universidad Nacional Aut\u00f3noma de M\u00e9xico", + "email": "mar.esther23@gmail.com", + "external": false, + "name": "Mariana Martinez-Sanchez" + } + ] + }, + "curationStatus": "CURATED", + "description": "Mariana Esther Martinez-Sanchez, Luis Mendoza, Carlos Villarreal & Elena R. Alvarez-Buylla. A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity. PLOS Computational Biology 11, 6 (2015).

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-\u03b2 and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. 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While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-\u03b2 and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.", + "title": "A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity.", + "type": "PubMed ID", + "volume": "11", + "year": 2015 + }, + "publicationId": "BIOMD0000000592", + "submissionId": "MODEL1411170000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000593": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "Universidad Nacional Aut\u00f3noma de M\u00e9xico", + "email": "mar.esther23@gmail.com", + "external": false, + "name": "Mariana Martinez-Sanchez" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Martinez-Sanchez2015 - T CD4+ lymphocytetranscriptional-signaling regulatory network

This model is described in the article:

Martinez-Sanchez ME, Mendoza L, Villarreal C, Alvarez-Buylla ER.
PLoS Comput. Biol. 2015 Jun; 11(6): e1004324

Abstract:

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-? and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000593.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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\r
Ally Hume: \r
\r
This model should correspond to figure 2A of the paper. The model has 10 nodes but only 5 are shown in the paper. I assume you wanted to keep the diagram simple in the paper. Are you happy with this difference?\r
\r
When I ran the model (in GinSIM) to find the attractors and it shows 9 steady states but the paper (fig 2B) only show 5. Is this OK? I assume you only showed the relevant attractors in the paper? The steady states in the paper are all in the above result.\r
\r
Author reply:\r
\r
Yes, we only included part of the network and the attractors for simplicity. The result you obtain is correct.", + "submitted": 1507126473, + "submitter": "administrator", + "version": 3 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724279267, + "submitter": "Lucian Smith", + "version": 4 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL1411170001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1411170001" + }, + { + "accession": "BIOMD0000000593", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000593" + }, + { + "accession": "26090929", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/26090929" + }, + { + "accession": "GO:0030154", + "name": "cell differentiation", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0030154" + }, + { + "accession": "7742", + "name": "Vertebrata", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/7742" + }, + { + "accession": "MAMO_0000030", + "name": "Logical model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000030" + } + ], + "modellingApproach": { + "accession": "MAMO_0000030", + "name": "logical model", + "resource": "http://identifiers.org/mamo/MAMO_0000030" + }, + "name": "Martinez-Sanchez2015 - T CD4+ lymphocyte transcriptional-signaling regulatory network", + "publication": { + "accession": "26090929", + "affiliation": "Departamento de Ecolog\u00eda Funcional, Instituto de Ecolog\u00eda, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Coyoac\u00e1n, M\u00e9xico Distrito Federal, M\u00e9xico; Centro de Ciencias de la Complejidad, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Coyoac\u00e1n, M\u00e9xico Distrito Federal, M\u00e9xico.", + "authors": [ + { + "institution": "Departamento de Ecolog\u00eda Funcional, Instituto de Ecolog\u00eda, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Coyoac\u00e1n, M\u00e9xico Distrito Federal, M\u00e9xico; Centro de Ciencias de la Complejidad, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Coyoac\u00e1n, M\u00e9xico Distrito Federal, M\u00e9xico.", + "name": "Mariana Esther Martinez-Sanchez", + "orcid": "0000-0002-6115-1449" + }, + { + "institution": "Departamento de Biolog\u00eda Molecular y Biotecnolog\u00eda, Instituto de Investigaciones Biom\u00e9dicas, Universidad Nacional Aut\u00f3noma de M\u00e9xico, M\u00e9xico Distrito Federal, M\u00e9xico.", + "name": "Luis Mendoza", + "orcid": "0000-0002-3321-3083" + }, + { + "name": "Carlos Villarreal", + "orcid": "0000-0003-3569-2820" + }, + { + "institution": "Departamento de Ecolog\u00eda Funcional, Instituto de Ecolog\u00eda, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Coyoac\u00e1n, M\u00e9xico Distrito Federal, M\u00e9xico; Centro de Ciencias de la Complejidad, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Coyoac\u00e1n, M\u00e9xico Distrito Federal, M\u00e9xico.", + "name": "Elena R Alvarez-Buylla" + } + ], + "issue": "6", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/26090929", + "month": "6", + "pages": "e1004324", + "synopsis": "CD4+ T cells orchestrate the adaptive immune response in vertebrates. 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Philipson, Josep Bassaganya-Riera, Monica Viladomiu, Barbara Kronsteiner, Vida Abedi, Stefan Hoops, Pawel Michalak, Lin Kang, Stephen E. Girardin & Raquel Hontecillas. Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection. PLOS ONE 10, 9 (2015).

Helicobacter pylori colonizes half of the world's population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. 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Flis2015 - Plant clock gene circuit(P2011.1.2 PLM_71 ver 1)

This model is described in the article:

Flis A, Fern\u00e1ndez AP, Zielinski T, Mengin V, Sulpice R, Stratford K, Hume A, Pokhilko A, Southern MM, Seaton DD, McWatters HG, Stitt M, Halliday KJ, Millar AJ.
Open Biol 2015 Oct; 5(10):

Abstract:

Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in Arabidopsis thaliana seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of PRR9 and ELF4, and regulation of PRR5 by GI. Absolute RNA quantification revealed low levels of PRR9 transcripts (peak approx. 50 copies cell(-1)) compared with other clock genes, and threefold higher levels of LHY RNA (more than 1500 copies cell(-1)) than of its close relative CCA1. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.


 cL_m_degr, param m1, modified to ensure light rate > dark rate.  

This model is hosted on BioModels Database and identified by: MODEL1510190000.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.", + "title": "Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure.", + "type": "PubMed ID", + "volume": "5", + "year": 2015 + }, + "publicationId": "BIOMD0000000597", + "submissionId": "MODEL1510190000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000598": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Institute of Molecular Plant Sciences, University of Edinburgh", + "email": "andrew.millar@ed.ac.uk", + "external": false, + "name": "Andrew J Millar" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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Waddington Building, Edinburgh EH9 3JD, UK.", + "name": "Aurora Pi\u00f1as Fern\u00e1ndez" + }, + { + "institution": "SynthSys and School of Biological Sciences, University of Edinburgh, C.H. Waddington Building, Edinburgh EH9 3JD, UK.", + "name": "Tomasz Zielinski", + "orcid": "0000-0002-0194-5706" + }, + { + "institution": "Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany.", + "name": "Virginie Mengin", + "orcid": "0000-0002-4958-4209" + }, + { + "institution": "Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany.", + "name": "Ronan Sulpice", + "orcid": "0000-0002-6113-9570" + }, + { + "institution": "EPCC, University of Edinburgh, James Clerk Maxwell Building, Edinburgh EH9 3JZ, UK.", + "name": "Kevin Stratford" + }, + { + "institution": "SynthSys and School of Biological Sciences, University of Edinburgh, C.H. Waddington Building, Edinburgh EH9 3JD, UK EPCC, University of Edinburgh, James Clerk Maxwell Building, Edinburgh EH9 3JZ, UK.", + "name": "Alastair Hume" + }, + { + "institution": "SynthSys and School of Biological Sciences, University of Edinburgh, C.H. Waddington Building, Edinburgh EH9 3JD, UK Institute of Molecular Cell and Systems Biology, University of Glasgow, Bower Building, Glasgow G12 8QQ, UK.", + "name": "Alexandra Pokhilko" + }, + { + "institution": "Department of Biological Sciences, University of Warwick, Coventry CV4 7AL, UK.", + "name": "Megan M Southern" + }, + { + "institution": "SynthSys and School of Biological Sciences, University of Edinburgh, C.H. Waddington Building, Edinburgh EH9 3JD, UK.", + "name": "Daniel D Seaton", + "orcid": "0000-0002-5222-3893" + }, + { + "institution": "SynthSys and School of Biological Sciences, University of Edinburgh, C.H. Waddington Building, Edinburgh EH9 3JD, UK.", + "name": "Harriet G McWatters" + }, + { + "institution": "Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany mstitt@mpimp-golm.mpg.de.", + "name": "Mark Stitt", + "orcid": "0000-0002-4900-1763" + }, + { + "institution": "SynthSys and School of Biological Sciences, University of Edinburgh, C.H. Waddington Building, Edinburgh EH9 3JD, UK karen.halliday@ed.ac.uk.", + "name": "Karen J Halliday", + "orcid": "0000-0003-0467-104X" + }, + { + "name": "Andrew J Millar", + "orcid": "0000-0003-1756-3654" + } + ], + "issue": "10", + "journal": "Open biology", + "link": "http://identifiers.org/pubmed/26468131", + "month": "10", + "pages": "150042", + "synopsis": "Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in Arabidopsis thaliana seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of PRR9 and ELF4, and regulation of PRR5 by GI. Absolute RNA quantification revealed low levels of PRR9 transcripts (peak approx. 50 copies cell(-1)) compared with other clock genes, and threefold higher levels of LHY RNA (more than 1500 copies cell(-1)) than of its close relative CCA1. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.", + "title": "Defining the robust behaviour of the plant clock gene circuit with absolute RNA timeseries and open infrastructure.", + "type": "PubMed ID", + "volume": "5", + "year": 2015 + }, + "publicationId": "BIOMD0000000598", + "submissionId": "MODEL1510190001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000599": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Michigan", + "email": "linderma@umich.edu", + "external": false, + "name": "Jennifer Linderman" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "Nathaniel L. Coggins, Danielle Trakimas, S. Laura Chang, Anna Ehrlich, Paramita Ray, Kathryn E. Luker, Jennifer J. Linderman & Gary D. Luker. CXCR7 controls competition for recruitment of \u03b2-arrestin 2 in cells expressing both CXCR4 and CXCR7. PLoS ONE 9, 6 (2014).

Chemokine CXCL12 promotes growth and metastasis of more than 20 different human cancers, as well as pathogenesis of other common diseases. CXCL12 binds two different receptors, CXCR4 and CXCR7, both of which recruit and signal through the cytosolic adapter protein \u03b2-arrestin 2. Differences in CXCL12-dependent recruitment of \u03b2-arrestin 2 in cells expressing one or both receptors remain poorly defined. To quantitatively investigate parameters controlling association of \u03b2-arrestin 2 with CXCR4 or CXCR7 in cells co-expressing both receptors, we used a systems biology approach combining real-time, multi-spectral luciferase complementation imaging with computational modeling. Cells expressing only CXCR4 maintain low basal association with \u03b2-arrestin 2, and CXCL12 induces a rapid, transient increase in this interaction. In contrast, cells expressing only CXCR7 have higher basal association with \u03b2-arrestin 2 and exhibit more gradual, prolonged recruitment of \u03b2-arrestin 2 in response to CXCL12. We developed and fit a data-driven computational model for association of either CXCR4 or CXCR7 with \u03b2-arrestin 2 in cells expressing only one type of receptor. We then experimentally validated model predictions that co-expression of CXCR4 and CXCR7 on the same cell substantially decreases both the magnitude and duration of CXCL12-regulated recruitment of \u03b2-arrestin 2 to CXCR4. Co-expression of both receptors on the same cell only minimally alters recruitment of \u03b2-arrestin 2 to CXCR7. In silico experiments also identified \u03b2-arrestin 2 as a limiting factor in cells expressing both receptors, establishing that CXCR7 wins the \"competition\" with CXCR4 for CXCL12 and recruitment of \u03b2-arrestin 2. These results reveal how competition for \u03b2-arrestin 2 controls integrated responses to CXCL12 in cells expressing both CXCR4 and CXCR7. 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"0000-0003-1974-1880" + }, + { + "institution": "Department of Chemical Engineering, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "S Laura Chang" + }, + { + "institution": "Center for Molecular Imaging, Department of Radiology, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Anna Ehrlich" + }, + { + "institution": "Center for Molecular Imaging, Department of Radiology, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Paramita Ray" + }, + { + "institution": "Center for Molecular Imaging, Department of Radiology, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Kathryn E Luker" + }, + { + "institution": "Department of Chemical Engineering, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America; Department of Biomedical Engineering, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Jennifer J Linderman" + }, + { + "institution": "Center for Molecular Imaging, Department of Radiology, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America; Department of Biomedical Engineering, Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Gary D Luker" + } + ], + "issue": "6", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/24896823", + "month": "0", + "pages": "e98328", + "synopsis": "Chemokine CXCL12 promotes growth and metastasis of more than 20 different human cancers, as well as pathogenesis of other common diseases. CXCL12 binds two different receptors, CXCR4 and CXCR7, both of which recruit and signal through the cytosolic adapter protein \u03b2-arrestin 2. Differences in CXCL12-dependent recruitment of \u03b2-arrestin 2 in cells expressing one or both receptors remain poorly defined. To quantitatively investigate parameters controlling association of \u03b2-arrestin 2 with CXCR4 or CXCR7 in cells co-expressing both receptors, we used a systems biology approach combining real-time, multi-spectral luciferase complementation imaging with computational modeling. Cells expressing only CXCR4 maintain low basal association with \u03b2-arrestin 2, and CXCL12 induces a rapid, transient increase in this interaction. In contrast, cells expressing only CXCR7 have higher basal association with \u03b2-arrestin 2 and exhibit more gradual, prolonged recruitment of \u03b2-arrestin 2 in response to CXCL12. We developed and fit a data-driven computational model for association of either CXCR4 or CXCR7 with \u03b2-arrestin 2 in cells expressing only one type of receptor. We then experimentally validated model predictions that co-expression of CXCR4 and CXCR7 on the same cell substantially decreases both the magnitude and duration of CXCL12-regulated recruitment of \u03b2-arrestin 2 to CXCR4. Co-expression of both receptors on the same cell only minimally alters recruitment of \u03b2-arrestin 2 to CXCR7. In silico experiments also identified \u03b2-arrestin 2 as a limiting factor in cells expressing both receptors, establishing that CXCR7 wins the \"competition\" with CXCR4 for CXCL12 and recruitment of \u03b2-arrestin 2. These results reveal how competition for \u03b2-arrestin 2 controls integrated responses to CXCL12 in cells expressing both CXCR4 and CXCR7. These results advance understanding of normal and pathologic functions of CXCL12, which is critical for developing effective strategies to target these pathways therapeutically.", + "title": "CXCR7 controls competition for recruitment of \u03b2-arrestin 2 in cells expressing both CXCR4 and CXCR7.", + "type": "PubMed ID", + "volume": "9", + "year": 2014 + }, + "publicationId": "BIOMD0000000599", + "submissionId": "MODEL1408060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000600": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Eidgen\u00f6ssische Technische Hochschule Zurich (ETHZ)", + "email": "georgios.fengos@bsse.ethz.ch", + "external": false, + "name": "Georgios Fengos" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Celli\u00e8re2011 - Plasticity of TGF-\u03b2 Signalling

Transforming growth factor beta (TGF-\u03b2) signalling has been implicated as an important regulator of almost all major cell behaviours, including proliferation, differentiation, cell death, and motility. It remains unclear that how the TGF-\u03b2 signalling pathway accomplishes the flexibility in its responses. What and how many parameters have to be altered for cells to respond differently to perform complex tasks? This canonical response has been explored in this model, by considering the core signalling architecture of TGF-\u03b2 pathway.

This model is described in the article:

Celli\u00e8re G, Fengos G, Herv\u00e9 M, Iber D.
BMC Syst Biol. 2011 Nov 3;5:184.

Abstract:

The family of TGF-\u03b2 ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-\u03b2 ligands eliciting both sustained or transient responses. Members of the TGF-\u03b2 family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration. A number of different models have been proposed to reconcile these different behaviours. We were interested to define the set of minimal modifications that are required to change the type of signal processing in the TGF-\u03b2 signaling network.\t\tRESULTS:To define the key aspects for signaling plasticity we focused on the core of the TGF-\u03b2 signaling network. With the help of a parameter screen we identified ranges of kinetic parameters and protein concentrations that give rise to transient, sustained, or oscillatory responses to constant stimuli, as well as those parameter ranges that enable a proportional response to time-varying ligand concentrations (as expected in the read-out of morphogens). A combination of a strong negative feedback and fast shuttling to the nucleus biases signaling to a transient rather than a sustained response, while oscillations were obtained if ligand binding to the receptor is weak and the turn-over of the I-Smad is fast. A proportional read-out required inefficient receptor activation in addition to a low affinity of receptor-ligand binding. We find that targeted modification of single parameters suffices to alter the response type. The intensity of a constant signal (i.e. the ligand concentration), on the other hand, affected only the strength but not the type of the response.CONCLUSIONS:The architecture of the TGF-\u03b2 pathway enables the observed signaling plasticity. The observed range of signaling outputs to TGF-\u03b2 ligand in different cell types and under different conditions can be explained with differences in cellular protein concentrations and with changes in effective rate constants due to cross-talk with other signaling pathways. It will be interesting to uncover the exact cellular differences as well as the details of the cross-talks in future work.

This model is hosted on BioModels Database and identified by: MODEL1208280000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to [CC0 Public Domain Dedication>http://creativecommons.org/publicdomain/zero/1.0/] for more information.

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4058 Basel, Switzerland.", + "name": "Geraldine Celli\u00e8re" + }, + { + "name": "Georgios Fengos" + }, + { + "name": "Marianne Herv\u00e9" + }, + { + "name": "Dagmar Iber", + "orcid": "0000-0001-8051-1035" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/22051045", + "month": "11", + "pages": "184", + "synopsis": "

Background

The family of TGF-\u03b2 ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-\u03b2 ligands eliciting both sustained or transient responses. Members of the TGF-\u03b2 family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration. A number of different models have been proposed to reconcile these different behaviours. We were interested to define the set of minimal modifications that are required to change the type of signal processing in the TGF-\u03b2 signaling network.

Results

To define the key aspects for signaling plasticity we focused on the core of the TGF-\u03b2 signaling network. With the help of a parameter screen we identified ranges of kinetic parameters and protein concentrations that give rise to transient, sustained, or oscillatory responses to constant stimuli, as well as those parameter ranges that enable a proportional response to time-varying ligand concentrations (as expected in the read-out of morphogens). A combination of a strong negative feedback and fast shuttling to the nucleus biases signaling to a transient rather than a sustained response, while oscillations were obtained if ligand binding to the receptor is weak and the turn-over of the I-Smad is fast. A proportional read-out required inefficient receptor activation in addition to a low affinity of receptor-ligand binding. We find that targeted modification of single parameters suffices to alter the response type. The intensity of a constant signal (i.e. the ligand concentration), on the other hand, affected only the strength but not the type of the response.

Conclusions

The architecture of the TGF-\u03b2 pathway enables the observed signaling plasticity. The observed range of signaling outputs to TGF-\u03b2 ligand in different cell types and under different conditions can be explained with differences in cellular protein concentrations and with changes in effective rate constants due to cross-talk with other signaling pathways. It will be interesting to uncover the exact cellular differences as well as the details of the cross-talks in future work.", + "title": "Plasticity of TGF-\u03b2 signaling.", + "type": "PubMed ID", + "volume": "5", + "year": 2011 + }, + "publicationId": "BIOMD0000000600", + "submissionId": "MODEL1208280000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000601": { + "contributors": { + "Modeller": [ + { + "affiliation": "Centro de Investigacion y de Estudios Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Unidad Monterrey, Apodaca, Nuevo Leon, Mexico.", + "email": "ncperez@cinvestav.mx", + "external": false, + "name": "Norma Citlalcue Perez-Rosas" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This SBML model reproduced the calcium release from SR by application of 20 mM or 2mM caffeine, described in the paper. * Ca_i_Total and Ca_SR_Total respectively represent the total calcium concentration in the sarcoplasm and in the sarcoplasmic reticulum. * Ca_i  and Ca_SR respectively represent the free calcium concentration in the sarcoplasm and  the sarcoplasmic reticulum.* J1 is the calcium  flux due to all mechanisms (except SERCA pumps) that remove the excess of calcium from the sarcoplasm.* J2 is the calcium flux from the reticulum to the sarcoplasm via the ryanodine receptors (RyR) present in the reticulum membrane.* J3 is the calcium flux from the sarcoplasm to the reticulum by the SERCA pumps located in the reticulum membrane.*The parameters are a,  b, B c, Ca_i_basal, Ca_SR_basal, caff, csq,  gamma, KC, kf, KR, Ks, nf, ns and nv.* The value of KC for the model were calculated for J2=J3, after substituting Ca_i=Ca_i_basal, Ca_SR=Ca_SR_basal and caff=0. * Po represents the RyR open probability based on CICR. * Caffeine (caff)** increases the calcium affinity of smooth muscle's RyR so they open even when calcium is at basal level.** Due to caffeine-induced calcium release, a 5 seconds  pulse of caffeine (20 mM) was applied (event called Caff_ON) at 10 seconds after the simulation starts. The event called Caff_OFF starts when the pulse of caffeine finished (caff=0).* PE denotes the concentration of calcium binding sites. * Xi=Ca_SR_Total+PE+KR*In order to reproduce the dynamics of calcium following the application of 2 mM of caffeine, the value of some parameters needs to be change: b=35, Ca_i_basal=9.257e-6, gamma=7.45, caff=0.002 and the initial condition for Ca_i_Total=9.257e-6.*The unit of the calcium concentration is mol/L.* The unit of time is second. *The original SBML code was exported from COPASI 4.12 (Build 81).
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Phase 1 is characterized by a large increase of the intracellular Ca2+ concentration ([Ca2+]i) with a minimal reduction of the free luminal SR [Ca2+] ([Ca2+]FSR). Importantly, active SR Ca2+ ATPases (SERCA pumps) are necessary for phase 1 to occur. This situation cannot be explained by the standard kinetics that involves a fixed amount of luminal Ca2+ binding sites. A new mathematical model was developed that assumes an increasing SR Ca2+ buffering capacity in response to an increase of the luminal SR [Ca2+] that is called Kinetics-on-Demand (KonD) model. This approach can explain both phase 1 and the refractory period associated with a recovered [Ca2+]FSR. Additionally, our data suggest that active SERCA pumps are a requisite for KonD to be functional; otherwise luminal SR Ca2+ binding proteins switch to standard kinetics. The importance of KonD Ca2+ binding properties is twofold: a more efficient Ca2+ release process and that [Ca2+]FSR and Ca2+-bound to SR proteins ([Ca2+]BSR) can be regulated separately allowing for Ca2+ release to occur (provided by Ca2+-bound to luminal Ca2+ binding proteins) without an initial reduction of the [Ca2+]FSR.", + "title": "Kinetics on Demand Is a Simple Mathematical Solution that Fits Recorded Caffeine-Induced Luminal SR Ca2+ Changes in Smooth Muscle Cells.", + "type": "PubMed ID", + "volume": "10", + "year": 2015 + }, + "publicationId": "BIOMD0000000601", + "submissionId": "MODEL1406180001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000602": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Dept. of Arctic and Marine Biology, UiT The Arctic University of Norway, Troms\u00f8 Norway", + "email": "ines.heiland@uit.no", + "external": false, + "name": "Ines Heiland" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Stavrum2013 - Tryptophan Metabolism inLiver

This model is described in the article:

Stavrum AK, Heiland I, Schuster S, Puntervoll P, Ziegler M.
J. Biol. Chem. 2013 Nov; 288(48): 34555-34566

Abstract:

Tryptophan is utilized in various metabolic routes including protein synthesis, serotonin, and melatonin synthesis and the kynurenine pathway. Perturbations in these pathways have been associated with neurodegenerative diseases and cancer. Here we present a comprehensive kinetic model of the complex network of human tryptophan metabolism based upon existing kinetic data for all enzymatic conversions and transporters. By integrating tissue-specific expression data, modeling tryptophan metabolism in liver and brain returned intermediate metabolite concentrations in the physiological range. Sensitivity and metabolic control analyses identified expected key enzymes to govern fluxes in the branches of the network. Combining tissue-specific models revealed a considerable impact of the kynurenine pathway in liver on the concentrations of neuroactive derivatives in the brain. Moreover, using expression data from a cancer study predicted metabolite changes that resembled the experimental observations. We conclude that the combination of the kinetic model with expression data represents a powerful diagnostic tool to predict alterations in tryptophan metabolism. The model is readily scalable to include more tissues, thereby enabling assessment of organismal tryptophan metabolism in health and disease.

This model is hosted on BioModels Database and identified by: BIOMD0000000602.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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We conclude that the combination of the kinetic model with expression data represents a powerful diagnostic tool to predict alterations in tryptophan metabolism. 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"Dagmara Klein" + }, + { + "name": "Kuk-Ki Hong" + }, + { + "name": "Therese Jacobson" + }, + { + "name": "Peter Dahl" + }, + { + "institution": "Max Planck Institute for Molecular Genetics, Berlin, Germany.", + "name": "J\u00f6rg Schaber", + "orcid": "0000-0001-6971-2530" + }, + { + "institution": "Chalmers University of Technology, Gothenburg, Sweden.", + "name": "Jens Nielsen", + "orcid": "0000-0002-9955-6003" + }, + { + "name": "Stefan Hohmann", + "orcid": "0000-0002-0809-1985" + }, + { + "name": "Edda Klipp" + } + ], + "issue": "6", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/23762021", + "month": "0", + "pages": "e1003084", + "synopsis": "We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "title": "Quantitative analysis of glycerol accumulation, glycolysis and growth under hyper osmotic stress.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000604", + "submissionId": "MODEL1204190001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000605": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "Humboldt University Berlin", + "email": "clemenskuehn@posteo.de", + "external": false, + "name": "Clemens K\u00fchn" + } + ] + }, + "curationStatus": "CURATED", + "description": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. 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integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. 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Palsson2013 - Fully-integration immune response model (FIRM)

FIRM (The Fully-integrated Immune Response Modeling) is a hybrid construct incorporating multiple existing models of the immune system [De Boer et al., (1985);Bell, (1970); Marino and Kirschner, (2004) ]. FIRM used a pharmacokinetic / pharmacodynamic modelling approach to combine previously published individual models of humoral and cellular response with antigen exposure. This integrated model has a potential to simulate a range of responses under a variety of conditions, for example, the immune response against tuberculosis infection, blood borne pathogen infection, Spontaneous tumour rejection and influence of regulatory T cells (Treg) on tumour rejection.

The SBML model provided here was generated from the matlab code (provided by the authors). The matlab to SBML conversion was done using MOCCASIN version 1.1.0. This model describes the immune response against tuberculosis (TB) infection and reproduces figure 7 of the reference publication.

Note:The following minor edit to the original matlab code was done during the conversion to SBML: The model had two parameters named k3 and K3. To avoid case-insensitive issues during the conversion, K3 was changed to K3s in the original matlap code before using the conversion software. The matlab code of the model provided by the authors (with the above change) can be obtained from the curation tab.

This model is described in the article:

Palsson S, Hickling TP, Bradshaw-Pierce EL, Zager M, Jooss K, O'Brien PJ, Spilker ME, Palsson BO, Vicini P.
BMC Syst Biol. 2013 Sep 28;7:95.

Abstract:

BACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-\u03b3, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-\u03b2. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

This model is hosted on BioModels Database and identified by: MODEL1603310000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Paolo.Vicini@pfizer.com.", + "authors": [ + { + "institution": "Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research and Development, San Diego, CA, USA. Paolo.Vicini@pfizer.com.", + "name": "Sirus Palsson" + }, + { + "name": "Timothy P Hickling", + "orcid": "0000-0002-8918-2140" + }, + { + "name": "Erica L Bradshaw-Pierce" + }, + { + "name": "Michael Zager" + }, + { + "name": "Karin Jooss" + }, + { + "name": "Peter J O'Brien" + }, + { + "name": "Mary E Spilker" + }, + { + "name": "Bernhard O Palsson" + }, + { + "name": "Paolo Vicini" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/24074340", + "month": "9", + "pages": "95", + "synopsis": "

Background

The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach.

Results

A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-\u03b3, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-\u03b2. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells.

Conclusions

The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.", + "title": "The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000608", + "submissionId": "MODEL1603310000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000609": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department of Mathematical Sciences, Loughborough University, Loughborough, Leics LE11 3TU, UK.", + "email": "d.reddyhoff@lboro.ac.uk", + "external": false, + "name": "Dennis Reddyhoff" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Reddyhoff2015 - Acetaminophen metabolism and toxicity
This model examines acetaminophen metabolism and related hepatotoxicity. Multiple pathways associated with APAP metabolism has been included in the model. Using numerical, sensitivity and timescale analysis, key parameters involved in the toxicity has been identified. The model analysis highlights a critical acetaminophen dose in terms of the model parameters.

This model is described in the article:

Reddyhoff D, Ward J, Williams D, Regan S, Webb S
J Theor Biol. 2015 Dec 7;386:132-46.

Abstract:

Acetaminophen is a widespread and commonly used painkiller all over the world. However, it can cause liver damage when taken in large doses or at repeated chronic doses. Current models of acetaminophen metabolism are complex, and limited to numerical investigation though provide results that represent clinical investigation well. We derive a mathematical model based on mass action laws aimed at capturing the main dynamics of acetaminophen metabolism, in particular the contrast between normal and overdose cases, whilst remaining simple enough for detailed mathematical analysis that can identify key parameters and quantify their role in liver toxicity. We use singular perturbation analysis to separate the different timescales describing the sequence of events in acetaminophen metabolism, systematically identifying which parameters dominate during each of the successive stages. Using this approach we determined, in terms of the model parameters, the critical dose between safe and overdose cases, timescales for exhaustion and regeneration of important cofactors for acetaminophen metabolism and total toxin accumulation as a fraction of initial dose.

This model is hosted on BioModels Database and identified by: MODEL1603080000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Electronic address: d.reddyhoff@lboro.ac.uk.", + "name": "Dennis Reddyhoff", + "orcid": "0000-0002-4971-2346" + }, + { + "institution": "Department of Mathematical Sciences, Loughborough University, Loughborough, Leics LE11 3TU, UK.", + "name": "John Ward", + "orcid": "0000-0003-0570-2725" + }, + { + "institution": "Translational Safety, Drug Safety and Metabolism, Darwin Building, Cambridge Science Park, Milton Road, Cambridge, Cambs CB4 0FZ, UK.", + "name": "Dominic Williams", + "orcid": "0000-0002-0758-3152" + }, + { + "institution": "MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Sherrington Building, Ashton Street, Liverpool L69 3GE, UK.", + "name": "Sophie Regan" + }, + { + "institution": "MRC Centre for Drug Safety Science, Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Sherrington Building, Ashton Street, Liverpool L69 3GE, UK.", + "name": "Steven Webb" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/26348886", + "month": "12", + "pages": "132-146", + "synopsis": "Acetaminophen is a widespread and commonly used painkiller all over the world. 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Using this approach we determined, in terms of the model parameters, the critical dose between safe and overdose cases, timescales for exhaustion and regeneration of important cofactors for acetaminophen metabolism and total toxin accumulation as a fraction of initial dose.", + "title": "Timescale analysis of a mathematical model of acetaminophen metabolism and toxicity.", + "type": "PubMed ID", + "volume": "386", + "year": 2015 + }, + "publicationId": "BIOMD0000000609", + "submissionId": "MODEL1603080000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000610": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Humboldt University Berlin", + "email": "clemenskuehn@posteo.de", + "external": false, + "name": "Clemens K\u00fchn" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Petelenz-kurdzeil2013 - Osmo adaptationgpd1D

This model is described in the article:

Petelenz-Kurdziel E, Kuehn C, Nordlander B, Klein D, Hong KK, Jacobson T, Dahl P, Schaber J, Nielsen J, Hohmann S, Klipp E.
PLoS Comput. Biol. 2013; 9(6): e1003084

Abstract:

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000610.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"Kuk-Ki Hong" + }, + { + "name": "Therese Jacobson" + }, + { + "name": "Peter Dahl" + }, + { + "institution": "Max Planck Institute for Molecular Genetics, Berlin, Germany.", + "name": "J\u00f6rg Schaber", + "orcid": "0000-0001-6971-2530" + }, + { + "institution": "Chalmers University of Technology, Gothenburg, Sweden.", + "name": "Jens Nielsen", + "orcid": "0000-0002-9955-6003" + }, + { + "name": "Stefan Hohmann", + "orcid": "0000-0002-0809-1985" + }, + { + "name": "Edda Klipp" + } + ], + "issue": "6", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/23762021", + "month": "0", + "pages": "e1003084", + "synopsis": "We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. 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Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "title": "Quantitative analysis of glycerol accumulation, glycolysis and growth under hyper osmotic stress.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000000610", + "submissionId": "MODEL1204190004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000611": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "Pfizer", + "email": "satyaprakash.nayak@pfizer.com", + "external": false, + "name": "Satyaprakash Nayak" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
Note:
The SBML model is generated from SimBiology. The SimBiology (.sbproj) file is available for download from the curation tab.

This model is described in the article:

Nayak S, Lee D, Patel-Hett S, Pittman DD, Martin SW, Heatherington AC, Vicini P, Hua F.
CPT Pharmacometrics Syst Pharmacol. 2015 Jul;4(7):396-405.

Abstract:

A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.

This model is hosted on BioModels Database and identified by: MODEL1511160000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cambridge, Massachusetts, USA.", + "name": "A C Heatherington" + }, + { + "institution": "Pharmacokinetics, Dynamics and Metabolism, New Biological Entities, Pfizer Inc. Cambridge, Massachusetts, USA.", + "name": "P Vicini" + }, + { + "institution": "Quantitative Clinical Sciences, PharmaTherapeutics Clinical R&D, Pfizer Inc. Cambridge, Massachusetts, USA.", + "name": "F Hua" + } + ], + "issue": "7", + "journal": "CPT: pharmacometrics & systems pharmacology", + "link": "http://identifiers.org/pubmed/26312163", + "month": "7", + "pages": "396-405", + "synopsis": "A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.", + "title": "Using a Systems Pharmacology Model of the Blood Coagulation Network to Predict the Effects of Various Therapies on Biomarkers.", + "type": "PubMed ID", + "volume": "4", + "year": 2015 + }, + "publicationId": "BIOMD0000000611", + "submissionId": "MODEL1511160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000612": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2016 - Circadian rhythm of PTH and thedynamics of signaling molecules on bone remodeling

This model is described in the article:

Proctor CJ, Gartland A.
Front Endocrinol (Lausanne) 2016; 7: 61

Abstract:

Bone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-?, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing.

This model is hosted on BioModels Database and identified by: BIOMD0000000612.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Circadian rhythm of PTH and the dynamics of signaling molecules on bone remodeling", + "publication": { + "accession": "27379013", + "affiliation": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Newcastle University, Newcastle upon Tyne, UK; Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "authors": [ + { + "institution": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Newcastle University, Newcastle upon Tyne, UK; Musculoskeletal Research Group, Institute of Cellular Medicine, Medical School, Newcastle University, Newcastle upon Tyne, UK.", + "name": "Carole J Proctor" + }, + { + "institution": "MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), Newcastle University, Newcastle upon Tyne, UK; Department of Oncology and Metabolism, University of Sheffield Medical School, Sheffield, UK.", + "name": "Alison Gartland", + "orcid": "0000-0002-3712-2437" + } + ], + "journal": "Frontiers in endocrinology", + "link": "http://identifiers.org/pubmed/27379013", + "month": "0", + "pages": "61", + "synopsis": "Bone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-\u03b2, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing.", + "title": "Simulated Interventions to Ameliorate Age-Related Bone Loss Indicate the Importance of Timing.", + "type": "PubMed ID", + "volume": "7", + "year": 2016 + }, + "publicationId": "BIOMD0000000612", + "submissionId": "MODEL1602290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000613": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "<notes xmlns="http://www.sbml.org/sbml/level3/version1/core"> <body xmlns="http://www.w3.org/1999/xhtml"> <div class="dc:title">Peterson2010 - integrated calcium homeostasisand bone remodelling</div><div class="dc:description"> </div><div class="dc:bibliographicCitation"> <p>This model is described in the article:</p> <div class="bibo:title"> <a href="http://identifiers.org/pubmed/19732857" title="Access to this publication">A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling.</a> </div> <div class="bibo:authorList">Peterson MC, Riggs MM.</div> <div class="bibo:Journal">Bone 2010 Jan; 46(1): 49-63</div> <p>Abstract:</p> <div class="bibo:abstract"> <p>Bone biology is physiologically complex and intimately linked to calcium homeostasis. The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research.</p> </div></div><div class="dc:publisher"> <p>This model is hosted on <a href="http://www.ebi.ac.uk/biomodels/">BioModels Database</a> and identified by: <a href="http://identifiers.org/biomodels.db/BIOMD0000000613">BIOMD0000000613</a>.</p> <p>To cite BioModels Database, please use: <a href="http://identifiers.org/pubmed/20587024" title="Latest BioModels Database publication">BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models</a>.</p></div><div class="dc:license"> <p>To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to <a href="http://creativecommons.org/publicdomain/zero/1.0/" title="Access to: CC0 1.0 Universal (CC0 1.0), Public Domain Dedication">CC0 Public Domain Dedication</a> for more information.</p></div></body> </notes>", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2)", + "fileSize": "60432", + "md5sum": "ab11e99cce8ba262b3d0b9da6e98e110", + "mimeType": "application/rdf+xml", + "name": "BIOMD0000000613-biopax2.owl", + "sha1sum": "f9b2f25015454e610d3c8247c0d88e03d88dea76", + "sha256sum": "3d72a3c8252d1dd4bd8299119d910c7927fef86039465c156e3b1aea0d5aeb74" + }, + { + "description": "Auto-generated BioPAX (Level 3)", + "fileSize": "97368", + "md5sum": "4cf2caa949994474eb8fbce1a8b93bd0", + "mimeType": "application/rdf+xml", + "name": "BIOMD0000000613-biopax3.owl", + "sha1sum": "1c988e8ecef104befc322d6382f13558ef8531ac", + "sha256sum": "b8fa76af10402e22e117e674ab940e5849afd5207a23986e751970e6ad976003" + }, + { + "description": "Auto-generated 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The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research.", + "title": "A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling.", + "type": "PubMed ID", + "volume": "46", + "year": 2010 + }, + "publicationId": "BIOMD0000000613", + "submissionId": "MODEL1604270004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000614": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tvarusai@ebi.ac.uk", + "external": false, + "name": "Thawfeek Varusai" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kamihira2000 - calcitonin fibrillation kinetics

This model studies the kinetics of human calcitonin fibrillation described as a two-step process. Empirical data is used to determine the parameter values. Results show that the first step in fibrillation is a slow homogenous reaction and the second step is a fast autocatalytic heterogenous reaction.

This model is described in the article:

Kamihira M, Naito A, Tuzi S, Nosaka AY, Sait\u00f4 H.
Protein Sci. 2000 May; 9(5): 867-877

Abstract:

Conformational transitions of human calcitonin (hCT) during fibril formation in the acidic and neutral conditions were investigated by high-resolution solid-state 13C NMR spectroscopy. In aqueous acetic acid solution (pH 3.3), a local alpha-helical form is present around Gly10 whereas a random coil form is dominant as viewed from Phe22, Ala26, and Ala31 in the monomer form on the basis of the 13C chemical shifts. On the other hand, a local beta-sheet form as viewed from Gly10 and Phe22, and both beta-sheet and random coil as viewed from Ala26 and Ala31 were detected in the fibril at pH 3.3. The results indicate that conformational transitions from alpha-helix to beta-sheet, and from random coil to beta-sheet forms occurred in the central and C-terminus regions, respectively, during the fibril formation. The increased 13C resonance intensities of fibrils after a certain delay time suggests that the fibrillation can be explained by a two-step reaction mechanism in which the first step is a homogeneous association to form a nucleus, and the second step is an autocatalytic heterogeneous fibrillation. In contrast to the fibril at pH 3.3, the fibril at pH 7.5 formed a local beta-sheet conformation at the central region and exhibited a random coil at the C-terminus region. Not only a hydrophobic interaction among the amphiphilic alpha-helices, but also an electrostatic interaction between charged side chains can play an important role for the fibril formation at pH 7.5 and 3.3 acting as electrostatically favorable and unfavorable interactions, respectively. These results suggest that hCT fibrils are formed by stacking antiparallel beta-sheets at pH 7.5 and a mixture of antiparallel and parallel beta-sheets at pH 3.3.

This model is hosted on BioModels Database and identified by: BIOMD0000000614.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"name": "Kamihira2000 - calcitonin fibrillation kinetics", + "publication": { + "accession": "10850796", + "affiliation": "Department of Life Science, Himeji Institute of Technology, Harime Science Garden City, Kamigori, Hyogo, Japan.", + "authors": [ + { + "institution": "Department of Life Science, Himeji Institute of Technology, Harime Science Garden City, Kamigori, Hyogo, Japan.", + "name": "M Kamihira" + }, + { + "name": "A Naito" + }, + { + "name": "S Tuzi" + }, + { + "name": "A Y Nosaka" + }, + { + "name": "H Sait\u00f4" + } + ], + "issue": "5", + "journal": "Protein science : a publication of the Protein Society", + "link": "http://identifiers.org/pubmed/10850796", + "month": "5", + "pages": "867-877", + "synopsis": "Conformational transitions of human calcitonin (hCT) during fibril formation in the acidic and neutral conditions were investigated by high-resolution solid-state 13C NMR spectroscopy. In aqueous acetic acid solution (pH 3.3), a local alpha-helical form is present around Gly10 whereas a random coil form is dominant as viewed from Phe22, Ala26, and Ala31 in the monomer form on the basis of the 13C chemical shifts. On the other hand, a local beta-sheet form as viewed from Gly10 and Phe22, and both beta-sheet and random coil as viewed from Ala26 and Ala31 were detected in the fibril at pH 3.3. The results indicate that conformational transitions from alpha-helix to beta-sheet, and from random coil to beta-sheet forms occurred in the central and C-terminus regions, respectively, during the fibril formation. The increased 13C resonance intensities of fibrils after a certain delay time suggests that the fibrillation can be explained by a two-step reaction mechanism in which the first step is a homogeneous association to form a nucleus, and the second step is an autocatalytic heterogeneous fibrillation. In contrast to the fibril at pH 3.3, the fibril at pH 7.5 formed a local beta-sheet conformation at the central region and exhibited a random coil at the C-terminus region. Not only a hydrophobic interaction among the amphiphilic alpha-helices, but also an electrostatic interaction between charged side chains can play an important role for the fibril formation at pH 7.5 and 3.3 acting as electrostatically favorable and unfavorable interactions, respectively. These results suggest that hCT fibrils are formed by stacking antiparallel beta-sheets at pH 7.5 and a mixture of antiparallel and parallel beta-sheets at pH 3.3.", + "title": "Conformational transitions and fibrillation mechanism of human calcitonin as studied by high-resolution solid-state 13C NMR.", + "type": "PubMed ID", + "volume": "9", + "year": 2000 + }, + "publicationId": "BIOMD0000000614", + "submissionId": "MODEL1608100002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000615": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tvarusai@ebi.ac.uk", + "external": false, + "name": "Thawfeek Varusai" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kuznetsov2016(II) - \u03b1-syn aggregationkinetics in Parkinson's
This theoretical model uses 2-step Finke-Watzky (FW) kineticstodescribe the production, misfolding, aggregation, transport anddegradation of \u03b1-syn that may lead to Parkinson's Disease(PD). Deregulated \u03b1-syn degradation is predicted to becrucialfor PD pathogenesis.   

This model is described in the article:

Kuznetsov IA, Kuznetsov AV.
Math Biosci 2016 Aug; 278: 22-29

Abstract:

The aim of this paper is to develop a minimal model describing events leading to the onset of Parkinson's disease (PD). The model accounts for \u03b1-synuclein (\u03b1-syn) production in the soma, transport toward the synapse, misfolding, and aggregation. The production and aggregation of polymeric \u03b1-syn is simulated using a minimalistic 2-step Finke-Watzky model. We utilized the developed model to analyze what changes in a healthy neuron are likely to lead to the onset of \u03b1-syn aggregation. We checked the effects of interruption of \u03b1-syn transport toward the synapse, entry of misfolded (infectious) \u03b1-syn into the somatic and synaptic compartments, increasing the rate of \u03b1-syn synthesis in the soma, and failure of \u03b1-syn degradation machinery. Our model suggests that failure of \u03b1-syn degradation machinery is probably the most likely cause for the onset of \u03b1-syn aggregation leading to PD.

This model is hosted on BioModels Database and identified by: BIOMD0000000615.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Baltimore, MD 21218-2694, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.", + "authors": [ + { + "institution": "Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218-2694, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.", + "name": "I A Kuznetsov" + }, + { + "institution": "Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695-7910, USA. Electronic address: avkuznet@ncsu.edu.", + "name": "A V Kuznetsov" + } + ], + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/27211070", + "month": "8", + "pages": "22-29", + "synopsis": "The aim of this paper is to develop a minimal model describing events leading to the onset of Parkinson's disease (PD). The model accounts for \u03b1-synuclein (\u03b1-syn) production in the soma, transport toward the synapse, misfolding, and aggregation. The production and aggregation of polymeric \u03b1-syn is simulated using a minimalistic 2-step Finke-Watzky model. We utilized the developed model to analyze what changes in a healthy neuron are likely to lead to the onset of \u03b1-syn aggregation. We checked the effects of interruption of \u03b1-syn transport toward the synapse, entry of misfolded (infectious) \u03b1-syn into the somatic and synaptic compartments, increasing the rate of \u03b1-syn synthesis in the soma, and failure of \u03b1-syn degradation machinery. Our model suggests that failure of \u03b1-syn degradation machinery is probably the most likely cause for the onset of \u03b1-syn aggregation leading to PD.", + "title": "What can trigger the onset of Parkinson's disease - A modeling study based on a compartmental model of \u03b1-synuclein transport and aggregation in neurons.", + "type": "PubMed ID", + "volume": "278", + "year": 2016 + }, + "publicationId": "BIOMD0000000615", + "submissionId": "MODEL1608150000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000616": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tvarusai@ebi.ac.uk", + "external": false, + "name": "Thawfeek Varusai" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Dunster2014 - WBC Interactions (Model1)
This is a sub-model of a three-stepinflammatory response modelling study. The model includes distinctpopulations of white blood cells namely, macrophages and active andapoptotic neutrophil populations. Neutrophil apoptosis rate ispredicted to be crucial for the qualitative nature of thesystem.

This model is described in the article:

Dunster JL, Byrne HM, King JR.
Bull. Math. Biol. 2014 Aug; 76(8): 1953-1980

Abstract:

There is growing interest in inflammation due to its involvement in many diverse medical conditions, including Alzheimer's disease, cancer, arthritis and asthma. The traditional view that resolution of inflammation is a passive process is now being superceded by an alternative hypothesis whereby its resolution is an active, anti-inflammatory process that can be manipulated therapeutically. This shift in mindset has stimulated a resurgence of interest in the biological mechanisms by which inflammation resolves. The anti-inflammatory processes central to the resolution of inflammation revolve around macrophages and are closely related to pro-inflammatory processes mediated by neutrophils and their ability to damage healthy tissue. We develop a spatially averaged model of inflammation centring on its resolution, accounting for populations of neutrophils and macrophages and incorporating both pro- and anti-inflammatory processes. Our ordinary differential equation model exhibits two outcomes that we relate to healthy and unhealthy states. We use bifurcation analysis to investigate how variation in the system parameters affects its outcome. We find that therapeutic manipulation of the rate of macrophage phagocytosis can aid in resolving inflammation but success is critically dependent on the rate of neutrophil apoptosis. Indeed our model predicts that an effective treatment protocol would take a dual approach, targeting macrophage phagocytosis alongside neutrophil apoptosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000616.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The traditional view that resolution of inflammation is a passive process is now being superceded by an alternative hypothesis whereby its resolution is an active, anti-inflammatory process that can be manipulated therapeutically. This shift in mindset has stimulated a resurgence of interest in the biological mechanisms by which inflammation resolves. The anti-inflammatory processes central to the resolution of inflammation revolve around macrophages and are closely related to pro-inflammatory processes mediated by neutrophils and their ability to damage healthy tissue. We develop a spatially averaged model of inflammation centring on its resolution, accounting for populations of neutrophils and macrophages and incorporating both pro- and anti-inflammatory processes. Our ordinary differential equation model exhibits two outcomes that we relate to healthy and unhealthy states. We use bifurcation analysis to investigate how variation in the system parameters affects its outcome. We find that therapeutic manipulation of the rate of macrophage phagocytosis can aid in resolving inflammation but success is critically dependent on the rate of neutrophil apoptosis. Indeed our model predicts that an effective treatment protocol would take a dual approach, targeting macrophage phagocytosis alongside neutrophil apoptosis.", + "title": "The resolution of inflammation: a mathematical model of neutrophil and macrophage interactions.", + "type": "PubMed ID", + "volume": "76", + "year": 2014 + }, + "publicationId": "BIOMD0000000616", + "submissionId": "MODEL1608160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000617": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tvarusai@ebi.ac.uk", + "external": false, + "name": "Thawfeek Varusai" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Walsh2014 - Inhibition kinetics of DAPT onAPP Cleavage

This model is described in the article:

Walsh R.
PeerJ 2014; 2: e649

Abstract:

Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits ?-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000617.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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of DAPT on APP Cleavage", + "submitted": 1476113968, + "submitter": "Thawfeek Varusai", + "version": 2 + }, + { + "comment": "CRBM-sponsored manual and automated updates.", + "submitted": 1724280645, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "9606", + "name": "Homo sapiens", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/9606" + }, + { + "accession": "MODEL1609120000", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1609120000" + }, + { + "accession": "BIOMD0000000617", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000617" + }, + { + "accession": "25374788", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/25374788" + }, + { + "accession": "GO:0042987", + "name": "amyloid 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"Department of Chemistry, Carleton University , Ottawa, ON , Canada.", + "name": "Ryan Walsh", + "orcid": "0000-0002-3794-4721" + } + ], + "journal": "PeerJ", + "link": "http://identifiers.org/pubmed/25374788", + "month": "0", + "pages": "e649", + "synopsis": "Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits \u03b3-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.", + "title": "Are improper kinetic models hampering drug development?", + "type": "PubMed ID", + "volume": "2", + "year": 2014 + }, + "publicationId": "BIOMD0000000617", + "submissionId": "MODEL1609120000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000618": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Rostock", + "email": "winter@kapora.de", + "external": false, + "name": "Felix Winter" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Krohn2011 - Cerebral amyloid-\u03b2proteostasis regulated by membrane transport protein ABCC1

This model is described in the article:

Krohn M, Lange C, Hofrichter J, Scheffler K, Stenzel J, Steffen J, Schumacher T, Br\u00fcning T, Plath AS, Alfen F, Schmidt A, Winter F, Rateitschak K, Wree A, Gsponer J, Walker LC, Pahnke J.
J. Clin. Invest. 2011 Oct; 121(10): 3924-3931

Abstract:

In Alzheimer disease (AD), the intracerebral accumulation of amyloid-\u03b2 (A\u03b2) peptides is a critical yet poorly understood process. A\u03b2 clearance via the blood-brain barrier is reduced by approximately 30% in AD patients, but the underlying mechanisms remain elusive. ABC transporters have been implicated in the regulation of A\u03b2 levels in the brain. Using a mouse model of AD in which the animals were further genetically modified to lack specific ABC transporters, here we have shown that the transporter ABCC1 has an important role in cerebral A\u03b2 clearance and accumulation. Deficiency of ABCC1 substantially increased cerebral A\u03b2 levels without altering the expression of most enzymes that would favor the production of A\u03b2 from the A\u03b2 precursor protein. In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced A\u03b2 load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1. Thus, by altering the temporal aggregation profile of A\u03b2, pharmacological activation of ABC transporters could impede the neurodegenerative cascade that culminates in the dementia of AD.

This model is hosted on BioModels Database and identified by: BIOMD0000000618.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cerebral amyloid-\u03b2 proteostasis regulated by membrane transport protein ABCC1", + "publication": { + "accession": "21881209", + "affiliation": "Department of Neurology, Neurodegeneration Research Laboratory, University of Rostock, Rostock, Germany.", + "authors": [ + { + "institution": "Department of Neurology, Neurodegeneration Research Laboratory, University of Rostock, Rostock, Germany.", + "name": "Markus Krohn", + "orcid": "0000-0002-5345-9048" + }, + { + "name": "Cathleen Lange" + }, + { + "name": "Jacqueline Hofrichter" + }, + { + "name": "Katja Scheffler", + "orcid": "0000-0002-1734-7807" + }, + { + "name": "Jan Stenzel" + }, + { + "name": "Johannes Steffen" + }, + { + "name": "Toni Schumacher", + "orcid": "0000-0001-7077-1300" + }, + { + "name": "Thomas Br\u00fcning", + "orcid": "0000-0002-7060-6564" + }, + { + "name": "Anne-Sophie Plath" + }, + { + "name": "Franziska Alfen" + }, + { + "name": "Anke Schmidt" + }, + { + "institution": "1 ASD Advanced Simulation and Design GmbH, Rostock, Germany.2 Department of Systems Biology and Bioinformatics, Rostock University, Rostock, Germany.", + "name": "Felix Winter", + "orcid": "0000-0003-2987-6797" + }, + { + "name": "Katja Rateitschak" + }, + { + "name": "Andreas Wree" + }, + { + "name": "J\u00f6rg Gsponer" + }, + { + "name": "Lary C Walker", + "orcid": "0000-0001-9166-3261" + }, + { + "name": "Jens Pahnke", + "orcid": "0000-0001-7355-4213" + } + ], + "issue": "10", + "journal": "The Journal of clinical investigation", + "link": "http://identifiers.org/pubmed/21881209", + "month": "10", + "pages": "3924-3931", + "synopsis": "In Alzheimer disease (AD), the intracerebral accumulation of amyloid-\u03b2 (A\u03b2) peptides is a critical yet poorly understood process. A\u03b2 clearance via the blood-brain barrier is reduced by approximately 30% in AD patients, but the underlying mechanisms remain elusive. ABC transporters have been implicated in the regulation of A\u03b2 levels in the brain. Using a mouse model of AD in which the animals were further genetically modified to lack specific ABC transporters, here we have shown that the transporter ABCC1 has an important role in cerebral A\u03b2 clearance and accumulation. Deficiency of ABCC1 substantially increased cerebral A\u03b2 levels without altering the expression of most enzymes that would favor the production of A\u03b2 from the A\u03b2 precursor protein. In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced A\u03b2 load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1. Thus, by altering the temporal aggregation profile of A\u03b2, pharmacological activation of ABC transporters could impede the neurodegenerative cascade that culminates in the dementia of AD.", + "title": "Cerebral amyloid-\u03b2 proteostasis is regulated by the membrane transport protein ABCC1 in mice.", + "type": "PubMed ID", + "volume": "121", + "year": 2011 + }, + "publicationId": "BIOMD0000000618", + "submissionId": "MODEL1607270000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000619": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + }, + { + "affiliation": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "email": "JSluka@indiana.edu", + "external": false, + "name": "James P Sluka", + "orcid": "0000-0002-5901-1404" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Basic PBPK (Physiologically Based PharmacoKinetic) model of Acetaminophen.

This is a basic model of Acetaminophen (APAP, Paracetamol) pharmacokinetics in humans. Many of the model parameters (compartment volumes, volumetric flow rates, etc.) are scaled allometrically based on the body weight (BW) raised to the\t\t\t3/4 power. Because of that, the assigned values of many of the parameters are recalculated at run time and are different \t\t\tthan the default values for the particular entity (e.g., the volume of a compartment and the volumetric flow rate between compartments).

APAP dose is initially given in grams (APAP_Dose_gram), which is converted to moles via the APAP molecular weight (APAP_MW).\t\t\tAPAP quantities throughout the rest of the models are given in moles.

The base parameters are for a 70Kg human and a pharmacological oral dose of 1.4 gram of APAP. Metabolism is modelled as a single ODE in the liver compartment and the metabolite does not leave that compartment.

This model is loosely based on the model of Wambaugh and Shaw (PLoS Comput Biol. 2010 Apr 22;6(4):e1000756. doi: 10.1371/journal.pcbi.1000756. Pubmed ID: PMID- 20421935) with the following changes:

  1. The lung lumen compartment was omitted.
  2. A kidney compartment was added.
  3. Glomerular Filtration is from the kidney compartment.
  4. The APAP dose was changed to 1.4g.
  5. The gut adsorption rate constant (KGutabs), tissue partition coefficients and liver metabolism rate constant (CLmetabolism) were fit using the human in vivo data of Critchley (Critchley, J. A., Critchley, L. A. H., Anderson, P. J., and Tomlinson, B. 2005 Journal of clinical pharmacy and therapeutics, 30(2), 179-184).
  6. To model the extensive re-adsorption of APAP from the kidney tubules back into the blood the QGfr value is modified. This was done by \t\t\t\tchanging the scaling parameter QGFR_ref value from 0.31 to 0.039, resulting in a decrease in the QGfr value of 8 fold (from 7.2 L/hr to 0.91 L/hr).

The parameters in this file are the REFSIM parameters from our publication.

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"Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "James P Sluka", + "orcid": "0000-0002-5901-1404" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Xiao Fu", + "orcid": "0000-0002-8611-891X" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Maciej Swat" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Julio M Belmonte", + "orcid": "0000-0002-4315-9631" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Alin Cosmanescu" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Sherry G Clendenon", + "orcid": "0000-0002-3353-0919" + }, + { + "institution": "National Center for Computational Toxicology Office of Research and Development US EPA, Research Triangle Park, NC 27711, United States of America.", + "name": "John F Wambaugh", + "orcid": "0000-0002-4024-534X" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "James A Glazier", + "orcid": "0000-0003-3634-190X" + } + ], + "issue": "9", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/27636091", + "month": "0", + "pages": "e0162428", + "synopsis": "We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.", + "title": "A Liver-Centric Multiscale Modeling Framework for Xenobiotics.", + "type": "PubMed ID", + "volume": "11", + "year": 2016 + }, + "publicationId": "BIOMD0000000619", + "submissionId": "MODEL1509230010", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000620": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Disease Condition
\t

This is the model with disease state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetanoet al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb].\t

\t

This model is described in the article:

Palm\u00e9r R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118.

Abstract:

Recent clinical studies suggest sustained treatment effects of interleukin-1\u03b2 (IL-1\u03b2)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. We propose that improved \u03b2-cell function, rather than mass, is likely to explain the main IL-1\u03b2-blocking effects seen in current clinical data, but that improved \u03b2-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

This model is hosted on BioModels Database and identified by: MODEL1604270002.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. 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Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Healthy Condition
\t

This is the model with healthy state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetanoet al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb].\t

\t

This model is described in the article:

Palm\u00e9r R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118.

Abstract:

Recent clinical studies suggest sustained treatment effects of interleukin-1\u03b2 (IL-1\u03b2)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. We propose that improved \u03b2-cell function, rather than mass, is likely to explain the main IL-1\u03b2-blocking effects seen in current clinical data, but that improved \u03b2-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

This model is hosted on BioModels Database and identified by: MODEL1604270002.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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NguyenLK2011 - Ubiquitination dynamics inRing1B-Bmi1 system
This theoretical model investigates thedynamics of Ring1B/Bmi1 ubiquitination to identify bistableswitch-like and oscillatory behaviour in thesystem.\u00a0Michaelis-Menten (MM) equations are used to formulatethe model. However, the authors show that the dynamics persist evenfor Mass-Action kinetics. This SBML file is the MM version of themodel.

This model is described in the article:

Nguyen LK, Mu\u00f1oz-Garc\u00eda J, Maccario H, Ciechanover A, Kolch W, Kholodenko BN.
PLoS Comput. Biol. 2011 Dec; 7(12): e1002317

Abstract:

In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7.

This model is hosted on BioModels Database and identified by: BIOMD0000000622.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Australia. lan.k.nguyen@monash.edu.", + "name": "Lan K Nguyen", + "orcid": "0000-0003-4040-7705" + }, + { + "name": "Javier Mu\u00f1oz-Garc\u00eda", + "orcid": "0000-0002-7594-9507" + }, + { + "name": "Helene Maccario" + }, + { + "name": "Aaron Ciechanover", + "orcid": "0000-0001-9184-8944" + }, + { + "institution": "1] Systems Biology Ireland, University College Dublin, Dublin 4, Ireland [2] Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin 4, Ireland [3] School of Medicine and Medical Sciences, University College Dublin, Dublin 4, Ireland [4].", + "name": "Walter Kolch", + "orcid": "0000-0001-5777-5016" + }, + { + "name": "Boris N Kholodenko", + "orcid": "0000-0002-9483-4975" + } + ], + "issue": "12", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/22194680", + "month": "12", + "pages": "e1002317", + "synopsis": "In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7.", + "title": "Switches, excitable responses and oscillations in the Ring1B/Bmi1 ubiquitination system.", + "type": "PubMed ID", + "volume": "7", + "year": 2011 + }, + "publicationId": "BIOMD0000000622", + "submissionId": "MODEL1611160000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000623": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tvarusai@ebi.ac.uk", + "external": false, + "name": "Thawfeek Varusai" + }, + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Orton2009 - Modelling cancerous mutations inthe EGFR/ERK pathway - EGF Model
This model studies the aberrations inERK signalling for different cancer mutations. The authors alter apreviously existing EGF model (Brown et al 2004) to include newinteractions\u00a0that\u00a0better\u00a0fit empirical data.Predictions show that the ERK signalling is a robust mechanismtaking different courses for different cancer mutations. Mostparameter values are used from the previous model and the newparameters are\u00a0estimated using experimental data performed bythe authors on PC12 cells (adrenal gland, rat).\u00a0The authorsprovide an SBML version of the model in the paper.

This model is described in the article:

Orton RJ, Adriaens ME, Gormand A, Sturm OE, Kolch W, Gilbert DR.
BMC Syst Biol 2009 Oct; 3: 100

Abstract:

The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway.We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors.Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.

This model is hosted on BioModels Database and identified by: BIOMD0000000623.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Modelling cancerous mutations in the EGFR/ERK pathway - EGF Model", + "publication": { + "accession": "19804630", + "affiliation": "Institute of Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Glasgow, UK. r.orton@vet.gla.ac.uk", + "authors": [ + { + "institution": "Institute of Comparative Medicine, Faculty of Veterinary Medicine, University of Glasgow, Glasgow, UK. r.orton@vet.gla.ac.uk", + "name": "Richard J Orton" + }, + { + "name": "Michiel E Adriaens", + "orcid": "0000-0002-4472-7119" + }, + { + "name": "Amelie Gormand" + }, + { + "name": "Oliver E Sturm" + }, + { + "institution": "1] Systems Biology Ireland, University College Dublin, Dublin 4, Ireland [2] Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Dublin 4, Ireland [3] School of Medicine and Medical Sciences, University College Dublin, Dublin 4, Ireland [4].", + "name": "Walter Kolch", + "orcid": "0000-0001-5777-5016" + }, + { + "name": "David R Gilbert" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/19804630", + "month": "10", + "pages": "100", + "synopsis": "

Background

The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway.

Results

We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors.

Conclusion

Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.", + "title": "Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway.", + "type": "PubMed ID", + "volume": "3", + "year": 2009 + }, + "publicationId": "BIOMD0000000623", + "submissionId": "MODEL1611280000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000624": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "email": "JSluka@indiana.edu", + "external": false, + "name": "James P Sluka", + "orcid": "0000-0002-5901-1404" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Sluka2016 - Acetaminophen metabolism
Liver metabolism of Acetaminophen: Acetaminophen (APAP) ismetabolized in the liver in both Phase I and Phase II reactions.Phase II reactions convert APAP to APAP-glucuronide andAPAP-sulfate. Phase I reactions involve Cytochrome P450 mediated(mostly Cyp450-2E1 and -1A2) conversion of APAP toN-acetyle-p-quinoneimine (NAPQI), which goes on to react withcellular nucleophiles such as glutathione (GSH). At high doses ofAPAP significant GSH depletion in hepatocyte occurs resulting incell necrosis and and in extreme cases death.

This model is described in the article:

Sluka JP, Fu X, Swat M, Belmonte JM, Cosmanescu A, Clendenon SG, Wambaugh JF, Glazier JA.
PLoS ONE 2016; 11(9): e0162428

Abstract:

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.

This model is hosted on BioModels Database and identified by: BIOMD0000000624.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "authors": [ + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "James P Sluka", + "orcid": "0000-0002-5901-1404" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Xiao Fu", + "orcid": "0000-0002-8611-891X" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Maciej Swat" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Julio M Belmonte", + "orcid": "0000-0002-4315-9631" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Alin Cosmanescu" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "Sherry G Clendenon", + "orcid": "0000-0002-3353-0919" + }, + { + "institution": "National Center for Computational Toxicology Office of Research and Development US EPA, Research Triangle Park, NC 27711, United States of America.", + "name": "John F Wambaugh", + "orcid": "0000-0002-4024-534X" + }, + { + "institution": "Biocomplexity Institute Indiana University Bloomington, Bloomington, IN 47405-7105, United States of America.", + "name": "James A Glazier", + "orcid": "0000-0003-3634-190X" + } + ], + "issue": "9", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/27636091", + "month": "0", + "pages": "e0162428", + "synopsis": "We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.", + "title": "A Liver-Centric Multiscale Modeling Framework for Xenobiotics.", + "type": "PubMed ID", + "volume": "11", + "year": 2016 + }, + "publicationId": "BIOMD0000000624", + "submissionId": "MODEL1509230011", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000625": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Virginia Tech", + "email": "ajleber@vbi.vt.edu", + "external": false, + "name": "Andrew Leber" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Leber2016 - Expanded model of Tfh-Tfrdifferentiation - Helicobacter pylori infection

The parameters used in the model wereobtained from experiments conducted by the authors, previouspublications [ 1, 2, 3] andparameter optimisation carried out in the paper using particleswarm and genetic algorithms.\u00a0

This model is described in the article:

Leber A, Abedi V, Hontecillas R, Viladomiu M, Hoops S, Ciupe S, Caughman J, Andrew T, Bassaganya-Riera J.
J. Theor. Biol. 2016 Jun; 398: 74-84

Abstract:

T follicular helper (Tfh) cells are a highly plastic subset of CD4+ T cells specialized in providing B cell help and promoting inflammatory and effector responses during infectious and immune-mediate diseases. Helicobacter pylori is the dominant member of the gastric microbiota and exerts both beneficial and harmful effects on the host. Chronic inflammation in the context of H. pylori has been linked to an upregulation in T helper (Th)1 and Th17 CD4+ T cell phenotypes, controlled in part by the cytokine, interleukin-21. This study investigates the differentiation and regulation of Tfh cells, major producers of IL-21, in the immune response to H. pylori challenge. To better understand the conditions influencing the promotion and inhibition of a chronically elevated Tfh population, we used top-down and bottom-up approaches to develop computational models of Tfh and T follicular regulatory (Tfr) cell differentiation. Stability analysis was used to characterize the presence of two bi-stable steady states in the calibrated Tfh/Tfr models. Stochastic simulation was used to illustrate the ability of the parameter set to dictate two distinct behavioral patterns. Furthermore, sensitivity analysis helped identify the importance of various parameters on the establishment of Tfh and Tfr cell populations. The core network model was expanded into a more comprehensive and predictive model by including cytokine production and signaling pathways. From the expanded network, the interaction between TGFB-Induced Factor Homeobox 1 (Tgif1) and the retinoid X receptor (RXR) was displayed to exert control over the determination of the Tfh response. Model simulations predict that Tgif1 and RXR respectively induce and curtail Tfh responses. This computational hypothesis was validated experimentally by assaying Tgif1, RXR and Tfh in stomachs of mice infected with H. pylori.

The impulse of RXR as shown in the paper(figure 7C) can be implemented by creating an event in the curatedSBML file.

This model is hosted on BioModels Database and identified by: BIOMD0000000625.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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USA.", + "name": "Monica Viladomiu" + }, + { + "institution": "Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA; Center for Modeling Immunity to Enteric Pathogens, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA.", + "name": "Stefan Hoops", + "orcid": "0000-0001-8503-8371" + }, + { + "institution": "Center for Modeling Immunity to Enteric Pathogens, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA; Department of Mathematics, Virginia Tech, Blacksburg, VA, USA.", + "name": "Stanca Ciupe" + }, + { + "institution": "Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA; Center for Modeling Immunity to Enteric Pathogens, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA.", + "name": "John Caughman" + }, + { + "institution": "Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA; Center for Modeling Immunity to Enteric Pathogens, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA.", + "name": "Tricity Andrew" + }, + { + "institution": "Nutritional Immunology and Molecular Medicine Laboratory, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA; Center for Modeling Immunity to Enteric Pathogens, Biocomplexity Institute of Virginia Tech, Blacksburg, VA, USA. Electronic address: jbassaga@vt.edu.", + "name": "Josep Bassaganya-Riera" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/26947272", + "month": "6", + "pages": "74-84", + "synopsis": "T follicular helper (Tfh) cells are a highly plastic subset of CD4+ T cells specialized in providing B cell help and promoting inflammatory and effector responses during infectious and immune-mediate diseases. Helicobacter pylori is the dominant member of the gastric microbiota and exerts both beneficial and harmful effects on the host. Chronic inflammation in the context of H. pylori has been linked to an upregulation in T helper (Th)1 and Th17 CD4+ T cell phenotypes, controlled in part by the cytokine, interleukin-21. This study investigates the differentiation and regulation of Tfh cells, major producers of IL-21, in the immune response to H. pylori challenge. To better understand the conditions influencing the promotion and inhibition of a chronically elevated Tfh population, we used top-down and bottom-up approaches to develop computational models of Tfh and T follicular regulatory (Tfr) cell differentiation. Stability analysis was used to characterize the presence of two bi-stable steady states in the calibrated Tfh/Tfr models. Stochastic simulation was used to illustrate the ability of the parameter set to dictate two distinct behavioral patterns. Furthermore, sensitivity analysis helped identify the importance of various parameters on the establishment of Tfh and Tfr cell populations. The core network model was expanded into a more comprehensive and predictive model by including cytokine production and signaling pathways. From the expanded network, the interaction between TGFB-Induced Factor Homeobox 1 (Tgif1) and the retinoid X receptor (RXR) was displayed to exert control over the determination of the Tfh response. Model simulations predict that Tgif1 and RXR respectively induce and curtail Tfh responses. This computational hypothesis was validated experimentally by assaying Tgif1, RXR and Tfh in stomachs of mice infected with H. pylori.", + "title": "Bistability analyses of CD4+ T follicular helper and regulatory cells during Helicobacter pylori infection.", + "type": "PubMed ID", + "volume": "398", + "year": 2016 + }, + "publicationId": "BIOMD0000000625", + "submissionId": "MODEL1603010000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000626": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Washington State University", + "email": "debjit_ray@wsu.edu", + "external": false, + "name": "Debjit Ray" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ray2013 - Meiotic initiation in S. cerevisiae

A mathematical representation of early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signalling molecules for regulating protein activities, is described here

This model is described in the article:

Ray D, Su Y, Ye P.
BMC Syst Biol. 2013 May 1;7:37

Abstract:

BACKGROUND: Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities.

RESULTS: The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state.

CONCLUSIONS: By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000626 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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+ "accession": "23631506", + "affiliation": "School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, WA 99164, USA.", + "authors": [ + { + "institution": "United States of America ; Biological Systems Engineering, Washington State University, Pullman, Washington, United States of America.", + "name": "Debjit Ray", + "orcid": "0000-0002-7192-9859" + }, + { + "name": "Yongchun Su" + }, + { + "name": "Ping Ye" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/23631506", + "month": "5", + "pages": "37", + "synopsis": "

Background

Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities.

Results

The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state.

Conclusions

By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity.", + "title": "Dynamic modeling of yeast meiotic initiation.", + "type": "PubMed ID", + "volume": "7", + "year": 2013 + }, + "publicationId": "BIOMD0000000626", + "submissionId": "MODEL1303060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000627": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Rostock", + "email": "winter@kapora.de", + "external": false, + "name": "Felix Winter" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Winter2017 - Brain Energy Metabolism with PPP

This model is described in the article:

Felix Winter1,2, Catrin Bludszuweit-Philipp1 and Olaf Wolkenhauer2,3
Journal of Cerebral Blood Flow & Metabolism

Abstract:

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a standard clinical tool for the detection of brain activation. In Alzheimer\u2019s disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia- driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients.

This model is hosted on BioModels Database and identified by: BIOMD0000000627.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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In Alzheimer's disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia-driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients.", + "title": "Mathematical analysis of the influence of brain metabolism on the BOLD signal in Alzheimer's disease.", + "type": "PubMed ID", + "volume": "38", + "year": 2018 + }, + "publicationId": "BIOMD0000000627", + "submissionId": "MODEL1603240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000628": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Babraham Institute", + "email": "kothamav@babraham.ac.uk", + "external": false, + "name": "Varun Kothamachu" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Li2012 Calcium mediated synapticplasticity
This model is an extension of\u00a0 BIOMD0000000183.

This model is described in the article:

Li L, Stefan MI, Le Nov\u00e8re N.
PLoS ONE 2012; 7(9): e43810

Abstract:

NMDA receptor dependent long-term potentiation (LTP) and long-term depression (LTD) are two prominent forms of synaptic plasticity, both of which are triggered by post-synaptic calcium elevation. To understand how calcium selectively stimulates two opposing processes, we developed a detailed computational model and performed simulations with different calcium input frequencies, amplitudes, and durations. We show that with a total amount of calcium ions kept constant, high frequencies of calcium pulses stimulate calmodulin more efficiently. Calcium input activates both calcineurin and Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) at all frequencies, but increased frequencies shift the relative activation from calcineurin to CaMKII. Irrespective of amplitude and duration of the inputs, the total amount of calcium ions injected adjusts the sensitivity of the system to calcium input frequencies. At a given frequency, the quantity of CaMKII activated is proportional to the total amount of calcium. Thus, an input of a small amount of calcium at high frequencies can induce the same activation of CaMKII as a larger amount, at lower frequencies. Finally, the extent of activation of CaMKII signals with high calcium frequency is further controlled by other factors, including the availability of calmodulin, and by the potency of phosphatase inhibitors.

This model is hosted on BioModels Database and identified by: BIOMD0000000628.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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To understand how calcium selectively stimulates two opposing processes, we developed a detailed computational model and performed simulations with different calcium input frequencies, amplitudes, and durations. We show that with a total amount of calcium ions kept constant, high frequencies of calcium pulses stimulate calmodulin more efficiently. Calcium input activates both calcineurin and Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) at all frequencies, but increased frequencies shift the relative activation from calcineurin to CaMKII. Irrespective of amplitude and duration of the inputs, the total amount of calcium ions injected adjusts the sensitivity of the system to calcium input frequencies. At a given frequency, the quantity of CaMKII activated is proportional to the total amount of calcium. Thus, an input of a small amount of calcium at high frequencies can induce the same activation of CaMKII as a larger amount, at lower frequencies. 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Haffez2017 - RAR interaction with syntheticanalogues

This model is described in the article:

Hesham Haffez, David R. Chisholm, Roy Valentine, Ehmke Pohl, Christopher Redfern and Andrew Whiting
MedChemComm

Abstract:

All-trans-retinoic acid (ATRA) and its synthetic analogues EC23 and EC19 direct cellular differentiation by interacting as ligands for the retinoic acid receptor (RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3) family of nuclear receptor proteins. To date, a number of crystal structures of natural and synthetic ligands complexed to their target proteins have been solved, providing molecular level snap-shots of ligand binding. However, a deeper understanding of receptor and ligand flexibility and conformational freedom is required to develop stable and effective ATRA analogues for clinical use. Therefore, we have used molecular modelling techniques to define RAR interactions with ATRA and two synthetic analogues, EC19 and EC23, and compared their predicted biochemical activities to experimental measurements of relative ligand affinity and recruitment of coactivator proteins. A comprehensive molecular docking approach that explored the conformational space of the ligands indicated that ATRA is able to bind the three RAR proteins in a number of conformations with one extended structure being favoured. In contrast the biologically-distinct isomer, 9-cis-retinoic acid (9CRA), showed significantly less conformational flexibility in the RAR binding pockets. These findings were used to inform docking studies of the synthetic retinoids EC23 and EC19, and their respective methyl esters. EC23 was found to be an excellent mimic for ATRA, and occupied similar binding modes to ATRA in all three target RAR proteins. In comparison, EC19 exhibited an alternative binding mode which reduces the strength of key polar interactions in RAR\u00ce\u00b1/\u00ce\u00b3 but is well-suited to the larger RAR\u00ce\u00b2 binding pocket. In contrast, docking of the corresponding esters revealed the loss of key polar interactions which may explain the much reduced biological activity. Our computational results were complemented using an in vitro binding assay based on FRET measurements, which showed that EC23 was a strongly binding, pan-agonist of the RARs, while EC19 exhibited specificity for RAR\u00ce\u00b2, as predicted by the docking studies. These findings can account for the distinct behaviour of EC23 and EC19 in cellular differentiation assays, and additionally, the methods described herein can be further applied to the understanding of the molecular basis for the selectivity of different retinoids to RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3.

This model is hosted on BioModels Database and identified by: BIOMD0000000629.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"name": "Hesham Haffez" + }, + { + "name": "David R. Chisholm" + }, + { + "name": "Roy Valentine" + }, + { + "name": "Ehmke Pohl" + }, + { + "name": "Christopher Redfern and Andrew Whiting" + } + ], + "journal": "MedChemComm", + "link": "http://identifiers.org/doi/10.1039/C6MD00680A", + "month": "1", + "synopsis": "All-trans-retinoic acid (ATRA) and its synthetic analogues EC23 and EC19 direct cellular differentiation by interacting as ligands for the retinoic acid receptor (RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3) family of nuclear receptor proteins. To date, a number of crystal structures of natural and synthetic ligands complexed to their target proteins have been solved, providing molecular level snap-shots of ligand binding. However, a deeper understanding of\nreceptor and ligand flexibility and conformational freedom is required to develop stable and effective ATRA analogues for clinical use. Therefore, we have used molecular modelling techniques to define RAR interactions with ATRA and two synthetic analogues, EC19 and EC23, and compared their predicted biochemical\nactivities to experimental measurements of relative ligand affinity and recruitment of coactivator proteins. A comprehensive molecular docking approach that explored the conformational space of the ligands indicated that ATRA is able to bind the three RAR proteins in a number of conformations with one extended\nstructure being favoured. In contrast the biologically-distinct isomer, 9-cis-retinoic acid (9CRA), showed\nsignificantly less conformational flexibility in the RAR binding pockets. These findings were used to inform\ndocking studies of the synthetic retinoids EC23 and EC19, and their respective methyl esters. EC23 was\nfound to be an excellent mimic for ATRA, and occupied similar binding modes to ATRA in all three target\nRAR proteins. In comparison, EC19 exhibited an alternative binding mode which reduces the strength of\nkey polar interactions in RAR\u00ce\u00b1/\u00ce\u00b3 but is well-suited to the larger RAR\u00ce\u00b2 binding pocket. In contrast, docking of the corresponding esters revealed the loss of key polar interactions which may explain the much reduced biological activity. Our computational results were complemented using an in vitro binding assay\nbased on FRET measurements, which showed that EC23 was a strongly binding, pan-agonist of the RARs,\nwhile EC19 exhibited specificity for RAR\u00ce\u00b2, as predicted by the docking studies. These findings can account for the distinct behaviour of EC23 and EC19 in cellular differentiation assays, and additionally, the methods described herein can be further applied to the understanding of the molecular basis for the selectivity of different retinoids to RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3.", + "title": "The molecular basis of the interactions between synthetic retinoic acid analogues and the retinoic acid receptors", + "type": "DOI", + "year": 2017 + }, + "publicationId": "BIOMD0000000629", + "submissionId": "MODEL1702240000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000630": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Singapore MIT Alliance, Computational and Systems Biology", + "email": "lihuipengsmacsb@gmail.com", + "external": false, + "name": "Huipeng Li" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Venkatraman2011 - PLS-UPA behaviour in thepresence of substrate competition

The posibility of ultrasensitivity and bistable activation of PLS (Plasmin) and UPA (Urokinase-type plasminogen activator) in the presence of substrate competition is explained here using a mathematical model.\u00a0

This model is described in the article:

Venkatraman L, Li H, Dewey CF Jr, White JK, Bhowmick SS, Yu H, Tucker-Kellogg L.
Biophys. J. 2011 Oct; 101(8): 1825-1834

Abstract:

Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis.

The cooperativity parameter \"ci\" was missing in the original model. The parameter \"ci\" has been added to the added.

This model is hosted on BioModels Database and identified by: BIOMD0000000630.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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PLS-UPA behaviour in the presence of substrate competition_1_1_1_1", + "publication": { + "accession": "22004735", + "affiliation": "Singapore-MIT Alliance, National University of Singapore, Singapore.", + "authors": [ + { + "institution": "Singapore-MIT Alliance, National University of Singapore, Singapore.", + "name": "Lakshmi Venkatraman" + }, + { + "name": "Huipeng Li" + }, + { + "name": "C Forbes Dewey" + }, + { + "name": "Jacob K White" + }, + { + "name": "Sourav S Bhowmick" + }, + { + "name": "Hanry Yu", + "orcid": "0000-0002-0339-3685" + }, + { + "name": "Lisa Tucker-Kellogg", + "orcid": "0000-0002-1301-7069" + } + ], + "issue": "8", + "journal": "Biophysical journal", + "link": "http://identifiers.org/pubmed/22004735", + "month": "10", + "pages": "1825-1834", + "synopsis": "Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis.", + "title": "Steady states and dynamics of urokinase-mediated plasmin activation in silico and in vitro.", + "type": "PubMed ID", + "volume": "101", + "year": 2011 + }, + "publicationId": "BIOMD0000000630", + "submissionId": "MODEL1303130001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000631": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
DeCaluw\u00e92016 - Circadian Clock

This model is described in the article:

De Caluw\u00e9 J, Xiao Q, Hermans C, Verbruggen N, Leloup JC, Gonze D.
Front Plant Sci 2016; 7: 74

Abstract:

The circadian clock is an endogenous timekeeper that allows organisms to anticipate and adapt to the daily variations of their environment. The plant clock is an intricate network of interlocked feedback loops, in which transcription factors regulate each other to generate oscillations with expression peaks at specific times of the day. Over the last decade, mathematical modeling approaches have been used to understand the inner workings of the clock in the model plant Arabidopsis thaliana. Those efforts have produced a number of models of ever increasing complexity. Here, we present an alternative model that combines a low number of equations and parameters, similar to the very earliest models, with the complex network structure found in more recent ones. This simple model describes the temporal evolution of the abundance of eight clock gene mRNA/protein and captures key features of the clock on a qualitative level, namely the entrained and free-running behaviors of the wild type clock, as well as the defects found in knockout mutants (such as altered free-running periods, lack of entrainment, or changes in the expression of other clock genes). Additionally, our model produces complex responses to various light cues, such as extreme photoperiods and non-24 h environmental cycles, and can describe the control of hypocotyl growth by the clock. Our model constitutes a useful tool to probe dynamical properties of the core clock as well as clock-dependent processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000631.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Those efforts have produced a number of models of ever increasing complexity. Here, we present an alternative model that combines a low number of equations and parameters, similar to the very earliest models, with the complex network structure found in more recent ones. This simple model describes the temporal evolution of the abundance of eight clock gene mRNA/protein and captures key features of the clock on a qualitative level, namely the entrained and free-running behaviors of the wild type clock, as well as the defects found in knockout mutants (such as altered free-running periods, lack of entrainment, or changes in the expression of other clock genes). Additionally, our model produces complex responses to various light cues, such as extreme photoperiods and non-24 h environmental cycles, and can describe the control of hypocotyl growth by the clock. Our model constitutes a useful tool to probe dynamical properties of the core clock as well as clock-dependent processes.", + "title": "A Compact Model for the Complex Plant Circadian Clock.", + "type": "PubMed ID", + "volume": "7", + "year": 2016 + }, + "publicationId": "BIOMD0000000631", + "submissionId": "MODEL1601130000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000632": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "OvGU", + "email": "schaber@med.ovgu.de", + "external": false, + "name": "Joerg Schaber" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Kollarovic2016 - Cell fate decision at G1-Stransition

This model is described in the article:

Kollarovic G, Studencka M, Ivanova L, Lauenstein C, Heinze K, Lapytsko A, Talemi SR, Figueiredo AS, Schaber J.
Aging (Albany NY) 2016 Jan;

Abstract:

Excessive DNA damage can induce an irreversible cell cycle arrest, called senescence, which is generally perceived as an important tumour-suppressor mechanism. However, it is unclear how cells decide whether to senesce or not after DNA damage. By combining experimental data with a parameterized mathematical model we elucidate this cell fate decision at the G1-S transition. Our model provides a quantitative and conceptually new understanding of how human fibroblasts decide whether DNA damage is beyond repair and senesce. Model and data imply that the G1-S transition is regulated by a bistable hysteresis switch with respect to Cdk2 activity, which in turn is controlled by the Cdk2/p21 ratio rather than cyclin abundance. We experimentally confirm the resulting predictions that to induce senescence i) in healthy cells both high initial and elevated background DNA damage are necessary and sufficient, and ii) in already damaged cells much lower additional DNA damage is sufficient. Our study provides a mechanistic explanation of a) how noise in protein abundances allows cells to overcome the G1-S arrest even with substantial DNA damage, potentially leading to neoplasia, and b) how accumulating DNA damage with age increasingly sensitizes cells for senescence.

This model is hosted on BioModels Database and identified by: BIOMD0000000632.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cell fate decision at G1-S transition", + "publication": { + "accession": "26830321", + "affiliation": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "authors": [ + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.Cancer Research Institute, Slovak Academy of Sciences, Bratislava, Slovakia.", + "name": "Gabriel Kollarovic", + "orcid": "0000-0002-3159-7781" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Maja Studencka" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Lyubomira Ivanova" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Claudia Lauenstein" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Kristina Heinze" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Anastasiya Lapytsko" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Soheil Rastgou Talemi" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "Ana Sofia Figueiredo" + }, + { + "institution": "Institute for Experimental Internal Medicine, Medical Faculty, Otto-von-Guericke University, Magdeburg, Germany.", + "name": "J\u00f6rg Schaber" + } + ], + "issue": "1", + "journal": "Aging", + "link": "http://identifiers.org/pubmed/26830321", + "month": "1", + "pages": "158-177", + "synopsis": "Excessive DNA damage can induce an irreversible cell cycle arrest, called senescence, which is generally perceived as an important tumour-suppressor mechanism. 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Our study provides a mechanistic explanation of a) how noise in protein abundances allows cells to overcome the G1-S arrest even with substantial DNA damage, potentially leading to neoplasia, and b) how accumulating DNA damage with age increasingly sensitizes cells for senescence.", + "title": "To senesce or not to senesce: how primary human fibroblasts decide their cell fate after DNA damage.", + "type": "PubMed ID", + "volume": "8", + "year": 2016 + }, + "publicationId": "BIOMD0000000632", + "submissionId": "MODEL1505080000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000633": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Bulik2016 - Regulation of hepatic glucosemetabolism

This model is described in the article:

Bulik S, Holzh\u00fctter HG, Berndt N.
BMC Biol. 2016 Mar; 14: 15

Abstract:

Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in the activity of enzymes and transporters. Hormone-dependent reversible enzyme phosphorylation and concentration changes of reactants and allosteric effectors are the major types of rapid kinetic enzyme regulation, whereas on longer time scales changes in protein abundance may also become operative. Here, we used a comprehensive mathematical model of the hepatic glucose metabolism of rat hepatocytes to decipher the relative importance of different regulatory modes and their mutual interdependencies in the hepatic control of plasma glucose homeostasis.Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of plasma glucose in different physiological settings (starvation, ad libitum nutrient supply, diabetes). Changes in enzyme abundances adjust the metabolic output to the anticipated physiological demand but may turn into a regulatory disadvantage if sudden unexpected changes of the external conditions occur. Allosteric and hormonal control of enzyme activities allow the liver to assume a broad range of metabolic states and may even fully reverse flux changes resulting from changes of enzyme abundances alone. Metabolic control analysis reveals that control of the hepatic glucose metabolism is mainly exerted by enzymes alone, which are differently controlled by alterations in enzyme abundance, reversible phosphorylation, and allosteric effects.In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects, and reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.

This model is hosted on BioModels Database and identified by: BIOMD0000000633.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Regulation of hepatic glucose metabolism", + "publication": { + "accession": "26935066", + "affiliation": "Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Institute of Biochemistry, Computational Systems Biochemistry Group, Charit\u00e9platz 1, 10117, Berlin, Germany. sascha.bulik@charite.de.", + "authors": [ + { + "institution": "Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Institute of Biochemistry, Computational Systems Biochemistry Group, Charit\u00e9platz 1, 10117, Berlin, Germany. sascha.bulik@charite.de.", + "name": "Sascha Bulik" + }, + { + "name": "Hermann-Georg Holzh\u00fctter", + "orcid": "0000-0002-5054-6023" + }, + { + "institution": "Charit\u00e9 - Universit\u00e4tsmedizin Berlin, Institute of Biochemistry, Computational Systems Biochemistry Group, Charit\u00e9platz 1, 10117, Berlin, Germany. nikolaus.berndt@charite.de.", + "name": "Nikolaus Berndt", + "orcid": "0000-0001-5594-9940" + } + ], + "journal": "BMC biology", + "link": "http://identifiers.org/pubmed/26935066", + "month": "3", + "pages": "15", + "synopsis": "

Background

Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in the activity of enzymes and transporters. Hormone-dependent reversible enzyme phosphorylation and concentration changes of reactants and allosteric effectors are the major types of rapid kinetic enzyme regulation, whereas on longer time scales changes in protein abundance may also become operative. Here, we used a comprehensive mathematical model of the hepatic glucose metabolism of rat hepatocytes to decipher the relative importance of different regulatory modes and their mutual interdependencies in the hepatic control of plasma glucose homeostasis.

Results

Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of plasma glucose in different physiological settings (starvation, ad libitum nutrient supply, diabetes). Changes in enzyme abundances adjust the metabolic output to the anticipated physiological demand but may turn into a regulatory disadvantage if sudden unexpected changes of the external conditions occur. Allosteric and hormonal control of enzyme activities allow the liver to assume a broad range of metabolic states and may even fully reverse flux changes resulting from changes of enzyme abundances alone. Metabolic control analysis reveals that control of the hepatic glucose metabolism is mainly exerted by enzymes alone, which are differently controlled by alterations in enzyme abundance, reversible phosphorylation, and allosteric effects.

Conclusion

In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects, and reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.", + "title": "The relative importance of kinetic mechanisms and variable enzyme abundances for the regulation of hepatic glucose metabolism--insights from mathematical modeling.", + "type": "PubMed ID", + "volume": "14", + "year": 2016 + }, + "publicationId": "BIOMD0000000633", + "submissionId": "MODEL1603030000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000634": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2013 - Effect of A\u03b2 immunisationin Alzheimer's disease (stochastic version)
Extension of a previously publishedstochastic model (designed to examine some of the key pathwaysinvolved in the aggregation of amyloid-beta (A\u03b2)\u00a0andthe micro-tubular binding protein tau (BIOMD0000000286,BIOMD0000000462))to include the main processes involved in passive and activeimmunisation against A\u03b2and then to demonstrate the effects of this intervention on solubleA\u03b2.\u00a0Thisis the stochastic version of the model, the deterministic versionis BIOMD0000000488.\u00a0

This model is described in the article:

Proctor CJ, Boche D, Gray DA, Nicoll JA.
PLoS ONE 2013; 8(9): e73631

Abstract:

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000634.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. 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Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.", + "title": "Investigating interventions in Alzheimer's disease with computer simulation models.", + "type": "PubMed ID", + "volume": "8", + "year": 2013 + }, + "publicationId": "BIOMD0000000634", + "submissionId": "MODEL1704060000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000635": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "SciLifeLab Stockholm; KTH royal institute of technology", + "email": "nair@kth.se", + "external": false, + "name": "Anu G Nair" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Nair2015 - Interaction betweenneuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1Neuron)

This model is described in the article:

Nair AG, Gutierrez-Arenas O, Eriksson O, Vincent P, Hellgren Kotaleski J.
J. Neurosci. 2015 Oct; 35(41): 14017-14030

Abstract:

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000635.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.

Significance statement

Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.", + "title": "Sensing Positive versus Negative Reward Signals through Adenylyl Cyclase-Coupled GPCRs in Direct and Indirect Pathway Striatal Medium Spiny Neurons.", + "type": "PubMed ID", + "volume": "35", + "year": 2015 + }, + "publicationId": "BIOMD0000000635", + "submissionId": "MODEL1502200000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000636": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "SciLifeLab Stockholm; KTH royal institute of technology", + "email": "nair@kth.se", + "external": false, + "name": "Anu G Nair" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Nair2015 - Interaction betweenneuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2Neuron)

This model is described in the article:

Nair AG, Gutierrez-Arenas O, Eriksson O, Vincent P, Hellgren Kotaleski J.
J. Neurosci. 2015 Oct; 35(41): 14017-14030

Abstract:

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000636.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 Neuron)", + "publication": { + "accession": "26468202", + "affiliation": "Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, 11428 Stockholm, Sweden, Manipal University, 576104 Manipal, India.", + "authors": [ + { + "institution": "Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden.National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India.Manipal University, Manipal, India.", + "name": "Anu G Nair", + "orcid": "0000-0002-1952-9583" + }, + { + "institution": "Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, 11428 Stockholm, Sweden, jeanette@csc.kth.se tardebut@yahoo.es.", + "name": "Omar Gutierrez-Arenas" + }, + { + "institution": "Science for Life Laboratory, Department of Numerical Analysis and Computer Science, Stockholm University, 11418 Stockholm, Sweden.", + "name": "Olivia Eriksson" + }, + { + "institution": "CNRS, UMR8256 \"Biological Adaptation and Ageing\", Institut de Biologie Paris-Seine (IBPS), F-75005, Paris, France.Universit\u00e9 Pierre et Marie Curie (UPMC, Paris 6), Sorbonne Universit\u00e9s, F-75005, Paris, France.", + "name": "Pierre Vincent", + "orcid": "0000-0002-8479-1908" + }, + { + "institution": "Science for Life Laboratory, School of Computer Science and Communication, KTH Royal Institute of Technology, 11428 Stockholm, Sweden, Science for Life Laboratory, Department of Numerical Analysis and Computer Science, Stockholm University, 11418 Stockholm, Sweden, Department of Neuroscience, Karolinska Institute, 17177 Stockholm, Sweden, jeanette@csc.kth.se tardebut@yahoo.es.", + "name": "Jeanette Hellgren Kotaleski" + } + ], + "issue": "41", + "journal": "The Journal of neuroscience : the official journal of the Society for Neuroscience", + "link": "http://identifiers.org/pubmed/26468202", + "month": "10", + "pages": "14017-14030", + "synopsis": "Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.

Significance statement

Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.", + "title": "Sensing Positive versus Negative Reward Signals through Adenylyl Cyclase-Coupled GPCRs in Direct and Indirect Pathway Striatal Medium Spiny Neurons.", + "type": "PubMed ID", + "volume": "35", + "year": 2015 + }, + "publicationId": "BIOMD0000000636", + "submissionId": "MODEL1502200001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000637": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "IFIBYNE-UBA-CONICET", + "email": "abush84@gmail.com", + "external": false, + "name": "Alan Bush" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Bush2016 - Simplified Carrousel model ofGPCR

This model is described in the article:

Bush A, Vasen G, Constantinou A, Dunayevich P, Patop IL, Blaustein M, Colman-Lerner A.
Mol. Syst. Biol. 2016 Dec; 12(12): 898

Abstract:

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.


This model is hosted on BioModels Database and identified by: BIOMD0000000637.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Bush2016 - Extended Carrousel model ofGPCR-RGS

This model is described in the article:

Bush A, Vasen G, Constantinou A, Dunayevich P, Patop IL, Blaustein M, Colman-Lerner A.
Mol. Syst. Biol. 2016 Dec; 12(12): 898

Abstract:

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.


This model is hosted on BioModels Database and identified by: BIOMD0000000638.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Thiaville2016 - Wild type folate pathwaymodel with proposed PanB reaction
This is a wild type E. coli model, andis one amongst the three models described in the paper. The othertwo models are\u00a0MODEL1602280002 (wild type with PanB overexpression) and MODEL1602280003 (wild type with PanB overexpression and THF regulation).\u00a0

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: BIOMD0000000639.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. 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DallePezze2016 - Activation of AMPK and mTORby amino acids (Model 3)
This model is as\u00a0described in the Supplementary Software 3 of the reference publication: SBML model similar to Model S2, but including a more complex p70-S6K module.

This model is described in the article:

Dalle Pezze P, Ruf S, Sonntag AG, Langelaar-Makkinje M, Hall P, Heberle AM, Razquin Navas P, van Eunen K, T\u00f6lle RC, Schwarz JJ, Wiese H, Warscheid B, Deitersen J, Stork B, F\u00e4\u00dfler E, Sch\u00e4uble S, Hahn U, Horvatovich P, Shanley DP, Thedieck K.
Nat Commun 2016 Nov; 7: 13254

Abstract:

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase ? (CaMKK?). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000640.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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for Cell and Molecular Biosciences (ICaMB), Newcastle University, Newcastle upon Tyne NE2 4HH, UK.", + "authors": [ + { + "institution": "Institute for Cell and Molecular Biosciences (ICaMB), Newcastle University, Newcastle upon Tyne NE2 4HH, UK.Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University Institute for Ageing, Newcastle upon Tyne NE4 5PL, UK.Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK.", + "name": "Piero Dalle Pezze", + "orcid": "0000-0003-1695-6763" + }, + { + "institution": "Department of Bioinformatics and Molecular Genetics (Faculty of Biology), Institute for Biology 3, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany.Research Training Group (RTG) 1104, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany.Department of Pediatrics, University of Groningen, University Medical Center Groningen (UMCG), 9713 AV Groningen, The Netherlands.", + "name": "Stefanie Ruf" + }, + { + "institution": "Department of Bioinformatics and Molecular Genetics (Faculty of Biology), Institute for Biology 3, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany.", + "name": "Annika G Sonntag" + }, + { + "institution": "Department of Pediatrics, University of Groningen, University Medical Center Groningen (UMCG), 9713 AV Groningen, The Netherlands.", + "name": "Miriam Langelaar-Makkinje" + }, + { + "institution": "Institute for Cell and Molecular Biosciences (ICaMB), Newcastle University, Newcastle upon Tyne NE2 4HH, UK.Centre for Integrated Systems Biology of Ageing and Nutrition, Newcastle University Institute for Ageing, Newcastle upon Tyne NE4 5PL, UK.", + "name": "Philip Hall" + }, + { + "institution": "Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.", + "name": "Heberle AM", + "orcid": "0000-0001-6414-8707" + }, + { + "institution": "Department of Pediatrics, University of Groningen, University Medical Center Groningen (UMCG), 9713 AV Groningen, The Netherlands.Department of Neuroscience, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany.", + "name": "Patricia Razquin Navas" + }, + { + "institution": "Department of Pediatrics, University of Groningen, University Medical Center Groningen (UMCG), 9713 AV Groningen, The Netherlands.", + "name": "Karen van Eunen" + }, + { + "institution": "Department of Bioinformatics and Molecular Genetics (Faculty of Biology), Institute for Biology 3, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany.", + "name": "Regine C T\u00f6lle" + }, + { + "institution": "Department of Bioinformatics and Molecular Genetics (Faculty of Biology), Institute for Biology 3, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany.Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, 79104 Freiburg, Germany.Department of Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.", + "name": "Jennifer J Schwarz" + }, + { + "institution": "Department of Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.Institute of Pharmacology and Toxicology, University of Ulm, 89081 Ulm, Germany.", + "name": "Heike Wiese", + "orcid": "0000-0002-8941-3762" + }, + { + "institution": "Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, 79104 Freiburg, Germany.Department of Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.BIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany.", + "name": "Bettina Warscheid", + "orcid": "0000-0001-5096-1975" + }, + { + "institution": "Institute of Molecular Medicine I, Medical Faculty, Heinrich-Heine-University, 40225 D\u00fcsseldorf, Germany.", + "name": "Jana Deitersen" + }, + { + "institution": "Institute of Molecular Medicine I, Medical Faculty, Heinrich-Heine-University, 40225 D\u00fcsseldorf, Germany.", + "name": "Bj\u00f6rn Stork", + "orcid": "0000-0002-4167-7806" + }, + { + "institution": "Jena University Language &Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, 07743 Jena, Germany.", + "name": "Erik F\u00e4\u00dfler" + }, + { + "institution": "Jena University Language &Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, 07743 Jena, Germany.", + "name": "Sascha Sch\u00e4uble" + }, + { + "institution": "Jena University Language &Information Engineering (JULIE) Lab, Friedrich-Schiller-University Jena, 07743 Jena, Germany.", + "name": "Udo Hahn" + }, + { + "institution": "Faculty of Mathematics and Natural Sciences, Department of Pharmacy, Analytical Biochemistry, University of Groningen, 9713 AV Groningen, The Netherlands.", + "name": "Peter Horvatovich", + "orcid": "0000-0003-2218-1140" + }, + { + "name": "Daryl P Shanley", + "orcid": "0000-0003-3096-6386" + }, + { + "institution": "Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signaling, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands kathrin.thedieck@uibk.ac.at.Department for Neuroscience, School of Medicine and Health Sciences, Carl von Ossietzky University Oldenburg, Oldenburg, Germany.Institute of Biochemistry and Center for Molecular Biosciences Innsbruck, University of Innsbruck, Innsbruck, Austria.", + "name": "Kathrin Thedieck", + "orcid": "0000-0002-9069-2930" + } + ], + "journal": "Nature communications", + "link": "http://identifiers.org/pubmed/27869123", + "month": "11", + "pages": "13254", + "synopsis": "Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase \u03b2 (CaMKK\u03b2). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.", + "title": "A systems study reveals concurrent activation of AMPK and mTOR by amino acids.", + "type": "PubMed ID", + "volume": "7", + "year": 2016 + }, + "publicationId": "BIOMD0000000640", + "submissionId": "MODEL1702270000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000641": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Karolinska Institutet", + "email": "karen.akopyan@ki.se", + "external": false, + "name": "Karen Akopyan" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Jaiswal2017 - Cell cycle arrest

This model is described in the article:

Jaiswal H, Benada J, M\u00fcllers E, Akopyan K, Burdova K, Koolmeister T, Helleday T, Medema RH, Macurek L, Lindqvist A.
EMBO J. 2017 Jul; 36(14): 2161-2176

Abstract:

After DNA damage, the cell cycle is arrested to avoid propagation of mutations. Arrest in G2 phase is initiated by ATM-/ATR-dependent signaling that inhibits mitosis-promoting kinases such as Plk1. At the same time, Plk1 can counteract ATR-dependent signaling and is required for eventual resumption of the cell cycle. However, what determines when Plk1 activity can resume remains unclear. Here, we use FRET-based reporters to show that a global spread of ATM activity on chromatin and phosphorylation of ATM targets including KAP1 control Plk1 re-activation. These phosphorylations are rapidly counteracted by the chromatin-bound phosphatase Wip1, allowing cell cycle restart despite persistent ATM activity present at DNA lesions. Combining experimental data and mathematical modeling, we propose a model for how the minimal duration of cell cycle arrest is controlled. Our model shows how cell cycle restart can occur before completion of DNA repair and suggests a mechanism for checkpoint adaptation in human cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000641.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Mufudza2012 - Estrogen effect on the dynamicsof breast cancer
This deterministic model shows thedynamics of breast cancer with immune response. The effects ofestrogen are incorporated to study its effects as a risk factor forthe disease.\u00a0

This model is described in the article:

Mufudza C, Sorofa W, Chiyaka ET.
Comput Math Methods Med 2012; 2012: 473572

Abstract:

Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000642.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"accession": "23365616", + "affiliation": "Department of Applied Mathematics, National University of Science and Technology, Ascot, Bulawayo, Zimbabwe. chipmuf@gmail.com", + "authors": [ + { + "institution": "Department of Applied Mathematics, National University of Science and Technology, Ascot, Bulawayo, Zimbabwe. chipmuf@gmail.com", + "name": "Chipo Mufudza" + }, + { + "name": "Walter Sorofa" + }, + { + "name": "Edward T Chiyaka", + "orcid": "0000-0003-0150-8351" + } + ], + "journal": "Computational and mathematical methods in medicine", + "link": "http://identifiers.org/pubmed/23365616", + "month": "0", + "pages": "473572", + "synopsis": "Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer.", + "title": "Assessing the effects of estrogen on the dynamics of breast cancer.", + "type": "PubMed ID", + "volume": "2012", + "year": 2012 + }, + "publicationId": "BIOMD0000000642", + "submissionId": "MODEL1304190001", + "vcsIdentifier": "aab" + }, + "BIOMD0000000643": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "The Babraham Institute", + "email": "Lu.Li@babraham.ac.uk", + "external": false, + "name": "Lu Li" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000643.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions and PKA inhibitsMAST3

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000644.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3and dominant negative effect

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000645.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3 and dominant negative effect", + "publication": { + "accession": "10.7554/eLife.24998", + "affiliation": "Yale University School of Medicine, United States; Babraham Institute, United Kingdom; Yale University School Medicine, United States; The Rockefeller University, United States", + "authors": [ + { + "name": "Veronica Musante" + }, + { + "name": "Lu Li" + }, + { + "name": "Jean Kanyo" + }, + { + "name": "Tukiet T Lam" + }, + { + "name": "Christopher M Colangelo" + }, + { + "name": "Shuk Kei Cheng" + }, + { + "name": "Harrison Brody" + }, + { + "name": "Paul Greengard" + }, + { + "institution": "The Babraham Institute", + "name": "Nicolas Le Nov\u00e8re" + }, + { + "name": "Angus C Nairn" + } + ], + "journal": "eLIFE", + "link": "http://identifiers.org/doi/10.7554/eLife.24998", + "month": "9", + "synopsis": "ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. 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Barr2016 - All-or-nothing G1/Stransition

This model is described in the article:

Barr AR, Heldt FS, Zhang T, Bakal C, Nov\u00e1k B.
Cell Syst 2016 Jan; 2(1): 27-37

Abstract:

The transition from G1 into DNA replication (S phase) is an emergent behavior resulting from dynamic and complex interactions between cyclin-dependent kinases (Cdks), Cdk inhibitors (CKIs), and the anaphase-promoting complex/cyclosome (APC/C). Understanding the cellular decision to commit to S\u00a0phase requires a quantitative description of these interactions. We apply quantitative imaging of single human cells to track the expression of G1/S regulators and use these data to parametrize a stochastic mathematical model of the G1/S transition. We show that a rapid, proteolytic, double-negative feedback loop between Cdk2:Cyclin and the Cdk inhibitor p27(Kip1) drives a switch-like entry into S phase. Furthermore, our model predicts that increasing Emi1 levels throughout S phase are critical in maintaining irreversibility of the G1/S transition, which we validate using Emi1 knockdown and live imaging of G1/S reporters. This work provides insight into the general design principles of the signaling networks governing the temporally abrupt transitions between cell-cycle phases.

This model is hosted on BioModels Database and identified by: BIOMD0000000646.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Kwang2003 - The influence of RKIP on the ERKsignaling pathway

This model is described in the article:

Kwang-Hyun Cho, Sung-Young Shin, Hyun-Woo Kim, Olaf Wolkenhauer, Brian McFerran and Walter Kolch
Computational Methods in Systems Biology: First International Workshop, CMSB 2003 Rovereto, Italy, February 24\u00e2??26, 2003 Proceedings

Abstract:

This paper investigates the influence of the Raf Kinase Inhibitor Pro- tein (RKIP) on the Extracellular signal Regulated Kinase (ERK) signaling pathway through mathematical modeling and simulation. Using nonlinear ordi- nary differential equations to represent biochemical reactions in the pathway, we suggest a technique for parameter estimation, utilizing time series data of proteins involved in the signaling pathway. The mathematical model allows the simulation the sensitivity of the ERK pathway to variations of initial RKIP and ERK-PP (phosphorylated ERK) concentrations along with time. Throughout the simulation study, we can qualitatively validate the proposed mathematical model compared with experimental results.

This model is hosted on BioModels Database and identified by: BIOMD0000000647.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Organon Laboratories, Newhouse, Motherwell; Institute of Biomedical and Life Sciences, University of Glasgow", + "journal": "Computational Methods in Systems Biology: First International Workshop, CMSB 2003 Rovereto, Italy, February 24\u00e2??26, 2003 Proceedings", + "link": "http://identifiers.org/doi/10.1007/3-540-36481-1_11", + "month": "2", + "synopsis": "This paper investigates the influence of the Raf Kinase Inhibitor Pro- tein (RKIP) on the Extracellular signal Regulated Kinase (ERK) signaling pathway through mathematical modeling and simulation. Using nonlinear ordi- nary differential equations to represent biochemical reactions in the pathway, we suggest a technique for parameter estimation, utilizing time series data of proteins involved in the signaling pathway. The mathematical model allows the simulation the sensitivity of the ERK pathway to variations of initial RKIP and ERK-PP (phosphorylated ERK) concentrations along with time. 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Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (normal)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways under normal condition.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000648.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"name": "Asim Bikas Das", + "orcid": "0000-0003-1801-7487" + } + ], + "issue": "5", + "journal": "Molecular bioSystems", + "link": "http://identifiers.org/pubmed/28367561", + "month": "5", + "pages": "830-840", + "synopsis": "Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.", + "title": "Cancerous perturbations within the ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling network constitutively activate inter-pathway positive feedback loops.", + "type": "PubMed ID", + "volume": "13", + "year": 2017 + }, + "publicationId": "BIOMD0000000648", + "submissionId": "MODEL1708290005", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000650": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Owen1998 - tumour treatment model

This model is described in the article:

Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun; 15(2): 165-185

Abstract:

Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.

This model is hosted on BioModels Database and identified by: MODEL1708250002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Feedback regulation in cell signalling: Lessons for cancer therapeutics

This model is described in the article:

Nguyen LK, Kholodenko BN.
Semin. Cell Dev. Biol. 2016 Feb; 50: 85-94

Abstract:

The notion of feedback is fundamental for understanding signal transduction networks. Feedback loops attenuate or amplify signals, change the network dynamics and modify the input-output relationships between the signal and the target. Negative feedback provides robustness to noise and adaptation to perturbations, but as a double-edged sword can prevent effective pathway inhibition by a drug. Positive feedback brings about switch-like network responses and can convert analog input signals into digital outputs, triggering cell fate decisions and phenotypic changes. We show how a multitude of protein-protein interactions creates hidden feedback loops in signal transduction cascades. Drug treatments that interfere with feedback regulation can cause unexpected adverse effects. Combinatorial molecular interactions generated by pathway crosstalk and feedback loops often bypass the block caused by targeted therapies against oncogenic mutated kinases. We discuss mechanisms of drug resistance caused by network adaptations and suggest that development of effective drug combinations requires understanding of how feedback loops modulate drug responses.

This model is hosted on BioModels Database and identified by: MODEL1708250003.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Negative feedback provides robustness to noise and adaptation to perturbations, but as a double-edged sword can prevent effective pathway inhibition by a drug. Positive feedback brings about switch-like network responses and can convert analog input signals into digital outputs, triggering cell fate decisions and phenotypic changes. We show how a multitude of protein-protein interactions creates hidden feedback loops in signal transduction cascades. Drug treatments that interfere with feedback regulation can cause unexpected adverse effects. Combinatorial molecular interactions generated by pathway crosstalk and feedback loops often bypass the block caused by targeted therapies against oncogenic mutated kinases. We discuss mechanisms of drug resistance caused by network adaptations and suggest that development of effective drug combinations requires understanding of how feedback loops modulate drug responses.", + "title": "Feedback regulation in cell signalling: Lessons for cancer therapeutics.", + "type": "PubMed ID", + "volume": "50", + "year": 2016 + }, + "publicationId": "BIOMD0000000651", + "submissionId": "MODEL1708250003", + "vcsIdentifier": "aag" + }, + "BIOMD0000000652": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (PI3K mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with mutated PI3K.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000652.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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bioSystems", + "link": "http://identifiers.org/pubmed/28367561", + "month": "5", + "pages": "830-840", + "synopsis": "Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. 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We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.", + "title": "Cancerous perturbations within the ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling network constitutively activate inter-pathway positive feedback loops.", + "type": "PubMed ID", + "volume": "13", + "year": 2017 + }, + "publicationId": "BIOMD0000000652", + "submissionId": "MODEL1708290004", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000653": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (bRaf mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with bRaf mutation

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000653.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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bioSystems", + "link": "http://identifiers.org/pubmed/28367561", + "month": "5", + "pages": "830-840", + "synopsis": "Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.", + "title": "Cancerous perturbations within the ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling network constitutively activate inter-pathway positive feedback loops.", + "type": "PubMed ID", + "volume": "13", + "year": 2017 + }, + "publicationId": "BIOMD0000000653", + "submissionId": "MODEL1708290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000654": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (Ras mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with Ras mutation.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000654.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (PTEN mutation)
Crosstalk model of the ERK, Wnt and AktSignalling pathways with PTEN mutation.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000655.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (EGFR overexpression)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with EGFR overexpression.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000656.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.", + "title": "Cancerous perturbations within the ERK, PI3K/Akt, and Wnt/\u03b2-catenin signaling network constitutively activate inter-pathway positive feedback loops.", + "type": "PubMed ID", + "volume": "13", + "year": 2017 + }, + "publicationId": "BIOMD0000000656", + "submissionId": "MODEL1708290001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000657": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Araujo2016 - Positive feedback in Cdk1signalling keeps mitotic duration short and constant

This model is described in the article:

Araujo AR, Gelens L, Sheriff RS, Santos SD.
Mol. Cell 2016 Oct; 64(2): 362-375

Abstract:

Cell division is characterized by a sequence of events by which a cell gives rise to two daughter cells. Quantitative measurements of cell-cycle dynamics in single cells showed that despite variability in G1-, S-, and G2 phases, duration of mitosis is short and remarkably constant. Surprisingly, there is no correlation between cell-cycle length and mitotic duration, suggesting that mitosis is temporally insulated from variability in earlier cell-cycle phases. By combining live cell imaging and computational modeling, we showed that positive feedback is the molecular mechanism underlying the temporal insulation of mitosis. Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.

This model is hosted on BioModels Database and identified by: BIOMD0000000657.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.", + "title": "Positive Feedback Keeps Duration of Mitosis Temporally Insulated from Upstream Cell-Cycle Events.", + "type": "PubMed ID", + "volume": "64", + "year": 2016 + }, + "publicationId": "BIOMD0000000657", + "submissionId": "MODEL1710040001", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000658": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Lee2003 - Roles of APC and Axin in WntPathway (without regulatory loop)

This model is described in the article:

Lee E, Salic A, Kr\u00fcger R, Heinrich R, Kirschner MW.
PLoS Biol. 2003 Oct; 1(1): E10

Abstract:

Wnt signaling plays an important role in both oncogenesis and development. Activation of the Wnt pathway results in stabilization of the transcriptional coactivator beta-catenin. Recent studies have demonstrated that axin, which coordinates beta-catenin degradation, is itself degraded. Although the key molecules required for transducing a Wnt signal have been identified, a quantitative understanding of this pathway has been lacking. We have developed a mathematical model for the canonical Wnt pathway that describes the interactions among the core components: Wnt, Frizzled, Dishevelled, GSK3beta, APC, axin, beta-catenin, and TCF. Using a system of differential equations, the model incorporates the kinetics of protein-protein interactions, protein synthesis/degradation, and phosphorylation/dephosphorylation. We initially defined a reference state of kinetic, thermodynamic, and flux data from experiments using Xenopus extracts. Predictions based on the analysis of the reference state were used iteratively to develop a more refined model from which we analyzed the effects of prolonged and transient Wnt stimulation on beta-catenin and axin turnover. We predict several unusual features of the Wnt pathway, some of which we tested experimentally. An insight from our model, which we confirmed experimentally, is that the two scaffold proteins axin and APC promote the formation of degradation complexes in very different ways. We can also explain the importance of axin degradation in amplifying and sharpening the Wnt signal, and we show that the dependence of axin degradation on APC is an essential part of an unappreciated regulatory loop that prevents the accumulation of beta-catenin at decreased APC concentrations. By applying control analysis to our mathematical model, we demonstrate the modular design, sensitivity, and robustness of the Wnt pathway and derive an explicit expression for tumor suppression and oncogenicity.

This model is hosted on BioModels Database and identified by: BIOMD0000000658.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cursons2015 - Regulation of ERK-MAPKsignaling in human epidermis
Model comparing the abundance ofphosphorylated MAPK signalling proteins and calcium signalling inthe epidermis.

This model is described in the article:

Cursons J, Gao J, Hurley DG, Print CG, Dunbar PR, Jacobs MD, Crampin EJ.
BMC Syst Biol 2015; 9: 41

Abstract:

The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized.We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism.These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling.Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.

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To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

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Regulation of ERK-MAPK signaling in human epidermis", + "publication": { + "accession": "26209520", + "affiliation": "Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. joseph.cursons@unimelb.edu.au.", + "authors": [ + { + "institution": "Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. joseph.cursons@unimelb.edu.au.NICTA Victoria Research Lab, Melbourne, Australia. joseph.cursons@unimelb.edu.au.ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. joseph.cursons@unimelb.edu.au.Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. joseph.cursons@unimelb.edu.au.Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand. joseph.cursons@unimelb.edu.au.", + "name": "Joseph Cursons", + "orcid": "0000-0002-5053-4540" + }, + { + "institution": "Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. jerry.gao@unimelb.edu.au.", + "name": "Jerry Gao" + }, + { + "institution": "Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. daniel.hurley@unimelb.edu.au.NICTA Victoria Research Lab, Melbourne, Australia. daniel.hurley@unimelb.edu.au.Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. daniel.hurley@unimelb.edu.au.Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand. daniel.hurley@unimelb.edu.au.Bioinformatics Institute, University of Auckland, Auckland, New Zealand. daniel.hurley@unimelb.edu.au.Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand. daniel.hurley@unimelb.edu.au.", + "name": "Daniel G Hurley", + "orcid": "0000-0003-0171-1982" + }, + { + "institution": "Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand. c.print@auckland.ac.nz.Bioinformatics Institute, University of Auckland, Auckland, New Zealand. c.print@auckland.ac.nz.Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand. c.print@auckland.ac.nz.", + "name": "Cristin G Print", + "orcid": "0000-0001-8345-7812" + }, + { + "institution": "Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand. r.dunbar@auckland.ac.nz.School of Biological Sciences, University of Auckland, Auckland, New Zealand. r.dunbar@auckland.ac.nz.", + "name": "P Rod Dunbar", + "orcid": "0000-0001-9626-2600" + }, + { + "institution": "Department of Biology, New Zealand International College, ACG New Zealand, Auckland, New Zealand. marc.jacobs@gmx.com.", + "name": "Marc D Jacobs" + }, + { + "institution": "Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. edmund.crampin@unimelb.edu.au.ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, University of Melbourne, Melbourne, Australia. edmund.crampin@unimelb.edu.au.Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand. edmund.crampin@unimelb.edu.au.Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand. edmund.crampin@unimelb.edu.au.School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia. edmund.crampin@unimelb.edu.au.School of Medicine, University of Melbourne, Melbourne, Australia. edmund.crampin@unimelb.edu.au.", + "name": "Edmund J Crampin" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/26209520", + "month": "7", + "pages": "41", + "synopsis": "

Background

The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized.

Methods

We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism.

Results

These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling.

Conclusions

Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.", + "title": "Regulation of ERK-MAPK signaling in human epidermis.", + "type": "PubMed ID", + "volume": "9", + "year": 2015 + }, + "publicationId": "BIOMD0000000659", + "submissionId": "MODEL1503270000", + "vcsIdentifier": "aab" + }, + "BIOMD0000000660": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Department of Biochemistry, University of Oxford, OX1 3QU Oxford, United Kingdom; stefan.heldt@bioch.ox.ac.uk bela.novak@bioch.ox.ac.uk.", + "email": "st.heldt@gmail.com", + "external": false, + "name": "Frank Stefan Heldt", + "orcid": "0000-0002-9276-5013" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Barr2017 - Dynamics of p21 in hTert-RPE1cells
This deteministic model reveals that abistable switch created by Cdt2, promotes irreversible S-phaseentry by keeping p21 levels low, prevents premature S-phase exitupon DNA damage

This model is described in the article:

Barr AR, Cooper S, Heldt FS, Butera F, Stoy H, Mansfeld J, Nov\u00e1k B, Bakal C.
Nat Commun 2017 Mar; 8: 14728

Abstract:

Following DNA damage caused by exogenous sources, such as ionizing radiation, the tumour suppressor p53 mediates cell cycle arrest via expression of the CDK inhibitor, p21. However, the role of p21 in maintaining genomic stability in the absence of exogenous DNA-damaging agents is unclear. Here, using live single-cell measurements of p21 protein in proliferating cultures, we show that naturally occurring DNA damage incurred over S-phase causes p53-dependent accumulation of p21 during mother G2- and daughter G1-phases. High p21 levels mediate G1 arrest via CDK inhibition, yet lower levels have no impact on G1 progression, and the ubiquitin ligases CRL4Cdt2 and SCFSkp2 couple to degrade p21 prior to the G1/S transition. Mathematical modelling reveals that a bistable switch, created by CRL4Cdt2, promotes irreversible S-phase entry by keeping p21 levels low, preventing premature S-phase exit upon DNA damage. Thus, we characterize how p21 regulates the proliferation-quiescence decision to maintain genomic stability.

This model is hosted on BioModels Database and identified by: BIOMD0000000660.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Dynamics of p21 in hTert-RPE1 cells", + "publication": { + "accession": "28317845", + "affiliation": "Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.", + "authors": [ + { + "institution": "Division of Cancer Biology, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.", + "name": "Alexis R Barr", + "orcid": "0000-0002-6684-8114" + }, + { + "institution": "Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.Department of Computational Systems Medicine, Imperial College, South Kensington Campus, London SW7 2AZ, UK.", + "name": "Samuel Cooper" + }, + { + "institution": "Department of Biochemistry, University of Oxford, OX1 3QU Oxford, United Kingdom; stefan.heldt@bioch.ox.ac.uk bela.novak@bioch.ox.ac.uk.", + "name": "Frank S Heldt", + "orcid": "0000-0002-9276-5013" + }, + { + "institution": "Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.", + "name": "Francesca Butera", + "orcid": "0000-0002-6606-4678" + }, + { + "institution": "Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.", + "name": "Henriette Stoy" + }, + { + "institution": "Cell Cycle, Biotechnology Centre, TU Dresden, 01307 Dresden, Germany.", + "name": "J\u00f6rg Mansfeld", + "orcid": "0000-0002-0562-8206" + }, + { + "institution": "Department of Biochemistry, University of Oxford, Oxford, United Kingdom.", + "name": "B\u00e9la Nov\u00e1k", + "orcid": "0000-0002-6961-1366" + }, + { + "institution": "Division of Cancer Biology, The Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK.", + "name": "Chris Bakal", + "orcid": "0000-0002-0413-6744" + } + ], + "journal": "Nature communications", + "link": "http://identifiers.org/pubmed/28317845", + "month": "3", + "pages": "14728", + "synopsis": "Following DNA damage caused by exogenous sources, such as ionizing radiation, the tumour suppressor p53 mediates cell cycle arrest via expression of the CDK inhibitor, p21. However, the role of p21 in maintaining genomic stability in the absence of exogenous DNA-damaging agents is unclear. Here, using live single-cell measurements of p21 protein in proliferating cultures, we show that naturally occurring DNA damage incurred over S-phase causes p53-dependent accumulation of p21 during mother G2- and daughter G1-phases. High p21 levels mediate G1 arrest via CDK inhibition, yet lower levels have no impact on G1 progression, and the ubiquitin ligases CRL4Cdt2 and SCFSkp2 couple to degrade p21 prior to the G1/S transition. Mathematical modelling reveals that a bistable switch, created by CRL4Cdt2, promotes irreversible S-phase entry by keeping p21 levels low, preventing premature S-phase exit upon DNA damage. Thus, we characterize how p21 regulates the proliferation-quiescence decision to maintain genomic stability.", + "title": "DNA damage during S-phase mediates the proliferation-quiescence decision in the subsequent G1 via p21 expression.", + "type": "PubMed ID", + "volume": "8", + "year": 2017 + }, + "publicationId": "BIOMD0000000660", + "submissionId": "MODEL1607210001", + "vcsIdentifier": "aab" + }, + "BIOMD0000000661": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Webb2002 - Fas/FasL mediated tumor T-cell interaction
This deterministic model ofimmunological surveillance involving tumour cell\u2013T-lymphocyteinteraction, cell surface expression of Fas/FasL, and theirsecreted soluble forms.

This model is described in the article:

Webb SD, Sherratt JA, Fish RG.
Math Biosci 2002 Sep-Oct; 179(2): 113-129

Abstract:

One proposed mechanism of tumour escape from immune surveillance is tumour up-regulation of the cell surface ligand FasL, which can lead to apoptosis of Fas receptor (Fas) positive lymphocytes. Based upon this 'counterattack', we have developed a mathematical model involving tumour cell-lymphocyte interaction, cell surface expression of Fas/FasL, and their secreted soluble forms. The model predicts that (a) the production of soluble forms of Fas and FasL will lead to the down-regulation of the immune response; (b) matrix metalloproteinase (MMP) inactivation should lead to increased membrane FasL and result in a higher rate of Fas-mediated apoptosis for lymphocytes than for tumour cells. Recent studies on cancer patients lend support for these predictions. The clinical implications are two-fold. Firstly, the use of broad spectrum MMP inhibitors as anti-angiogenic agents may be compromised by their adverse effect on tumour FasL up-regulation. Also, Fas/FasL interactions may have an impact on the outcome of numerous ongoing immunotherapeutic trials since the final common pathway of all these approaches is the transduction of death signals within the tumour cell.

This model is hosted on BioModels Database and identified by: BIOMD0000000661.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Moore2004 - Chronic Myeloid Leukemic cellsand T-lymphocytes interaction
A mathematical model for theinteraction of between cancer cells and immune system, involvingCML cancer cells, naive and effector T-lymphocytes.

This model is described in the article:

Moore H, Li NK.
J. Theor. Biol. 2004 Apr; 227(4): 513-523

Abstract:

In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome.

This model is hosted on BioModels Database and identified by: BIOMD0000000662.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Chronic Myeloid Leukemic cells and T-lymphocyte interaction", + "publication": { + "accession": "15038986", + "affiliation": "American Institute of Mathematics, 360 Portage Avenue, Palo Alto, CA 94306, USA. moore@aimath.org", + "authors": [ + { + "institution": "American Institute of Mathematics, 360 Portage Avenue, Palo Alto, CA 94306, USA. moore@aimath.org", + "name": "Helen Moore" + }, + { + "name": "Natasha K Li" + } + ], + "issue": "4", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/15038986", + "month": "4", + "pages": "513-523", + "synopsis": "In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome.", + "title": "A mathematical model for chronic myelogenous leukemia (CML) and T cell interaction.", + "type": "PubMed ID", + "volume": "227", + "year": 2004 + }, + "publicationId": "BIOMD0000000662", + "submissionId": "MODEL1006230057", + "vcsIdentifier": "aab" + }, + "BIOMD0000000663": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Wodarz2007 - HIV/CD4 T-cell interaction
A deterministic model illustrating howCD4 T-cells can influence HIV infection.

This model is described in the article:

Wodarz D, Hamer DH.
Math Biosci 2007 Sep; 209(1): 14-29

Abstract:

Recent experimental data have shown that HIV-specific CD4 T cells provide a very important target for HIV replication. We use mathematical models to explore the effect of specific CD4 T cell infection on the dynamics of virus spread and immune responses. Infected CD4 T cells can provide antigen for their own stimulation. We show that such autocatalytic cell division can significantly enhance virus spread, and can also provide an additional reservoir for virus persistence during anti-viral drug therapy. In addition, the initial number of HIV-specific CD4 T cells is an important determinant of acute infection dynamics. A high initial number of HIV-specific CD4 T cells can lead to a sudden and fast drop of the population of HIV-specific CD4 T cells which results quickly in their extinction. On the other hand, a low initial number of HIV-specific CD4 T cells can lead to a prolonged persistence of HIV-specific CD4 T cell help at higher levels. The model suggests that boosting the population of HIV-specific CD4 T cells can increase the amount of virus-induced immune impairment, lead to less efficient anti-viral effector responses, and thus speed up disease progression, especially if effector responses such as CTL have not been sufficiently boosted at the same time.

This model is hosted on BioModels Database and identified by: BIOMD0000000663.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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immunodeficiency virus 1", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/11676" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Wodarz2007 - HIV/CD4 T-cell interaction", + "publication": { + "accession": "17379260", + "affiliation": "Department of Ecology and Evolutionary Biology, 321 Steinhaus Hall, University of California, Irvine, CA 92697, USA. dwodarz@uci.edu", + "authors": [ + { + "institution": "Department of Ecology and Evolutionary Biology, 321 Steinhaus Hall, University of California, Irvine, CA 92697, USA. dwodarz@uci.edu", + "name": "Dominik Wodarz" + }, + { + "name": "Dean H Hamer" + } + ], + "issue": "1", + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/17379260", + "month": "9", + "pages": "14-29", + "synopsis": "Recent experimental data have shown that HIV-specific CD4 T cells provide a very important target for HIV replication. We use mathematical models to explore the effect of specific CD4 T cell infection on the dynamics of virus spread and immune responses. Infected CD4 T cells can provide antigen for their own stimulation. We show that such autocatalytic cell division can significantly enhance virus spread, and can also provide an additional reservoir for virus persistence during anti-viral drug therapy. In addition, the initial number of HIV-specific CD4 T cells is an important determinant of acute infection dynamics. A high initial number of HIV-specific CD4 T cells can lead to a sudden and fast drop of the population of HIV-specific CD4 T cells which results quickly in their extinction. On the other hand, a low initial number of HIV-specific CD4 T cells can lead to a prolonged persistence of HIV-specific CD4 T cell help at higher levels. 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Muller2008 - Simplified MAPK activation Dynamics (Model B)
Simplified mathematical model (model B)for predicting MAPK signal expression.

This model is described in the article:

Muller M, Obeyesekere M, Mills GB, Ram PT.
FASEB J. 2008 May; 22(5): 1393-1403

Abstract:

Activation of the fibroblast growth factor (FGFR) and melanocyte stimulating hormone (MC1R) receptors stimulates B-Raf and C-Raf isoforms that regulate the dynamics of MAPK1,2 signaling. Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. Further reducing a 106-node ordinary differential equations network encompassing the complete network to a 6-node network encompassing rate-limiting processes sustains the feedback loops and the bifan, providing sufficient information to predict biological responses.

This model is hosted on BioModels Database and identified by: BIOMD0000000664.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. 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This a model from the article:
Computational model for effects of ligand/receptor binding properties oninterleukin-2 trafficking dynamics and T cell proliferation response.
Fallon EM, Lauffenburger DA. Biotechnol Prog 2000 Sep-Oct;16(5):905-16 11027188 ,
Abstract:
Multisubunit cytokine receptors such as the heterotrimeric receptor forinterleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance inbiotechnology and are crucial regulators of cell proliferation anddifferentiation behavior. Dynamics of cytokine/receptor endocytic traffickingcan significantly impact cell responses through effects of receptordown-regulation and ligand depletion, and in turn are governed byligand/receptor binding properties. We describe here a computational model fortrafficking dynamics of the IL-2 receptor (IL-2R) system, which is able topredict T cell proliferation responses to IL-2. This model comprises kineticequations describing binding, internalization, and postendocytic sorting of IL-2and IL-2R, including an experimentally derived dependence of cell proliferationrate on these properties. Computational results from this model predict thatIL-2 depletion can be reduced by decreasing its binding affinity for the IL-2Rbetagamma subunit relative to the alpha subunit at endosomal pH, as a result ofenhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2analogue with such altered binding properties should exhibit increased potencyfor stimulating the T cell proliferation response. These results are inagreement with our recent experimental findings for the IL-2 analogue termed 2D1[Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type ofmodel may enable prediction of beneficial cytokine/receptor binding propertiesto aid development of molecular design criteria for improvements in applicationssuch as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Fallon EM, Lauffenburger DA. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Dynamics of cytokine/receptor endocytic trafficking can significantly impact cell responses through effects of receptor down-regulation and ligand depletion, and in turn are governed by ligand/receptor binding properties. We describe here a computational model for trafficking dynamics of the IL-2 receptor (IL-2R) system, which is able to predict T cell proliferation responses to IL-2. This model comprises kinetic equations describing binding, internalization, and postendocytic sorting of IL-2 and IL-2R, including an experimentally derived dependence of cell proliferation rate on these properties. 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Pappalardo2016 - PI3K/AKT and MAPK SignalingPathways in Melanoma Cancer

This model is described in the article:

Pappalardo F, Russo G, Candido S, Pennisi M, Cavalieri S, Motta S, McCubrey JA, Nicoletti F, Libra M.
PLoS ONE 2016; 11(3): e0152104

Abstract:

Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.

This model is hosted on BioModels Database and identified by: MODEL1609190000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer", + "publication": { + "accession": "27015094", + "affiliation": "Department of Drug Sciences, University of Catania, 95125, Catania, Italy.", + "authors": [ + { + "institution": "Department of Drug Sciences, University of Catania, 95125, Catania, Italy.", + "name": "Francesco Pappalardo", + "orcid": "0000-0003-1668-3320" + }, + { + "institution": "Department of Drug Sciences, University of Catania, 95125, Catania, Italy.", + "name": "Giulia Russo", + "orcid": "0000-0001-6616-7856" + }, + { + "institution": "Department of Biomedical and Biotechnological Sciences, University of Catania, 95125, Catania, Italy.", + "name": "Saverio Candido", + "orcid": "0000-0003-1255-6666" + }, + { + "name": "Marzio Pennisi", + "orcid": "0000-0003-0231-7653" + }, + { + "institution": "Department of Engineering, University of Catania, 95125, Catania, Italy.", + "name": "Salvatore Cavalieri", + "orcid": "0000-0001-9077-3688" + }, + { + "institution": "Department of Mathematics and Computer Science, University of Catania, 95125, Catania, Italy.", + "name": "Santo Motta" + }, + { + "institution": "Department of Microbiology and Immunology, Brody School of Medicine at East Carolina University, Greenville, NC, United States of America.", + "name": "James A McCubrey", + "orcid": "0000-0001-6027-3156" + }, + { + "institution": "Department of Biomedical and Biotechnological Sciences, University of Catania, 95125, Catania, Italy.", + "name": "Ferdinando Nicoletti", + "orcid": "0000-0002-4570-8462" + }, + { + "institution": "Department of Biomedical and Biotechnological Sciences, University of Catania, 95125, Catania, Italy.", + "name": "Massimo Libra" + } + ], + "issue": "3", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/27015094", + "month": "0", + "pages": "e0152104", + "synopsis": "

Background

Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.

Result

Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.", + "title": "Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer.", + "type": "PubMed ID", + "volume": "11", + "year": 2016 + }, + "publicationId": "BIOMD0000000666", + "submissionId": "MODEL1609190000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000667": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Hornberg2005 - MAPKsignalling
Large model of the ERK signallingnetwork. Results from this model were used to generate a simplifiedversion of the network.

This model is described in the article:

Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R, Westerhoff HV.
Oncogene 2005 Aug; 24(36): 5533-5542

Abstract:

Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.

This model is hosted on BioModels Database and identified by: BIOMD0000000667.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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H.V.Westerhoff@VU.NL.Molecular Cell Biology, VU University Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. H.V.Westerhoff@VU.NL.Manchester Centre for Integrative Systems Biology, Manchester, UK. H.V.Westerhoff@VU.NL.", + "name": "Hans V Westerhoff", + "orcid": "0000-0002-0443-6114" + } + ], + "issue": "36", + "journal": "Oncogene", + "link": "http://identifiers.org/pubmed/16007170", + "month": "8", + "pages": "5533-5542", + "synopsis": "Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. 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Zhu2015 - Combined gemcitabine and birinapantin pancreatic cancer cells - basic PD model
Mathematical model to illustrate theeffectiveness of combination chemotherapy involving gemcitabine andbirinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000668.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.", + "title": "Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells.", + "type": "PubMed ID", + "volume": "42", + "year": 2015 + }, + "publicationId": "BIOMD0000000668", + "submissionId": "MODEL1604270000", + "vcsIdentifier": "aab" + }, + "BIOMD0000000669": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Zhu2015 - combined gemcitabine and birinapantin pancreatic cancer cells - mechanistic PD model
Mechanistic mathematical model toillustrate the effectiveness of combination chemotherapy involvinggemcitabine and birinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000669.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.", + "title": "Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells.", + "type": "PubMed ID", + "volume": "42", + "year": 2015 + }, + "publicationId": "BIOMD0000000669", + "submissionId": "MODEL1604270001", + "vcsIdentifier": "aab" + }, + "BIOMD0000000670": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Owen1998 - tumour growth model
Deterministic model for the early,avascular growth of a tumour, concentrating on the inhibitoryeffect of macrophages.

This model is described in the article:

Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun; 15(2): 165-185

Abstract:

Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.

This model is hosted on BioModels Database and identified by: BIOMD0000000670.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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tumour growth model", + "publication": { + "accession": "9661282", + "affiliation": "Nonlinear Systems Laboratory, University of Warwick, Coventry, UK. owen@maths.warwick.ac.uk", + "authors": [ + { + "institution": "Nonlinear Systems Laboratory, University of Warwick, Coventry, UK. owen@maths.warwick.ac.uk", + "name": "M R Owen" + }, + { + "name": "J A Sherratt" + } + ], + "issue": "2", + "journal": "IMA journal of mathematics applied in medicine and biology", + "link": "http://identifiers.org/pubmed/9661282", + "month": "6", + "pages": "165-185", + "synopsis": "Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.", + "title": "Modelling the macrophage invasion of tumours: effects on growth and composition.", + "type": "PubMed ID", + "volume": "15", + "year": 1998 + }, + "publicationId": "BIOMD0000000670", + "submissionId": "MODEL1708240000", + "vcsIdentifier": "aab" + }, + "BIOMD0000000671": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Nottingham", + "email": "emmafairbanks93@gmail.com", + "external": false, + "name": "Emma Fairbanks" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Murphy2016 - Differences in predictions ofODE models of tumor growth
Comparison of 7 ODE models for tumoursize. This models have been compared to experimental data.

This model is described in the article:

Murphy H, Jaafari H, Dobrovolny HM.
BMC Cancer 2016 Feb; 16: 163

Abstract:

While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.

This model is hosted on BioModels Database and identified by: BIOMD0000000671.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"Department of Physics & Astronomy, Texas Christian University, 2800 S. University Drive, TX, 76129, Fort Worth, USA. H.K.JAAFARI@tcu.edu.", + "name": "Hana Jaafari" + }, + { + "institution": "Department of Physics & Astronomy, Texas Christian University, 2800 S. University Drive, TX, 76129, Fort Worth, USA. h.dobrovolny@tcu.edu.", + "name": "Hana M Dobrovolny", + "orcid": "0000-0003-3592-6770" + } + ], + "journal": "BMC cancer", + "link": "http://identifiers.org/pubmed/26921070", + "month": "2", + "pages": "163", + "synopsis": "

Background

While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.

Methods

We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.

Results

We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.

Conclusion

Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.", + "title": "Differences in predictions of ODE models of tumor growth: a cautionary example.", + "type": "PubMed ID", + "volume": "16", + "year": 2016 + }, + "publicationId": "BIOMD0000000671", + "submissionId": "MODEL1708250001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000672": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Brown1997 - Plasma Melatonin Levels
A mathematical model that incorporatesa piecewise function for NAT activity to predict melatoninconcentration.

This model is described in the article:

Brown EN, Choe Y, Shanahan TL, Czeisler CA.
Am. J. Physiol. 1997 Mar; 272(3 Pt 1): E506-16

Abstract:

Studies in animals and humans suggest that the diurnal pattern in plasma melatonin levels is due to the hormone's rates of synthesis, circulatory infusion and clearance, circadian control of synthesis onset and offset, environmental lighting conditions, and error in the melatonin immunoassay. A two-dimensional linear differential equation model of the hormone is formulated and is used to analyze plasma melatonin levels in 18 normal healthy male subjects during a constant routine. Recently developed Bayesian statistical procedures are used to incorporate correctly the magnitude of the immunoassay error into the analysis. The estimated parameters [median (range)] were clearance half-life of 23.67 (14.79-59.93) min, synthesis onset time of 2206 (1940-0029), synthesis offset time of 0621 (0246-0817), and maximum N-acetyltransferase activity of 7.17(2.34-17.93) pmol x l(-1) x min(-1). All were in good agreement with values from previous reports. The difference between synthesis offset time and the phase of the core temperature minimum was 1 h 15 min (-4 h 38 min-2 h 43 min). The correlation between synthesis onset and the dim light melatonin onset was 0.93. Our model provides a more physiologically plausible estimate of the melatonin synthesis onset time than that given by the dim light melatonin onset and the first reliable means of estimating the phase of synthesis offset. Our analysis shows that the circadian and pharmacokinetics parameters of melatonin can be reliably estimated from a single model.

This model is hosted on BioModels Database and identified by: BIOMD0000000672.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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A two-dimensional linear differential equation model of the hormone is formulated and is used to analyze plasma melatonin levels in 18 normal healthy male subjects during a constant routine. Recently developed Bayesian statistical procedures are used to incorporate correctly the magnitude of the immunoassay error into the analysis. The estimated parameters [median (range)] were clearance half-life of 23.67 (14.79-59.93) min, synthesis onset time of 2206 (1940-0029), synthesis offset time of 0621 (0246-0817), and maximum N-acetyltransferase activity of 7.17(2.34-17.93) pmol x l(-1) x min(-1). All were in good agreement with values from previous reports. The difference between synthesis offset time and the phase of the core temperature minimum was 1 h 15 min (-4 h 38 min-2 h 43 min). The correlation between synthesis onset and the dim light melatonin onset was 0.93. Our model provides a more physiologically plausible estimate of the melatonin synthesis onset time than that given by the dim light melatonin onset and the first reliable means of estimating the phase of synthesis offset. Our analysis shows that the circadian and pharmacokinetics parameters of melatonin can be reliably estimated from a single model.", + "title": "A mathematical model of diurnal variations in human plasma melatonin levels.", + "type": "PubMed ID", + "volume": "272", + "year": 1997 + }, + "publicationId": "BIOMD0000000672", + "submissionId": "MODEL1006230019", + "vcsIdentifier": "aab" + }, + "BIOMD0000000673": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Lockwood2006 - AlzheimersDisease PBPKmodel
A mathematical model to predict theeffectiveness of CI-1017 (muscarinic agonist) for Alzheimer'sdisease by evaluating changes in ADAS-cog score.

This model is described in the article:

Lockwood P, Ewy W, Hermann D, Holford N.
Pharm. Res. 2006 Sep; 23(9): 2050-2059

Abstract:

OBJECTIVE: Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials. MATERIALS AND METHODS: A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and literature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p < 0.05) and pair-wise comparisons to placebo (p < 0.025). RESULTS: A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability. CONCLUSION: CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.

This model is hosted on BioModels Database and identified by: BIOMD0000000673.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Alzheimer's Disease PBPK model", + "publication": { + "accession": "16906456", + "affiliation": "Pfizer Global Research and Development, Ann Arbor, Michigan, USA. peter.lockwood@pfizer.com", + "authors": [ + { + "institution": "Pfizer Global Research and Development, Ann Arbor, Michigan, USA. peter.lockwood@pfizer.com", + "name": "Peter Lockwood" + }, + { + "name": "Wayne Ewy" + }, + { + "name": "David Hermann" + }, + { + "institution": "University of Auckland", + "name": "Nick Holford", + "orcid": "0000-0002-4031-2514" + } + ], + "issue": "9", + "journal": "Pharmaceutical research", + "link": "http://identifiers.org/pubmed/16906456", + "month": "9", + "pages": "2050-2059", + "synopsis": "

Objective

Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials.

Materials and methods

A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and literature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p < 0.05) and pair-wise comparisons to placebo (p < 0.025).

Results

A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability.

Conclusion

CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.", + "title": "Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease.", + "type": "PubMed ID", + "volume": "23", + "year": 2006 + }, + "publicationId": "BIOMD0000000673", + "submissionId": "MODEL1006230054", + "vcsIdentifier": "aab" + }, + "BIOMD0000000674": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "University of M?laga. CIBERER (Enfermedades Raras)", + "email": "armando@uma.es", + "external": false, + "name": "Armando Reyes-Palomares" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Reyes-Palomares2012 - a combined modelhepatic polyamine and sulfur aminoacid metabolism - version1

Mammalian polyamine metabolism consists of a bi-cycle with two required entrances, omithine and S-adenosyl methionine (SAM), and several alternative exists. The relevant regulatory roles of the short half-life enzymes ornithine decarboxylase (ODC), S-adenosyl methione decarboxylase (SAMDC) and spermindine/spermine acetyl transferase (SSAT) in polyamine metabolism are well studied, and has been modelled here.

This model is described in the article:

Reyes-Palomares A, Monta\u00f1ez R, S\u00e1nchez-Jim\u00e9nez F, Medina MA.
Amino Acids 2012 Feb; 42(2-3): 597-610

Abstract:

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

Notes by the author:

This model combines BIOMD0000000190 and BIOMD0000000268 from BioModels Database, both models include corrections respect to their originals publications.

This model is hosted on BioModels Database and identified by: BIOMD0000000674.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. 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This a model from the article:
Kinetic analysis of a molecular model of the budding yeast cell cycle.
Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. Mol Biol Cell 2000 Jan;11(1):369-91 10637314 ,
Abstract:
The molecular machinery of cell cycle control is known in more detail forbudding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism.In recent years, many elegant experiments on budding yeast have dissected theroles of cyclin molecules (Cln1-3 and Clb1-6) in coordinating the events of DNAsynthesis, bud emergence, spindle formation, nuclear division, and cellseparation. These experimental clues suggest a mechanism for the principalmolecular interactions controlling cyclin synthesis and degradation. Usingstandard techniques of biochemical kinetics, we convert the mechanism into a setof differential equations, which describe the time courses of three majorclasses of cyclin-dependent kinase activities. Model in hand, we examine themolecular events controlling \"Start\" (the commitment step to a new round ofchromosome replication, bud formation, and mitosis) and \"Finish\" (the transitionfrom metaphase to anaphase, when sister chromatids are pulled apart and the budseparates from the mother cell) in wild-type cells and 50 mutants. The modelaccounts for many details of the physiology, biochemistry, and genetics of cellcycle control in budding yeast.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Chen2006 - Nitric Oxide Release fromEndothelial Cells

This model is described in the article:

Chen K, Popel AS.
Free Radic. Biol. Med. 2006 Aug; 41(4): 668-680

Abstract:

Vascular endothelium expressing endothelial nitric oxide synthase (eNOS) produces nitric oxide (NO), which has a number of important physiological functions in the microvasculature. The rate of NO production by the endothelium is a critical determinant of NO distribution in the vascular wall. We have analyzed the biochemical pathways of NO synthesis and formulated a model to estimate NO production by the microvascular endothelium under physiological conditions. The model quantifies the NO produced by eNOS based on the kinetics of NO synthesis and the availability of eNOS and its intracellular substrates. The predicted NO production from microvessels was in the range of 0.005-0.1 microM/s. This range of predicted values is in agreement with some experimental values but is much lower than other rates previously measured or estimated from experimental data with the help of mathematical modeling. Paradoxical discrepancies between the model predictions and previously reported results based on experimental measurements of NO concentration in the vicinity of the arteriolar wall suggest that NO can also be released through eNOS-independent mechanisms, such as catalysis by neuronal NOS (nNOS). We also used our model to test the sensitivity of NO production to substrate availability, eNOS concentration, and potential rate-limiting factors. The results indicated that the predicted low level of NO production can be attributed primarily to a low expression of eNOS in the microvascular endothelial cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000676.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Nitric Oxide Release from Endothelial Cells", + "publication": { + "accession": "16864000", + "affiliation": "Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, 720 Rutland Avenue, 613 Traylor Building, Baltimore, MD 21205, USA. kchen21@jhu.edu", + "authors": [ + { + "institution": "Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, 720 Rutland Avenue, 613 Traylor Building, Baltimore, MD 21205, USA. kchen21@jhu.edu", + "name": "Kejing Chen" + }, + { + "name": "Aleksander S Popel", + "orcid": "0000-0002-6706-9235" + } + ], + "issue": "4", + "journal": "Free radical biology & medicine", + "link": "http://identifiers.org/pubmed/16864000", + "month": "8", + "pages": "668-680", + "synopsis": "Vascular endothelium expressing endothelial nitric oxide synthase (eNOS) produces nitric oxide (NO), which has a number of important physiological functions in the microvasculature. The rate of NO production by the endothelium is a critical determinant of NO distribution in the vascular wall. We have analyzed the biochemical pathways of NO synthesis and formulated a model to estimate NO production by the microvascular endothelium under physiological conditions. The model quantifies the NO produced by eNOS based on the kinetics of NO synthesis and the availability of eNOS and its intracellular substrates. The predicted NO production from microvessels was in the range of 0.005-0.1 microM/s. This range of predicted values is in agreement with some experimental values but is much lower than other rates previously measured or estimated from experimental data with the help of mathematical modeling. Paradoxical discrepancies between the model predictions and previously reported results based on experimental measurements of NO concentration in the vicinity of the arteriolar wall suggest that NO can also be released through eNOS-independent mechanisms, such as catalysis by neuronal NOS (nNOS). We also used our model to test the sensitivity of NO production to substrate availability, eNOS concentration, and potential rate-limiting factors. The results indicated that the predicted low level of NO production can be attributed primarily to a low expression of eNOS in the microvascular endothelial cells.", + "title": "Theoretical analysis of biochemical pathways of nitric oxide release from vascular endothelial cells.", + "type": "PubMed ID", + "volume": "41", + "year": 2006 + }, + "publicationId": "BIOMD0000000676", + "submissionId": "MODEL1006230005", + "vcsIdentifier": "aab" + }, + "BIOMD0000000677": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Holmes2006 - Hill's model of muscle contraction

This model is described in the article:

Holmes JW.
Adv Physiol Educ 2006 Jun; 30(2): 67-72

Abstract:

A. V. Hill's 1938 paper \"The heat of shortening and the dynamic constants of muscle\" is an enduring classic, presenting detailed methods, meticulous experiments, and the model of muscle contraction that now bears Hill's name. Pairing a simulation based on Hill's model with a reading of his paper allows students to follow his thought process to discover key principles of muscle physiology and gain insight into how to develop quantitative models of physiological processes. In this article, the experience of the author using this approach in a graduate biomedical engineering course is outlined, along with suggestions for adapting this approach to other audiences.

This model is hosted on BioModels Database and identified by: BIOMD0000000677.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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V. Hill's 1938 paper \"The heat of shortening and the dynamic constants of muscle\" is an enduring classic, presenting detailed methods, meticulous experiments, and the model of muscle contraction that now bears Hill's name. Pairing a simulation based on Hill's model with a reading of his paper allows students to follow his thought process to discover key principles of muscle physiology and gain insight into how to develop quantitative models of physiological processes. In this article, the experience of the author using this approach in a graduate biomedical engineering course is outlined, along with suggestions for adapting this approach to other audiences.", + "title": "Teaching from classic papers: Hill's model of muscle contraction.", + "type": "PubMed ID", + "volume": "30", + "year": 2006 + }, + "publicationId": "BIOMD0000000677", + "submissionId": "MODEL1006230007", + "vcsIdentifier": "aab" + }, + "BIOMD0000000678": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Tomida2003 - NFAT functions CalciumOscillation

This model is described in the article:

Tomida T, Hirose K, Takizawa A, Shibasaki F, Iino M.
EMBO J. 2003 Aug; 22(15): 3825-3832

Abstract:

Transcription by the nuclear factor of activated T cells (NFAT) is regulated by the frequency of Ca(2+) oscillation. However, why and how Ca(2+) oscillation regulates NFAT activity remain elusive. NFAT is dephosphorylated by Ca(2+)-dependent phosphatase calcineurin and translocates from the cytoplasm to the nucleus to initiate transcription. We analyzed the kinetics of dephosphorylation and translocation of NFAT. We show that Ca(2+)-dependent dephosphorylation proceeds rapidly, while the rephosphorylation and nuclear transport of NFAT proceed slowly. Therefore, after brief Ca(2+) stimulation, dephosphorylated NFAT has a lifetime of several minutes in the cytoplasm. Thus, Ca(2+) oscillation induces a build-up of dephosphorylated NFAT in the cytoplasm, allowing effective nuclear translocation, provided that the oscillation interval is shorter than the lifetime of dephosphorylated NFAT. We also show that Ca(2+) oscillation is more cost-effective in inducing the translocation of NFAT than continuous Ca(2+) signaling. Thus, the lifetime of dephosphorylated NFAT functions as a working memory of Ca(2+) signals and enables the control of NFAT nuclear translocation by the frequency of Ca(2+) oscillation at a reduced cost of Ca(2+) signaling.

This model is hosted on BioModels Database and identified by: BIOMD0000000678.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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However, why and how Ca(2+) oscillation regulates NFAT activity remain elusive. NFAT is dephosphorylated by Ca(2+)-dependent phosphatase calcineurin and translocates from the cytoplasm to the nucleus to initiate transcription. We analyzed the kinetics of dephosphorylation and translocation of NFAT. We show that Ca(2+)-dependent dephosphorylation proceeds rapidly, while the rephosphorylation and nuclear transport of NFAT proceed slowly. Therefore, after brief Ca(2+) stimulation, dephosphorylated NFAT has a lifetime of several minutes in the cytoplasm. Thus, Ca(2+) oscillation induces a build-up of dephosphorylated NFAT in the cytoplasm, allowing effective nuclear translocation, provided that the oscillation interval is shorter than the lifetime of dephosphorylated NFAT. We also show that Ca(2+) oscillation is more cost-effective in inducing the translocation of NFAT than continuous Ca(2+) signaling. 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This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Complex bursting in pancreatic islets: a potential glycolytic mechanism.
Wierschem K, Bertram R. J Theor Biol 2004 Jun 21;228(4):513-21 15178199 ,
Abstract:
The electrical activity of insulin-secreting pancreatic islets of Langerhans ischaracterized by bursts of action potentials. Most often this bursting isperiodic, but in some cases it is modulated by an underlying slower rhythm. Wesuggest that the modulatory rhythm for this complex bursting pattern is due tooscillations in glycolysis, while the bursting itself is generated by some otherslow process. To demonstrate this hypothesis, we couple a minimal model ofglycolytic oscillations to a minimal model for activity-dependent bursting inislets. We show that the combined model can reproduce several complex burstingpatterns from mouse islets published in the literature, and we illustrate howthese complex oscillations are produced through the use of a fast/slow analysis.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wierschem K, Bertram R. () - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Specific therapy regimes could lead to long-term immunological control of HIV.
Wodarz D, Nowak MA. Proc Natl Acad Sci U S A 1999 Dec 7;96(25):14464-9 10588728 ,
Abstract:
We use mathematical models to study the relationship between HIV and the immunesystem during the natural course of infection and in the context of differentantiviral treatment regimes. The models suggest that an efficient cytotoxic Tlymphocyte (CTL) memory response is required to control the virus. We define CTLmemory as long-term persistence of CTL precursors in the absence of antigen.Infection and depletion of CD4(+) T helper cells interfere with CTL memorygeneration, resulting in persistent viral replication and disease progression.We find that antiviral drug therapy during primary infection can enable thedevelopment of CTL memory. In chronically infected patients, specific treatmentschedules, either including deliberate drug holidays or antigenic boosts of theimmune system, can lead to a re-establishment of CTL memory. Whether suchtreatment regimes would lead to long-term immunologic control deservesinvestigation under carefully controlled conditions.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Nowak MA. (1999) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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course of infection and in the context of different antiviral treatment regimes. 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This a model from the article:
Evolution of immunological memory and the regulation of competition betweenpathogens.
Wodarz D. Curr Biol 2003 Sep 16;13(18):1648-52 13678598 ,
Abstract:
Memory is a central characteristic of immune responses. It is defined as anelevated number of specific immune cells that remain after resolution ofinfection and can protect the host against reinfection. The evolution ofimmunological memory is subject to debate. The advantages of memory discussed sofar include protection from reinfection, control of chronic infection, and thetransfer of immune function to the next generation. Mathematical models are usedto identify a new force that can drive the evolution of immunological memory:the duration of memory can regulate the degree of competition between differentpathogens. While a long duration of memory provides lasting protection againstreinfection, it may also allow an inferior pathogen species to persist. This canbe detrimental for the host if the inferior pathogen is more virulent. On theother hand, a shorter duration of memory ensures that an inferior pathogenspecies is excluded. This can be beneficial for the host if the inferiorpathogen is more virulent. Thus, while in the absence of pathogen diversitymemory is always expected to evolve to a long duration, under specificcircumstances, memory can evolve toward shorter durations in the presence ofpathogen diversity.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A dynamical perspective of CTL cross-priming and regulation: implications forcancer immunology.
Wodarz D, Jansen VA. Immunol Lett 2003 May 1;86(3):213-27 12706524 ,
Abstract:
Cytotoxic T lymphocytes (CTL) responses are required to fight many diseases suchas viral infections and tumors. At the same time, they can cause disease wheninduced inappropriately. Which factors regulate CTL and decide whether theyshould remain silent or react is open to debate. The phenomenon calledcross-priming has received attention in this respect. That is, CTL expansionoccurs if antigen is recognized on the surface of professional antigenpresenting cells (APCs). This is in contrast to direct presentation whereantigen is seen on the surface of the target cells (e.g. infected cells or tumorcells). Here we introduce a mathematical model, which takes the phenomenon ofcross-priming into account. We propose a new mechanism of regulation which isimplicit in the dynamics of the CTL: According to the model, the ability of aCTL response to become established depends on the ratio of cross-presentation todirect presentation of the antigen. If this ratio is relatively high, CTLresponses are likely to become established. If this ratio is relatively low,tolerance is the likely outcome. The behavior of the model includes a parameterregion where the outcome depends on the initial conditions. We discuss ourresults with respect to the idea of self/non-self discrimination and the dangersignal hypothesis. We apply the model to study the role of CTL in cancerinitiation, cancer evolution/progression, and therapeutic vaccination againstcancers.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Jansen VA. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Cytotoxic T lymphocyte cross-priming", + "publication": { + "accession": "12706524", + "affiliation": "Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP-665 Seattle, WA 98109, USA. wodarz@fhcrc.org", + "authors": [ + { + "institution": "Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, MP-665 Seattle, WA 98109, USA. wodarz@fhcrc.org", + "name": "Dominik Wodarz" + }, + { + "name": "Vincent A A Jansen", + "orcid": "0000-0002-6518-2090" + } + ], + "issue": "3", + "journal": "Immunology letters", + "link": "http://identifiers.org/pubmed/12706524", + "month": "5", + "pages": "213-227", + "synopsis": "Cytotoxic T lymphocytes (CTL) responses are required to fight many diseases such as viral infections and tumors. At the same time, they can cause disease when induced inappropriately. Which factors regulate CTL and decide whether they should remain silent or react is open to debate. The phenomenon called cross-priming has received attention in this respect. That is, CTL expansion occurs if antigen is recognized on the surface of professional antigen presenting cells (APCs). This is in contrast to direct presentation where antigen is seen on the surface of the target cells (e.g. infected cells or tumor cells). Here we introduce a mathematical model, which takes the phenomenon of cross-priming into account. We propose a new mechanism of regulation which is implicit in the dynamics of the CTL: According to the model, the ability of a CTL response to become established depends on the ratio of cross-presentation to direct presentation of the antigen. If this ratio is relatively high, CTL responses are likely to become established. If this ratio is relatively low, tolerance is the likely outcome. The behavior of the model includes a parameter region where the outcome depends on the initial conditions. We discuss our results with respect to the idea of self/non-self discrimination and the danger signal hypothesis. We apply the model to study the role of CTL in cancer initiation, cancer evolution/progression, and therapeutic vaccination against cancers.", + "title": "A dynamical perspective of CTL cross-priming and regulation: implications for cancer immunology.", + "type": "PubMed ID", + "volume": "86", + "year": 2003 + }, + "publicationId": "BIOMD0000000685", + "submissionId": "MODEL1006230094", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000686": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model further suggests that weaker NK cell mediated protection can correlate with more pronounced CTL inflation dynamics over time. We present experimental data from mice infected with MCMV which are consistent with the theoretical predictions. 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This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Henry2016 Folate pathway model with inducedPanB reaction

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: MODEL1602280002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Henry2016 Folate pathway model with inducedPanB reaction

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: MODEL1602280002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.", + "title": "Experimental and Metabolic Modeling Evidence for a Folate-Cleaving Side-Activity of Ketopantoate Hydroxymethyltransferase (PanB).", + "type": "PubMed ID", + "volume": "7", + "year": 2016 + }, + "publicationId": "BIOMD0000000690", + "submissionId": "MODEL1602280003", + "vcsIdentifier": "aab" + }, + "BIOMD0000000691": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Wolf2000 - Cellular interaction on glycolyticoscillations in yeast
A two-cell model of glycolysis.

This model is described in the article:

Wolf J, Heinrich R.
Biochem. J. 2000 Jan; 345 Pt 2: 321-334

Abstract:

On the basis of a detailed model of yeast glycolysis, the effect of intercellular dynamics is analysed theoretically. The model includes the main steps of anaerobic glycolysis, and the production of ethanol and glycerol. Transmembrane diffusion of acetaldehyde is included, since it has been hypothesized that this substance mediates the interaction. Depending on the kinetic parameter, the single-cell model shows both stationary and oscillatory behaviour. This agrees with experimental data with respect to metabolite concentrations and phase shifts. The inclusion of intercellular coupling leads to a variety of dynamical modes, such as synchronous oscillations, and different kinds of asynchronous behavior. These oscillations can co-exist, leading to bi- and tri-rhythmicity. The corresponding parameter regions have been identified by a bifurcation analysis. The oscillatory dynamics of synchronized cell populations are investigated by calculating the phase responses to acetaldehyde pulses. Simulations are performed with respect to the synchronization of two subpopulations that are oscillating out of phase before mixing. The effect of the various process on synchronization is characterized quantitatively. While continuous exchange of acetaldehyde might synchronize the oscillations for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization. for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization.

This model is hosted on BioModels Database and identified by: BIOMD0000000691.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Cellular interaction on glycolytic oscillations in yeast", + "publication": { + "accession": "10702114", + "affiliation": "Humboldt-Universit\u00e4t zu Berlin, Institut f\u00fcr Biologie, Theoretische Biophysik, Invalidenstrasse 42, D-10115 Berlin, Germany.", + "authors": [ + { + "institution": "Mathematical Modelling of Cellular Processes, Max Delbr\u00fcck Center for Molecular Medicine, Berlin, Germany.", + "name": "Jana Wolf", + "orcid": "0000-0003-3254-5868" + }, + { + "name": "R Heinrich" + } + ], + "journal": "The Biochemical journal", + "link": "http://identifiers.org/pubmed/10702114", + "month": "1", + "pages": "321-334", + "synopsis": "On the basis of a detailed model of yeast glycolysis, the effect of intercellular dynamics is analysed theoretically. The model includes the main steps of anaerobic glycolysis, and the production of ethanol and glycerol. Transmembrane diffusion of acetaldehyde is included, since it has been hypothesized that this substance mediates the interaction. Depending on the kinetic parameter, the single-cell model shows both stationary and oscillatory behaviour. This agrees with experimental data with respect to metabolite concentrations and phase shifts. The inclusion of intercellular coupling leads to a variety of dynamical modes, such as synchronous oscillations, and different kinds of asynchronous behavior. These oscillations can co-exist, leading to bi- and tri-rhythmicity. The corresponding parameter regions have been identified by a bifurcation analysis. The oscillatory dynamics of synchronized cell populations are investigated by calculating the phase responses to acetaldehyde pulses. Simulations are performed with respect to the synchronization of two subpopulations that are oscillating out of phase before mixing. The effect of the various process on synchronization is characterized quantitatively. While continuous exchange of acetaldehyde might synchronize the oscillations for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization. for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization.", + "title": "Effect of cellular interaction on glycolytic oscillations in yeast: a theoretical investigation.", + "type": "PubMed ID", + "volume": "345 Pt 2", + "year": 2000 + }, + "publicationId": "BIOMD0000000691", + "submissionId": "MODEL1006230022", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000692": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "DOQCS", + "email": "doqcs@ncbs.res.in", + "external": false, + "name": "Sharat Vayttaden" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Phillips2003 - The Mechanism of Ras GTPaseActivation by Neurofibromin
A mathematical model for Ras-GTPactivation by neurofibromin and the kinetic rates of the relevantreactions.

This model is described in the article:

Phillips RA, Hunter JL, Eccleston JF, Webb MR.
Biochemistry 2003 Apr; 42(13): 3956-3965

Abstract:

Individual rate constants have been determined for each step of the Ras.GTP hydrolysis mechanism, activated by neurofibromin. Fluorescence intensity and anisotropy stopped-flow measurements used the fluorescent GTP analogue, mantGTP (2'(3')-O-(N-methylanthraniloyl)GTP), to determine rate constants for binding and release of neurofibromin. Quenched flow measurements provided the kinetics of the hydrolytic cleavage step. The fluorescent phosphate sensor, MDCC-PBP was used to measure phosphate release kinetics. Phosphate-water oxygen exchange, using (18)O-substituted GTP and inorganic phosphate (P(i)), was used to determine the extent of reversal of the hydrolysis step and of P(i) binding. The data show that neurofibromin and P(i) dissociate from the NF1.Ras.GDP.P(i) complex with identical kinetics, which are 3-fold slower than the preceding cleavage step. A model is presented in which the P(i) release is associated with the change of Ras from \"GTP\" to \"GDP\" conformation. In this model, the conformation change on P(i) release causes the large change in affinity of neurofibromin, which then dissociates rapidly.

This model is hosted on BioModels Database and identified by: BIOMD0000000692.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Wang2008 - Mimicking the inhibitory effect ofriluzole on membrane conductance in skeletal fibres

This model is described in the article:

Wang YJ, Lin MW, Lin AA, Wu SN.
Life Sci. 2008 Jan; 82(1-2): 11-20

Abstract:

Riluzole is known to be of therapeutic use in the management of amyotrophic lateral sclerosis. In this study, we investigated the effects of riluzole on ion currents in cultured differentiated human skeletal muscle cells (dHSkMCs). Western blotting revealed the protein expression of alpha-subunits for both large-conductance Ca2+-activated K+ (BK(Ca)) channel and Na+ channel (Na(v)1.5) in these cells. Riluzole could reduce the frequency of spontaneous beating in dHSkMCs. In whole-cell configuration, riluzole suppressed voltage-gated Na+ current (I(Na)) in a concentration-dependent manner with an IC50 value of 2.3 microM. Riluzole (10 microM) also effectively increased Ca2+-activated K+ current (I(K(Ca))) which could be reversed by iberiotoxin (200 nM) and paxilline (1 microM), but not by apamin (200 nM). In inside-out patches, when applied to the inside of the cell membrane, riluzole (10 microM) increased BK(Ca)-channel activity with a decrease in mean closed time. Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole actions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials. Taken together, inhibition of I(Na) and stimulation of BK(Ca)-channel activity caused by this drug are partly, if not entirely, responsible for its muscle relaxant actions in clinical setting.

This model is hosted on BioModels Database and identified by: BIOMD0000000693.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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FelixGarza2017 - Blue Light Treatment ofPsoriasis (simplified)

This model is described in the article:

F\u00e9lix Garza ZC, Liebmann J, Born M, Hilbers PA, van Riel NA.
Front Physiol 2017; 8: 28

Abstract:

Clinical investigations prove that blue light irradiation reduces the severity of psoriasis vulgaris. Nevertheless, the mechanisms involved in the management of this condition remain poorly defined. Despite the encouraging results of the clinical studies, no clear guidelines are specified in the literature for the irradiation scheme regime of blue light-based therapy for psoriasis. We investigated the underlying mechanism of blue light irradiation of psoriatic skin, and tested the hypothesis that regulation of proliferation is a key process. We implemented a mechanistic model of cellular epidermal dynamics to analyze whether a temporary decrease of keratinocytes hyper-proliferation can explain the outcome of phototherapy with blue light. Our results suggest that the main effect of blue light on keratinocytes impacts the proliferative cells. They show that the decrease in the keratinocytes proliferative capacity is sufficient to induce a transient decrease in the severity of psoriasis. To study the impact of the therapeutic regime on the efficacy of psoriasis treatment, we performed simulations for different combinations of the treatment parameters, i.e., length of treatment, fluence (also referred to as dose), and intensity. These simulations indicate that high efficacy is achieved by regimes with long duration and high fluence levels, regardless of the chosen intensity. Our modeling approach constitutes a framework for testing diverse hypotheses on the underlying mechanism of blue light-based phototherapy, and for designing effective strategies for the treatment of psoriasis.

This model is hosted on BioModels Database and identified by: BIOMD0000000695.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Blue Light Treatment of Psoriasis (simplified)", + "publication": { + "accession": "28184200", + "affiliation": "Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands.", + "authors": [ + { + "institution": "Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands.", + "name": "Zandra C F\u00e9lix Garza", + "orcid": "0000-0001-7262-2165" + }, + { + "institution": "Philips Technologie GmbH, Innovative Technologies Aachen, Germany.", + "name": "Joerg Liebmann" + }, + { + "institution": "Philips Technologie GmbH, Innovative Technologies Aachen, Germany.", + "name": "Matthias Born" + }, + { + "institution": "Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands.", + "name": "Peter A J Hilbers" + }, + { + "institution": "Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.Amsterdam Diabetes Center, Department of Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.", + "name": "Natal A W van Riel", + "orcid": "0000-0001-9375-4730" + } + ], + "journal": "Frontiers in physiology", + "link": "http://identifiers.org/pubmed/28184200", + "month": "0", + "pages": "28", + "synopsis": "Clinical investigations prove that blue light irradiation reduces the severity of psoriasis vulgaris. Nevertheless, the mechanisms involved in the management of this condition remain poorly defined. Despite the encouraging results of the clinical studies, no clear guidelines are specified in the literature for the irradiation scheme regime of blue light-based therapy for psoriasis. We investigated the underlying mechanism of blue light irradiation of psoriatic skin, and tested the hypothesis that regulation of proliferation is a key process. We implemented a mechanistic model of cellular epidermal dynamics to analyze whether a temporary decrease of keratinocytes hyper-proliferation can explain the outcome of phototherapy with blue light. Our results suggest that the main effect of blue light on keratinocytes impacts the proliferative cells. They show that the decrease in the keratinocytes proliferative capacity is sufficient to induce a transient decrease in the severity of psoriasis. 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Boada2016 - Incoherent type 1 feed-forwardloop (I1-FFL)
A synthetic-biology mathematicalmodelling framework that was constructed to provide guidelines forexperimental implementation and parameter optimisation resulted ina biological device demonstrating desired behaviour.

This model is described in the article:

Boada Y, Reynoso-Meza G, Pic\u00f3 J, Vignoni A.
BMC Syst Biol 2016 Mar; 10: 27

Abstract:

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.

This model is hosted on BioModels Database and identified by: BIOMD0000000696.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Background

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.

Results

We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.

Conclusion

The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.", + "title": "Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case.", + "type": "PubMed ID", + "volume": "10", + "year": 2016 + }, + "publicationId": "BIOMD0000000696", + "submissionId": "MODEL1511290000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000697": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Ciliberto2003 - CyclinE / Cdk2 timer in thecell cycle of Xenopus laevis embryo

This model is described in the article:

Ciliberto A, Petrus MJ, Tyson JJ, Sible JC.
Biophys. Chem. 2003 Jul; 104(3): 573-589

Abstract:

Early cell cycles of Xenopus laevis embryos are characterized by rapid oscillations in the activity of two cyclin-dependent kinases. Cdk1 activity peaks at mitosis, driven by periodic degradation of cyclins A and B. In contrast, Cdk2 activity oscillates twice per cell cycle, despite a constant level of its partner, cyclin E. Cyclin E degrades at a fixed time after fertilization, normally corresponding to the midblastula transition. Based on published data and new experiments, we constructed a mathematical model in which: (1) oscillations in Cdk2 activity depend upon changes in phosphorylation, (2) Cdk2 participates in a negative feedback loop with the inhibitory kinase Wee1; (3) cyclin E is cooperatively removed from the oscillatory system; and (4) removed cyclin E is degraded by a pathway activated by cyclin E/Cdk2 itself. The model's predictions about embryos injected with Xic1, a stoichiometric inhibitor of cyclin E/Cdk2, were experimentally validated.

This model is hosted on BioModels Database and identified by: BIOMD0000000697.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Reed2004 - Methionine Cycle

This model is described in the article:

Reed MC, Nijhout HF, Sparks R, Ulrich CM.
J. Theor. Biol. 2004 Jan; 226(1): 33-43

Abstract:

Building on the work of Martinov et al. (2000), a mathematical model is developed for the methionine cycle. A large amount of information is available about the enzymes that catalyse individual reaction steps in the cycle, from methionine to S-adenosylmethionine to S-adenosylhomocysteine to homocysteine, and the removal of mass from the cycle by the conversion of homocysteine to cystathionine. Nevertheless, the behavior of the cycle is very complicated since many substrates alter the activities of the enzymes in the reactions that produce them, and some can also alter the activities of other enzymes in the cycle. The model consists of four differential equations, based on known reaction kinetics, that can be solved to give the time course of the concentrations of the four main substrates in the cycle under various circumstances. We show that the behavior of the model in response to genetic abnormalities and dietary deficiencies is similar to the changes seen in a wide variety of experimental studies. We conduct computational \"experiments\" that give understanding of the regulatory behavior of the methionine cycle under normal conditions and the behavior in the presence of genetic variation and dietary deficiencies.

This model is hosted on BioModels Database and identified by: BIOMD0000000698.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Caydasi2012 - Regulation of Tem1 by the GAPcomplex in spindle position cell cycle checkpoint - Ubiquitousassociation model

This model is described in the article:

Caydasi AK, Lohel M, Gr\u00fcnert G, Dittrich P, Pereira G, Ibrahim B.
Mol. Syst. Biol. 2012; 8: 582

Abstract:

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000699.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Heldt2018 - Proliferation-quiescence decisionin response to DNA damage

This model is described in the article:

Heldt FS, Barr AR, Cooper S, Bakal C, Nov\u00e1k B.
Proc. Natl. Acad. Sci. U.S.A. 2018 Feb; :

Abstract:

Human cells that suffer mild DNA damage can enter a reversible state of growth arrest known as quiescence. This decision to temporarily exit the cell cycle is essential to prevent the propagation of mutations, and most cancer cells harbor defects in the underlying control system. Here we present a mechanistic mathematical model to study the proliferation-quiescence decision in nontransformed human cells. We show that two bistable switches, the restriction point (RP) and the G1/S transition, mediate this decision by integrating DNA damage and mitogen signals. In particular, our data suggest that the cyclin-dependent kinase inhibitor p21 (Cip1/Waf1), which is expressed in response to DNA damage, promotes quiescence by blocking positive feedback loops that facilitate G1 progression downstream of serum stimulation. Intriguingly, cells exploit bistability in the RP to convert graded p21 and mitogen signals into an all-or-nothing cell-cycle response. The same mechanism creates a window of opportunity where G1 cells that have passed the RP can revert to quiescence if exposed to DNA damage. We present experimental evidence that cells gradually lose this ability to revert to quiescence as they progress through G1 and that the onset of rapid p21 degradation at the G1/S transition prevents this response altogether, insulating S phase from mild, endogenous DNA damage. Thus, two bistable switches conspire in the early cell cycle to provide both sensitivity and robustness to external stimuli.

This model is hosted on BioModels Database and identified by: MODEL1703030000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model is from the article:
A dynamical model of the spindle position checkpoint
Ayse Koca Caydasi, Maiko Lohel, Gerd Gr\u00fcnert, Peter Dittrich, Gislene Pereira, Bashar Ibrahim Molecular Systems Biology 2012; 582 doi: 10.1038/msb.2012.15
Abstract:
The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1\u2013Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1\u2013Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1\u2013Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally 32093, Kuwait.", + "name": "Bashar Ibrahim", + "orcid": "0000-0001-7773-0122" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/22580890", + "month": "5", + "pages": "582", + "synopsis": "The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.", + "title": "A dynamical model of the spindle position checkpoint.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000701", + "submissionId": "MODEL1202090001", + "vcsIdentifier": "aab" + }, + "BIOMD0000000702": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research center and Friedrich Schiller University Jena", + "email": "bashar.ibrahim@uni-jena.de", + "external": false, + "name": "Bashar Ibrahim" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Regulation of Tem1 by the GAP complex inSpindle Position Checkpoint - Ubiquitous inactive

This model is described in the article:

Caydasi AK, Lohel M, Gr\u00fcnert G, Dittrich P, Pereira G, Ibrahim B.
Mol. Syst. Biol. 2012; 8: 582

Abstract:

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000702.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Regulation of Tem1 by the GAP complex in Spindle Position Checkpoint - Ubiquitous inactive model", + "publication": { + "accession": "22580890", + "affiliation": "Molecular Biology of Centrosomes and Cilia, German Cancer Research Center, DKFZ-ZMBH Alliance, Heidelberg, Germany.", + "authors": [ + { + "institution": "Molecular Biology of Centrosomes and Cilia, German Cancer Research Center, DKFZ-ZMBH Alliance, Heidelberg, Germany.", + "name": "Ayse Koca Caydasi", + "orcid": "0000-0003-2570-1367" + }, + { + "name": "Maiko Lohel" + }, + { + "name": "Gerd Gr\u00fcnert" + }, + { + "name": "Peter Dittrich" + }, + { + "name": "Gislene Pereira" + }, + { + "institution": "Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.Centre for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, Hawally 32093, Kuwait.Department of Mathematics and Natural Sciences, Gulf University for Science and Technology, Hawally 32093, Kuwait.", + "name": "Bashar Ibrahim", + "orcid": "0000-0001-7773-0122" + } + ], + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/22580890", + "month": "5", + "pages": "582", + "synopsis": "The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. 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Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.", + "title": "A dynamical model of the spindle position checkpoint.", + "type": "PubMed ID", + "volume": "8", + "year": 2012 + }, + "publicationId": "BIOMD0000000702", + "submissionId": "MODEL1202090003", + "vcsIdentifier": "aab" + }, + "BIOMD0000000703": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Wheaton College", + "email": "danilo.diedrichs@wheaton.edu", + "external": false, + "name": "Danilo R. Diedrichs" + } + ] + }, + "curationStatus": "CURATED", + "description": "
A data-entrained computational model fortesting the regulatory logic of the vertebrate unfolded proteinresponse

This model is described in the article:

Diedrichs DR, Gomez JA, Huang CS, Rutkowski DT, Curtu R.
Mol. Biol. Cell 2018 Apr; : mbcE17090565

Abstract:

The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2? phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feed-forward circuit within the PERK/CHOP axis, and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to ER stress yet also to be rapidly deactivated by improved protein folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR.

This model is hosted on BioModels Database and identified by: MODEL1803300000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model should serve as a valuable resource for further exploring the regulatory logic of the UPR.", + "title": "A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response.", + "type": "PubMed ID", + "volume": "29", + "year": 2018 + }, + "publicationId": "BIOMD0000000703", + "submissionId": "MODEL1803300000", + "vcsIdentifier": "aag" + }, + "BIOMD0000000704": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "Baltazar D. Aguda. A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint system. Proceedings of the National Academy of Sciences 96, 20 (1999).

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"publication": { + "accession": "10500180", + "affiliation": "Department of Chemistry, Laurentian University, Sudbury, Ontario, Canada P3E 2C6. daguda@nickel.laurentian.ca", + "authors": [ + { + "institution": "Department of Chemistry, Laurentian University, Sudbury, Ontario, Canada P3E 2C6. daguda@nickel.laurentian.ca", + "name": "B D Aguda" + } + ], + "issue": "20", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/pubmed/10500180", + "month": "9", + "pages": "11352-11357", + "synopsis": "A detailed model of the G(2) DNA damage checkpoint (G2DDC) system is presented that includes complex regulatory networks of the mitotic kinase Cdc2, phosphatase Cdc25, Wee1 kinase, and damage signal transduction pathways involving Chk1 and p53. 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It also is shown that, under certain conditions, p53 is necessary to sustain a G(2) arrest.", + "title": "A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint system.", + "type": "PubMed ID", + "volume": "96", + "year": 1999 + }, + "publicationId": "BIOMD0000000704", + "submissionId": "MODEL6960055446", + "vcsIdentifier": "aab" + }, + "BIOMD0000000705": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "Graham R. Smith & Daryl P. Shanley. Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways. PLoS ONE 5, 6 (2010).

FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. 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The approach should be generally applicable to other proteins experiencing multiple regulations.", + "title": "Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways.", + "type": "PubMed ID", + "volume": "5", + "year": 2010 + }, + "publicationId": "BIOMD0000000705", + "submissionId": "MODEL1112260000", + "vcsIdentifier": "aab" + }, + "BIOMD0000000706": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL - European Bioinformatics Institute (EBI)", + "email": "biomodels-developers@ebi.ac.uk", + "external": false, + "name": "administrator" + }, + { + "affiliation": "EMBL-EBI", + "email": "ajmera@ebi.ac.uk", + "external": false, + "name": "Ishan Ajmera" + } + ] + }, + "curationStatus": "CURATED", + "description": "Graham R. Smith & Daryl P. Shanley. Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways. PLoS ONE 5, 6 (2010).

FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. The use of the ancillary program to generate the SBML is necessary because the possibility that many regulatory PTMs may be added, each independently of the others, means that a large number of chemically distinct forms of the FOXO molecule must be taken into account, and the program is used to generate them. Although the model does not yet include detailed representations of events upstream and downstream of FOXO, we show how it can qualitatively, and in some cases quantitatively, reproduce the known effects of certain treatments that induce various single and multiple PTMs, and allows for a complex spatiotemporal interplay of effects due to the activation of multiple PTM-inducing treatments. Thus, it provides an important framework to integrate current knowledge about the behaviour of FOXO. 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This a model from the article:
Fighting a virus with a virus: a dynamic model for HIV-1 therapy.
Revilla T, Garcia-Ramos G. Math Biosci 2003 Oct;185(2):191-203 12941536 ,
Abstract:
A mathematical model examined a potential therapy for controlling viralinfections using genetically modified viruses. The control of the infection isan indirect effect of the selective elimination by an engineered virus ofinfected cells that are the source of the pathogens. Therefore, this engineeredvirus could greatly compensate for a dysfunctional immune system compromised byAIDS. In vitro studies using engineered viruses have been shown to decrease theHIV-1 load about 1000-fold. However, the efficacy of this potential treatmentfor reducing the viral load in AIDS patients is unknown. The present modelstudied the interactions among the HIV-1 virus, its main host cell (activatedCD4+ T cells), and a therapeutic engineered virus in an in vivo context; and itexamined the conditions for controlling the pathogen. This model predicted asignificant drop in the HIV-1 load, but the treatment does not eradicate HIV. Abasic estimation using a currently engineered virus indicated an HIV-1 loadreduction of 92% and a recovery of host cells to 17% of their normal level.Greater success (98% HIV reduction, 44% host cells recovery) is expected as morecompetent engineered viruses are designed. These results suggest that therapyusing viruses could be an alternative to extend the survival of AIDS patients.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Revilla T, Garcia-Ramos G. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Postal 47058, Caracas 1041-A, Venezuela.", + "authors": [ + { + "institution": "Instituto de Zoolog\u00eda Tropical, Universidad Central de Venezuela, Apdo. Postal 47058, Caracas 1041-A, Venezuela.", + "name": "Tom\u00e1s Revilla", + "orcid": "0000-0001-6772-3412" + }, + { + "name": "Gisela Garc\u00eda-Ramos" + } + ], + "issue": "2", + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/12941536", + "month": "10", + "pages": "191-203", + "synopsis": "A mathematical model examined a potential therapy for controlling viral infections using genetically modified viruses. The control of the infection is an indirect effect of the selective elimination by an engineered virus of infected cells that are the source of the pathogens. Therefore, this engineered virus could greatly compensate for a dysfunctional immune system compromised by AIDS. In vitro studies using engineered viruses have been shown to decrease the HIV-1 load about 1000-fold. However, the efficacy of this potential treatment for reducing the viral load in AIDS patients is unknown. The present model studied the interactions among the HIV-1 virus, its main host cell (activated CD4+ T cells), and a therapeutic engineered virus in an in vivo context; and it examined the conditions for controlling the pathogen. This model predicted a significant drop in the HIV-1 load, but the treatment does not eradicate HIV. A basic estimation using a currently engineered virus indicated an HIV-1 load reduction of 92% and a recovery of host cells to 17% of their normal level. Greater success (98% HIV reduction, 44% host cells recovery) is expected as more competent engineered viruses are designed. These results suggest that therapy using viruses could be an alternative to extend the survival of AIDS patients.", + "title": "Fighting a virus with a virus: a dynamic model for HIV-1 therapy.", + "type": "PubMed ID", + "volume": "185", + "year": 2003 + }, + "publicationId": "BIOMD0000000707", + "submissionId": "MODEL1006230047", + "vcsIdentifier": "aab" + }, + "BIOMD0000000708": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + } + ] + }, + "curationStatus": "CURATED", + "description": "Sanhong Liu, Shigui Ruan & Xinan Zhang. Nonlinear dynamics of avian influenza epidemic models. Mathematical Biosciences 283 (2017).

Avian influenza is a zoonotic disease caused by the transmission of the avian influenza A virus, such as H5N1 and H7N9, from birds to humans. The avian influenza A H5N1 virus has caused more than 500 human infections worldwide with nearly a 60% death rate since it was first reported in Hong Kong in 1997. The four outbreaks of the avian influenza A H7N9 in China from March 2013 to June 2016 have resulted in 580 human cases including 202 deaths with a death rate of nearly 35%. In this paper, we construct two avian influenza bird-to-human transmission models with different growth laws of the avian population, one with logistic growth and the other with Allee effect, and analyze their dynamical behavior. We obtain a threshold value for the prevalence of avian influenza and investigate the local or global asymptotical stability of each equilibrium of these systems by using linear analysis technique or combining Liapunov function method and LaSalle's invariance principle, respectively. 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In this paper, we construct two avian influenza bird-to-human transmission models with different growth laws of the avian population, one with logistic growth and the other with Allee effect, and analyze their dynamical behavior. We obtain a threshold value for the prevalence of avian influenza and investigate the local or global asymptotical stability of each equilibrium of these systems by using linear analysis technique or combining Liapunov function method and LaSalle's invariance principle, respectively. Moreover, we give necessary and sufficient conditions for the occurrence of periodic solutions in the avian influenza system with Allee effect of the avian population. 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We present a simplified dynamical model of immune response to uncomplicated influenza A virus (IAV) infection, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells (cytotoxic T-cells and natural killer cells). Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov and Romanyukha [1994. Mathematical model of antiviral immune response. III. Influenza A virus infection. J. Theor. Biol. 167, 323-360], the model is constructed as a system of 10 ordinary differential equations with 27 parameters characterizing the rates of various processes contributing to the course of disease. The parameters are derived from published experimental data or estimated so as to reproduce available data about the time course of IAV infection in a na\u00efve host. 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The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load.", + "title": "A dynamical model of human immune response to influenza A virus infection.", + "type": "PubMed ID", + "volume": "246", + "year": 2007 + }, + "publicationId": "BIOMD0000000711", + "submissionId": "MODEL1808280004", + "vcsIdentifier": "aag" + }, + "BIOMD0000000712": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + } + ] + }, + "curationStatus": "CURATED", + "description": "Himanshu Manchanda, Nora Seidel, Andi Krumbholz, Andreas Sauerbrei, Michaela Schmidtke & Reinhard Guthke. Within-host influenza dynamics: a small-scale mathematical modeling approach. Biosystems 118 (2014).

The emergence of new influenza viruses like the pandemic H1N1 influenza A virus in 2009 (A(H1N1)pdm09) with unpredictable difficulties in vaccine coverage and established antiviral treatment protocols emphasizes the need of new murine models to prove the activity of novel antiviral compounds in vivo. The aim of the present study was to develop a small-scale mathematical model based on easily attainable experimental data to explain differences in influenza kinetics induced by different virus strains in mice. To develop a three-dimensional ordinary differential equation model of influenza dynamics, the following variables were included: (i) viral pathogenicity (P), (ii) antiviral immune defense (D), and (iii) inflammation due to pro-inflammatory response (I). Influenza virus-induced symptoms (clinical score S) in mice provided the basis for calculations of P and I. 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"name": "Nora Seidel" + }, + { + "institution": "Jena University Hospital, Department of Virology and Antiviral Therapy, Jena, Germany; Institute for Infection Medicine, Christian-Albrecht University of Kiel and University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany.", + "name": "Andi Krumbholz" + }, + { + "institution": "Jena University Hospital, Department of Virology and Antiviral Therapy, Jena, Germany.", + "name": "Andreas Sauerbrei" + }, + { + "institution": "Jena University Hospital, Department of Virology and Antiviral Therapy, Jena, Germany.", + "name": "Michaela Schmidtke" + }, + { + "institution": "Leibniz Institute for Natural Product Research and Infection Biology - Hans Kn\u00f6ll Institute, Jena, Germany. Electronic address: reinhard.guthke@hki-jena.de.", + "name": "Reinhard Guthke" + } + ], + "journal": "Bio Systems", + "link": "http://identifiers.org/pubmed/24614233", + "month": "4", + "pages": "51-59", + "synopsis": "The emergence of new influenza viruses like the pandemic H1N1 influenza A virus in 2009 (A(H1N1)pdm09) with unpredictable difficulties in vaccine coverage and established antiviral treatment protocols emphasizes the need of new murine models to prove the activity of novel antiviral compounds in vivo. The aim of the present study was to develop a small-scale mathematical model based on easily attainable experimental data to explain differences in influenza kinetics induced by different virus strains in mice. To develop a three-dimensional ordinary differential equation model of influenza dynamics, the following variables were included: (i) viral pathogenicity (P), (ii) antiviral immune defense (D), and (iii) inflammation due to pro-inflammatory response (I). Influenza virus-induced symptoms (clinical score S) in mice provided the basis for calculations of P and I. Both, mono- and biphasic course of mild to severe influenza induced by three clinical A(H1N1)pdm09 strains and one European swine H1N2 virus were comparatively and quantitatively studied by fitting the mathematical model to the experimental data. The model hypothesizes reasons for mild and severe influenza with mono- as well as biphasic course of disease. According to modeling results, the second peak of the biphasic course of infection is caused by inflammation. The parameters (i) maximum primary pathogenicity, (ii) viral infection rate, and (iii) rate of activation of the immune system represent most important parameters that quantitatively characterize the different pattern of virus-specific influenza kinetics.", + "title": "Within-host influenza dynamics: a small-scale mathematical modeling approach.", + "type": "PubMed ID", + "volume": "118", + "year": 2014 + }, + "publicationId": "BIOMD0000000712", + "submissionId": "MODEL1808280001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000713": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + } + ] + }, + "curationStatus": "CURATED", + "description": "Philip Aston. A New Model for the Dynamics of Hepatitis C Infection: Derivation, Analysis and Implications. Viruses 10, 4 (2018).

We review various existing models of hepatitis C virus (HCV) infection and show that there are inconsistencies between the models and known behaviour of the infection. A new model for HCV infection is proposed, based on various dynamical processes that occur during the infection that are described in the literature. This new model is analysed, and three steady state branches of solutions are found when there is no stem cell generation of hepatocytes. Unusually, the branch of infected solutions that connects the uninfected branch and the pure infection branch can be found analytically and always includes a limit point, subject to a few conditions on the parameters. When the action of stem cells is included, the bifurcation between the pure infection and infected branches unfolds, leaving a single branch of infected solutions. It is shown that this model can generate various viral load profiles that have been described in the literature, which is confirmed by fitting the model to four viral load datasets. 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This a model from the article:
A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells.
Shenghua Li, Paul Brazhnik, Bruno Sobral, John J. Tyson PLoS Comput Biol 2008 Jan 25:4(1): e9 18225942 ,
Abstract:
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genomereplication and partitioning to daughter cells. From published experimental evidence, we propose a molecularmechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on thesynthesis and degradation of three \u2018\u2018master regulator\u2019\u2019 proteins (CtrA, GcrA, and DnaA), is converted into a quantitativemodel, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts forimportant details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. Itreproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, andpredicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating thecell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may beapplicable to other species of importance in agriculture and medicine.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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During the early development of Xenopus laevis embryos, the first mitotic cell cycle is long (\u223c85 min) and the subsequent 11 cycles are short (\u223c30 min) and clock-like. Here we address the question of how the Cdk1 cell cycle oscillator changes between these two modes of operation. We found that the change can be attributed to an alteration in the balance between Wee1/Myt1 and Cdc25. The change in balance converts a circuit that acts like a positive-plus-negative feedback oscillator, with spikes of Cdk1 activation, to one that acts like a negative-feedback-only oscillator, with a shorter period and smoothly varying Cdk1 activity. Shortening the first cycle, by treating embryos with the Wee1A/Myt1 inhibitor PD0166285, resulted in a dramatic reduction in embryo viability, and restoring the length of the first cycle in inhibitor-treated embryos with low doses of cycloheximide partially rescued viability. Computations with an experimentally parameterized mathematical model show that modest changes in the Wee1/Cdc25 ratio can account for the observed qualitative changes in the cell cycle. The high ratio in the first cycle allows the period to be long and tunable, and decreasing the ratio in the subsequent cycles allows the oscillator to run at a maximal speed. Thus, the embryo rewires its feedback regulation to meet two different developmental requirements during early development.

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However, their regulatory roles have remained largely mysterious. E2F is a positive regulator of cell cycle progression and also a potent inducer of apoptosis. Positive feedback loops in the regulation of Rb-E2F pathway are predicted and shown experimentally. Recently, it has been discovered that E2F induce a cluster of miRNAs called miR449. In turn, E2F is inhibited by miR449 through regulating different transcripts, thus forming negative feedback loops in the interaction network. Here, based on the integration of experimental evidence and quantitative data, we studied Rb-E2F pathway coupling the positive feedback loops and negative feedback loops mediated by miR449. Therefore, a mathematical model is constructed based in part on the model proposed in Yao-Lee et al. (2008) and nonlinear dynamical behaviors including the stability and bifurcations of the model are discussed. 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A better knowledge of the dynamical behaviors of miRNAs mediated networks is also of interest for bio-engineering and artificial control.", + "title": "Dynamical behaviors of Rb-E2F pathway including negative feedback loops involving miR449.", + "type": "PubMed ID", + "volume": "7", + "year": 2012 + }, + "publicationId": "BIOMD0000000720", + "submissionId": "MODEL1811080001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000721": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + } + ] + }, + "curationStatus": "CURATED", + "description": "Jason M. Graham, Bruce P. Ayati, Sarah A. Holstein & James A. Martin. The role of osteocytes in targeted bone remodeling: a mathematical model. PLoS ONE 8, 5 (2013).

Until recently many studies of bone remodeling at the cellular level have focused on the behavior of mature osteoblasts and osteoclasts, and their respective precursor cells, with the role of osteocytes and bone lining cells left largely unexplored. This is particularly true with respect to the mathematical modeling of bone remodeling. However, there is increasing evidence that osteocytes play important roles in the cycle of targeted bone remodeling, in serving as a significant source of RANKL to support osteoclastogenesis, and in secreting the bone formation inhibitor sclerostin. Moreover, there is also increasing interest in sclerostin, an osteocyte-secreted bone formation inhibitor, and its role in regulating local response to changes in the bone microenvironment. Here we develop a cell population model of bone remodeling that includes the role of osteocytes, sclerostin, and allows for the possibility of RANKL expression by osteocyte cell populations. 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Through numerical simulations we demonstrate that our model can be used to explore theoretically many of the qualitative features of the role of osteocytes in bone biology as presented in recent literature.", + "title": "The role of osteocytes in targeted bone remodeling: a mathematical model.", + "type": "PubMed ID", + "volume": "8", + "year": 2013 + }, + "publicationId": "BIOMD0000000721", + "submissionId": "MODEL1811150001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000722": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + } + ] + }, + "curationStatus": "CURATED", + "description": "Arianna Bianchi, Kevin J. Painter & Jonathan A. Sherratt. A mathematical model for lymphangiogenesis in normal and diabetic wounds. Journal of Theoretical Biology 383 (2015).

Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis) very few have been proposed for the regeneration of the lymphatic network. Moreover, lymphangiogenesis is markedly distinct from angiogenesis, occurring at different times and in a different manner. Here a model of five ordinary differential equations is presented to describe the formation of lymphatic capillaries following a skin wound. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from experimental and clinical data. The system is then solved numerically and the results are compared with the available biological literature. Finally, a parameter sensitivity analysis of the model is taken as a starting point for suggesting new therapeutic approaches targeting the enhancement of lymphangiogenesis in diabetic wounds. The work provides a deeper understanding of the phenomenon in question, clarifying the main factors involved. In particular, the balance between TGF-\u03b2 and VEGF levels, rather than their absolute values, is identified as crucial to effective lymphangiogenesis. In addition, the results indicate lowering the macrophage-mediated activation of TGF-\u03b2 and increasing the basal lymphatic endothelial cell growth rate, inter alia, as potential treatments. 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"affiliation": "Department of Mathematics and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK. Electronic address: ab584@hw.ac.uk.", + "authors": [ + { + "institution": "Department of Mathematics and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK. Electronic address: ab584@hw.ac.uk.", + "name": "Arianna Bianchi" + }, + { + "institution": "Department of Mathematics and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK.", + "name": "Kevin J Painter", + "orcid": "0000-0003-3273-6031" + }, + { + "institution": "Department of Mathematics and Maxwell Institute for Mathematical Sciences, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK.", + "name": "Jonathan A Sherratt" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/26254217", + "month": "10", + "pages": "61-86", + "synopsis": "Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis) very few have been proposed for the regeneration of the lymphatic network. Moreover, lymphangiogenesis is markedly distinct from angiogenesis, occurring at different times and in a different manner. Here a model of five ordinary differential equations is presented to describe the formation of lymphatic capillaries following a skin wound. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from experimental and clinical data. The system is then solved numerically and the results are compared with the available biological literature. Finally, a parameter sensitivity analysis of the model is taken as a starting point for suggesting new therapeutic approaches targeting the enhancement of lymphangiogenesis in diabetic wounds. The work provides a deeper understanding of the phenomenon in question, clarifying the main factors involved. In particular, the balance between TGF-\u03b2 and VEGF levels, rather than their absolute values, is identified as crucial to effective lymphangiogenesis. In addition, the results indicate lowering the macrophage-mediated activation of TGF-\u03b2 and increasing the basal lymphatic endothelial cell growth rate, inter alia, as potential treatments. It is hoped the findings of this paper may be considered in the development of future experiments investigating novel lymphangiogenic therapies.", + "title": "A mathematical model for lymphangiogenesis in normal and diabetic wounds.", + "type": "PubMed ID", + "volume": "383", + "year": 2015 + }, + "publicationId": "BIOMD0000000722", + "submissionId": "MODEL1811210001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000723": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ashleythomasxavier@gmail.com", + "external": false, + "name": "Ashley Xavier", + "orcid": "0000-0001-5372-3024" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation.
Michael C. Weis, Jayant Avva, James W. Jacobberger, Sree N. Sreenath PLoS ONE 2014 May 13: 9(5): e97130 24824602 ,
Abstract:
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genomereplication and partitioning to daughter cells. From published experimental evidence, we propose a molecularmechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on thesynthesis and degradation of three \u2018\u2018master regulator\u2019\u2019 proteins (CtrA, GcrA, and DnaA), is converted into a quantitativemodel, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts forimportant details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. Itreproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, andpredicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating thecell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may beapplicable to other species of importance in agriculture and medicine.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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published cell cycle modeling efforts use significant levels of data for tuning and validation. This reflects the difficultly to generate correlated quantitative data, and it points out a critical uncertainty in modeling efforts. To develop a data-driven model of cell cycle regulation, we used contiguous, dynamic measurements over two time scales (minutes and hours) calculated from static multiparametric cytometry data. The approach provided expression profiles of cyclin A2, cyclin B1, and phospho-S10-histone H3. The model was built by integrating and modifying two previously published models such that the model outputs for cyclins A and B fit cyclin expression measurements and the activation of B cyclin/Cdk1 coincided with phosphorylation of histone H3. The model depends on Cdh1-regulated cyclin degradation during G1, regulation of B cyclin/Cdk1 activity by cyclin A/Cdk via Wee1, and transcriptional control of the mitotic cyclins that reflects some of the current literature. 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Its a Deterministic ODE model showcasing mechanism of PDL1 induced TCR and CD38 signalling inhibition. The model also contains the LCK activation and inactivation phenomenon dependent on the particular phosphorylation site. This model is relevant in immunotherapy. 
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Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation

This model is described in the article:

Enrique Salcedo-Sora J, Mc Auley MT.
Mol Biosyst. 2016 Jan 21.

Abstract:

The metabolic biochemistry of folate biosynthesis and utilisation has evolved into a complex network of reactions. Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole.

This model is hosted on BioModels Database and identified by: MODEL1511020000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole.", + "title": "A mathematical model of microbial folate biosynthesis and utilisation: implications for antifolate development.", + "type": "PubMed ID", + "volume": "12", + "year": 2016 + }, + "publicationId": "BIOMD0000000725", + "submissionId": "MODEL1511020000", + "vcsIdentifier": "aab" + }, + "BIOMD0000000726": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + } + ] + }, + "curationStatus": "CURATED", + "description": "Shigui Ruan. Modeling the transmission dynamics and control of rabies in China. Mathematical Biosciences 286 (2017).

Human rabies was first recorded in ancient China in about 556 BC and is still one of the major public-health problems in China. From 1950 to 2015, 130,494 human rabies cases were reported in Mainland China with an average of 1977 cases per year. It is estimated that 95% of these human rabies cases are due to dog bites. The purpose of this article is to provide a review about the models, results, and simulations that we have obtained recently on studying the transmission of rabies in China. We first construct a basic susceptible, exposed, infectious, and recovered (SEIR) type model for the spread of rabies virus among dogs and from dogs to humans and use the model to simulate the human rabies data in China from 1996 to 2010. Then we modify the basic model by including both domestic and stray dogs and apply the model to simulate the human rabies data from Guangdong Province, China. To study the seasonality of rabies, in Section\u00a04 we further propose a SEIR model with periodic transmission rates and employ the model to simulate the monthly data of human rabies cases reported by the Chinese Ministry of Health from January 2004 to December 2010. To understand the spatial spread of rabies, in Section\u00a05 we add diffusion to the dog population in the basic SEIR model to obtain a reaction-diffusion equation model and determine the minimum wave speed connecting the disease-free equilibrium to the endemic equilibrium. Finally, in order to investigate how the movement of dogs affects the geographically inter-provincial spread of rabies in Mainland China, in Section\u00a06 we propose a multi-patch model to describe the transmission dynamics of rabies between dogs and humans and use the two-patch submodel to investigate the rabies virus clades lineages and to simulate the human rabies data from Guizhou and Guangxi, Hebei and Fujian, and Sichuan and Shaanxi, respectively. 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Electronic address: ruan@math.miami.edu.", + "authors": [ + { + "institution": "Department of Mathematics, University of Miami, Coral Gables, FL 33146, USA. Electronic address: ruan@math.miami.edu.", + "name": "Shigui Ruan" + } + ], + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/28188732", + "month": "4", + "pages": "65-93", + "synopsis": "Human rabies was first recorded in ancient China in about 556 BC and is still one of the major public-health problems in China. From 1950 to 2015, 130,494 human rabies cases were reported in Mainland China with an average of 1977 cases per year. It is estimated that 95% of these human rabies cases are due to dog bites. The purpose of this article is to provide a review about the models, results, and simulations that we have obtained recently on studying the transmission of rabies in China. We first construct a basic susceptible, exposed, infectious, and recovered (SEIR) type model for the spread of rabies virus among dogs and from dogs to humans and use the model to simulate the human rabies data in China from 1996 to 2010. Then we modify the basic model by including both domestic and stray dogs and apply the model to simulate the human rabies data from Guangdong Province, China. To study the seasonality of rabies, in Section\u00a04 we further propose a SEIR model with periodic transmission rates and employ the model to simulate the monthly data of human rabies cases reported by the Chinese Ministry of Health from January 2004 to December 2010. To understand the spatial spread of rabies, in Section\u00a05 we add diffusion to the dog population in the basic SEIR model to obtain a reaction-diffusion equation model and determine the minimum wave speed connecting the disease-free equilibrium to the endemic equilibrium. Finally, in order to investigate how the movement of dogs affects the geographically inter-provincial spread of rabies in Mainland China, in Section\u00a06 we propose a multi-patch model to describe the transmission dynamics of rabies between dogs and humans and use the two-patch submodel to investigate the rabies virus clades lineages and to simulate the human rabies data from Guizhou and Guangxi, Hebei and Fujian, and Sichuan and Shaanxi, respectively. Some discussions are provided in Section\u00a07.", + "title": "Modeling the transmission dynamics and control of rabies in China.", + "type": "PubMed ID", + "volume": "286", + "year": 2017 + }, + "publicationId": "BIOMD0000000726", + "submissionId": "MODEL1808280012", + "vcsIdentifier": "aag" + }, + "BIOMD0000000727": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ashleythomasxavier@gmail.com", + "external": false, + "name": "Ashley Xavier", + "orcid": "0000-0001-5372-3024" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine
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A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the interaction of the maturation promotion factor (MPF) and cyclin. The model retrieves the dynamics observed in early embryos and explains how multiple cycles of MPF activity can be produced and how the internal clock that determines durations and number of cycles can be adjusted by modulating the rate of change in MPF or cyclin concentrations. Experiments are suggested for verifying the role of MPF activity in determining the length of the somatic cell cycle.
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We consider a minimal cascade model previously proposed for the mitotic oscillator driving the embryonic cell division cycle. The model is based on a bicyclic phosphorylation-dephosphorylation cascade involving cyclin and cdc2 kinase. By constructing stability diagrams showing domains of periodic behavior as a function of the maximum rates of the kinases and phosphatases involved in the two cycles of the cascade, we investigate the role of these converter enzymes in the oscillatory mechanism. Oscillations occur when the balance of kinase and phosphatase rates in each cycle is in a range bounded by two critical values. The results suggest ways to arrest the mitotic oscillator by altering the maximum rates of the converter enzymes. These results bear on the control of cell proliferation.

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The model is based on a bicyclic phosphorylation-dephosphorylation cascade involving cyclin and cdc2 kinase. By constructing stability diagrams showing domains of periodic behavior as a function of the maximum rates of the kinases and phosphatases involved in the two cycles of the cascade, we investigate the role of these converter enzymes in the oscillatory mechanism. Oscillations occur when the balance of kinase and phosphatase rates in each cycle is in a range bounded by two critical values. The results suggest ways to arrest the mitotic oscillator by altering the maximum rates of the converter enzymes. These results bear on the control of cell proliferation.", + "title": "Arresting the mitotic oscillator and the control of cell proliferation: insights from a cascade model for cdc2 kinase activation.", + "type": "PubMed ID", + "volume": "52", + "year": 1996 + }, + "publicationId": "BIOMD0000000729", + "submissionId": "MODEL1812120005", + "vcsIdentifier": "aag" + }, + "BIOMD0000000730": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ashleythomasxavier@gmail.com", + "external": false, + "name": "Ashley Xavier", + "orcid": "0000-0001-5372-3024" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

We propose an integrated computational model for the network of cyclin-dependent kinases (Cdks) that controls the dynamics of the mammalian cell cycle. The model contains four Cdk modules regulated by reversible phosphorylation, Cdk inhibitors, and protein synthesis or degradation. Growth factors (GFs) trigger the transition from a quiescent, stable steady state to self-sustained oscillations in the Cdk network. These oscillations correspond to the repetitive, transient activation of cyclin D/Cdk4-6 in G(1), cyclin E/Cdk2 at the G(1)/S transition, cyclin A/Cdk2 in S and at the S/G(2) transition, and cyclin B/Cdk1 at the G(2)/M transition. The model accounts for the following major properties of the mammalian cell cycle: (i) repetitive cell cycling in the presence of suprathreshold amounts of GF; (ii) control of cell-cycle progression by the balance between antagonistic effects of the tumor suppressor retinoblastoma protein (pRB) and the transcription factor E2F; and (iii) existence of a restriction point in G(1), beyond which completion of the cell cycle becomes independent of GF. The model also accounts for endoreplication. Incorporating the DNA replication checkpoint mediated by kinases ATR and Chk1 slows down the dynamics of the cell cycle without altering its oscillatory nature and leads to better separation of the S and M phases. The model for the mammalian cell cycle shows how the regulatory structure of the Cdk network results in its temporal self-organization, leading to the repetitive, sequential activation of the four Cdk modules that brings about the orderly progression along cell-cycle phases.

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The model for the mammalian cell cycle shows how the regulatory structure of the Cdk network results in its temporal self-organization, leading to the repetitive, sequential activation of the four Cdk modules that brings about the orderly progression along cell-cycle phases.", + "title": "Temporal self-organization of the cyclin/Cdk network driving the mammalian cell cycle.", + "type": "PubMed ID", + "volume": "106", + "year": 2009 + }, + "publicationId": "BIOMD0000000730", + "submissionId": "MODEL1812210001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000731": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Its a mathematical model presenting the interaction between a growing tumor and immune system. Model involves tumor cells, dendritic cell, helper Tcells, regulatory Tcells, effector cells and certain cytokines (e.g. TGFbeta, IL10, IL2 ) produced by these cells. It represent a dynamic regulation of tumor production/killing by different immune cells and cytokines.

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+ "month": "2", + "pages": "56-73", + "synopsis": "A mathematical model of the interactions between a growing tumor and the immune system is presented. The equations and parameters of the model are based on experimental and clinical results from published studies. The model includes the primary cell populations involved in effector T-cell mediated tumor killing: regulatory T cells, helper T cells, and dendritic cells. A key feature is the inclusion of multiple mechanisms of immunosuppression through the main cytokines and growth factors mediating the interactions between the cell populations. Decreased access of effector cells to the tumor interior with increasing tumor size is accounted for. The model is applied to tumors with different growth rates and antigenicities to gauge the relative importance of various immunosuppressive mechanisms. The most important factors leading to tumor escape are TGF-\u03b2-induced immunosuppression, conversion of helper T cells into regulatory T cells, and the limitation of immune cell access to the full tumor at large tumor sizes. The results suggest that for a given tumor growth rate, there is an optimal antigenicity maximizing the response of the immune system. Further increases in antigenicity result in increased immunosuppression, and therefore a decrease in tumor killing rate. This result may have implications for immunotherapies which modulate the effective antigenicity. Simulation of dendritic cell therapy with the model suggests that for some tumors, there is an optimal dose of transfused dendritic cells.", + "title": "A mathematical model of tumor-immune interactions.", + "type": "PubMed ID", + "volume": "294", + "year": 2012 + }, + "publicationId": "BIOMD0000000731", + "submissionId": "MODEL1901250001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000732": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "laibe@ebi.ac.uk", + "external": false, + "name": "Camille Laibe", + "orcid": "0000-0002-4625-743X" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Modeling immunotherapy of the tumor-immune interaction.
Kirschner D, Panetta JC. J Math Biol 1998 Sep;37(3):235-52 9785481 ,
Abstract:
A number of lines of evidence suggest that immunotherapy with the cytokineinterleukin-2 (IL-2) may boost the immune system to fight tumors. CD4+ T cells,the cells that orchestrate the immune response, use these cytokines as signalingmechanisms for immune-response stimulation as well as lymphocyte stimulation,growth, and differentiation. Because tumor cells begin as 'self', the immunesystem may not respond in an effective way to eradicate them. Adoptive cellularimmunotherapy can potentially restore or enhance these effects. We illustratethrough mathematical modeling the dynamics between tumor cells, immune-effectorcells, and IL-2. These efforts are able to explain both short tumor oscillationsin tumor sizes as well as long-term tumor relapse. We then explore the effectsof adoptive cellular immunotherapy on the model and describe under whatcircumstances the tumor can be eliminated.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Kirschner D, Panetta JC. (1998) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome.", + "title": "A mathematical model for chronic myelogenous leukemia (CML) and T cell interaction.", + "type": "PubMed ID", + "volume": "227", + "year": 2004 + }, + "publicationId": "BIOMD0000000733", + "submissionId": "MODEL1902200001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000734": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model attempts to fit the radioiron tracer data from Lopes et al. 2010 for mice fed iron deficient and rich diets by adjusting the rate of iron intake (vDiet) and the hepcidin synthesis rate (vhepcidin) independently for each experiment. All other parameters are those that provide the best fit for the adequate diet.

This model includes the radioiron tracer species.


Differences in parameter values between deficient, rich, and adequate diets:

Diet vDiet vhepcidin
Adequate 0.00377422 1.7393e-08
Deficient 0 8.54927e-09
Rich 0.00415624 2.30942e-08
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Rich and Deficient iron diets (tracer)", + "publication": { + "accession": "28521769", + "affiliation": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "authors": [ + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Jignesh H Parmar" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Grey Davis" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Hope Shevchuk" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.", + "name": "Pedro Mendes", + "orcid": "0000-0001-6507-9168" + } + ], + "issue": "1", + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/28521769", + "month": "5", + "pages": "57", + "synopsis": "

Background

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

Results

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

Conclusions

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.", + "title": "Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient.", + "type": "PubMed ID", + "volume": "11", + "year": 2017 + }, + "publicationId": "BIOMD0000000734", + "submissionId": "MODEL1903040001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000735": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "UConn Health", + "email": "pmendes@uchc.edu", + "external": false, + "name": "Pedro Mendes" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes normal iron and radioactive labelled tracer iron species and was used for parameter estimation given the data from Lopes et al. 2010 for mice fed an adequate iron diet.


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Background

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

Results

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

Conclusions

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.", + "title": "Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient.", + "type": "PubMed ID", + "volume": "11", + "year": 2017 + }, + "publicationId": "BIOMD0000000735", + "submissionId": "MODEL1605030002", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000736": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "UConn Health", + "email": "pmendes@uchc.edu", + "external": false, + "name": "Pedro Mendes" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed an adequate iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


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Background

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

Results

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

Conclusions

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.", + "title": "Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient.", + "type": "PubMed ID", + "volume": "11", + "year": 2017 + }, + "publicationId": "BIOMD0000000736", + "submissionId": "MODEL1605030003", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000737": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "UConn Health", + "email": "pmendes@uchc.edu", + "external": false, + "name": "Pedro Mendes" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Mouse Iron Distribution Dynamics

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This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


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UConn Health, Farmington, CT, 06030, USA.", + "authors": [ + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Jignesh H Parmar" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Grey Davis" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Hope Shevchuk" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.", + "name": "Pedro Mendes", + "orcid": "0000-0001-6507-9168" + } + ], + "issue": "1", + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/28521769", + "month": "5", + "pages": "57", + "synopsis": "

Background

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

Results

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

Conclusions

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.", + "title": "Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient.", + "type": "PubMed ID", + "volume": "11", + "year": 2017 + }, + "publicationId": "BIOMD0000000737", + "submissionId": "MODEL1605030004", + "vcsIdentifier": "aab" + }, + "BIOMD0000000738": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "UConn Health", + "email": "pmendes@uchc.edu", + "external": false, + "name": "Pedro Mendes" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed a rich iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


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"http://identifiers.org/pubmed/20704761" + }, + { + "accession": "GO:0055072", + "name": "iron ion homeostasis", + "qualifier": "bqbiol:is", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0055072" + }, + { + "accession": "MODEL1605030005", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1605030005" + }, + { + "accession": "BIOMD0000000738", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000738" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Mouse Iron Distribution - Rich iron diet (No Tracer)", + "publication": { + "accession": "28521769", + "affiliation": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "authors": [ + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Jignesh H Parmar" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Grey Davis" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, UConn Health, Farmington, CT, 06030, USA.", + "name": "Hope Shevchuk" + }, + { + "institution": "Center for Quantitative Medicine and Department of Cell Biology, University of Connecticut School of Medicine, Farmington, Connecticut, United States of America.", + "name": "Pedro Mendes", + "orcid": "0000-0001-6507-9168" + } + ], + "issue": "1", + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/28521769", + "month": "5", + "pages": "57", + "synopsis": "

Background

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

Results

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

Conclusions

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. 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Modelling blood coagulation factor Va inactivation by APC", + "publication": { + "accession": "22607732", + "affiliation": "Cell and Molecular Biology Program, University of Vermont, 89 Beaumont Ave, Burlington, VT 05405, USA.", + "authors": [ + { + "institution": "Cell and Molecular Biology Program, University of Vermont, 89 Beaumont Ave, Burlington, VT 05405, USA.", + "name": "Maria Cristina Bravo", + "orcid": "0000-0002-3270-7217" + }, + { + "name": "Thomas Orfeo" + }, + { + "name": "Kenneth G Mann" + }, + { + "name": "Stephen J Everse" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/22607732", + "month": "5", + "pages": "45", + "synopsis": "

Background

Because understanding of the inventory, connectivity and dynamics of the components characterizing the process of coagulation is relatively mature, it has become an attractive target for physiochemical modeling. Such models can potentially improve the design of therapeutics. The prothrombinase complex (composed of the protease factor (F)Xa and its cofactor FVa) plays a central role in this network as the main producer of thrombin, which catalyses both the activation of platelets and the conversion of fibrinogen to fibrin, the main substances of a clot. A key negative feedback loop that prevents clot propagation beyond the site of injury is the thrombin-dependent generation of activated protein C (APC), an enzyme that inactivates FVa, thus neutralizing the prothrombinase complex. APC inactivation of FVa is complex, involving the production of partially active intermediates and \"protection\" of FVa from APC by both FXa and prothrombin. An empirically validated mathematical model of this process would be useful in advancing the predictive capacity of comprehensive models of coagulation.

Results

A model of human APC inactivation of prothrombinase was constructed in a stepwise fashion by analyzing time courses of FVa inactivation in empirical reaction systems with increasing number of interacting components and generating corresponding model constructs of each reaction system. Reaction mechanisms, rate constants and equilibrium constants informing these model constructs were initially derived from various research groups reporting on APC inactivation of FVa in isolation, or in the presence of FXa or prothrombin. Model predictions were assessed against empirical data measuring the appearance and disappearance of multiple FVa degradation intermediates as well as prothrombinase activity changes, with plasma proteins derived from multiple preparations. Our work integrates previously published findings and through the cooperative analysis of in vitro experiments and mathematical constructs we are able to produce a final validated model that includes 24 chemical reactions and interactions with 14 unique rate constants which describe the flux in concentrations of 24 species.

Conclusion

This study highlights the complexity of the inactivation process and provides a module of equations describing the Protein C pathway that can be integrated into existing comprehensive mathematical models describing tissue factor initiated coagulation.", + "title": "Modeling of human factor Va inactivation by activated protein C.", + "type": "PubMed ID", + "volume": "6", + "year": 2012 + }, + "publicationId": "BIOMD0000000739", + "submissionId": "MODEL1807020001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000740": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mroberts@ebi.ac.uk", + "external": false, + "name": "Matthew Roberts" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "Full and reduced mathematical model of blood coagulation focusing on fibrin formation and the response to varied TF and V.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "13442", + "md5sum": "3cce50a9397774c62f67b2090855cfc7", + "mimeType": "application/rdf+xml", + "name": "Panteleev2010_full_model-biopax2.owl", + "sha1sum": "881c431381717af24dbaea9e74976cb67a94a542", + "sha256sum": "8686798ff05fb205cd509057cfc0122ca10b2d1e90664ef75250c31f4910d954" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "17828", + "md5sum": "261a9bd7f38ef122321aa2c1d6d5d378", + "mimeType": "application/rdf+xml", + "name": "Panteleev2010_full_model-biopax3.owl", + "sha1sum": "020e2eced51585eb07b4e6fac7c6d2a737806cc8", + "sha256sum": "275ffec705e17f7a6a01b4678e89aa20e28c7371faff105ffc2ad4e11c325308" + }, + { + "description": "Auto-generated Matlab file.", + "fileSize": "17222", + "md5sum": "6ffda54b5d3b701240219214e8557fd1", + "mimeType": "text/x-matlab", + "name": "Panteleev2010_full_model-matlab.m", + "sha1sum": "9846181d15738c0d0e6966c0e158ac0cee7196b1", + "sha256sum": "159d525efc4f0c339cebd633bb2be4f3d3cc390cc9a78cb25b858e60b0760526" + }, + { + "description": "Copasi 4.24 (build 197) file ready to reproduce figure 1A", + "fileSize": "143402", + "md5sum": "a18cdc8dcb6ee5e8ab7d98905198d485", + "mimeType": "application/xml", + "name": "Panteleev2010_full_model.cps", + "sha1sum": "c23a0eee80dadb0b4a76fe2f2aae34fa2ae87176", + "sha256sum": "e5017bfae6721849d1f390d2e91dfd235f75249180dded8eb170aacdb99bcdbd" + }, + { + "description": "Auto-generated ODE file for XPP.", + "fileSize": "12616", + "md5sum": "de861bd076dd4affe7cb815c24921267", + "mimeType": "text/plain", + "name": "Panteleev2010_full_model.ode", + "sha1sum": "9a4d51f4ee4b7057a60504300b61b5d80a30fe4d", + "sha256sum": "89bcfb348c00b5ac88eda4552a6be31778344b3cffd0bba79682ab772b8e27d9" + }, + { + "description": "SEDML file (additionally CRBM-validated and adjusted).", + "fileSize": "58819", + "md5sum": "602a6d272fa1e2aa04465b68f13f68d4", + "mimeType": "application/xml", + "name": "Panteleev2010_full_model.sedml", + "sha1sum": "cd1668f739a83862f1ec6bce1e11e703907d3bbb", + "sha256sum": "51dab0ce2e42153877d7f8fd4130fd1753faa3dc58717e45367ca6ff83f20d41" + }, + { + "description": "Reproduced figure(s) from original curation.", + "fileSize": "156344", + "md5sum": "95e655e6ebc9d6ac54124a0d2817b7cc", + "mimeType": "image/jpeg", + "name": "curation_image.jpeg", + "sha1sum": "081d53dd2fac1b664d69f277bacd50c6ed069712", + "sha256sum": "ae997fa6fd63d4ece3b11272fbc91e8e19f07ff3dc0fe24c5b0bde3efa40fe97" + }, + { + "description": "Notes from original curation (describes curation_image).", + "fileSize": "341", + "md5sum": "51c746461b6de7ca29dcf99487c4629a", + "mimeType": "text/plain", + "name": "curation_notes.txt", + "sha1sum": "565b7311ca6058238bdcb5677bdb2d4e3a80ad73", + "sha256sum": "8c6375e3166e68dd8dfffc4c4e5453ff8ad02ca5d9567759316b0dcecbe0ec92" + }, + { + "description": "Auto-generated manifest file for OMEX archives.", + "fileSize": "1397", + "md5sum": "9b6ded4bed47f97a4823fd5e368deecd", + "mimeType": "application/xml", + "name": "manifest.xml", + "sha1sum": "7899137b1425cfd66636a529910c219dd5dacd0e", + "sha256sum": "7725f91a684a73629abbf9631fe94d44569fd7a542bc3b4806715fbbe731da16" + }, + { + "description": "Auto-generated annotation metadata file.", + "fileSize": "1402", + "md5sum": "32dfb9c7404b2e107d13b2577892f6ee", + "mimeType": "application/rdf+xml", + "name": "metadata.rdf", + "sha1sum": "8ceeec7a6f9fd8d34782c8bad90797a5e325e56a", + "sha256sum": "fdfa284fdfebbd1ba577eaea7c91a9daa0aa729aabaa65a424355f3dda7a933c" + } + ], + "main": [ + { + "description": "SBML L2V4 representation of Panteleev2010 - 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We propose a middle-out approach for decomposition and analysis of complex time-dependent biological networks based on: 1), creation of a detailed mechanism-driven mathematical model of the network; 2), network response decomposition into several physiologically relevant subtasks; and 3), subsequent decomposition of the model, with the help of task-oriented necessity and sensitivity analysis into several modules that each control a single specific subtask, which is followed by further simplification employing temporal hierarchy reduction. The technique is tested and illustrated by studying blood coagulation. Five subtasks (threshold, triggering, control by blood flow velocity, spatial propagation, and localization), together with responsible modules, can be identified for the coagulation network. We show that the task of coagulation triggering is completely regulated by a two-step pathway containing a single positive feedback of factor V activation by thrombin. 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The paper describes a model on the detection of cancer based on cancer and immune biomarkers. Created by COPASI 4.25 (Build 207) This model is described in the article: Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach Raluca Eftimie and and Esraa Hassanein J Transl Med (2018) 16:73 Abstract: Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific. Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times This model is hosted on BioModels Database and identified by: MODEL1907050002. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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"accession": "10.1186/s12967-018-1432-8", + "affiliation": "Correspondence: Raluca Eftimie\r\nE-mail address: r.a.eftimie@dundee.ac.uk\r\nDivision of Mathematics, University of Dundee, Dundee DD1 4HN, UK", + "authors": [ + { + "name": "Raluca Eftimie" + }, + { + "name": "Esraa Hassanein" + } + ], + "issue": "16:73", + "journal": "Journal of Translational Medicine", + "link": "http://identifiers.org/doi/10.1186/s12967-018-1432-8", + "month": "3", + "synopsis": "Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.\r\nMethods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.\r\nResults: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times.\r\nConclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.\r\nKeywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times", + "title": "Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach", + "type": "DOI", + "year": 2018 + }, + "publicationId": "BIOMD0000000741", + "submissionId": "MODEL1907050002", + "vcsIdentifier": "aah" + }, + "BIOMD0000000742": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "
The paper describes a basic model of immune-tumor cell interactions. Created by COPASI 4.25 (Build 207) This model is described in the article: Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination Victor Garcia, Sebastian Bonhoeffer and Feng Fu bioRxiv, 2018 Abstract: Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can reproduce biologically plausible behavior. This behavior is consistent with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible, consistent with the notion of an immunological barrier. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into cancer-free state. In combination with the administration of immune effector cells, modifications in cancer cell killing may also be harnessed for immunotherapy without resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers. This model is hosted on BioModels Database and identified by: MODEL1907050005. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers.", + "title": "Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination", + "type": "DOI", + "year": 2018 + }, + "publicationId": "BIOMD0000000742", + "submissionId": "MODEL1907050005", + "vcsIdentifier": "aah" + }, + "BIOMD0000000743": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The paper describes a model on the key components for tumor\u2013immune dynamics in multiple myeloma.Created by COPASI 4.25 (Build 207)This model is described in the article:Methods for determining key components in a mathematical model for tumor\u2013immune dynamics in multiple myelomaJill Gallaher, Kamila Larripa, Marissa Renardy, Blerta Shtylla, Nessy Tania, Diana White, Karen Wood, Li Zhu, Chaitali Passey, Michael Robbins, Natalie Bezman, Suresh Shelat, Hearn Jay Choo, Helen MooreJournal of Theoretical Biology 458 (2018) 31\u201346Abstract:In this work, we analyze a mathematical model we introduced previously for the dynamics of multiple myeloma and the immune system. We focus on four main aspects: (1) obtaining and justifying ranges and values for all parameters in the model; (2) determining a subset of parameters to which the model is most sensitive; (3) determining which parameters in this subset can be uniquely estimated given cer- tain types of data; and (4) exploring the model numerically. Using global sensitivity analysis techniques, we found that the model is most sensitive to certain growth, loss, and efficacy parameters. This anal- ysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.This model is hosted on BioModels Database and identified by: MODEL1907050001To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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}, + { + "name": "Michael Robbins" + }, + { + "name": "Natalie Bezman" + }, + { + "name": "Suresh Shelat" + }, + { + "name": "Hearn Jay Cho" + }, + { + "name": "Helen Moore" + } + ], + "journal": "Journal of Theoretical Biology", + "link": "http://identifiers.org/doi/10.1016/j.jtbi.2018.08.037", + "month": "8", + "pages": "31-46", + "synopsis": "In this work, we analyze a mathematical model we introduced previously for the dynamics of multiple myeloma and the immune system. We focus on four main aspects: (1) obtaining and justifying ranges and values for all parameters in the model; (2) determining a subset of parameters to which the model is most sensitive; (3) determining which parameters in this subset can be uniquely estimated given cer- tain types of data; and (4) exploring the model numerically. Using global sensitivity analysis techniques, we found that the model is most sensitive to certain growth, loss, and efficacy parameters. This anal- ysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.", + "title": "Methods for determining key components in a mathematical model for tumor\u2013immune dynamics in multiple myeloma", + "type": "DOI", + "volume": "458", + "year": 2018 + }, + "publicationId": "BIOMD0000000743", + "submissionId": "MODEL1907050001", + "vcsIdentifier": "aah" + }, + "BIOMD0000000744": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "
The paper describes a model on the size of pancreatic tumour. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling Pancreatic Cancer Dynamics with Immunotherapy Xiaochuan Hu, Guoyi Ke and Sophia R.-J. Jang Bulletin of Mathematical Biology (2019) 81:1885\u20131915 Abstract: We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor- suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sen- sitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive trans- fers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunother- apies between adoptive ex vivo expanded immune cells and TGF-\u03b2 inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-\u03b2 and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-\u03b2 inhibition first followed by adoptive immune cell transfers can yield better survival outcomes. This model is hosted on BioModels Database and identified by: MODEL1907050003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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Jang\r\nE-mail address: sophia.jang@ttu.edu\r\n\r\nDepartment of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409-1042, USA", + "authors": [ + { + "name": "Xiaochuan Hu" + }, + { + "name": "Guoyi Ke" + }, + { + "name": "Sophia R.-J. Jang" + } + ], + "journal": "Bulletin of Mathematical Biology", + "link": "http://identifiers.org/doi/10.1007/s11538-019-00591-3", + "month": "3", + "pages": "1885-1915", + "synopsis": "We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor- suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sen- sitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive trans- fers of immune cells are applied. 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Applying TGF-\u03b2 inhibition first followed by adoptive immune cell transfers can yield better survival outcomes.", + "title": "Modeling Pancreatic Cancer Dynamics with Immunotherapy", + "type": "DOI", + "volume": "81", + "year": 2019 + }, + "publicationId": "BIOMD0000000744", + "submissionId": "MODEL1907050003", + "vcsIdentifier": "aah" + }, + "BIOMD0000000745": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "
The paper describes a model on the trastuzumab-induced immune response in murine(mouse) HER2+ breast cancer.Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer Angela M. Jarrett, Meghan J. Bloom, Wesley Godfrey, Anum K. Syed, David A. Ekrut, Lauren I. Ehrlich, Thomas E. Yankeelov, Anna G. Sorace Mathematical Medicine and Biology: A Journal of the IMA (2018) 00, 1\u201330 Abstract: The goal of this study is to develop an integrated, mathematical\u2013experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls. We posit that the proposed mathematical\u2013experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth\u2014conclusions revealed by the mathematical model that were not deducible from the experimental data alone. This model is hosted on BioModels Database and identified by: MODEL1907050004. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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Sorace\r\nE-mail address: anna.sorace@austin.utexas.edu\r\nDepartment of Biomedical Engineering; Department of Diagnostic Medicine, Department of Oncology, and Livestrong Cancer Institutes at The University of Texas at Austin, Austin, TX 78712, USA", + "authors": [ + { + "name": "Angela M. Jarrett" + }, + { + "name": "Meghan J. Bloom" + }, + { + "name": "Wesley Godfrey" + }, + { + "name": "Anum K. Syed" + }, + { + "name": "David A. Ekrut" + }, + { + "name": "Lauren I. Ehrlich" + }, + { + "name": "Thomas E. Yankeelov" + }, + { + "name": "Anna G. Sorace" + } + ], + "issue": "00", + "journal": "Mathematical Medicine and Biology: A Journal of the IMA", + "link": "http://identifiers.org/doi/10.1093/imammb/dqy014", + "month": "8", + "pages": "1-30", + "synopsis": "The goal of this study is to develop an integrated, mathematical\u2013experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls. We posit that the proposed mathematical\u2013experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth\u2014conclusions revealed by the mathematical model that were not deducible from the experimental data alone.", + "title": "Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer", + "type": "DOI", + "year": 2018 + }, + "publicationId": "BIOMD0000000745", + "submissionId": "MODEL1907050004", + "vcsIdentifier": "aah" + }, + "BIOMD0000000746": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model on the Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of Superficial Bladder Cancer. Created by COPASI 4.25 (Build 207) This model is described in the article: Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of Superficial Bladder CancerFarouk Tijjani Saad, Evren Hincal, and Bilgen KaymakamzadeComputational and Mathematical Methods in Medicine, vol. 2017, no. 3573082Abstract: This paper aims to study the dynamics of immune suppressors/checkpoints, immune system, and BCG in the treatment of superficial bladder cancer. Programmed cell death protein-1 (PD-1), cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), and transforming growth factor-beta (TGF-b) are some of the examples of immune suppressors/checkpoints. They are responsible for deactivating the immune system and enhancing immunological tolerance. Moreover, they categorically downregulate and suppress the immune system by preventing and blocking the activation of T-cells, which in turn decreases autoimmunity and enhances self- tolerance. In cancer immunotherapy, the immune checkpoints/suppressors prevent and block the immune cells from attacking, spreading, and killing the cancer cells, which leads to cancer growth and development. We formulate a mathematical model that studies three possible dynamics of the treatment and establish the effects of the immune checkpoints on the immune system and the treatment at large. Although the effect cannot be seen explicitly in the analysis of the model, we show it by numerical simulations.This model is hosted on BioModels Database and identified by: MODEL1907100001.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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The innate immunity may complicate virotherapy in the way of creating more equilibria when the viral burst size is not too big, while the dynamics is similar to the system without innate immunity when the viral burst size is big.", + "title": "The Role of the Innate Immune System in Oncolytic Virotherapy.", + "type": "PubMed ID", + "volume": "2017", + "year": 2017 + }, + "publicationId": "BIOMD0000000748", + "submissionId": "MODEL1907110001", + "vcsIdentifier": "aah" + }, + "BIOMD0000000749": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of tumor control via alternating immunostimulating and immunosuppressive phases. Created by COPASI 4.25 (Build 207) This model is described in the article: In silico tumor control induced via alternating immunostimulating and immunosuppressive phasesAI Reppas, JCL Alfonso, and H HatzikirouVirulence 7:2, 174--186Abstract: Despite recent advances in the field of Oncoimmunology, the success potential of immunomodulatory therapies against cancer remains to be elucidated. One of the reasons is the lack of understanding on the complex interplay between tumor growth dynamics and the associated immune system responses. Toward this goal, we consider a mathematical model of vascularized tumor growth and the corresponding effector cell recruitment dynamics. Bifurcation analysis allows for the exploration of model\u2019s dynamic behavior and the determination of these parameter regimes that result in immune-mediated tumor control. In this work, we focus on a particular tumor evasion regime that involves tumor and effector cell concentration oscillations of slowly increasing and decreasing amplitude, respectively. Considering a temporal multiscale analysis, we derive an analytically tractable mapping of model solutions onto a weakly negatively damped harmonic oscillator. Based on our analysis, we propose a theory-driven intervention strategy involving immunostimulating and immunosuppressive phases to induce long-term tumor control.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Based on our analysis, we propose a theory-driven intervention strategy involving immunostimulating and immunosuppressive phases to induce long-term tumor control.", + "title": "In silico tumor control induced via alternating immunostimulating and immunosuppressive phases.", + "type": "PubMed ID", + "volume": "7", + "year": 2016 + }, + "publicationId": "BIOMD0000000749", + "submissionId": "MODEL1907110002", + "vcsIdentifier": "aah" + }, + "BIOMD0000000750": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of tumour-induced neoneurogenesis and perineural tumour growth. Created by COPASI 4.25 (Build 207) This model is described in the article: Tumour-induced neoneurogenesis and perineural tumour growth: a mathematical approachGeorgios Lolas, Arianna Bianchi and Konstantinos N. SyrigosScientific Reports 6:20684Abstract:It is well-known that tumours induce the formation of a lymphatic and a blood vasculature around themselves. A similar but far less studied process occurs in relation to the nervous system and is referred to as neoneurogenesis. The relationship between tumour progression and the nervous system is still poorly understood and is likely to involve a multitude of factors. It is therefore relevant to study tumour-nerve interactions through mathematical modelling: this may reveal the most significant factors of the plethora of interacting elements regulating neoneurogenesis. The present work is a first attempt to model the neurobiological aspect of cancer development through a system of differential equations. 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A similar but far less studied process occurs in relation to the nervous system and is referred to as neoneurogenesis. The relationship between tumour progression and the nervous system is still poorly understood and is likely to involve a multitude of factors. It is therefore relevant to study tumour-nerve interactions through mathematical modelling: this may reveal the most significant factors of the plethora of interacting elements regulating neoneurogenesis. The present work is a first attempt to model the neurobiological aspect of cancer development through a system of differential equations. The model confirms the experimental observations that a tumour is able to promote nerve formation/elongation around itself, and that high levels of nerve growth factor and axon guidance molecules are recorded in the presence of a tumour. Our results also reflect the observation that high stress levels (represented by higher norepinephrine release by sympathetic nerves) contribute to tumour development and spread, indicating a mutually beneficial relationship between tumour cells and neurons. 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its a nine species reduced model of Hockin 2002. Model uses different level of reduction (5,7,9,11) and testing the best alignment with Hockin model results
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"modelLevelAnnotations": [ + { + "accession": "9606", + "name": "Homo sapiens", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/9606" + }, + { + "accession": "GO:0007596", + "name": "blood coagulation", + "qualifier": "bqbiol:hasProperty", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0007596" + }, + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + }, + { + "accession": "31161687", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/31161687" + }, + { + "accession": "MODEL1907180004", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1907180004" + }, + { + "accession": "BIOMD0000000755", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000755" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Hansen2019 - Nine species reduced model of blood coagulation", + "publication": { + "accession": "31161687", + "affiliation": "Department of Mechanical Engineering, University of California, Berkeley, Berkeley, California.", + "authors": [ + { + "institution": "Department of Mechanical Engineering, University of California, Berkeley, Berkeley, California.", + "name": "Kirk B Hansen", + "orcid": "0000-0001-7146-3983" + }, + { + "institution": "Department of Mechanical Engineering, University of California, Berkeley, Berkeley, California.", + "name": "Shawn C Shadden", + "orcid": "0000-0001-7561-1568" + } + ], + "issue": "10", + "journal": "International journal for numerical methods in biomedical engineering", + "link": "http://identifiers.org/pubmed/31161687", + "month": "10", + "pages": "e3220", + "synopsis": "Mathematical modeling of thrombosis typically involves modeling the coagulation cascade. Models of coagulation generally involve the reaction kinetics for dozens of proteins. The resulting system of equations is difficult to parameterize, and its numerical solution is challenging when coupled to blood flow or other physics important to clotting. Prior research suggests that essential aspects of coagulation may be reproduced by simpler models. This evidence motivates a systematic approach to model reduction. We herein introduce an automated framework to generate reduced-order models of blood coagulation. The framework consists of nested optimizations, where an outer optimization selects the optimal species for the reduced-order model and an inner optimization selects the optimal reaction rates for the new coagulation network. The framework was tested on an established 34-species coagulation model to rigorously consider what level of model fidelity is necessary to capture essential coagulation dynamics. The results indicate that a nine-species reduced-order model is sufficient to reproduce the thrombin dynamics of the benchmark 34-species model for a range of tissue factor concentrations, including those not included in the optimization process. Further model reduction begins to compromise the ability to capture the thrombin generation process. The framework proposed herein enables automated development of reduced-order models of coagulation that maintain essential dynamics used to model thrombosis.", + "title": "Automated reduction of blood coagulation models.", + "type": "PubMed ID", + "volume": "35", + "year": 2019 + }, + "publicationId": "BIOMD0000000755", + "submissionId": "MODEL1907180004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000756": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a full model of immune-itumor interaction.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "14780", + "md5sum": "81e1fd0e9592c4a443c3f82168d82279", + "mimeType": "application/rdf+xml", + "name": "Figueredo2013_3-biopax2.owl", + "sha1sum": "918519b4858ac0e227ef5d5dcc8a726a08eb34c6", + "sha256sum": "a51a1421d6e7633e7b86d77cd09896f8af499afcd839bd12a7c34d097d623f62" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "20276", + "md5sum": "fa462e8673dc75471c64bcff66b11caa", + "mimeType": "application/rdf+xml", + "name": "Figueredo2013_3-biopax3.owl", + "sha1sum": "1609589dd2b19aa367a1832ce6ecda2efa01b828", + "sha256sum": "6261d3ecf6ff308a83a77de6fa7859b81025cfbedf0a9931dbe9389800b12f7e" + }, + { + "description": "Auto-generated Matlab file.", + "fileSize": "6620", + "md5sum": "fa2a41ed9ed0139d8aa2450f805ec015", + "mimeType": "text/x-matlab", + "name": "Figueredo2013_3-matlab.m", 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"name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + }, + { + "accession": "GO:0002418", + "name": "immune response to tumor cell", + "qualifier": "bqbiol:isVersionOf", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0002418" + }, + { + "accession": "23734575", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/23734575" + }, + { + "accession": "MODEL1907220001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1907220001" + }, + { + "accession": "BIOMD0000000756", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000756" + } + ], + "modelTags": [ + "Immuno-oncology" + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Figueredo2013/3 - immunointeraction full model", + "publication": { + "accession": "23734575", + "affiliation": "Intelligent Modelling and Analysis Research Group, School of Computer Science, The University of Nottingham, UK. grazziela.figueredo@nottingham.ac.uk", + "authors": [ + { + "institution": "Intelligent Modelling and Analysis Research Group, School of Computer Science, The University of Nottingham, UK. grazziela.figueredo@nottingham.ac.uk", + "name": "Grazziela P Figueredo", + "orcid": "0000-0003-4094-7680" + }, + { + "name": "Peer-Olaf Siebers", + "orcid": "0000-0002-0603-5904" + }, + { + "name": "Uwe Aickelin", + "orcid": "0000-0002-2679-2275" + } + ], + "journal": "BMC bioinformatics", + "link": "http://identifiers.org/pubmed/23734575", + "month": "0", + "pages": "S6", + "synopsis": "Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.", + "title": "Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective.", + "type": "PubMed ID", + "volume": "14 Suppl 6", + "year": 2013 + }, + "publicationId": "BIOMD0000000756", + "submissionId": "MODEL1907220001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000757": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of glioblastoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling the Treatment of Glioblastoma Multiforme and Cancer Stem Cells with Ordinary Differential EquationsKristen Abernathy and Jeremy Burke BMC Computational and Mathematical Methods in Medicine Volume 2016, Article ID 1239861, 11 pagesAbstract: Despite improvements in cancer therapy and treatments, tumor recurrence is a common event in cancer patients. One explanation of recurrence is that cancer therapy focuses on treatment of tumor cells and does not eradicate cancer stem cells (CSCs). CSCs are postulated to behave similar to normal stem cells in that their role is to maintain homeostasis. That is, when the population of tumor cells is reduced or depleted by treatment, CSCs will repopulate the tumor, causing recurrence. In this paper, we study the application of the CSC Hypothesis to the treatment of glioblastoma multiforme by immunotherapy. We extend the work of Kogan et al. (2008) to incorporate the dynamics of CSCs, prove the existence of a recurrence state, and provide an analysis of possible cancerous states and their dependence on treatment levels.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Created by COPASI 4.25 (Build 207) This model is described in the article: Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model Charles F BabbsAm J Cancer Res 2012;2(2):204-213Abstract: The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, \u03bb; and immune cell half-life decay, \u03bc. The predator-prey equations as functions of time, t, for nor- malized tumor cell numbers, y, (the prey) and immunocyte numbers, x, (the predators) are: dy/dt = gy \u2013 kx and dx/dt = \u03bbxy \u2013 \u03bcx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell num- bers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parame- ters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. 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IL-21 is now developed as a cancer immunotherapeutic drug, but conditions for efficacious therapy, and the conflicting immunostimulatory and immunoinhibitory influence of the cytokine, are yet to be defined. We studied the effects of IL-21 on tumor eradication in a mathematical model focusing on natural killer (NK) cell-mediated and CD8+ T-cell-mediated lysis of tumor cells. Model parameters were estimated using results in tumor-bearing mice treated with IL-21 via cytokine gene therapy (CGT), hydrodynamics-based gene delivery (HGD), or standard interval dosing (SID). Our model accurately retrieved experimental growth dynamics in the nonimmunogenic B16 melanoma and the immunogenic MethA and MCA205 fibrosarcomas, showing a strong dependence of the NK-cell/CD8+ T-cell balance on tumor immunogenicity. Moreover, in melanoma, simulations of CGT-like dosing regimens, dynamically determined according to tumor mass changes, resulted in efficient disease elimination. In contrast, in fibrosarcoma, such a strategy was not superior to that of fixed dosing regimens, HGD or SID. Our model supports clinical use of IL-21 as a potent stimulator of cellular immunity against cancer, and suggests selecting the immunotherapy strategy according to tumor immunogenicity. Nonimmunogenic tumors, but not highly immunogenic tumors, should be controlled by IL-21 dosing, which depends on tumor mass at the time of administration. This method imitates, yet amplifies, the natural anticancer immune response rather than accelerates only one of the response arms in an unbalanced manner.", + "title": "Cancer immunotherapy by interleukin-21: potential treatment strategies evaluated in a mathematical model.", + "type": "PubMed ID", + "volume": "66", + "year": 2006 + }, + "publicationId": "BIOMD0000000761", + "submissionId": "MODEL1907230001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000762": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This mathematical model describes the response of cytotoxic T lymphocytes to the growth of an immunogenic tumor, with the inclusion on a number of in vivo phenomena, such as the immunostimulation of tumor growth, evasion of immune function by the tumor, and the formation of a tumor \"dormant state\". Specifically, this model is used to describe the kinetics of growth and regression of the B-cell lymphoma BCL1 in the spleen of mice.
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The model exhibits a number of phenomena that are seen in vivo, including immunostimulation of tumor growth, \"sneaking through\" of the tumor, and formation of a tumor \"dormant state\". The model is used to describe the kinetics of growth and regression of the B-lymphoma BCL1 in the spleen of mice. By comparing the model with experimental data, numerical estimates of parameters describing processes that cannot be measured in vivo are derived. Local and global bifurcations are calculated for realistic values of the parameters. 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This model examines the role of helper and cytotoxic T cells in an anti-tumour response, with implicit inclusions of immunosuppressive effects. The model demonstrates the dependence of immunoediting on infilftration by helper and cytotoxic T cells, as well as the importance of these cells in mediating tumour elimination.
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Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical Analysis of a Mathematical Model of Chemovirotherapy: Effect of Drug Infusion Method Joseph Malinzi Computational and Mathematical Methods in Medicine Volume 2019, Article ID 7576591, 16 pages Abstract: IA mathematical model for the treatment of cancer using chemovirotherapy is developed with the aim of determining the efficacy of three drug infusion methods: constant, single bolus, and periodic treatments. The model is in the form of ODEs and is further extended into DDEs to account for delays as a result of the infection of tumor cells by the virus and chemotherapeutic drug responses. Analysis of the model is carried out for each of the three drug infusion methods. Analytic solutions are determined where possible and stability analysis of both steady state solutions for the ODEs and DDEs is presented. The results indicate that constant and periodic drug infusion methods are more efficient compared to a single bolus injection. Numerical simulations show that with a large virus burst size, irrespective of the drug infusion method, chemovirotherapy is highly effective compared to either treatments. The simulations further show that both delays increase the period within which a tumor can be cleared from body tissue.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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This model was developed with the aim of constructing an equilibrium model of the pharmacokinetic behaviour of a drug exhibiting target-mediated drug disposition (TMDD). TMDD involves the inclusion of drug-target interactions within a pharmacokinetic description, something which is usually considered negligible and subsequently excluded. Two approaches were used, one of which involved a quasi-equilibrium method to describe the kinetics of TMDD.
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Purpose

The aim of this study is to derive and evaluate an equilibrium model of a previously developed general pharmacokinetic model for drugs exhibiting target-mediated drug disposition (TMDD).

Methods

A quasi-equilibrium solution to the system of ordinary differential equations that describe the kinetics of TMDD was obtained. Computer simulations of the equilibrium model were carried out to generate plasma concentration-time profiles resulting from a large range of intravenous bolus doses. Additionally, the final model was fitted to previously published pharmacokinetic profiles of leukemia inhibitory factor (LIF), a cytokine that seems to exhibit TMDD, following intravenous administration of 12.5, 25, 100, 250, 500, or 750 microg/kg in sheep.

Results

Simulations show that pharmacokinetic profiles display steeper distribution phases for lower doses and similar terminal disposition phases, but with slight underestimation at early time points as theoretically expected. The final model well-described LIF pharmacokinetics, and the final parameters, which were estimated with relatively good precision, were in good agreement with literature values.

Conclusions

An equilibrium model of TMDD is developed that recapitulates the essential features of the full general model and eliminates the need for estimating drug-binding microconstants that are often difficult or impossible to identify from typical in vivo pharmacokinetic data.", + "title": "Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition.", + "type": "PubMed ID", + "volume": "22", + "year": 2005 + }, + "publicationId": "BIOMD0000000765", + "submissionId": "MODEL1907290001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000766": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a full model of oncolytic virotherapy. 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Created by COPASI 4.25 (Build 207) This model is described in the article: Memory versus effector immune responses in oncolytic virotherapiesCicely Macnamara, Raluca Eftimie Abstract: The main priority when designing cancer immuno-therapies has been to seek viable biological mechanisms that lead to permanent cancer eradica- tion or cancer control. Understanding the delicate balance between the role of effector and memory cells on eliminating cancer cells remains an elusive problem in immunology. Here we make an initial investigation into this problem with the help of a mathematical model for oncolytic virotherapy; although the model can in fact be made general enough to be applied also to other immunological problems. Our results show that long-term cancer con- trol is associated with a large number of persistent effector cells (irrespective of the initial peak in effector cell numbers). 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+ "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Macnamara2015/2 - virotherapy virus-free submodel", + "publication": { + "accession": "25882747", + "affiliation": "Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom. Electronic address: c.k.macnamara@dundee.ac.uk.", + "authors": [ + { + "institution": "Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom. Electronic address: c.k.macnamara@dundee.ac.uk.", + "name": "Cicely Macnamara", + "orcid": "0000-0003-4961-6052" + }, + { + "institution": "Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK. r.a.eftimie@dundee.ac.uk.", + "name": "Eftimie R", + "orcid": "0000-0002-9726-1498" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/25882747", + "month": "7", + "pages": "1-9", + "synopsis": "The main priority when designing cancer immuno-therapies has been to seek viable biological mechanisms that lead to permanent cancer eradication or cancer control. Understanding the delicate balance between the role of effector and memory cells on eliminating cancer cells remains an elusive problem in immunology. Here we make an initial investigation into this problem with the help of a mathematical model for oncolytic virotherapy; although the model can in fact be made general enough to be applied also to other immunological problems. According to this model, we find that long-term cancer control is associated with a large number of persistent effector cells (irrespective of the initial peak in effector cell numbers). However, this large number of persistent effector cells is sustained by a relatively large number of memory cells. Moreover, the results of the mathematical model suggest that cancer control from a dormant state cannot be predicted by the size of the memory population.", + "title": "Memory versus effector immune responses in oncolytic virotherapies.", + "type": "PubMed ID", + "volume": "377", + "year": 2015 + }, + "publicationId": "BIOMD0000000767", + "submissionId": "MODEL1907290003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000768": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of immunity to melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling anti-tumor Th1 and Th2 immunity in the rejection of melanoma Raluca Eftimie, Jonathan L. Bramson, David J.D. Earn Journal of Theoretical Biology 265 (2010) 467\u2013480Abstract: Recent experiments indicate that CD4+ Th2 cells can reject skin tumors in mice, while CD4+ Th1 cells cannot (Mattes et al., 2003; Zhang et al., 2009). These results are surprising because CD4+ Th1 cells are typically considered to be capable of tumor rejection. We used mathematical models to investigate this unexpected outcome. We found that neither CD4+ Th1 nor CD4+ Th2 cells could eliminate the cancer cells when acting alone, but that tumor elimination could be induced by recruitment of eosinophils by the Th2 cells. These recruited eosinophils had unexpected indirect effects on the decay rate of type 2 cytokines and the rate at which Th2 cells are inactivated through interactions with cancer cells. Strikingly, the presence of eosinophils impacted tumor growth more significantly than the release of tumor-suppressing cytokines such as IFN-g and TNF-a. Our simulations suggest that novel strategies to enhance eosinophil recruitment into skin tumors may improve cancer immunotherapies. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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"Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + }, + { + "accession": "GO:0002418", + "name": "immune response to tumor cell", + "qualifier": "bqbiol:hasProperty", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0002418" + }, + { + "accession": "20450922", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/20450922" + }, + { + "accession": "BIOMD0000000768", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000000768" + }, + { + "accession": "MODEL1907300001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL1907300001" + }, + { + "accession": "0000756", + "name": "melanoma", + "qualifier": "bqbiol:hasProperty", + "resource": "Experimental Factor Ontology", + "uri": "http://identifiers.org/efo/0000756" + } + ], + "modelTags": [ + "Immuno-oncology" + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Eftimie2010 - immunity to melanoma", + "publication": { + "accession": "20450922", + "affiliation": "Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada L8S 4K1. reftimie@math.mcmaster.ca", + "authors": [ + { + "institution": "Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK. r.a.eftimie@dundee.ac.uk.", + "name": "Eftimie R", + "orcid": "0000-0002-9726-1498" + }, + { + "name": "Jonathan L Bramson", + "orcid": "0000-0003-2874-6886" + }, + { + "name": "David J D Earn", + "orcid": "0000-0002-7562-1341" + } + ], + "issue": "3", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/20450922", + "month": "8", + "pages": "467-480", + "synopsis": "Recent experiments indicate that CD4(+) Th2 cells can reject skin tumors in mice, while CD4(+) Th1 cells cannot (Mattes et al., 2003; Zhang et al., 2009). These results are surprising because CD4(+) Th1 cells are typically considered to be capable of tumor rejection. We used mathematical models to investigate this unexpected outcome. We found that neither CD4(+) Th1 nor CD4(+) Th2 cells could eliminate the cancer cells when acting alone, but that tumor elimination could be induced by recruitment of eosinophils by the Th2 cells. These recruited eosinophils had unexpected indirect effects on the decay rate of type 2 cytokines and the rate at which Th2 cells are inactivated through interactions with cancer cells. Strikingly, the presence of eosinophils impacted tumor growth more significantly than the release of tumor-suppressing cytokines such as IFN-gamma and TNF-alpha. Our simulations suggest that novel strategies to enhance eosinophil recruitment into skin tumors may improve cancer immunotherapies.", + "title": "Modeling anti-tumor Th1 and Th2 immunity in the rejection of melanoma.", + "type": "PubMed ID", + "volume": "265", + "year": 2010 + }, + "publicationId": "BIOMD0000000768", + "submissionId": "MODEL1907300001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000769": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of interaction of Th cells and macrophage in melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling and investigation of the CD4 T cells \u2013 Macrophages paradox in melanoma immunotherapiesRaluca Eftimie, Haneen HamamJournal of Theoretical Biology 420 (2017) 82\u2013104 Abstract: It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10\u201315 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. In some studies, elimination of B16 melanoma was associated with a Th1 immune response (i.e., elimination occurred in the presence of cytokines produced by Th1 cells), while in other studies melanoma elimination was associated with a Th2 immune response (i.e., elimination occurred in the presence of cytokines produced by Th2 cells). Moreover, macrophages have been shown to be present inside the tumours, during both Th1 and Th2 immune responses. To investigate the possible biological mechanisms behind these apparently contradictory results, we develop a class of mathematical models for the dynamics of Th1 and Th2 cells, and M1 and M2 macrophages in the presence/absence of tumour cells. Using this mathematical model, we show that depending on the re- polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type- II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Electronic address: r.a.eftimie@dundee.ac.uk.", + "authors": [ + { + "institution": "Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK. r.a.eftimie@dundee.ac.uk.", + "name": "Eftimie R", + "orcid": "0000-0002-9726-1498" + }, + { + "institution": "Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom.", + "name": "Haneen Hamam", + "orcid": "0000-0002-7083-0654" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/28219660", + "month": "5", + "pages": "82-104", + "synopsis": "It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10-15 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. 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Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.", + "title": "Modelling and investigation of the CD4+ T cells - Macrophages paradox in melanoma immunotherapies.", + "type": "PubMed ID", + "volume": "420", + "year": 2017 + }, + "publicationId": "BIOMD0000000769", + "submissionId": "MODEL1907310003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000770": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of interaction of Th cells and macrophage in melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling and investigation of the CD4 T cells \u2013 Macrophages paradox in melanoma immunotherapiesRaluca Eftimie, Haneen HamamJournal of Theoretical Biology 420 (2017) 82\u2013104 Abstract: It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10\u201315 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. In some studies, elimination of B16 melanoma was associated with a Th1 immune response (i.e., elimination occurred in the presence of cytokines produced by Th1 cells), while in other studies melanoma elimination was associated with a Th2 immune response (i.e., elimination occurred in the presence of cytokines produced by Th2 cells). Moreover, macrophages have been shown to be present inside the tumours, during both Th1 and Th2 immune responses. To investigate the possible biological mechanisms behind these apparently contradictory results, we develop a class of mathematical models for the dynamics of Th1 and Th2 cells, and M1 and M2 macrophages in the presence/absence of tumour cells. Using this mathematical model, we show that depending on the re- polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type- II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Electronic address: r.a.eftimie@dundee.ac.uk.", + "authors": [ + { + "institution": "Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK. r.a.eftimie@dundee.ac.uk.", + "name": "Eftimie R", + "orcid": "0000-0002-9726-1498" + }, + { + "institution": "Division of Mathematics, University of Dundee, Dundee DD1 4HN, United Kingdom.", + "name": "Haneen Hamam", + "orcid": "0000-0002-7083-0654" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/28219660", + "month": "5", + "pages": "82-104", + "synopsis": "It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10-15 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. 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Using this mathematical model, we show that depending on the re-polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type-II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.", + "title": "Modelling and investigation of the CD4+ T cells - Macrophages paradox in melanoma immunotherapies.", + "type": "PubMed ID", + "volume": "420", + "year": 2017 + }, + "publicationId": "BIOMD0000000770", + "submissionId": "MODEL1907310004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000771": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model describes the interactions between tumor cells and virus particles, with particular reference to virus-induced syncytium formation and ultimately death of tumor cells. Dynamics of infected cells, and production of new virus particules by infected cells, is also included.
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A mathematical model describing oncolytic virotherapy with incorporation the viral lytic cycle and the virus-specific CTL response. The thresholds for viral treatment and virus-specific CTl response are also obtained.
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A mathematical model of oncolytic virotherapy with time delay", + "publication": { + "accession": "31137188", + "affiliation": "School of Mathematics and Electronic Engineering, Wenzhou University, Wenzhou, 325035, PR China.", + "authors": [ + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "name": "Zizi Wang", + "orcid": "0000-0002-4652-7954" + }, + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, PR China.", + "name": "Zhi Ming Guo" + }, + { + "institution": "School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, 85287, USA.", + "name": "Hal Smith" + } + ], + "issue": "4", + "journal": "Mathematical biosciences and engineering : MBE", + "link": "http://identifiers.org/pubmed/31137188", + "month": "3", + "pages": "1836-1860", + "synopsis": "Oncolytic virotherapy is an emerging treatment modality which uses replication-competent viruses to destroy cancers without causing harm to normal tissues. By the development of molecular biotechnology, many effective viruses are adapted or engineered to make them cancer-specific, such as measles, adenovirus, herpes simplex virus and M1 virus. A successful design of virus needs a full understanding about how viral and host parameters influence the tumor load. In this paper, we propose a mathematical model on the oncolytic virotherapy incorporating viral lytic cycle and virus-specific CTL response. Thresholds for viral treatment and virus-specific CTL response are obtained. Different protocols are given depending on the thresholds. Our results also support that immune suppressive drug can enhance the oncolytic effect of virus as reported in recent literature.", + "title": "A mathematical model of oncolytic virotherapy with time delay.", + "type": "PubMed ID", + "volume": "16", + "year": 2019 + }, + "publicationId": "BIOMD0000000772", + "submissionId": "MODEL1908010001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000773": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells. Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulationDominik Wodarz J Theor Biol. 2018 July 07; 448: 86\u201393Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulationDominik Wodarz J Theor Biol. 2018 July 07; 448: 86\u201393Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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These results indicate that the presence of de-differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.", + "title": "Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulation.", + "type": "PubMed ID", + "volume": "448", + "year": 2018 + }, + "publicationId": "BIOMD0000000774", + "submissionId": "MODEL1908010003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000775": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of brain tumor. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical model of brain tumour with glia-neuron interactions and chemotherapy treatmentKelly C. Iarosz, Fernando S. Borges, Antonio M. Batista, Murilo S. Baptista, Regiane A. N. Siqueira, Ricardo L. Viana, Sergio R. LopesAbstract: In recent years, it became clear that a better understanding of the interactions among the main elements involved in the cancer network is necessary for the treatment of cancer and the suppression of cancer growth. In this work we propose a system of coupled differential equations that model brain tumour under treatment by chemotherapy, which considers interactions among the glial cells, the glioma, the neurons, and the chemotherapeutic agents. We study the conditions for the glioma growth to be eliminated, and identify values of the parameters for which the inhibition of the glioma growth is obtained with a minimal loss of healthy cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling chemotherapy resistance in palliation and failed cure Helen C. Monro, Eamonn A. Gaffney J Theor Biol. 2009, 257 (2), pp.292 Abstract: The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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chemotherapy resistance", + "publication": { + "accession": "19135065", + "affiliation": "University of Birmingham, Edgbaston, UK. monro@mat.bham.ac.uk", + "authors": [ + { + "institution": "University of Birmingham, Edgbaston, UK. monro@mat.bham.ac.uk", + "name": "Helen C Monro" + }, + { + "institution": "Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK.", + "name": "Eamonn A Gaffney", + "orcid": "0000-0002-6888-4362" + } + ], + "issue": "2", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/19135065", + "month": "3", + "pages": "292-302", + "synopsis": "The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. 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Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times.", + "title": "Modelling chemotherapy resistance in palliation and failed cure.", + "type": "PubMed ID", + "volume": "257", + "year": 2009 + }, + "publicationId": "BIOMD0000000776", + "submissionId": "MODEL1908020002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000777": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a reinvestigation of a previous model depicting cancer remission. It involves application of mathematical tools from control theory to assess the optimal approach during the use of Adaptive Cellular Immunotherapy and interleukin-2 treatment.
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Mathematical tools from control theory have been used to analyze and determine how an optimal external treatment of Adaptive Cellular Immunotherapy and interleukin-2 can result in more effective remission of malignant tumors while minimizing any adverse affect on the immune response.", + "title": "A CONTROL THEORY APPROACH TO CANCER REMISSION AIDED BY AN OPTIMAL THERAPY", + "type": "DOI", + "volume": "18", + "year": 2010 + }, + "publicationId": "BIOMD0000000777", + "submissionId": "MODEL1908040001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000778": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of tumor-immune interaction. Created by COPASI 4.25 (Build 207) This model is described in the article: Periodically Pulsed Immunotherapy in a Mathematical Model of Tumor, CD4+ T Cells, and Antitumor Cytokine InteractionsHsiu-Chuan Wei, Jui-Ling Yu, Chia-Yu Hsu Computational and Mathematical Methods in Medicine Volume 2017, Article ID 2906282, 12 pages Abstract: Immunotherapy is one of the most recent approaches for controlling and curing malignant tumors. In this paper, we consider a mathematical model of periodically pulsed immunotherapy using CD4+ T cells and an antitumor cytokine. Mathematical analyses are performed to determine the threshold of a successful treatment. The interindividual variability is explored by one-, two-, and three-parameter bifurcation diagrams for a nontreatment case. 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This is an updated version of a previous model that described the dynamics of cancer treatment, with descriptions of tumour cell numbers, specific and non-specific immune cells (NK, CD8+ T cells and other lymphocytes) with inclusion of chemo- and immunotherapy. This model incorporates new data to provide an improved and more comprehensive model, with specific emphasis on better descriptions of IL-2 dynamics.
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This task becomes increasingly difficult if a model involves several cell populations and treatment modalities. A sophisticated model constructed by de Pillis et al., Mixed immunotherapy and chemotherapy of tumours: Modelling, applications and biological interpretations, J. Theor. Biol. 238 (2006), pp. 841-862; involves tumour cells, specific and non-specific immune cells (natural killer (NK) cells, CD8+T cells and other lymphocytes) and employs chemotherapy and two types of immunotherapy (IL-2 supplementation and CD8+T-cell infusion) as treatment modalities. Despite the overall success of the aforementioned model, the problem of illustrating the effects of IL-2 on a growing tumour remains open. In this paper, we update the model of de Pillis et al. and then carefully identify appropriate values for the parameters of the new model according to recent empirical data. 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Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. 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Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China. Electronic address: gzm100@21cn.com.", + "name": "Zhiming Guo", + "orcid": "0000-0002-8351-8571" + }, + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "name": "Huaqin Peng" + } + ], + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/26976483", + "month": "6", + "pages": "19-27", + "synopsis": "Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. 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Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "authors": [ + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "name": "Zizi Wang", + "orcid": "0000-0002-4652-7954" + }, + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China. Electronic address: gzm100@21cn.com.", + "name": "Zhiming Guo", + "orcid": "0000-0002-8351-8571" + }, + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "name": "Huaqin Peng" + } + ], + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/26976483", + "month": "6", + "pages": "19-27", + "synopsis": "Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. 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Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.", + "title": "A mathematical model verifying potent oncolytic efficacy of M1 virus.", + "type": "PubMed ID", + "volume": "276", + "year": 2016 + }, + "publicationId": "BIOMD0000000781", + "submissionId": "MODEL1908060002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000782": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a basic model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "name": "Zizi Wang", + "orcid": "0000-0002-4652-7954" + }, + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China. Electronic address: gzm100@21cn.com.", + "name": "Zhiming Guo", + "orcid": "0000-0002-8351-8571" + }, + { + "institution": "School of Mathematics and Information Science, Guangzhou University, Guangzhou 510006, PR China; Key Laboratory of Mathematics and Interdisciplinary Science of Guangdong, Higher Education Institutes, Guangzhou University, Guangzhou 510006, PR China.", + "name": "Huaqin Peng" + } + ], + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/26976483", + "month": "6", + "pages": "19-27", + "synopsis": "Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. 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Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.", + "title": "A mathematical model verifying potent oncolytic efficacy of M1 virus.", + "type": "PubMed ID", + "volume": "276", + "year": 2016 + }, + "publicationId": "BIOMD0000000782", + "submissionId": "MODEL1908060003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000783": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model gives a mathematical description of the interactions between tumor cells, cytotoxic T lymphocytes and helper T cells (HTCs) within the tumor microenvironment, with emphasis on the role played by HTCs. The effects and dynamics of adoptive cell immunotherapy and HTC recruitment are also specifically discussed.
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In contrast, CD4+ T cells are commonly classified as helper T cells (HTCs) on the basis of their roles in providing help to the generation and maintenance of effective CD8+ cytotoxic and memory T cells. In order to get a better insight on the role of HTCs in a tumor immune system, we incorporate the third population of HTCs into a previous two dimensional ordinary differential equations (ODEs) model. Further we introduce the adoptive cellular immunotherapy (ACI) as the treatment to boost the immune system to fight against tumors. Compared tumor cells (TCs) and effector cells (ECs), the recruitment of HTCs changes the dynamics of the system substantially, by the effects through particular parameters, i.e., the activation rate of ECs by HTCs, \u03c0 (scaled as \u03c0), and the HTCs stimulation rate by the presence of identified tumor antigens, k2 (scaled as \u03c52). We describe the stability regions of the interior equilibria \u00c9 (no treatment case) and E+ (treatment case) in the scaled (\u03c0,\u03c52) parameter space respectively. Both \u03c0 and \u03c52 can destabilize \u00c9 and E+ and cause Hopf bifurcations. Our results show that HTCs might play a crucial role in the long term periodic oscillation behaviors of tumor immune system interactions. They also show that TCs may be eradicated from the patient's body under the ACI treatment.", + "title": "Mathematical modeling on helper t cells in a tumor immune system", + "type": "DOI", + "volume": "19", + "year": 2014 + }, + "publicationId": "BIOMD0000000783", + "submissionId": "MODEL1908080001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000784": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a dynamical model of cancer growth that includes three interacting cell populations of tumor cells, healthy host cells and immune effector cells. The tumor-immune and the tumor-host interactions are characterized to reproduce experimental results.
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This is a coupled ordinary differential equation model of tumour-immune dynamics, accounting for biological and clinical factors which regulate the interaction rates of cytotoxic T lymphocytes on the surface of the tumour mass.
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Electronic address: ffrascoli@swin.edu.au.", + "authors": [ + { + "institution": "Department of Mathematics, Swinburne University of Technology, VIC, Australia. 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A semi-analytic procedure to calculate oscillation periods and determine their sensitivity to model parameters is also presented. Numerical results show that the period of oscillations exhibits notable nonlinear dependence on biologically relevant conditions.", + "title": "A dynamical model of tumour immunotherapy.", + "type": "PubMed ID", + "volume": "253", + "year": 2014 + }, + "publicationId": "BIOMD0000000787", + "submissionId": "MODEL1908120001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000788": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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These molecules have shown promise as immuno-oncology drugs, and the TC is considered the pharmacologically active species that drives their pharmacodynamic effect. Here, we have presented a general target-mediated drug disposition (TMDD) model for these BsAbs, which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters and, if necessary, reduced internalization parameters is presented. The model is further used to investigate the kinetics of BsAb and TC concentrations. Our analysis shows that larger doses of BsAbs may delay the build-up of the TC. Consequently, a method to compute the optimal dosing strategy of BsAbs, which will immediately create and maintain maximal possible TC concentration, is presented.", + "title": "Target-Mediated Drug Disposition Model for Bispecific Antibodies: Properties, Approximation, and Optimal Dosing Strategy.", + "type": "PubMed ID", + "volume": "8", + "year": 2019 + }, + "publicationId": "BIOMD0000000788", + "submissionId": "MODEL1907240003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000789": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical Modelling of the Interaction Between Cancer Cells and an Oncolytic Virus: Insights into the Effects of Treatment ProtocolsAdrianne L. Jenner, Chae-Ok Yun, Peter S. Kim, Adelle C. F. CosterBull Math Biol (2018) 80:1615\u20131629Abstract: Oncolyticvirotherapyisanexperimentalcancertreatmentthatusesgenet- ically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity Kim et al. (Biomaterials 32(9):2314\u20132326, 2011) modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. Whilst PEG mod- ification appeared to improve plasma retention and initial infectivity, it also increased the virus particle arrival time. We derive a mathematical model that describes the inter- action between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations, we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy are explored.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. 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This is a mathematical model of pancreatic cancer that includes descriptions of pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor-suppressing cytokines to investigate the effects of immunotherapies on patient survival.
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Modeling Pancreatic Cancer Dynamics with Immunotherapy", + "publication": { + "accession": "30843136", + "affiliation": "Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA.", + "authors": [ + { + "institution": "Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA.Department of Mathematics and Statistics, University of the Incarnate Word, San Antonio, TX, 78209, USA.", + "name": "Xiaochuan Hu" + }, + { + "institution": "Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA.Department of Mathematics and Physical Sciences, Louisiana State University of Alexandria, Alexandria, LA, 71302, USA.", + "name": "Guoyi Ke" + }, + { + "institution": "Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, 79409-1042, USA. sophia.jang@ttu.edu.", + "name": "Sophia R-J Jang" + } + ], + "issue": "6", + "journal": "Bulletin of mathematical biology", + "link": "http://identifiers.org/pubmed/30843136", + "month": "6", + "pages": "1885-1915", + "synopsis": "We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor-suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sensitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive transfers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunotherapies between adoptive ex vivo expanded immune cells and TGF-[Formula: see text] inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-[Formula: see text] and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-[Formula: see text] inhibition first followed by adoptive immune cell transfers can yield better survival outcomes.", + "title": "Modeling Pancreatic Cancer Dynamics with Immunotherapy.", + "type": "PubMed ID", + "volume": "81", + "year": 2019 + }, + "publicationId": "BIOMD0000000792", + "submissionId": "MODEL1908130001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000793": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is described in the article: Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principlesKun-Wan Chen, Kenneth J Pienta Theoretical Biology and Medical Modelling 2011, 8:36 Abstract: Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Background

The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).

Results

Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.

Conclusions

The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.", + "title": "Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principles.", + "type": "PubMed ID", + "volume": "8", + "year": 2011 + }, + "publicationId": "BIOMD0000000793", + "submissionId": "MODEL1908130002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000794": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
its a mathematical model studying impact of b_TrCP on NFKB nuclear dynamics. This model is derived from Lipniacki2004 (PMID:15094015).
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regulates central processes in mammalian cells and plays a fundamental role in the regulation of inflammation and immunity. Aberrant regulation of the activation of the transcription factor NF-\u03baB is associated with severe diseases such as inflammatory bowel disease and arthritis. In the canonical pathway, the inhibitor I\u03baB suppresses NF-\u03baB's transcriptional activity. NF-\u03baB becomes active upon the degradation of I\u03baB, a process that is, in turn, regulated by the \u03b2-transducin repeat-containing protein (\u03b2-TrCP). \u03b2-TrCP has therefore been proposed as a promising pharmacological target in the development of novel therapeutic approaches to control NF-\u03baB's activity in diseases. This study explores the extent to which \u03b2-TrCP affects the dynamics of nuclear NF-\u03baB using a computational model of canonical NF-\u03baB signaling. The analysis predicts that \u03b2-TrCP influences the steady-state concentration of nuclear NF-\u03baB, as well as changes characteristic dynamic properties of nuclear NF-\u03baB, such as fold-change and the duration of its response to pathway stimulation. The results suggest that the modulation of \u03b2-TrCP has a high potential to regulate the transcriptional activity of NF-\u03baB.", + "title": "Controlling Nuclear NF-\u03baB Dynamics by \u03b2-TrCP-Insights from a Computational Model.", + "type": "PubMed ID", + "volume": "7", + "year": 2019 + }, + "publicationId": "BIOMD0000000794", + "submissionId": "MODEL1908130003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000795": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is described in the article: Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principlesKun-Wan Chen, Kenneth J Pienta Theoretical Biology and Medical Modelling 2011, 8:36 Abstract: Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Background

The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).

Results

Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.

Conclusions

The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.", + "title": "Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principles.", + "type": "PubMed ID", + "volume": "8", + "year": 2011 + }, + "publicationId": "BIOMD0000000795", + "submissionId": "MODEL1908130004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000796": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of tumor growth with angiogenesis. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical modeling of solid cancer growth with angiogenesisHyun M Yang Theoretical Biology and Medical Modelling 2012, 9:2 Abstract: Background: Cancer arises when within a single cell multiple malfunctions of control systems occur, which are, broadly, the system that promote cell growth and the system that protect against erratic growth. Additional systems within the cell must be corrupted so that a cancer cell, to form a mass of any real size, produces substances that promote the growth of new blood vessels. Multiple mutations are required before a normal cell can become a cancer cell by corruption of multiple growth-promoting systems.Methods: We develop a simple mathematical model to describe the solid cancer growth dynamics inducing angiogenesis in the absence of cancer controlling mechanisms.Results: The initial conditions supplied to the dynamical system consist of a perturbation in form of pulse: The origin of cancer cells from normal cells of an organ of human body. Thresholds of interacting parameters were obtained from the steady states analysis. The existence of two equilibrium points determine the strong dependency of dynamical trajectories on the initial conditions. The thresholds can be used to control cancer.Conclusions: Cancer can be settled in an organ if the following combination matches: better fitness of cancer cells, decrease in the efficiency of the repairing systems, increase in the capacity of sprouting from existing vascularization, and higher capacity of mounting up new vascularization. However, we show that cancer is rarely induced in organs (or tissues) displaying an efficient (numerically and functionally) reparative or regenerative mechanism.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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cancer growth with angiogenesis", + "publication": { + "accession": "22300422", + "affiliation": "UNICAMP - IMECC - DMA, Pra\u00e7a S\u00e9rgio Buarque de Holanda, Campinas, SP, Brazil. hyunyang@ime.unicamp.br", + "authors": [ + { + "institution": "UNICAMP - IMECC - DMA, Pra\u00e7a S\u00e9rgio Buarque de Holanda, Campinas, SP, Brazil. hyunyang@ime.unicamp.br", + "name": "Hyun M Yang", + "orcid": "0000-0002-1711-363X" + } + ], + "journal": "Theoretical biology & medical modelling", + "link": "http://identifiers.org/pubmed/22300422", + "month": "2", + "pages": "2", + "synopsis": "

Background

Cancer arises when within a single cell multiple malfunctions of control systems occur, which are, broadly, the system that promote cell growth and the system that protect against erratic growth. Additional systems within the cell must be corrupted so that a cancer cell, to form a mass of any real size, produces substances that promote the growth of new blood vessels. Multiple mutations are required before a normal cell can become a cancer cell by corruption of multiple growth-promoting systems.

Methods

We develop a simple mathematical model to describe the solid cancer growth dynamics inducing angiogenesis in the absence of cancer controlling mechanisms.

Results

The initial conditions supplied to the dynamical system consist of a perturbation in form of pulse: The origin of cancer cells from normal cells of an organ of human body. Thresholds of interacting parameters were obtained from the steady states analysis. The existence of two equilibrium points determine the strong dependency of dynamical trajectories on the initial conditions. The thresholds can be used to control cancer.

Conclusions

Cancer can be settled in an organ if the following combination matches: better fitness of cancer cells, decrease in the efficiency of the repairing systems, increase in the capacity of sprouting from existing vascularization, and higher capacity of mounting up new vascularization. However, we show that cancer is rarely induced in organs (or tissues) displaying an efficient (numerically and functionally) reparative or regenerative mechanism.", + "title": "Mathematical modeling of solid cancer growth with angiogenesis.", + "type": "PubMed ID", + "volume": "9", + "year": 2012 + }, + "publicationId": "BIOMD0000000796", + "submissionId": "MODEL1908140001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000797": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a proposed mathematical model describing interactions between tumor cells, CD4+ T cells, cytokines, and host cells within the context of CD4+ T cells inducing tumor regression. A platform is provided to assess the effectiveness of single or combination therapy with CD4+ T cells and/or cytokines.
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Our results suggest that host cells along with the mechanism of production of CD4+ T cells play important roles in driving tumor dynamics. Cancer cells can be eradicated if the tumor has a small growth rate and is also not competitive. Treatments by either CD4+, cytokines, or a combination of the two are applied to study their effectiveness. It is concluded that doses of treatments along with the tumor size are critical in determining the fate of the tumor. Tumor cells can be eliminated completely if doses of treatments by cytokine are large. The treatments are in general more effective if the tumor size is smaller. Bistability is observed in all of the models with or without the treatment strategies indicating that there is a window of opportunity for clearing off the tumor cells.", + "title": "Dynamics of tumor\u2013CD4+\u2013cytokine\u2013host cells interactions with treatments", + "type": "DOI", + "volume": "321", + "year": 2018 + }, + "publicationId": "BIOMD0000000797", + "submissionId": "MODEL1908150001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000798": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of acute myeloid leukaemia. Created by COPASI 4.26 (Build 213) This model is described in the article: Optimal control of acute myeloid leukaemia Jesse A. Sharp, Alexander P Browning, Tarunendu Mapder, Kevin Burrage, Matthew J SimpsonJournal of Theoretical Biology 470 (2019) 30\u201342 Abstract: Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corre- sponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin\u2019s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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This is a simple mathematical model describing the growth and removal of normal and leukemic haematopoietic stem cell populations and the role of these cellular processes in generating monoclonal leukemic patterns.
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Haematopoiesis can be seen as an evolutionary tree, starting with one cell that undergoes several divisions during the expansion phase, afterwards losing functional cells during the aging-related contraction phase. During divisions, offspring cells acquire variations, which can be either normal or abnormal. If an abnormal variation is present in more than 25% of the final cells, a monoclonal, leukemic pattern occurs. Such a pattern develops if: (A1) The abnormal variation occurs early, during the first or second divisions; (A2) The variation confers exceptional proliferative capacity; (B) A sizable proportion of the normal clones are destroyed and a previously non-significant abnormal clone gains relative dominance over a depleted environment; (C) The abnormal variation confers relative immortality, rendering it significant during the contraction phase. Combinations of these pathways further enhance the leukemic risk of the system. 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Their evolution is one of competitive type and depends upon kinetic and cellcell interaction parameters. Numerical simulations prove that the evolution can ultimately lead either to the normal hematopoietic state achieved by the expansion of the donor cells and the elimination of the host cells, or to the leukemic hematopoietic state characterized by the proliferation of the cancer line and the suppression of the other cell lines. One state or the other is reached depending on cellcell interactions (anti-host, anti-leukemia and anti-graft effects) and initial cell concentrations at transplantation. 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Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of pre-diagnostic glioma growth Marc Sturrock, Wenrui Hao, Judith Schwartzbaum, Grzegorz A. Rempala J Theor Biol. 2015 September 7; 380: 299\u2013308 Abstract: Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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"accession": "26073722", + "affiliation": "Mathematical Biosciences Institute, The Ohio State University, Columbus 43210, OH, USA.", + "authors": [ + { + "institution": "Mathematical Biosciences Institute, The Ohio State University, Columbus 43210, OH, USA.", + "name": "Marc Sturrock" + }, + { + "institution": "Mathematical Biosciences Institute, The Ohio State University, Columbus 43210, OH, USA.", + "name": "Wenrui Hao", + "orcid": "0000-0002-6925-7424" + }, + { + "institution": "Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA; Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.", + "name": "Judith Schwartzbaum", + "orcid": "0000-0002-1523-9941" + }, + { + "institution": "Mathematical Biosciences Institute, The Ohio State University, Columbus 43210, OH, USA; Division of Biostatistics, College of Public Health, The Ohio State University, Columbus 43210, OH, USA. Electronic address: rempala.3@osu.edu.", + "name": "Grzegorz A Rempala" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/26073722", + "month": "9", + "pages": "299-308", + "synopsis": "Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.", + "title": "A mathematical model of pre-diagnostic glioma growth.", + "type": "PubMed ID", + "volume": "380", + "year": 2015 + }, + "publicationId": "BIOMD0000000801", + "submissionId": "MODEL1908200001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000802": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of ADCC. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of antibody-dependent cellular cytotoxicity (ADCC) F. Hoffman, D. Gavaghan, J. Osborne, I.P. Barrett, T. You, H. Ghadially, R. Sainson, R.W. Wilkinson, H.M. ByrneJournal of Theoretical Biology 436 (2018) 39\u201350 Abstract: Immunotherapies exploit the immune system to target and kill cancer cells, while sparing healthy tis- sue. Antibody therapies, an important class of immunotherapies, involve the binding to specific antigens on the surface of the tumour cells of antibodies that activate natural killer (NK) cells to kill the tu- mour cells. Preclinical assessment of molecules that may cause antibody-dependent cellular cytotoxicity (ADCC) involves co-culturing cancer cells, NK cells and antibody in vitro for several hours and measuring subsequent levels of tumour cell lysis. Here we develop a mathematical model of such an in vitro ADCC assay, formulated as a system of time-dependent ordinary differential equations and in which NK cells kill cancer cells at a rate which depends on the amount of antibody bound to each cancer cell. Numerical simulations generated using experimentally-based parameter estimates reveal that the system evolves on two timescales: a fast timescale on which antibodies bind to receptors on the surface of the tumour cells, and NK cells form complexes with the cancer cells, and a longer time-scale on which the NK cells kill the cancer cells. We construct approximate model solutions on each timescale, and show that they are in good agreement with numerical simulations of the full system. Our results show how the processes involved in ADCC change as the initial concentration of antibody and NK-cancer cell ratio are varied. We use these results to explain what information about the tumour cell kill rate can be extracted from the cytotoxicity assays.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Hoffman2018- ADCC against cancer", + "publication": { + "accession": "28970093", + "affiliation": "Department of Computer Science, University of Oxford, Oxford, UK.", + "authors": [ + { + "institution": "Department of Computer Science, University of Oxford, Oxford, UK.", + "name": "F Hoffman" + }, + { + "institution": "Department of Computer Science, University of Oxford, Oxford, UK.", + "name": "D Gavaghan" + }, + { + "institution": "Department of Computer Science, University of Oxford, Oxford, UK; School of Mathematics and Statistics, University of Melbourne, AUS.", + "name": "J Osborne" + }, + { + "institution": "Quantitative Biology, Astrazeneca Ltd., Cambridge, UK.", + "name": "I P Barrett" + }, + { + "institution": "Computational Biology, Astrazeneca Ltd., Alderley Edge, UK; Beyond Consulting Ltd., BioHub, Alderley Park, Cheshire SK12 4TG, UK.", + "name": "T You" + }, + { + "institution": "Medimmune Ltd., Cambridge, UK.", + "name": "H Ghadially" + }, + { + "institution": "Medimmune Ltd., Cambridge, UK.", + "name": "R Sainson" + }, + { + "institution": "Medimmune Ltd., Cambridge, UK.", + "name": "R W Wilkinson", + "orcid": "0000-0001-8180-2643" + }, + { + "institution": "Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK.", + "name": "H M Byrne", + "orcid": "0000-0003-1771-5910" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/28970093", + "month": "1", + "pages": "39-50", + "synopsis": "Immunotherapies exploit the immune system to target and kill cancer cells, while sparing healthy tissue. Antibody therapies, an important class of immunotherapies, involve the binding to specific antigens on the surface of the tumour cells of antibodies that activate natural killer (NK) cells to kill the tumour cells. Preclinical assessment of molecules that may cause antibody-dependent cellular cytotoxicity (ADCC) involves co-culturing cancer cells, NK cells and antibody in vitro for several hours and measuring subsequent levels of tumour cell lysis. Here we develop a mathematical model of such an in vitro ADCC assay, formulated as a system of time-dependent ordinary differential equations and in which NK cells kill cancer cells at a rate which depends on the amount of antibody bound to each cancer cell. Numerical simulations generated using experimentally-based parameter estimates reveal that the system evolves on two timescales: a fast timescale on which antibodies bind to receptors on the surface of the tumour cells, and NK cells form complexes with the cancer cells, and a longer time-scale on which the NK cells kill the cancer cells. We construct approximate model solutions on each timescale, and show that they are in good agreement with numerical simulations of the full system. Our results show how the processes involved in ADCC change as the initial concentration of antibody and NK-cancer cell ratio are varied. We use these results to explain what information about the tumour cell kill rate can be extracted from the cytotoxicity assays.", + "title": "A mathematical model of antibody-dependent cellular cytotoxicity (ADCC).", + "type": "PubMed ID", + "volume": "436", + "year": 2018 + }, + "publicationId": "BIOMD0000000802", + "submissionId": "MODEL1908200002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000803": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model is an attempt to provide a mathematical description of IL-7 dependent T cell homeostasis at the molecular and cellular level, with inclusion of gamma-chain and ligand binding in the context of receptors for IL-7 and IL-15 receptors.
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IL7 receptor signaling in T cells", + "publication": { + "accession": "31137085", + "affiliation": "Experimental Immunology Branch, National Cancer Institute, NIH, Bethesda, Maryland.", + "authors": [ + { + "institution": "Experimental Immunology Branch, National Cancer Institute, NIH, Bethesda, Maryland.", + "name": "Jung-Hyun Park" + }, + { + "institution": "Experimental Immunology Branch, National Cancer Institute, NIH, Bethesda, Maryland.", + "name": "Adam T Waickman", + "orcid": "0000-0002-4536-6714" + }, + { + "institution": "Safety and Environmental Assurance Centre (SEAC), Unilever, UK.", + "name": "Joseph Reynolds" + }, + { + "institution": "Universidad Pontificia Comillas, GISC and DNL, Madrid, Spain.Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK.", + "name": "Mario Castro", + "orcid": "0000-0003-3288-6144" + }, + { + "institution": "Department of Applied Mathematics, School of Mathematics, University of Leeds, Leeds, UK.", + "name": "Carmen Molina-Par\u00eds", + "orcid": "0000-0001-9828-6737" + } + ], + "issue": "5", + "journal": "Wiley interdisciplinary reviews. Systems biology and medicine", + "link": "http://identifiers.org/pubmed/31137085", + "month": "9", + "pages": "e1447", + "synopsis": "Interleukin-7 (IL7) plays a nonredundant role in T cell survival and homeostasis, which is illustrated in the severe T cell lymphopenia of IL7-deficient mice, or demonstrated in animals or humans that lack expression of either the IL7R\u03b1 or \u03b3 c chain, the two subunits that constitute the functional IL7 receptor. Remarkably, IL7 is not expressed by T cells themselves, but produced in limited amounts by radio-resistant stromal cells. Thus, T cells need to constantly compete for IL7 to survive. How T cells maintain homeostasis and further maximize the size of the peripheral T cell pool in face of such competition are important questions that have fascinated both immunologists and mathematicians for a long time. Exceptionally, IL7 downregulates expression of its own receptor, so that IL7-signaled T cells do not consume extracellular IL7, and thus, the remaining extracellular IL7 can be shared among unsignaled T cells. Such an altruistic behavior of the IL7R\u03b1 chain is quite unique among members of the \u03b3 c cytokine receptor family. However, the consequences of this altruistic signaling behavior at the molecular, single cell and population levels are less well understood and require further investigation. In this regard, mathematical modeling of how a limited resource can be shared, while maintaining the clonal diversity of the T cell pool, can help decipher the molecular or cellular mechanisms that regulate T cell homeostasis. Thus, the current review aims to provide a mathematical modeling perspective of IL7-dependent T cell homeostasis at the molecular, cellular and population levels, in the context of recent advances in our understanding of the IL7 biology. This article is categorized under: Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Biological Mechanisms > Cell Signaling Models of Systems Properties and Processes > Mechanistic Models Analytical and Computational Methods > Computational Methods.", + "title": "IL7 receptor signaling in T cells: A mathematical modeling perspective.", + "type": "PubMed ID", + "volume": "11", + "year": 2019 + }, + "publicationId": "BIOMD0000000803", + "submissionId": "MODEL1908210001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000804": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of multiple myeloma. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of cell equilibrium and joint cell formation in multiple myelomaM.A. Koenders, R. Saso Journal of Theoretical Biology 390 (2016) 73\u201379Abstract: In Multiple Myeloma Bone Disease healthy bone remodelling is affected by tumour cells by means of paracrine cytokinetic signalling in such a way that osteoclast formation is enhanced and the growth of osteoblast cells inhibited. The participating cytokines are described in the literature. Osteoclast-induced myeloma cell growth is also reported. Based on existing mathematical models for healthy bone remo- delling a three-way equilibrium model is presented for osteoclasts, osteoblasts and myeloma cell populations to describe the progress of the illness in a scenario in which there is a secular increase in the cytokinetic interactive effectiveness of paracrine processes. The equilibrium state for the system is obtained. The paracrine interactive effectiveness is explored by parameter variation and the stable region in the parameter space is identified. Then recently-discovered joint myeloma\u2013osteoclast cells are added to the model to describe the populations inside lytic lesions. It transpires that their presence expands the available parameter space for stable equilibrium, thus permitting a detrimental, larger population of osteoclasts and myeloma cells. A possible relapse mechanism for the illness is explored by letting joint cells dissociate. The mathematics then permits the evaluation of the evolution of the cell populations as a function of time during relapse.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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Electronic address: c_koenders@yahoo.com.", + "authors": [ + { + "institution": "Department Engineering and The Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK. Electronic address: c_koenders@yahoo.com.", + "name": "M A Koenders" + }, + { + "institution": "Bud Flanagan Ambulatory Outpatients Unit, The Royal Marsden, Downs Road, Sutton, Surrey SM2 5 PT, UK.", + "name": "R Saso" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/26643942", + "month": "2", + "pages": "73-79", + "synopsis": "In Multiple Myeloma Bone Disease healthy bone remodelling is affected by tumour cells by means of paracrine cytokinetic signalling in such a way that osteoclast formation is enhanced and the growth of osteoblast cells inhibited. The participating cytokines are described in the literature. Osteoclast-induced myeloma cell growth is also reported. Based on existing mathematical models for healthy bone remodelling a three-way equilibrium model is presented for osteoclasts, osteoblasts and myeloma cell populations to describe the progress of the illness in a scenario in which there is a secular increase in the cytokinetic interactive effectiveness of paracrine processes. The equilibrium state for the system is obtained. The paracrine interactive effectiveness is explored by parameter variation and the stable region in the parameter space is identified. Then recently-discovered joint myeloma-osteoclast cells are added to the model to describe the populations inside lytic lesions. It transpires that their presence expands the available parameter space for stable equilibrium, thus permitting a detrimental, larger population of osteoclasts and myeloma cells. A possible relapse mechanism for the illness is explored by letting joint cells dissociate. The mathematics then permits the evaluation of the evolution of the cell populations as a function of time during relapse.", + "title": "A mathematical model of cell equilibrium and joint cell formation in multiple myeloma.", + "type": "PubMed ID", + "volume": "390", + "year": 2016 + }, + "publicationId": "BIOMD0000000804", + "submissionId": "MODEL1908290002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000805": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "jm2187@cam.ac.uk", + "external": false, + "name": "Jinghao Men", + "orcid": "0000-0003-4781-8144" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a model of pH control in tumor. Created by COPASI 4.26 (Build 213) This model is described in the article: Regulation of tumour intracellular pH: A mathematical model examining the interplay between H and lactateMaymona Al-Husari, Steven D. WebbJournal of Theoretical Biology 322 (2013) 58\u201371 Abstract:Non-invasive measurements of pH have shown that both tumour and normal cells have intracellular pH (pHi) that lies on the alkaline side of neutrality (7.1\u20137.2). However, extracellular pH (pHe) is reported to be more acidic in some tumours compared to normal tissues. Many cellular processes and therapeutic agents are known to be tightly pH dependent which makes the study of intracellular pH regulation of paramount importance. We develop a mathematical model that examines the role of various membrane-based ion transporters in tumour pH regulation, in particular, with a focus on the interplay between lactate and H ions and whether the lactate/H symporter activity is sufficient to give rise to the observed reversed pH gradient that is seen is some tumours. Using linear stability analysis and H ions. We extend this analysis using perturbation techniques to specifically examine a rapid change in H-ion concentrations relative to variations in lactate. We then perform a parameter sensitivity analysis to explore solution robustness to parameter variations. An important result from our study is that a reversed pH gradient is possible in our system but for unrealistic parameter estimates\u2014pointing to the possible involvement of other mechanisms in cellular pH gradient reversal, for example acidic vesicles, lysosomes, golgi and endosomes.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. 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However, extracellular pH (pHe) is reported to be more acidic in some tumours compared to normal tissues. Many cellular processes and therapeutic agents are known to be tightly pH dependent which makes the study of intracellular pH regulation of paramount importance. We develop a mathematical model that examines the role of various membrane-based ion transporters in tumour pH regulation, in particular, with a focus on the interplay between lactate and H(+) ions and whether the lactate/H(+) symporter activity is sufficient to give rise to the observed reversed pH gradient that is seen is some tumours. Using linear stability analysis and numerical methods, we are able to gain a clear understanding of the relationship between lactate and H(+) ions. We extend this analysis using perturbation techniques to specifically examine a rapid change in H(+)-ion concentrations relative to variations in lactate. We then perform a parameter sensitivity analysis to explore solution robustness to parameter variations. An important result from our study is that a reversed pH gradient is possible in our system but for unrealistic parameter estimates-pointing to the possible involvement of other mechanisms in cellular pH gradient reversal, for example acidic vesicles, lysosomes, golgi and endosomes.", + "title": "Regulation of tumour intracellular pH: a mathematical model examining the interplay between H+ and lactate.", + "type": "PubMed ID", + "volume": "322", + "year": 2013 + }, + "publicationId": "BIOMD0000000805", + "submissionId": "MODEL1909030001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000806": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This paper describes the complex interactions between two extreme types of macrophages (M1 and M2 cells), effector T cells and an oncolytic Vesicular Stomatitis Virus (VSV), on the growth/elimination of B16F10 melanoma. The mathematics model descirbes, in terms of VSV and macrophages levels, two different types of immune responses which could ensure tumour control and eventual elimination. It shows that both innate and adaptive anti-tumour immune responses, as well as the oncolytic virus, could be very important in delaying tumour relapse and eventually eliminating the tumour. Overall this study supports the use mathematical modelling to increase our understanding of the complex immune interaction following oncolytic virotherapies. However, the complexity of the model combined with a lack of sufficient data for model parametrisation has an impact on the possibility of making quantitative predictions. 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systemic anti-tumour immunity and selectively killing tumour cells. However, the effectiveness of these viruses depends significantly on their interactions with the host immune responses, both innate (e.g., macrophages, which accumulate in high numbers inside solid tumours) and adaptive (e.g., [Formula: see text] T cells). In this article, we consider a mathematical approach to investigate the possible outcomes of the complex interactions between two extreme types of macrophages (M1 and M2 cells), effector [Formula: see text] T cells and an oncolytic Vesicular Stomatitis Virus (VSV), on the growth/elimination of B16F10 melanoma. We discuss, in terms of VSV, [Formula: see text] and macrophages levels, two different types of immune responses which could ensure tumour control and eventual elimination. We show that both innate and adaptive anti-tumour immune responses, as well as the oncolytic virus, could be very important in delaying tumour relapse and eventually eliminating the tumour. Overall this study supports the use mathematical modelling to increase our understanding of the complex immune interaction following oncolytic virotherapies. However, the complexity of the model combined with a lack of sufficient data for model parametrisation has an impact on the possibility of making quantitative predictions.", + "title": "Investigating Macrophages Plasticity Following Tumour-Immune Interactions During Oncolytic Therapies.", + "type": "PubMed ID", + "volume": "67", + "year": 2019 + }, + "publicationId": "BIOMD0000000806", + "submissionId": "MODEL1909050001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000807": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model describes the multistep process that transform a normal cell and its descendants into a malignant tumour by considering three populations: normal, premalignant and cancer cells. Created by COPASI 4.24(Build 197)Abstract:Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: self-sufficiency on growth signals, insensibility to anti-growth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. 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This mathematical model describes interactions between glioma tumors and the immune system that may occur following direct intra-tumoral administration of ex-vivo activated alloreactive cytotoxic-T-lymphocytes (aCTLs) as part of adoptive immunotherapy. The model includes descriptions of aCTL, neoplastic cells, MHC class I and II molecules, TGF-beta and IFN-gamma.
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Background

Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.

Methodology/principal findings

We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient's training set and his validation set. The training set, used for model personalization, contained the patient's initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R(2)\u200a=\u200a0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.

Conclusions/significance

Using a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols.", + "title": "Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models.", + "type": "PubMed ID", + "volume": "5", + "year": 2010 + }, + "publicationId": "BIOMD0000000808", + "submissionId": "MODEL1909090001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000809": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "The paper describes a spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. 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Electronic address: josephmalinzi1@gmail.com.", + "authors": [ + { + "institution": "Department of Mathematics, University of Eswatini, Private Bag 4, Kwaluseni, Eswatini.", + "name": "Joseph Malinzi", + "orcid": "0000-0001-9184-8506" + }, + { + "institution": "Department of Mathematical Sciences, Stellenbosch University, Private Bag X1 Matieland, 7602, South Africa. Electronic address: innocenter@aims.ac.za.", + "name": "Innocenter Amima", + "orcid": "0000-0001-6822-0235" + } + ], + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/30537482", + "month": "2", + "pages": "8-19", + "synopsis": "A spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Analysis of the model is carried out for both temporal and spatio-temporal cases. Stability analysis and numerical simulations of the temporal model replicate experimental observations of immune-induced tumour dormancy. Travelling wave solutions of the spatio-temporal model are determined using the hyperbolic tangent method and minimum wave speeds of invasion are calculated. Travelling wave analysis depicts that cell invasion dynamics are mainly driven by their motion and growth rates. A stability analysis of the spatio-temporal model shows a possibility of dynamical stabilization of the tumour-free steady state. Simulation results reveal that the tumour swells to a dormant level.", + "title": "Mathematical analysis of a tumour-immune interaction model: A moving boundary problem.", + "type": "PubMed ID", + "volume": "308", + "year": 2019 + }, + "publicationId": "BIOMD0000000809", + "submissionId": "MODEL1909060001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000810": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model describes the concept of Cancer Stem Cells(CSC) differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. 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This is a mathematical model of pancreatic cancer which includes descriptions of regulatory T cell activity and inhibition therapy. Descriptions of cytokine induced killer immunotherapy are also included.
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We used 220 sets of experiments in-silico to show that a clinical trial incorporating 100 patients per arm (standard intensive treatment versus 5 + 12 scheme) could demonstrate the superiority of the novel scheme after a follow-up period of 10 years. 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Camilo Jos\u00e9 Cela 3, 13071 Ciudad Real, Spain", + "authors": [ + { + "institution": "Department of Mathematics, University of Castilla-La Mancha, Avda. Camilo Jos\u00e9 Cela 3, 13071 Ciudad Real, Spain", + "name": "Chrobak, J.M." + }, + { + "institution": "Department of Mathematics, University of Castilla-La Mancha, Avda. Camilo Jos\u00e9 Cela 3, 13071 Ciudad Real, Spain", + "name": "Herrero, H." + } + ], + "issue": "3", + "journal": "Journal of Biological Systems", + "link": "http://identifiers.org/doi/10.1142/S0218339011004111", + "month": "9", + "pages": "521-532", + "synopsis": "We present a model of competition between an artificially induced tumor and the adaptive immune system based on the use of an autonomous system of ordinary differential equations (ODE). The aim of this work is to reproduce experimental results which find two possible outcomes depending on the initial quantities of the tumor and the adaptive immune cells. The ODE system is positively invariant and its solutions are bounded. The linear stability analysis of the fixed points of the model yields two groups of solutions depending on the initial conditions. In the first one, the immune system wins against the tumor cells, so the cancer disappears (elimination). In the second one, the cancer keeps on growing (escape). These results are coherent with experimental results which show these two possibilities, so the model reproduces the macroscopic behavior of the experiments. From the model some conclusions on the underlying competitive behavior can be derived.", + "title": "A mathematical model of induced cancer-adaptive immune system competition", + "type": "DOI", + "volume": "19", + "year": 2011 + }, + "publicationId": "BIOMD0000000815", + "submissionId": "MODEL1909170003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000816": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "The model is based on 'Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections', PMID:30510594. Author:Jana L.Gevertz and Joanna R.Wares. This model describes the original mathematical model described in section 2.1. Built by COPASI 4.24( Build 197)Abstract:Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. 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To maintain a normal cell physiology, it is critical that the control of Myc dynamics is precisely orchestrated. Recent studies suggest that such control of Myc can be achieved at the post-translational level via protein stability modulation. Myc is regulated by two Ras effector pathways: the extracellular signal-regulated kinase (Erk) and phosphatidylinositol 3-kinase (PI3K) pathways. To gain quantitative insight into Myc dynamics, we have developed a mathematical model to analyze post-translational regulation of Myc via sequential phosphorylation by Erk and PI3K. Our results suggest that Myc integrates Erk and PI3K signals to result in various cellular responses by differential stability control of Myc protein isoforms. Such signal integration confers a flexible dynamic range for the system output, governed by stability change. In addition, signal integration may require saturation of the input signals, leading to sensitive signal integration to the temporal features of the input signals, insensitive response to their amplitudes, and resistance to input fluctuations. We further propose that these characteristics of the protein stability control module in Myc may be commonly utilized in various cell types and classes of proteins.", + "title": "Sensing and integration of Erk and PI3K signals by Myc.", + "type": "PubMed ID", + "volume": "4", + "year": 2008 + }, + "publicationId": "BIOMD0000000818", + "submissionId": "MODEL1909170004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000819": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This a model from the article: A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatmentFereshteh Nazari, Alexander T. Pearson, Jacques Eduardo Nor, Trachette L. Jackson. PloS Computational Biology, 2018 Jan.Abstract:Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth.Author Summary:A small population of cancer stem cells that share many of the biological characteristics of normal adult stem cells are believed to initiate and sustain tumor growth for a wide variety of malignancies. Growth and survival of these cancer stem cells is highly influenced by tumor micro-environmental factors and molecular signaling initiated by cytokines and growth factors. This work focuses on quantifying the influence of IL-6, a pleiotropic cytokine secreted by a variety of cell types, on cancer stem cell self-renewal and survival. We present a mathematical model for IL-6 mediated, cancer stem cell driven tumor growth that operates at the following levels: (1) the molecular level\u2014capturing cell surface dynamics of receptor-ligand binding and receptor activation that lead to intra-cellular signal transduction cascades; and (2) the cellular level\u2014describing tumor growth, cellular composition, and response to treatments targeted against IL-6.This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. 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IL6 mediated stem cell driven tumor growth and targeted treatment", + "publication": { + "accession": "29351275", + "affiliation": "Simon A. Levin Mathematical, Computational, and Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America.", + "authors": [ + { + "institution": "Simon A. Levin Mathematical, Computational, and Modeling Sciences Center, School of Human Evolution and Social Change, Arizona State University, Tempe, Arizona, United States of America.", + "name": "Fereshteh Nazari" + }, + { + "institution": "Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan Cancer Center, Ann Arbor, Michigan, United States of America.", + "name": "Alexander T Pearson", + "orcid": "0000-0003-2801-7456" + }, + { + "institution": "Departments of Cardiology, Restorative Sciences, and Endontics, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Jacques Eduardo N\u00f6r" + }, + { + "institution": "Department of Mathematics, University of Michigan, Ann Arbor, Michigan, United States of America.", + "name": "Trachette L Jackson", + "orcid": "0000-0003-2803-3341" + } + ], + "issue": "1", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/pubmed/29351275", + "month": "1", + "pages": "e1005920", + "synopsis": "Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. 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We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth.", + "title": "A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment.", + "type": "PubMed ID", + "volume": "14", + "year": 2018 + }, + "publicationId": "BIOMD0000000819", + "submissionId": "MODEL1909230001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000820": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "These selections of models are described in the paper:Cellular interactions constrain tumor growthby Jeffrey West and Paul K. 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Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of states at the cellular level is what determines macroscopic growth characteristics.", + "title": "Cellular interactions constrain tumor growth.", + "type": "PubMed ID", + "volume": "116", + "year": 2019 + }, + "publicationId": "BIOMD0000000820", + "submissionId": "MODEL1909240001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000821": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is based on:Reinforcement learning-based control of tumor growth under anti-angiogenic therapyAuthors: Parisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi, Ala-Eddin Al Moustafa, Levente KovacsAbstract:Background and objectives: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration.Methods: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient.Results: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to \u202f\u00b1\u202f20% of tumor growth parameters changes.Conclusion: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.", + "files": { + "additional": [ + { + "description": "PNG plot of the model simulation Figure 1", + "fileSize": "27219", + "md5sum": "bd6e92df0cf49618b3155921d8d60bcf", + "mimeType": "image/png", + "name": "Fig 1.png", + "sha1sum": "fc5c61aee5d203dcbdcf9be884e9d839dd75a381", + "sha256sum": "66b5f0af79deced4d894e779a956269f2f216f70dcb2bc84b6d3add2073473ef" + }, + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "11547", + "md5sum": "dc88c446e899e08b30bc6a762aef3b6a", + "mimeType": "application/rdf+xml", + "name": "Yazdjer2019 - 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Electronic address: kovacs.levente@nik.uni-obuda.hu.", + "name": "Levente Kov\u00e1cs" + } + ], + "journal": "Computer methods and programs in biomedicine", + "link": "http://identifiers.org/pubmed/31046990", + "month": "5", + "pages": "15-26", + "synopsis": "

Background and objectives

In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration.

Methods

A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient.

Results

The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to \u202f\u00b1\u202f20% of tumor growth parameters changes.

Conclusion

The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.", + "title": "Reinforcement learning-based control of tumor growth under anti-angiogenic therapy.", + "type": "PubMed ID", + "volume": "173", + "year": 2019 + }, + "publicationId": "BIOMD0000000821", + "submissionId": "MODEL1909240003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000822": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is based on:Dynamic modeling of signal transduction by mTOR complexes in cancerAuthor:Mohammadreza Dorvash, Mohammad Farahmandnia, Pouria Mosaddeghi, Mitra Farahmandnejad, Hosein Saber, Mohammadhossein Khorraminejad-Shirazi, Amir Azadi, Iman TavassolyAbstract:Signal integration has a crucial role in the cell fate decision and dysregulation of the cellular signaling pathways is a primary characteristic of cancer. 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Consisting of the major kinase complexes mTOR Complex 1 and 2 (mTORC1/2), the mTOR network harbours complex interactions and feedback loops. The DEP domain-containing mTOR-interacting protein (DEPTOR) was recently identified as an endogenous inhibitor of both mTORC1 and 2 through direct interactions, and is in turn degraded by mTORC1/2, adding an extra layer of complexity to the mTOR network. Yet, the dynamic properties of the DEPTOR-mTOR network and the roles of DEPTOR in coordinating mTORC1/2 activation dynamics have not been characterised. Using computational modelling, systems analysis and dynamic simulations we show that DEPTOR confers remarkably rich and complex dynamic behaviours to mTOR signalling, including abrupt, bistable switches, oscillations and co-existing bistable/oscillatory responses. Transitions between these distinct modes of behaviour are enabled by modulating DEPTOR expression alone. We characterise the governing conditions for the observed dynamics by elucidating the network in its vast multi-dimensional parameter space, and develop strategies to identify core network design motifs underlying these dynamics. Our findings provide new systems-level insights into the complexity of mTOR signalling contributed by DEPTOR.", + "title": "Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR.", + "type": "PubMed ID", + "volume": "8", + "year": 2018 + }, + "publicationId": "BIOMD0000000823", + "submissionId": "MODEL1909250003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000824": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is built by COPASI 4.24(Build197), based on paper:A mathematical model of the effects of aging on naive T-cell population and diversityAuthors:Stephanie Lewkiewicz, Yao-li Chuang, Tom ChouAbstract:The human adaptive immune response is known to weaken in advanced age, resulting in increased severity of pathogen-born illness, poor vaccine efficacy, and a higher prevalence of cancer in the elderly. 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We show that diversity decreases with age primarily due to diminished thymic output of new T cells and the resulting overall loss of small immunoclones.", + "title": "A Mathematical Model of the Effects of Aging on Naive T Cell Populations and Diversity.", + "type": "PubMed ID", + "volume": "81", + "year": 2019 + }, + "publicationId": "BIOMD0000000824", + "submissionId": "MODEL1909260001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000825": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is built by COPASI 4.24(Build 197), based on paper:Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment.Author:James M. Greene, Jana L. Gevertz, Eduardo D. sontagAbstract:PURPOSE:Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols. MATERIALS AND METHODS:To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance. RESULTS:Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. 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Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment", + "publication": { + "accession": "30969799", + "affiliation": "Rutgers University, New Brunswick, NJ.", + "authors": [ + { + "institution": "Rutgers University, New Brunswick, NJ.", + "name": "James M Greene", + "orcid": "0000-0003-0954-2730" + }, + { + "institution": "The College of New Jersey, Ewing Township, NJ.", + "name": "Jana L Gevertz" + }, + { + "institution": "Department of Mathematics and Center for Quantitative Biology, Rutgers University, New Brunswick, NJ 08903, USA. Electronic address: eduardo.sontag@rutgers.edu.", + "name": "Sontag ED", + "orcid": "0000-0001-8020-5783" + } + ], + "journal": "JCO clinical cancer informatics", + "link": "http://identifiers.org/pubmed/30969799", + "month": "4", + "pages": "1-20", + "synopsis": "

Purpose

Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols.

Materials and methods

To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance.

Results

Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment's propensity to induce resistance.", + "title": "Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment.", + "type": "PubMed ID", + "volume": "3", + "year": 2019 + }, + "publicationId": "BIOMD0000000825", + "submissionId": "MODEL1909260002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000826": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer
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As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.", + "title": "Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.", + "type": "PubMed ID", + "volume": "14", + "year": 2018 + }, + "publicationId": "BIOMD0000000826", + "submissionId": "MODEL1909270001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000827": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "The model is based on publication:Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplificationAbstract:Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5\u201322% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. 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Giantsos-Adams2013 - Growth of glycocalyxunder static conditions

This model is described in the article:

Giantsos-Adams KM, Koo AJ, Song S, Sakai J, Sankaran J, Shin JH, Garcia-Cardena G, Dewey CF.
Cell Mol Bioeng 2013 Jun; 6(2): 160-174

Abstract:

The local hemodynamic shear stress waveforms present in an artery dictate the endothelial cell phenotype. The observed decrease of the apical glycocalyx layer on the endothelium in atheroprone regions of the circulation suggests that the glycocalyx may have a central role in determining atherosclerotic plaque formation. However, the kinetics for the cells' ability to adapt its glycocalyx to the environment have not been quantitatively resolved. Here we report that the heparan sulfate component of the glycocalyx of HUVECs increases by 1.4-fold following the onset of high shear stress, compared to static cultured cells, with a time constant of 19\u00a0h. Cell morphology experiments show that 12\u00a0h are required for the cells to elongate, but only after 36\u00a0h have the cells reached maximal alignment to the flow vector. Our findings demonstrate that following enzymatic degradation, heparan sulfate is restored to the cell surface within 12\u00a0h under flow whereas the time required is 20\u00a0h under static conditions. We also propose a model describing the contribution of endocytosis and exocytosis to apical heparan sulfate expression. The change in HS regrowth kinetics from static to high-shear EC phenotype implies a differential in the rate of endocytic and exocytic membrane turnover.

This model is hosted on BioModels Database and identified by: MODEL1609100001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Sakai", + "orcid": "0000-0002-2526-2766" + }, + { + "name": "Jagadish Sankaran", + "orcid": "0000-0001-7021-9807" + }, + { + "name": "Jennifer H Shin", + "orcid": "0000-0001-8448-6236" + }, + { + "name": "Guillermo Garcia-Cardena" + }, + { + "name": "C Forbes Dewey" + } + ], + "issue": "2", + "journal": "Cellular and molecular bioengineering", + "link": "http://identifiers.org/pubmed/23805169", + "month": "6", + "pages": "160-174", + "synopsis": "The local hemodynamic shear stress waveforms present in an artery dictate the endothelial cell phenotype. The observed decrease of the apical glycocalyx layer on the endothelium in atheroprone regions of the circulation suggests that the glycocalyx may have a central role in determining atherosclerotic plaque formation. However, the kinetics for the cells' ability to adapt its glycocalyx to the environment have not been quantitatively resolved. Here we report that the heparan sulfate component of the glycocalyx of HUVECs increases by 1.4-fold following the onset of high shear stress, compared to static cultured cells, with a time constant of 19\u00a0h. Cell morphology experiments show that 12\u00a0h are required for the cells to elongate, but only after 36\u00a0h have the cells reached maximal alignment to the flow vector. Our findings demonstrate that following enzymatic degradation, heparan sulfate is restored to the cell surface within 12\u00a0h under flow whereas the time required is 20\u00a0h under static conditions. We also propose a model describing the contribution of endocytosis and exocytosis to apical heparan sulfate expression. The change in HS regrowth kinetics from static to high-shear EC phenotype implies a differential in the rate of endocytic and exocytic membrane turnover.", + "title": "Heparan Sulfate Regrowth Profiles Under Laminar Shear Flow Following Enzymatic Degradation.", + "type": "PubMed ID", + "volume": "6", + "year": 2013 + }, + "publicationId": "BIOMD0000000830", + "submissionId": "MODEL1609100000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000831": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This a model from the article:
Hypothalamic regulation of pituitary secretion of luteinizing hormone.II. Feedback control of gonadotropin secretion.
Smith WR Bull Math Biol. (1980) 42(1): 57-78 6986927 ,
Abstract:
No Abstract Available

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: smith,1980,version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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In mammals, the pathway contains the core kinases MST1/2, which phosphorylate and activate LATS1/2 kinases. The pro-apoptotic function of the MST/LATS signalling axis was previously linked to the Akt and ERK MAPK pathways, demonstrating that the Hippo pathway does not act alone but crosstalks with other signalling pathways to coordinate network dynamics and cellular outcomes. These crosstalks were characterised by a multitude of complex regulatory mechanisms involving competitive protein-protein interactions and phosphorylation mediated feedback loops. However, how these different mechanisms interplay in different cellular contexts to drive the context-specific network dynamics of Hippo-ERK signalling remains elusive. Using mathematical modelling and computational analysis, we uncovered that the Hippo-ERK network can generate highly diverse dynamical profiles that can be clustered into distinct dose-response patterns. For each pattern, we offered mechanistic explanation that defines when and how the observed phenomenon can arise. We demonstrated that Akt displays opposing, dose-dependent functions towards ERK, which are mediated by the balance between the Raf-1/MST2 protein interaction module and the LATS1 mediated feedback regulation. Moreover, Ras displays a multi-functional role and drives biphasic responses of both MST2 and ERK activities; which are critically governed by the competitive protein interaction between MST2 and Raf-1. Our study represents the first in-depth and systematic analysis of the Hippo-ERK network dynamics and provides a concrete foundation for future studies.", + "title": "Unveiling Hidden Dynamics of Hippo Signalling: A Systems Analysis.", + "type": "PubMed ID", + "volume": "7", + "year": 2016 + }, + "publicationId": "BIOMD0000000832", + "submissionId": "MODEL1909250001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000833": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "Barbara Di Camillo, Azzurra Carlon, Federica Eduati & Gianna Maria Toffolo. 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Background

The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as \"combinatorial complexity\", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions.

Results

In this work, we present a comprehensive model of the ISP, which integrates three models previously available in the literature by using the rule-based modelling (RBM) approach. RBM allows for a simple description of a number of signalling pathway characteristics, such as the phosphorylation of signalling proteins at multiple sites with different effects, the simultaneous interaction of many molecules of the signalling pathways with several binding partners, and the information about subcellular localization where reactions take place. Thanks to its modularity, it also allows an easy integration of different pathways. After RBM specification, we simulated the dynamic behaviour of the ISP model and validated it using experimental data. We the examined the predicted profiles of all the active species and clustered them in four clusters according to their dynamic behaviour. Finally, we used parametric sensitivity analysis to show the role of negative feedback loops in controlling the robustness of the system.

Conclusions

The presented ISP model is a powerful tool for data simulation and can be used in combination with experimental approaches to guide the experimental design. The model is available at http://sysbiobig.dei.unipd.it/ was submitted to Biomodels Database ( https://www.ebi.ac.uk/biomodels-main/ # MODEL\u00a01604100005).", + "title": "A rule-based model of insulin signalling pathway.", + "type": "PubMed ID", + "volume": "10", + "year": 2016 + }, + "publicationId": "BIOMD0000000833", + "submissionId": "MODEL1604100005", + "vcsIdentifier": "aab" + }, + "BIOMD0000000834": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "University of Delaware", + "email": "averma@udel.edu", + "external": false, + "name": "Aalap Verma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Verma2016 - Ca(2+) Signal Propagation Along Hepatocyte Cords

This model is described in the article:

Verma A, Makadia H, Hoek JB, Ogunnaike BA, Vadigepalli R.
IEEE Trans Biomed Eng 2016 Oct; 63(10): 2047-2055

Abstract:

The purpose of this study is to model the dynamics of lobular Ca(2+) wave propagation induced by an extracellular stimulus, and to analyze the effect of spatially systematic variations in cell-intrinsic signaling parameters on sinusoidal Ca(2+) response.We developed a computational model of lobular scale Ca(2+) signaling that accounts for receptor- mediated initiation of cell-intrinsic Ca(2+) signal in hepatocytes and its propagation to neighboring hepatocytes through gap junction-mediated molecular exchange.Analysis of the simulations showed that a pericentral-to-periportal spatial gradient in hormone sensitivity and/or rates of IP3 synthesis underlies the Ca(2+) wave propagation. We simulated specific cases corresponding to localized disruptions in the graded pattern of these parameters along a hepatic sinusoid. Simulations incorporating locally altered parameters exhibited Ca(2+) waves that do not propagate throughout the hepatic plate. Increased gap junction coupling restored normal Ca(2+) wave propagation when hepatocytes with low Ca(2+) signaling ability were localized in the midlobular or the pericentral region.Multiple spatial patterns in intracellular signaling parameters can lead to Ca(2+) wave propagation that is consistent with the experimentally observed spatial patterns of Ca(2+) dynamics. Based on simulations and analysis, we predict that increased gap junction-mediated intercellular coupling can induce robust Ca(2+) signals in otherwise poorly responsive hepatocytes, at least partly restoring the sinusoidally oriented Ca (2+) waves.Our bottom-up model of agonist-evoked spatial Ca(2+) patterns can be integrated with detailed descriptions of liver histology to study Ca(2+) regulation at the tissue level.

This model is hosted on BioModels Database and identified by: MODEL1603110003.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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"accession": "27076052", + "authors": [ + { + "name": "Aalap Verma" + }, + { + "name": "Hirenkumar Makadia" + }, + { + "name": "Jan B Hoek", + "orcid": "0000-0001-7127-4218" + }, + { + "name": "Babatunde A Ogunnaike" + }, + { + "name": "Rajanikanth Vadigepalli", + "orcid": "0000-0002-8405-1037" + } + ], + "issue": "10", + "journal": "IEEE transactions on bio-medical engineering", + "link": "http://identifiers.org/pubmed/27076052", + "month": "10", + "pages": "2047-2055", + "synopsis": "

Objective

The purpose of this study is to model the dynamics of lobular Ca(2+) wave propagation induced by an extracellular stimulus, and to analyze the effect of spatially systematic variations in cell-intrinsic signaling parameters on sinusoidal Ca(2+) response.

Methods

We developed a computational model of lobular scale Ca(2+) signaling that accounts for receptor- mediated initiation of cell-intrinsic Ca(2+) signal in hepatocytes and its propagation to neighboring hepatocytes through gap junction-mediated molecular exchange.

Results

Analysis of the simulations showed that a pericentral-to-periportal spatial gradient in hormone sensitivity and/or rates of IP3 synthesis underlies the Ca(2+) wave propagation. We simulated specific cases corresponding to localized disruptions in the graded pattern of these parameters along a hepatic sinusoid. Simulations incorporating locally altered parameters exhibited Ca(2+) waves that do not propagate throughout the hepatic plate. Increased gap junction coupling restored normal Ca(2+) wave propagation when hepatocytes with low Ca(2+) signaling ability were localized in the midlobular or the pericentral region.

Conclusion

Multiple spatial patterns in intracellular signaling parameters can lead to Ca(2+) wave propagation that is consistent with the experimentally observed spatial patterns of Ca(2+) dynamics. Based on simulations and analysis, we predict that increased gap junction-mediated intercellular coupling can induce robust Ca(2+) signals in otherwise poorly responsive hepatocytes, at least partly restoring the sinusoidally oriented Ca (2+) waves.

Significance

Our bottom-up model of agonist-evoked spatial Ca(2+) patterns can be integrated with detailed descriptions of liver histology to study Ca(2+) regulation at the tissue level.", + "title": "Computational Modeling of Spatiotemporal Ca(2+) Signal Propagation Along Hepatocyte Cords.", + "type": "PubMed ID", + "volume": "63", + "year": 2016 + }, + "publicationId": "BIOMD0000000834", + "submissionId": "MODEL1603110003", + "vcsIdentifier": "aab" + }, + "BIOMD0000000835": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "University Medical Center, Groningen", + "email": "shodhanr@gmail.com", + "external": false, + "name": "Shodhan Rao" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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der Schaft", + "orcid": "0000-0002-2383-9234" + }, + { + "name": "Karen van Eunen" + }, + { + "institution": "Microbial Physiology, BioCentrum Amsterdam, Vrije Universiteit, De Boelelaan 1087, NL-1081 HV Amsterdam, BioCentrum Amsterdam, University of Amsterdam, Plantage Muidergracht 12, NL-1018 TV Amsterdam, The Netherlands.", + "name": "Barbara M Bakker", + "orcid": "0000-0001-6274-3633" + }, + { + "institution": "Systems Biology Center for Energy Metabolism and Ageing, University of Groningen, ERIBA, Antonius Deusinglaan 1 9713 AV Groningen, Netherlands. b.jayawardhana@rug.nl.", + "name": "Bayu Jayawardhana", + "orcid": "0000-0003-0987-0347" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/24885656", + "month": "5", + "pages": "52", + "synopsis": "

Background

In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model.

Results

We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%.

Conclusions

The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse-grained yet realistic environment.", + "title": "A model reduction method for biochemical reaction networks.", + "type": "PubMed ID", + "volume": "8", + "year": 2014 + }, + "publicationId": "BIOMD0000000835", + "submissionId": "MODEL1403250000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000836": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Epidemics of panic during a bioterrorist attack--a mathematical model.
Radosavljevic V, Radunovic D, Belojevic G. Med Hypotheses 2009 Sep;73(3):342-6 19423234 ,
Abstract:
A bioterrorist attacks usually cause epidemics of panic in a targeted population. We have presented epidemiologic aspect of this phenomenon as a three-component model--host, information on an attack and social network. We have proposed a mathematical model of panic and counter-measures as the function of time in a population exposed to a bioterrorist attack. The model comprises ordinary differential equations and graphically presented combinations of the equations parameters. Clinically, we have presented a model through a sequence of psychic conditions and disorders initiated by an act of bioterrorism. This model might be helpful for an attacked community to timely and properly apply counter-measures and to minimize human mental suffering during a bioterrorist attack.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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population. 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This model provides an in silico mathematical platform to explore the interactions between chimeric antigen receptor-modified T cells, inflammatory toxicitiy, and the tumour burdens of individual patients.
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This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic.\r\n\r\nTo inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients\u2019 tumour burdens in silico. 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This is a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, used to predict the response of melanoma patients to immune checkpoint inhibitors.
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At increased efficacy of the immune system, e.g., due to immunotherapy, two steady states were found, one leading to uncontrollable tumor growth, and the other resulting in tumor size stabilization. Model analysis indicates that a sufficient increase in the activation of CD8+ T cells results in stable disease, whereas a significant reduction in T-cell exhaustion, another process contributing CD8+ T cell activity, temporarily reduces the tumor mass, but fails to control disease progression in the long run. Importantly, the initial tumor burden influences the response to treatment: small tumors respond better to treatment than larger tumors. In conclusion, our model suggests that disease progression and response to ICI depend on the ratio between activation and exhaustion rates of CD8+ T cells. The analysis of the model provides a foundation for the use of computational methods to personalize immunotherapy.", + "title": "Response of patients with melanoma to immune checkpoint blockade\u00a0- insights gleaned from analysis of a new mathematical mechanistic model.", + "type": "PubMed ID", + "volume": "485", + "year": 2020 + }, + "publicationId": "BIOMD0000000838", + "submissionId": "MODEL1910250002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000839": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a transcriptional-based mathematical model centered on linear combinations of the clock controlled elements (CCEs): E-box, R-box and D-box, used to identify the essential interactions needed to generate phase opposition between the activating CLOCK:BMAL1 and the repressing PER:CRY complexes.
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Electronic address: sofia.figueiredo-almeida@inria.fr.", + "authors": [ + { + "institution": "1600 Universit\u00e9 C\u00f4te d'Azur, Inria, INRA, CNRS, UPMC Univ Paris 06, Biocore team, Sophia Antipolis, France; Universit\u00e9 C\u00f4te d'Azur, CNRS, INSERM, iBV, France. Electronic address: sofia.figueiredo-almeida@inria.fr.", + "name": "Sofia Almeida", + "orcid": "0000-0002-7547-8784" + }, + { + "institution": "1600 Universit\u00e9 C\u00f4te d'Azur, Inria, INRA, CNRS, UPMC Univ Paris 06, Biocore team, Sophia Antipolis, France. Electronic address: http://www-sop.inria.fr/members/Madalena.Chaves/.", + "name": "Madalena Chaves" + }, + { + "institution": "Universit\u00e9 C\u00f4te d'Azur, CNRS, INSERM, iBV, France. Electronic address: http://www.ibv.unice.fr/research-team/delaunay/.", + "name": "Franck Delaunay", + "orcid": "0000-0003-4927-1701" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/31539528", + "month": "1", + "pages": "110015", + "synopsis": "The molecular oscillator of the mammalian circadian clock consists in a dynamical network of genes and proteins whose main regulatory mechanisms occur at the transcriptional level. From a dynamical point of view, the mechanisms leading to an oscillatory solution with an orderly protein peak expression and a clear day/night phase distinction remain unclear. Our goal is to identify the essential interactions needed to generate phase opposition between the activating CLOCK:BMAL1 and the repressing PER:CRY complexes and to better distinguish these two main clock molecular phases relating to rest/activity and fast/feeding cycles. To do this, we develop a transcription-based mathematical model centered on linear combinations of the clock controlled elements (CCEs): E-box, R-box and D-box. Each CCE is responsive to activators and repressors. After model calibration with single-cell data, we explore entrainment and period tuning via interplay with metabolism. Variation of the PER degradation rate \u03b3p, relating to the tau mutation, results in asymmetric changes in the duration of the different clock molecular phases. Time spent at the state of high PER/PER:CRY decreases with \u03b3p, while time spent at the state of high BMAL1 and CRY1, both proteins with activity in promoting insulin sensitivity, remains constant. This result suggests a possible mechanism behind the altered metabolism of tau mutation animals. Furthermore, we expose the clock system to two regulatory inputs, one relating to the fast/feeding cycle and the other to the light-dependent synchronization signaling. We observe the phase difference between these signals to also affect the relative duration of molecular clock states. Simulated circadian misalignment, known to correlate with insulin resistance, leads to decreased duration of BMAL1 expression. 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This is a mathematical model of Vicodin use and abuse used to investigate methods of combating Vicodin abuse in a population of patients who have obtained the drug through prescription. Mathematical descriptions of transitions through acute, chronic, abusive, and in-treatment populations are included.
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The Vicodin abuse problem", + "publication": { + "accession": "31513802", + "affiliation": "School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States; X Computational Physics Division, Los Alamos National Laboratory, Los Alamos, NM, United States. Electronic address: wendy.caldwell@asu.edu.", + "authors": [ + { + "institution": "School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, United States; X Computational Physics Division, Los Alamos National Laboratory, Los Alamos, NM, United States. Electronic address: wendy.caldwell@asu.edu.", + "name": "Wendy K Caldwell", + "orcid": "0000-0001-6076-5636" + }, + { + "institution": "Department of Applied Mathematics, North Carolina State University, Raleigh, NC, United States.", + "name": "Benjamin Freedman" + }, + { + "institution": "Department of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, IL, United States.", + "name": "Luke Settles" + }, + { + "institution": "Georgia Department of Behavioral Health & Developmental Disabilities, Atlanta, GA, United States.", + "name": "Michael M Thomas" + }, + { + "institution": "School of Mathematical and Natural Sciences, Arizona State University, Glendale, AZ,United States.", + "name": "Erika T Camacho", + "orcid": "0000-0003-4286-5224" + }, + { + "institution": "School of Mathematical and Natural Sciences, Arizona State University, Glendale, AZ,United States.", + "name": "Stephen Wirkus" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/31513802", + "month": "12", + "pages": "110003", + "synopsis": "The prescription drug epidemic in the United States has gained attention in recent years. Vicodin, along with its generic version, is the country's mostly widely prescribed pain reliever, and it contains a narcotic component that can lead to physical and chemical dependency. The majority of Vicodin abusers were first introduced via prescription, unlike other drugs which are often experienced for the first time due to experimentation. Most abusers report obtaining their supply from a prescription, either their own or someone else's. Although the problem with prescription drug abuse is well known, there is no standard method of addressing the problem. To better understand how to do this, we develop and analyze a mathematical model of Vicodin use and abuse, considering only those patients who were initially prescribed the drug. Through global sensitivity analysis, we show that focusing efforts on abuse prevention rather than treatment has greater success at reducing the population of Vicodin abusers. 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These results help to provide a paradigm for understanding functional differences between the many redundant, but distinct RNA species thought to act as miRNA sponges in nature, such as long non-coding RNA, pseudogenes, competing mRNA, circular RNA, and3' UTRs.", + "title": "Endogenous miRNA sponges mediate the generation of oscillatory dynamics for a non-coding RNA network.", + "type": "PubMed ID", + "volume": "481", + "year": 2019 + }, + "publicationId": "BIOMD0000000841", + "submissionId": "MODEL1911010001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000842": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "INRA", + "email": "Eric.Reiter@tours.inra.fr", + "external": false, + "name": "Eric Reiter" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model is from the article:
Competing G protein-coupled receptor kinases balance G protein and \u03b2-arrestin signaling
Heitzler D, Durand G, Gallay N, Rizk A, Ahn S, Kim J, Violin JD, Dupuy L, Gauthier C, Piketty V, Cr\u00e9pieux P, Poupon A, Cl\u00e9ment F, Fages F, Lefkowitz RJ, Reiter E. Mol Syst Biol. 2012; 8: 590. 22735336 ,
Abstract:
Seven-transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through \u03b2-arrestins, whose recruitment to the activated receptor is regulated by G protein-coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal-regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT(1A)R) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)-based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well-established function in the desensitization of G-protein activation, GRK2 exerts a strong negative effect on \u03b2-arrestin-dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2-dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT(1A)R, and HEK293 cells expressing other 7TMRs.

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Electronic address: a.gambin@mimuw.edu.pl.", + "name": "Anna Gambin" + }, + { + "institution": "ICM, University of Warsaw, Pawi\u0144skiego 5a, 02-106 Warszawa, Poland. mysz@icm.edu.pl", + "name": "Zuzanna Szyma\u0144ska", + "orcid": "0000-0002-2968-5627" + }, + { + "institution": "Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, ul. Banacha 2, Warsaw 02-097, Poland; Department of Biosystems, Science and Engineering, ETH Zurich, Basel, Switzerland. Electronic address: mikolaj.rybinski@bsse.ethz.ch.", + "name": "Miko\u0142aj Rybi\u0144ski", + "orcid": "0000-0003-1397-6291" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/31181241", + "month": "10", + "pages": "74-101", + "synopsis": "A\u00a0proper response to rapid environmental changes is essential for cell survival and requires efficient modifications in the pattern of gene expression. 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Finally, based on a sensitivity analysis of the model carried out in both deterministic and stochastic settings, we suggest that the regulation of the binding of transcriptional complexes plays a key role in Hsp70 induction upon heat shock. In conclusion, we provide a model that is able to capture the essential dynamics of the Hsp70 heat induction whilst being biologically sound in terms of temperature dependencies, description of protein denaturation and imposed calibration constraints.", + "title": "Biologically sound formal model of Hsp70 heat induction.", + "type": "PubMed ID", + "volume": "478", + "year": 2019 + }, + "publicationId": "BIOMD0000000843", + "submissionId": "MODEL1807160001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000844": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical conductance-based model of the bursting activity in external tufted (ET) cells of the olfactory bulb. The model includes ion-current based descriptions of the mechanisms underlying bursting in ET cells, with facilitation of blocking various currents to characterise bursting behaviour.
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The consequences of chronic infections for the population dynamics of viruses and microbes remains under-explored. In this paper we present a model of chronic infections as well as a model of interactions between lytic and chronic viruses competing for the same microbial population. In the chronic only model, we identify conditions underlying complex bifurcations such as saddle-node, backward and Hopfbifurcations, leading to parameter regions with multiple attractors and/or oscillatory behavior. We then utilize invasion analysis to examine the coupled nonlinear system of microbes, lytic viruses, and chronic viruses. In so doing we find unexpected results, including a regime in which the chronic virus requires the lytic virus for survival, invasion, and persistence. In this regime, lytic viruses decrease total cell densities, so that a subpopulation of chronically infected cells experience decreased niche competition. As such, even when chronically infected cells have a growth disadvantage, lytic viruses can, paradoxically, enable the proliferation of both chronically infected cells and chronic viruses. 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Electronic address: hayriye.gulbudak@louisiana.edu.", + "authors": [ + { + "institution": "Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA. Electronic address: hayriye.gulbudak@louisiana.edu.", + "name": "Hayriye Gulbudak" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA; School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Joshua S Weitz" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/30389532", + "month": "2", + "pages": "65-84", + "synopsis": "Viral infections of microbial cells often culminate in lysis and the release of new virus particles. However, viruses of microbes can also initiate chronic infections, in which new viruses particles are released via budding and without cell lysis. In chronic infections, viral genomes may also be passed on from mother to daughter cells during division. The consequences of chronic infections for the population dynamics of viruses and microbes remains under-explored. In this paper we present a model of chronic infections as well as a model of interactions between lytic and chronic viruses competing for the same microbial population. In the chronic only model, we identify conditions underlying complex bifurcations such as saddle-node, backward and Hopfbifurcations, leading to parameter regions with multiple attractors and/or oscillatory behavior. We then utilize invasion analysis to examine the coupled nonlinear system of microbes, lytic viruses, and chronic viruses. In so doing we find unexpected results, including a regime in which the chronic virus requires the lytic virus for survival, invasion, and persistence. In this regime, lytic viruses decrease total cell densities, so that a subpopulation of chronically infected cells experience decreased niche competition. As such, even when chronically infected cells have a growth disadvantage, lytic viruses can, paradoxically, enable the proliferation of both chronically infected cells and chronic viruses. We discuss the implications of our results for understanding the ecology and long-term evolution of heterogeneous viral strategies.", + "title": "Heterogeneous viral strategies promote coexistence in virus-microbe systems.", + "type": "PubMed ID", + "volume": "462", + "year": 2019 + }, + "publicationId": "BIOMD0000000846", + "submissionId": "MODEL1911100004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000847": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical model of phenylalanine metabolism in plants as influenced by shikimate, with specific evidence of how shikimate dynamics influence phenylalanine metabolism as a function of phenylalanine availability.
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The regulatory role of shikimate in plant phenylalanine metabolism", + "publication": { + "accession": "30412698", + "affiliation": "Max Planck Institute for Mathematics in the Sciences, Inselstra\u00dfe 22, 04103 Leipzig, Germany. Electronic address: zachary.adams@mis.mpg.de.", + "authors": [ + { + "institution": "Max Planck Institute for Mathematics in the Sciences, Inselstra\u00dfe 22, 04103 Leipzig, Germany. Electronic address: zachary.adams@mis.mpg.de.", + "name": "Z P Adams", + "orcid": "0000-0002-7716-6338" + }, + { + "institution": "Department of Biology, University of Victoria, PO Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada. Electronic address: je@uvic.ca.", + "name": "J Ehlting", + "orcid": "0000-0003-2302-696X" + }, + { + "institution": "Department of Mathematics and Statistics, University of Victoria, PO Box 1700 STN CSC, Victoria, BC, V8W 2Y2, Canada. Electronic address: edwards@uvic.ca.", + "name": "R Edwards", + "orcid": "0000-0002-8239-5987" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/30412698", + "month": "2", + "pages": "158-170", + "synopsis": "In higher plants, the amino acid phenylalanine is a substrate of both primary and secondary metabolic pathways. The primary pathway that consumes phenylalanine, protein biosynthesis, is essential for the viability of all cells. Meanwhile, the secondary pathways are not necessary for the survival of individual cells, but benefit of the plant as a whole. Here we focus on the monolignol pathway, a secondary metabolic pathway in the cytosol that rapidly consumes phenylalanine to produce the precursors of lignin during wood formation. In planta\u00a0monolignol biosynthesis involves a series of seemingly redundant steps wherein shikimate, a precursor of phenylalanine synthesized in the plastid, is transiently ligated to the main substrate of the pathway. However, shikimate is not catalytically involved in the reactions of the monolignol pathway, and is only needed for pathway enzymes to recognize their main substrates. After some steps the shikimate moiety is removed unaltered, and the main substrate continues along the pathway. It has been suggested that this portion of the monolignol pathway fulfills a regulatory role in the following way. Low phenylalanine concentrations (viz. availability) correlate with low shikimate concentrations. When shikimate concentratios are low, flux into the monolignol pathway will be limited by means of the steps requiring shikimate. Thus, when the concentration of phenylalanine is low it will be reserved for protein biosynthesis. Here we employ a theoretical approach to test this hypothesis. Simplified versions of plant phenylalanine metabolism are modelled as systems of ordinary differential equations. Our analysis shows that the seemingly redundant steps can be sufficient for the prioritization of protein biosynthesis over the monolignol pathway when the availability of phenylalanine is low, depending on system parameters. Thus, the phenylalanine precursor shikimate may signal low phenylalanine availability to secondary pathways. Because our models have been abstracted from plant phenylalanine metabolism, this mechanism of metabolic signalling, which we call the Precursor Shutoff Valve (PSV), may also be present in other biochemical networks comprised of two pathways that share a common substrate.", + "title": "The regulatory role of shikimate in plant phenylalanine metabolism.", + "type": "PubMed ID", + "volume": "462", + "year": 2019 + }, + "publicationId": "BIOMD0000000847", + "submissionId": "MODEL1911100002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000848": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical model describing the dynamics of the immune response to hepatitis B, which takes into account contributions form innate and adaptive immune responses, as well as cytokines.
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Mathematical model of immune response to hepatitis B", + "publication": { + "accession": "29574141", + "affiliation": "Department of Mathematics, University of Sussex, Brighton BN1 9QH, UK.", + "authors": [ + { + "institution": "Department of Mathematics, University of Sussex, Brighton BN1 9QH, UK.", + "name": "F Fatehi Chenar", + "orcid": "0000-0003-3743-5772" + }, + { + "institution": "Department of Mathematics, University of Sussex, Brighton BN1 9QH, UK.", + "name": "Y N Kyrychko" + }, + { + "institution": "Department of Mathematics, University of Sussex, Brighton BN1 9QH, UK. 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Effects of nucleoside analogues and interferon treatments are analysed, and the critical drug efficiency is determined.", + "title": "Mathematical model of immune response to hepatitis B.", + "type": "PubMed ID", + "volume": "447", + "year": 2018 + }, + "publicationId": "BIOMD0000000848", + "submissionId": "MODEL1911110001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000849": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "Zuse Institute Berlin", + "email": "ploentzke@zib.de", + "external": false, + "name": "Julia Pl\u00f6ntzke" + } + ] + }, + "curationStatus": "CURATED", + "description": "M. Berg, J. Pl\u00f6ntzke, S. Leonhard-Marek, K.E. M\u00fcller & S. R\u00f6blitz. A dynamic model to simulate potassium balance in dairy cows. Journal of Dairy Science 100, 12 (2017).

High-performing dairy cows require a particular composition of nutritional ingredients, adapted to their individual requirements and depending on their production status. The optimal dimensioning of minerals in the diet, one being potassium, is indispensable for the prevention of imbalances. Potassium balance in cows is the result of potassium intake, distribution in the organism, and excretion, and it is closely related to glucose and electrolyte metabolism. In this paper, we present a dynamical model for potassium balance in lactating and nonlactating dairy cows based on ordinary differential equations. Parameter values were obtained from clinical trial data and from the literature. To verify the consistency of the model, we present simulation outcomes for 3 different scenarios: potassium balance in (1) nonlactating cows with varying feed intake, (2) nonlactating cows with varying potassium fraction in the diet, and (3) lactating cows with varying milk production levels. 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This is the simple version of the two mathematical models presented by Ho et al. It is a model comprised of simple ordinary differential equations describing the overall epidemic dynamics of influenza infection in the 2017-2018 winter influenza season in Hong Kong.
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Electronic address: shingheiho@gmail.com.", + "authors": [ + { + "institution": "Faculty of Education, University of Hong Kong, Pokfulam Road, Hong Kong. Electronic address: shingheiho@gmail.com.", + "name": "Shing Hei Ho" + }, + { + "institution": "Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.", + "name": "Daihai He", + "orcid": "0000-0003-3253-654X" + }, + { + "institution": "Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK. r.a.eftimie@dundee.ac.uk.", + "name": "Eftimie R", + "orcid": "0000-0002-9726-1498" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/31128142", + "month": "9", + "pages": "74-94", + "synopsis": "Two mathematical models described by simple ordinary differential equations are developed to investigate the Hong Kong influenza epidemic during 2017-2018 winter, based on overall epidemic dynamics and different influenza subtypes. The first model, describing the overall epidemic dynamics, provides the starting data for the second model which different influenza subtypes, and whose dynamics is further investigated. Weekly data from December 2017 to May 2018 are obtained from the data base of the Centre of Health Protection in Hong Kong, and used to parametrise the models. With the help of these models, we investigate the impact of different vaccination strategies and determine the corresponding critical vaccination coverage for different vaccine efficacies. The results suggest that at least 72% of Hong Kong population should have been vaccinated during 2017-2018 winter to prevent the seasonal epidemic by herd immunity (while data showed that only a maximum of 11.6% of the population were vaccinated). Our results also show that the critical vaccination coverage decreases with increasing vaccine efficacy, and the increase in one influenza subtype vaccine efficacy may lead to an increase in infections caused by a different subtype.", + "title": "Mathematical models of transmission dynamics and vaccine strategies in Hong Kong during the 2017-2018 winter influenza season.", + "type": "PubMed ID", + "volume": "476", + "year": 2019 + }, + "publicationId": "BIOMD0000000851", + "submissionId": "MODEL1911120003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000852": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical model investigating the role of chronic inflammation in the development and progression of myeloproliferative neoplasms (MPNs). The model describes the proliferation from stem cells to mature cells, including mutations of healthy stem cells to become malignant stem cells. The model also features a simple inflammatory coupling coping with cell death and affecting the basic model beneath.
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Mathematical modelling as a proof of concept for MPNs as a human inflammation model for cancer development", + "publication": { + "accession": "28859112", + "affiliation": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "authors": [ + { + "institution": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "name": "Morten Andersen", + "orcid": "0000-0001-7582-0685" + }, + { + "institution": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "name": "Zamra Sajid" + }, + { + "institution": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "name": "Rasmus K Pedersen", + "orcid": "0000-0001-5946-8220" + }, + { + "institution": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "name": "Johanne Gudmand-Hoeyer", + "orcid": "0000-0002-4972-0653" + }, + { + "institution": "Department of Laboratory Medicine at Boston Children's Hospital, Boston, Massachusetts, United States of America.", + "name": "Christina Ellervik", + "orcid": "0000-0002-3088-4375" + }, + { + "institution": "Department of Hematology, Zealand University Hospital, University of Copenhagen, Roskilde, Denmark.", + "name": "Vibe Skov" + }, + { + "institution": "Department of Hematology, Zealand University Hospital, University of Copenhagen, Roskilde, Denmark.", + "name": "Lasse Kj\u00e6r" + }, + { + "institution": "Department of Pathology, Zealand University Hospital, University of Copenhagen, Roskilde, Denmark.", + "name": "Niels Pallisgaard", + "orcid": "0000-0002-0749-0810" + }, + { + "institution": "Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.", + "name": "Torben A Kruse" + }, + { + "institution": "Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.", + "name": "Mads Thomassen" + }, + { + "institution": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "name": "Jesper Troelsen", + "orcid": "0000-0002-4177-3267" + }, + { + "institution": "Department of Hematology, Zealand University Hospital, University of Copenhagen, Roskilde, Denmark.", + "name": "Hans Carl Hasselbalch", + "orcid": "0000-0003-3936-8032" + }, + { + "institution": "Department of Science and Environment, Roskilde University, Roskilde, Denmark.", + "name": "Johnny T Ottesen" + } + ], + "issue": "8", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/28859112", + "month": "0", + "pages": "e0183620", + "synopsis": "The chronic Philadelphia-negative myeloproliferative neoplasms (MPNs) are acquired stem cell neoplasms which ultimately may transform to acute myelogenous leukemia. Most recently, chronic inflammation has been described as an important factor for the development and progression of MPNs in the biological continuum from early cancer stage to the advanced myelofibrosis stage, the MPNs being described as \"A Human Inflammation Model for Cancer Development\". This novel concept has been built upon clinical, experimental, genomic, immunological and not least epidemiological studies. Only a few studies have described the development of MPNs by mathematical models, and none have addressed the role of inflammation for clonal evolution and disease progression. Herein, we aim at using mathematical modelling to substantiate the concept of chronic inflammation as an important trigger and driver of MPNs.The basics of the model describe the proliferation from stem cells to mature cells including mutations of healthy stem cells to become malignant stem cells. We include a simple inflammatory coupling coping with cell death and affecting the basic model beneath. First, we describe the system without feedbacks or regulatory interactions. Next, we introduce inflammatory feedback into the system. Finally, we include other feedbacks and regulatory interactions forming the inflammatory-MPN model. Using mathematical modeling, we add further proof to the concept that chronic inflammation may be both a trigger of clonal evolution and an important driving force for MPN disease progression. Our findings support intervention at the earliest stage of cancer development to target the malignant clone and dampen concomitant inflammation.", + "title": "Mathematical modelling as a proof of concept for MPNs as a human inflammation model for cancer development.", + "type": "PubMed ID", + "volume": "12", + "year": 2017 + }, + "publicationId": "BIOMD0000000852", + "submissionId": "MODEL1911120005", + "vcsIdentifier": "aai" + }, + "BIOMD0000000853": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical model describing the formation of long-term potentiation (LTP) at the Schaffer collateral of CA1 pyramidal cell synapse. The model consists of nine ordinary differential equations that denote synaptic states associated with different degrees of LTP, as well as the levels of regulatory proteins involved in the formation and maintenance of LTP.
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M. Keck Center for the Neurobiology of Learning and Memory, McGovern Medical School of the University of Texas Health Science Center at Houston, Houston, TX 77030, USA. Electronic address: Paul.D.Smolen@uth.tmc.edu.", + "authors": [ + { + "institution": "Laboratory of Origin: Department of Neurobiology and Anatomy, W. M. Keck Center for the Neurobiology of Learning and Memory, McGovern Medical School of the University of Texas Health Science Center at Houston, Houston, TX 77030, USA. Electronic address: Paul.D.Smolen@uth.tmc.edu.", + "name": "Paul Smolen" + }, + { + "institution": "Laboratory of Origin: Department of Neurobiology and Anatomy, W. M. Keck Center for the Neurobiology of Learning and Memory, McGovern Medical School of the University of Texas Health Science Center at Houston, Houston, TX 77030, USA.", + "name": "Douglas A Baxter" + }, + { + "institution": "Laboratory of Origin: Department of Neurobiology and Anatomy, W. M. Keck Center for the Neurobiology of Learning and Memory, McGovern Medical School of the University of Texas Health Science Center at Houston, Houston, TX 77030, USA.", + "name": "John H Byrne" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/30138630", + "month": "11", + "pages": "79-87", + "synopsis": "The transition from early long-term potentiation (E-LTP) to late long-term potentiation (L-LTP) is a multistep process that involves both protein synthesis and degradation. The ways in which these two opposing processes interact to establish L-LTP are not well understood, however. For example, L-LTP is attenuated by inhibiting either protein synthesis or proteasome-dependent degradation prior to and during a tetanic stimulus (e.g., Huang et\u00a0al., 1996; Karpova et\u00a0al., 2006), but paradoxically, L-LTP is not attenuated when synthesis and degradation are inhibited simultaneously (Fonseca\u00a0et\u00a0al.,\u00a02006). These paradoxical results suggest that counter-acting 'positive' and 'negative' proteins regulate L-LTP. To investigate the basis of this paradox, we developed a model of LTP at the Schaffer collateral to CA1 pyramidal cell synapse. The model consists of nine ordinary differential equations that describe the levels of both positive- and negative-regulator proteins (PP and NP, respectively) and the transitions among five discrete synaptic states, including a basal state (BAS), three states corresponding to E-LTP (EP1, EP2, and ED), and a L-LTP state (LP). An LTP-inducing stimulus: 1) initiates the transition from BAS to EP1 and from EP1 to EP2; 2) initiates the synthesis of PP and NP; and finally; 3) activates the ubiquitin-proteasome system (UPS), which in turn, mediates transitions of EP1 and EP2 to ED and the degradation of NP. The conversion of E-LTP to L-LTP is mediated by the PP-dependent transition from ED to LP, whereas NP mediates reversal of EP2 to BAS. We found that the inclusion of the five discrete synaptic states was necessary to simulate key empirical observations: 1) normal L-LTP, 2) block of L-LTP by either proteasome inhibitor or protein synthesis inhibitor alone, and 3) preservation of L-LTP when both inhibitors are applied together. Although our model is abstract, elements of the model can be correlated with specific molecular processes. Moreover, the model correctly captures the dynamics of protein synthesis- and degradation-dependent phases of LTP, and it makes testable predictions, such as a unique synaptic state (ED) that precedes the transition from E-LTP to L-LTP, and a well-defined time window for the action of the UPS (i.e., during the transitions from EP1 and EP2 to ED). Tests of these predictions will provide new insights into the processes and dynamics of long-term synaptic plasticity.", + "title": "Paradoxical LTP maintenance with inhibition of protein synthesis and the proteasome suggests a novel protein synthesis requirement for early LTP reversal.", + "type": "PubMed ID", + "volume": "457", + "year": 2018 + }, + "publicationId": "BIOMD0000000853", + "submissionId": "MODEL1911120006", + "vcsIdentifier": "aai" + }, + "BIOMD0000000854": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a simple, linear, four-compartment ordinary differential equation (ODE) model Akt activation that tracks both the phosphorylation state and the physical location (cytosol, plasma membrane) of Akt.
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The Akt switch model", + "publication": { + "accession": "26992575", + "affiliation": "School of Mathematics and Statistics, UNSW, Sydney, Australia. Electronic address: catheryn.gray@unsw.edu.au.", + "authors": [ + { + "institution": "School of Mathematics and Statistics, UNSW, Sydney, Australia. Electronic address: catheryn.gray@unsw.edu.au.", + "name": "Catheryn W Gray" + }, + { + "institution": "School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia. a.coster@unsw.edu.au.", + "name": "Adelle C F Coster", + "orcid": "0000-0002-5572-6832" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/26992575", + "month": "6", + "pages": "103-111", + "synopsis": "Akt/PKB is a biochemical regulator that functions as an important cross-talk node between several signalling pathways in the mammalian cell. In particular, Akt is a key mediator of glucose transport in response to insulin. The phosphorylation (activation) of only a small percentage of the Akt pool of insulin-sensitive cells results in maximal translocation of glucose transporter 4 (GLUT4) to the plasma membrane (PM). This enables the diffusion of glucose into the cell. The dysregulation of Akt signalling is associated with the development of diabetes, cancer and cardiovascular disease. Akt is synthesised in the cytoplasm in the inactive state. Under the influence of insulin, it moves to the PM, where it is phosphorylated to form pAkt. Although phosphorylation occurs only at the PM, pAkt is found in many cellular locations, including the PM, the cytoplasm, and the nucleus. Indeed, the spatial distribution of pAkt within the cell appears to be an important determinant of downstream regulation. Here we present a simple, linear, four-compartment ordinary differential equation (ODE) model of Akt activation that tracks both the biochemical state and the physical location of Akt. This model embodies the main features of the activation of this important cross-talk node and is consistent with the experimental data. In particular, it allows different downstream signalling motifs without invoking separate feedback pathways. Moreover, the model is computationally tractable, readily analysed, and elucidates some of the apparent anomalies in insulin signalling via Akt.", + "title": "The Akt switch model: Is location sufficient?", + "type": "PubMed ID", + "volume": "398", + "year": 2016 + }, + "publicationId": "BIOMD0000000854", + "submissionId": "MODEL1911130001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000855": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is an ordinary differential equation-based mathematical model describing the inflammatory phase of the wound healing response. The model describes the interactions in the wound between wound debris, pathogens, neutrophils and macrophages, as well as the modulation of these interactions by estrogen and cortisol.
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Modeling the effects of systemic mediators on the inflammatory phase of wound healing", + "publication": { + "accession": "25446708", + "affiliation": "Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284-2014, USA; The VCU Johnson Center, Virginia Commonwealth University Medical Center, Richmond, VA 23298-0614, USA.", + "authors": [ + { + "institution": "Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284-2014, USA; The VCU Johnson Center, Virginia Commonwealth University Medical Center, Richmond, VA 23298-0614, USA.", + "name": "Racheal L Cooper" + }, + { + "institution": "Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284-2014, USA; Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284-2030, USA; The VCU Johnson Center, Virginia Commonwealth University Medical Center, Richmond, VA 23298-0614, USA.", + "name": "Rebecca A Segal" + }, + { + "institution": "The VCU Johnson Center, Virginia Commonwealth University Medical Center, Richmond, VA 23298-0614, USA; Department of Biochemistry & Molecular Biology, Virginia Commonwealth University Medical Center, Richmond, VA 23298-0614, USA.", + "name": "Robert F Diegelmann" + }, + { + "institution": "Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284-2014, USA; The VCU Johnson Center, Virginia Commonwealth University Medical Center, Richmond, VA 23298-0614, USA. Electronic address: areynolds2@vcu.edu.", + "name": "Angela M Reynolds" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/25446708", + "month": "2", + "pages": "86-99", + "synopsis": "The normal wound healing response is characterized by a progression from clot formation, to an inflammatory phase, to a repair phase, and finally, to remodeling. In many chronic wounds there is an extended inflammatory phase that stops this progression. In order to understand the inflammatory phase in more detail, we developed an ordinary differential equation model that accounts for two systemic mediators that are known to modulate this phase, estrogen (a protective hormone during wound healing) and cortisol (a hormone elevated after trauma that slows healing). This model describes the interactions in the wound between wound debris, pathogens, neutrophils and macrophages and the modulation of these interactions by estrogen and cortisol. A collection of parameter sets, which qualitatively match published data on the dynamics of wound healing, was chosen using Latin Hypercube Sampling. This collection of parameter sets represents normal healing in the population as a whole better than one single parameter set. Including the effects of estrogen and cortisol is a necessary step to creating a patient specific model that accounts for gender and trauma. Utilization of math modeling techniques to better understand the wound healing inflammatory phase could lead to new therapeutic strategies for the treatment of chronic wounds. This inflammatory phase model will later become the inflammatory subsystem of our full wound healing model, which includes fibroblast activity, collagen accumulation and remodeling.", + "title": "Modeling the effects of systemic mediators on the inflammatory phase of wound healing.", + "type": "PubMed ID", + "volume": "367", + "year": 2015 + }, + "publicationId": "BIOMD0000000855", + "submissionId": "MODEL1911130002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000856": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "Department of Biochemistry, University of Oxford, OX1 3QU Oxford, United Kingdom; stefan.heldt@bioch.ox.ac.uk bela.novak@bioch.ox.ac.uk.", + "email": "st.heldt@gmail.com", + "external": false, + "name": "Frank Stefan Heldt", + "orcid": "0000-0002-9276-5013" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model is decribed in the article:Dilution and titration of cell-cycle regulators may control cell size in budding yeastFrank S. Heldt, Reece Lunstone, John J. Tyson, Bela NovakPLoS Comput Biol, October 2018, 14(10), e1006548, doi: 10.1371/journal.pcbi.1006548Abstract:The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the START transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cell\u2019s overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect \u2018sizer\u2019 behaviour in G1 and a \u2018timer\u2019 in S/G2/M, which combine to yield an \u2018adder\u2019 over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.
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The model can describe the complete starch consumption during the night for plants adapted to a certain light/dark regime when grown on sufficient nitrate and can account for an increased accumulation of starch observed under nitrate limitation.", + "title": "Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light/dark conditions.", + "type": "PubMed ID", + "volume": "402", + "year": 2016 + }, + "publicationId": "BIOMD0000000859", + "submissionId": "MODEL1911130006", + "vcsIdentifier": "aai" + }, + "BIOMD0000000860": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + }, + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2017- Role of microRNAs inosteoarthritis (Positive Feedforward Incoherent By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100004.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Proctor2017- Role of microRNAs inosteoarthritis (Positive Feedback By Micro RNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. 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Background

Numerous oncology combination therapies involving modulators of the cancer immune cycle are being developed, yet quantitative simulation models predictive of outcome are lacking. We here present a model-based analysis of tumor size dynamics and immune markers, which integrates experimental data from multiple studies and provides a validated simulation framework predictive of biomarkers and anti-tumor response rates, for untested dosing sequences and schedules of combined radiation (RT) and anti PD-(L)1 therapies.

Methods

A quantitative systems pharmacology model, which includes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features, was developed to reproduce experimental data of CT26 tumor size dynamics upon administration of RT and/or a pharmacological IO treatment such as an anti-PD-L1 agent. Variability in individual tumor size dynamics was taken into account using a mixed-effects model at the level of tumor-infiltrating T cell influx.

Results

The model allowed for a detailed quantitative understanding of the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation, under these treatments. The model showed that the ability of T cells to infiltrate tumor tissue is a primary determinant of variability in individual tumor size dynamics and tumor response. The model was further used as an in silico evaluation tool to quantitatively predict, prospectively, untested treatment combination schedules and sequences. We demonstrate that anti-PD-L1 administration prior to, or concurrently with RT reveal further synergistic effects, which, according to the model, may materialize due to more favorable dynamics between RT-induced immuno-modulation and reduced immuno-suppression of T cells through anti-PD-L1.

Conclusions

This study provides quantitative mechanistic explanations of the links between RT and anti-tumor immune responses, and describes how optimized combinations and schedules of immunomodulation and radiation may tip the immune balance in favor of the host, sufficiently to lead to tumor shrinkage or rejection.", + "title": "Radiation and PD-(L)1 treatment combinations: immune response and dose optimization via a predictive systems model.", + "type": "PubMed ID", + "volume": "6", + "year": 2018 + }, + "publicationId": "BIOMD0000000863", + "submissionId": "MODEL1911140003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000864": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Newcastle University", + "email": "carole.proctor@ncl.ac.uk", + "external": false, + "name": "Carole Proctor" + }, + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Proctor2017- Role of microRNAs inosteoarthritis (Negative Feedback By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100001.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-\u03baB, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.", + "title": "Computer simulation models as a tool to investigate the role of microRNAs in osteoarthritis.", + "type": "PubMed ID", + "volume": "12", + "year": 2017 + }, + "publicationId": "BIOMD0000000864", + "submissionId": "MODEL1610100001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000865": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical mechanistic immunobiochemical model that incorporates T cell pathways that control programmed cell death protein 1 (PD-1) expression. A core component of the model is a kinetic motif, termed a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), which reflects known interactions between IRF4, Blimp-1, and Bcl-6.
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Immunobiochemical reconstruction of influenza lung infection-melanoma skin cancer interactions", + "publication": { + "accession": "30745900", + "affiliation": "Center for Quantitative Biology, Rutgers University, Piscataway, NJ, United States.", + "authors": [ + { + "institution": "Center for Quantitative Biology, Rutgers University, Piscataway, NJ, United States.Clinical Investigations and Precision Therapeutics Program, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States.", + "name": "Evgeni V Nikolaev" + }, + { + "institution": "Section of Surgical Oncology Research, Division of Surgical Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, United States.Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, United States.", + "name": "Andrew Zloza" + }, + { + "institution": "Department of Mathematics and Center for Quantitative Biology, Rutgers University, New Brunswick, NJ 08903, USA. Electronic address: eduardo.sontag@rutgers.edu.", + "name": "Sontag ED", + "orcid": "0000-0001-8020-5783" + } + ], + "journal": "Frontiers in immunology", + "link": "http://identifiers.org/pubmed/30745900", + "month": "0", + "pages": "4", + "synopsis": "It was recently reported that acute influenza infection of the lung promoted distal melanoma growth in the dermis of mice. Melanoma-specific CD8+ T cells were shunted to the lung in the presence of the infection, where they expressed high levels of inflammation-induced cell-activation blocker PD-1, and became incapable of migrating back to the tumor site. At the same time, co-infection virus-specific CD8+ T cells remained functional while the infection was cleared. It was also unexpectedly found that PD-1 blockade immunotherapy reversed this effect. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates T cell pathways that control PD-1 expression. A core component of our model is a kinetic motif, which we call a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6. The different activity levels of the PD-1 DIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes. Collectively, the model allowed us to put forward a few working hypotheses as follows: (i) the melanoma-specific CD8+ T cells re-circulating with the blood flow enter the lung where they express high levels of inflammation-induced cell-activation blocker PD-1 in the presence of infection; (ii) when PD-1 receptors interact with abundant PD-L1, constitutively expressed in the lung, T cells loose motility; (iii) at the same time, virus-specific cells adapt to strong stimulation by their cognate antigen by lowering the transiently-elevated expression of PD-1, remaining functional and mobile in the inflamed lung, while the infection is cleared. The role that T cell receptor (TCR) activation and feedback loops play in the underlying processes are also highlighted and discussed. We hope that the results reported in our study could potentially contribute to the advancement of immunological approaches to cancer treatment and, as well, to a better understanding of a broader complexity of fundamental interactions between pathogens and tumors.", + "title": "Immunobiochemical Reconstruction of Influenza Lung Infection-Melanoma Skin Cancer Interactions.", + "type": "PubMed ID", + "volume": "10", + "year": 2019 + }, + "publicationId": "BIOMD0000000865", + "submissionId": "MODEL1911180001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000866": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "University of California, Los Angeles", + "email": "Simon.mitchell@ucla.edu", + "external": false, + "name": "Simon Mitchell", + "orcid": "0000-0003-1091-6349" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model represents NIK-dependent p100 processing into p52 with Michaelis-Menten kinetics. While this model shows identical dose-response to the mass action representation, when IkBd degradation is included the dose-response is no-longer monotonic in mass action models due to substrate complex competition.

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"link": "http://identifiers.org/doi/10.1073/pnas.1816000116", + "month": "4", + "synopsis": "Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NF\u03baB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (I\u03baB\u03b4) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose\u2013response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis\u2013Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context.", + "title": "Substrate complex competition is a regulatory motif that allows NF\u03baB RelA to license but not amplify NF\u03baB RelB", + "type": "DOI", + "year": 2019 + }, + "publicationId": "BIOMD0000000866", + "submissionId": "MODEL1903280001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000867": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical model comprised of non-linear ordinary differential equations describing the dynamic relationship between hypoxia-inducible factor-1 alpha (HIF-1a) mRNA, HIF-1a protein, and interleukin-15-mediated upstream signalling events in natural killer cells from human blood. Regulatory expressions are also included for mammalian target of rapamycin (mTOR), nuclear factor-kappa beta, and signal transducer and activator of transcription 3 (STAT3).
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Interleukin-15 Signaling in HIF-1a Regulation in Natural Killer Cells", + "publication": { + "accession": "31681292", + "affiliation": "Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.", + "authors": [ + { + "institution": "Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.", + "name": "Anna Coulibaly", + "orcid": "0000-0001-5746-4496" + }, + { + "institution": "Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany.", + "name": "Anja Bettendorf" + }, + { + "institution": "Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany.Institute for Applied Mathematics, Heidelberg University, Heidelberg, Germany.", + "name": "Ekaterina Kostina" + }, + { + "institution": "Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.", + "name": "Ana Sofia Figueiredo" + }, + { + "institution": "Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.", + "name": "Sonia Y Vel\u00e1squez", + "orcid": "0009-0007-0777-8455" + }, + { + "institution": "Interdisciplinary Center for Scientific Computing, Heidelberg University, Heidelberg, Germany.", + "name": "Hans-Georg Bock" + }, + { + "institution": "Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.", + "name": "Manfred Thiel" + }, + { + "institution": "Department of Anesthesiology and Surgical Intensive Care Medicine, Medical Faculty Mannheim, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany.", + "name": "Holger A Lindner", + "orcid": "0000-0002-6679-4355" + }, + { + "institution": "Institute for Applied Mathematics, Heidelberg University, Heidelberg, Germany.", + "name": "Maria Vittoria Barbarossa", + "orcid": "0000-0001-8788-1709" + } + ], + "journal": "Frontiers in immunology", + "link": "http://identifiers.org/pubmed/31681292", + "month": "0", + "pages": "2401", + "synopsis": "Natural killer (NK) cells belong to the first line of host defense against infection and cancer. Cytokines, including interleukin-15 (IL-15), critically regulate NK cell activity, resulting in recognition and direct killing of transformed and infected target cells. NK cells have to adapt and respond in inflamed and often hypoxic areas. Cellular stabilization and accumulation of the transcription factor hypoxia-inducible factor-1\u03b1 (HIF-1\u03b1) is a key mechanism of the cellular hypoxia response. At the same time, HIF-1\u03b1 plays a critical role in both innate and adaptive immunity. While the HIF-1\u03b1 hydroxylation and degradation pathway has been recently described with the help of mathematical methods, less is known concerning the mechanistic mathematical description of processes regulating the levels of HIF-1\u03b1 mRNA and protein. In this work we combine mathematical modeling with experimental laboratory analysis and examine the dynamic relationship between HIF-1\u03b1 mRNA, HIF-1\u03b1 protein, and IL-15-mediated upstream signaling events in NK cells from human blood. We propose a system of non-linear ordinary differential equations with positive and negative feedback loops for describing the complex interplay of HIF-1\u03b1 regulators. The experimental design is optimized with the help of mathematical methods, and numerical optimization techniques yield reliable parameter estimates. The mathematical model allows for the investigation and prediction of HIF-1\u03b1 stabilization under different inflammatory conditions and provides a better understanding of mechanisms mediating cellular enrichment of HIF-1\u03b1. Thanks to the combination of in vitro experimental data and in silico predictions we identified the mammalian target of rapamycin (mTOR), the nuclear factor-\u03baB (NF-\u03baB), and the signal transducer and activator of transcription 3 (STAT3) as central regulators of HIF-1\u03b1 accumulation. We hypothesize that the regulatory pathway proposed here for NK cells can be extended to other types of immune cells. Understanding the molecular mechanisms involved in the dynamic regulation of the HIF-1\u03b1 pathway in immune cells is of central importance to the immune cell function and could be a promising strategy in the design of treatments for human inflammatory diseases and cancer.", + "title": "Interleukin-15 Signaling in HIF-1\u03b1 Regulation in Natural Killer Cells, Insights Through Mathematical Models.", + "type": "PubMed ID", + "volume": "10", + "year": 2019 + }, + "publicationId": "BIOMD0000000867", + "submissionId": "MODEL1911180002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000868": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "University of California, Los Angeles", + "email": "Simon.mitchell@ucla.edu", + "external": false, + "name": "Simon Mitchell", + "orcid": "0000-0003-1091-6349" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model represents NIK-dependent p100 processing into p52 with mass action kinetics. While this model shows identical dose-response to the Michaelis-Menten representation, when IkBd degradation is included the dose-response is no longer monotonic in mass action models due to substrate complex competition.

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In the NF\u03baB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (I\u03baB\u03b4) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose\u2013response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis\u2013Menten kinetics, but require more detailed mass action formulations. 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This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with Michaelis-Menten kinetics. Compare this Michaelis-Menten representation to the mass action model in which the dose-response to increasing p100 mRNA is no longer monotonic due to substrate complex competition.

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This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with mass action kinetics. Compare this mass action representation to the Michaelis-Menten model. In this model the dose-response to increasing p100 mRNA is not monotonic due to substrate complex competition.

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This model represents NIK-dependent p100 processing into p52 followed by binding to RelB and NIK-dependent IkBd degradation. This model assesses the impact of substrate complex competition on RelB-p52.

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United States of America", + "link": "http://identifiers.org/doi/10.1073/pnas.1816000116", + "month": "4", + "synopsis": "Signaling pathways often share molecular components, tying the activity of one pathway to the functioning of another. In the NF\u03baB signaling system, distinct kinases mediate inflammatory and developmental signaling via RelA and RelB, respectively. Although the substrates of the developmental, so-called noncanonical, pathway are induced by inflammatory/canonical signaling, crosstalk is limited. Through dynamical systems modeling, we identified the underlying regulatory mechanism. We found that as the substrate of the noncanonical kinase NIK, the nfkb2 gene product p100, transitions from a monomer to a multimeric complex, it may compete with and inhibit p100 processing to the active p52. Although multimeric complexes of p100 (I\u03baB\u03b4) are known to inhibit preexisting RelA:p50 through sequestration, here we report that p100 complexes can inhibit the enzymatic formation of RelB:p52. We show that the dose\u2013response systems properties of this complex substrate competition motif are poorly accounted for by standard Michaelis\u2013Menten kinetics, but require more detailed mass action formulations. In sum, although tonic inflammatory signaling is required for adequate expression of the noncanonical pathway precursors, the substrate complex competition motif identified here can prevent amplification of the active RelB:p52 dimer in elevated inflammatory conditions to ensure reliable RelB-dependent developmental signaling independent of inflammatory context.", + "title": "Substrate complex competition is a regulatory motif that allows NF\u03baB RelA to license but not amplify NF\u03baB RelB", + "type": "DOI", + "year": 2019 + }, + "publicationId": "BIOMD0000000871", + "submissionId": "MODEL1904030001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000872": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "Virginia Polytechnic Institute and State University", + "email": "meghna89@vt.edu", + "external": false, + "name": "Meghna Verma" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a COPASI version of the HIV/HPV coinfection model submitted to PLoS One.Title: Modeling the mechanisms by which HIV-associated immunosuppression influences HPV persistence at the oral mucosaAuthors: Meghna Verma*, Samantha Erwin*, Vida Abedi, Raque Hontecillas, Stefan Hoops, Andrew Leber, Josep Bassaganya-Riera, Stanca Ciupe* Contributed equally to the workCorresponding Authors: Josep Bassaganya-Riera and Stanca Ciupe
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This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci 1993 Mar;114(1):81-125 8096155 ,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considersfour populations: uninfected T cells, latently infected T cells, activelyinfected T cells, and free virus. Using this model we show that many of thepuzzling quantitative features of HIV infection can be explained simply. We alsoconsider effects of AZT on viral growth and T-cell population dynamics. Themodel exhibits two steady states, an uninfected state in which no virus ispresent and an endemically infected state, in which virus and infected T cellsare present. We show that if N, the number of infectious virions produced peractively infected T cell, is less a critical value, Ncrit, then the uninfectedstate is the only steady state in the nonnegative orthant, and this state isstable. For N > Ncrit, the uninfected state is unstable, and the endemicallyinfected state can be either stable, or unstable and surrounded by a stablelimit cycle. Using numerical bifurcation techniques we map out the parameterregimes of these various behaviors. oscillatory behavior seems to lie outsidethe region of biologically realistic parameter values. When the endemicallyinfected state is stable, it is characterized by a reduced number of T cellscompared with the uninfected state. Thus T-cell depletion occurs through theestablishment of a new steady state. The dynamics of the establishment of thisnew steady state are examined both numerically and via the quasi-steady-stateapproximation. We develop approximations for the dynamics at early times inwhich the free virus rapidly binds to T cells, during an intermediate time scalein which the virus grows exponentially, and a third time scale on which viralgrowth slows and the endemically infected steady state is approached. Using thequasi-steady-state approximation the model can be simplified to two ordinarydifferential equations the summarize much of the dynamical behavior. We computethe level of T cells in the endemically infected state and show how that levelvaries with the parameters in the model. The model predicts that different viralstrains, characterized by generating differing numbers of infective virionswithin infected T cells, can cause different amounts of T-cell depletion andgenerate depletion at different rates. Two versions of the model are studied. Inone the source of T cells from precursors is constant, whereas in the other thesource of T cells decreases with viral load, mimicking the infection and killingof T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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a model for the interaction of HIV with CD4+ T cells that considers four populations: uninfected T cells, latently infected T cells, actively infected T cells, and free virus. Using this model we show that many of the puzzling quantitative features of HIV infection can be explained simply. We also consider effects of AZT on viral growth and T-cell population dynamics. The model exhibits two steady states, an uninfected state in which no virus is present and an endemically infected state, in which virus and infected T cells are present. We show that if N, the number of infectious virions produced per actively infected T cell, is less a critical value, Ncrit, then the uninfected state is the only steady state in the nonnegative orthant, and this state is stable. For N > Ncrit, the uninfected state is unstable, and the endemically infected state can be either stable, or unstable and surrounded by a stable limit cycle. Using numerical bifurcation techniques we map out the parameter regimes of these various behaviors. oscillatory behavior seems to lie outside the region of biologically realistic parameter values. When the endemically infected state is stable, it is characterized by a reduced number of T cells compared with the uninfected state. Thus T-cell depletion occurs through the establishment of a new steady state. The dynamics of the establishment of this new steady state are examined both numerically and via the quasi-steady-state approximation. We develop approximations for the dynamics at early times in which the free virus rapidly binds to T cells, during an intermediate time scale in which the virus grows exponentially, and a third time scale on which viral growth slows and the endemically infected steady state is approached. Using the quasi-steady-state approximation the model can be simplified to two ordinary differential equations the summarize much of the dynamical behavior. We compute the level of T cells in the endemically infected state and show how that level varies with the parameters in the model. The model predicts that different viral strains, characterized by generating differing numbers of infective virions within infected T cells, can cause different amounts of T-cell depletion and generate depletion at different rates. Two versions of the model are studied. In one the source of T cells from precursors is constant, whereas in the other the source of T cells decreases with viral load, mimicking the infection and killing of T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)", + "title": "Dynamics of HIV infection of CD4+ T cells.", + "type": "PubMed ID", + "volume": "114", + "year": 1993 + }, + "publicationId": "BIOMD0000000874", + "submissionId": "MODEL1006230079", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000875": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is the general model without delay described by the equation system (1) in: A model of HIV-1 pathogenesis that includes an intracellular delay.
Nelson PW, Murray JD, Perelson AS; Math Biosci. 2000 Feb;163(2):201-15. PMID: 10701304 ; doi: 10.1016/S0025-5564(99)00055-3
Abstract:
Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection.
Author Keywords: HIV; Delay; Viral life cycle; T-cells

As there are no results given for this model in the article it cannot be checked for MIRIAM compliance. The SBML file should be equivalent to the described ODE file though.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
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To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is a mathematical model comprised of a simple system of four ordinary differential equations that account for the two opposing roles of infected and healthy cytotoxic T lymphocytes within the context of HIV infection, with each type responsible for the production of virus and the activation and stimulation of immune responses, respectively.
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The role of CD4 T cells in immune system activation and viral reproduction in a simple model for HIV infection", + "publication": { + "accession": "10.1016/j.apm.2019.05.028", + "affiliation": "Department of Biological Sciences, Texas Tech University, 2901 Main St., Lubbock, TX 79409, United States", + "authors": [ + { + "institution": "Department of Biological Sciences, Texas Tech University, 2901 Main St., Lubbock, TX 79409, United States", + "name": "Aavani, P." + }, + { + "institution": "Department of Mathematics & Statistics, Texas Tech University, Broadway and Boston, Lubbock, TX 79409-1042, United States", + "name": "Allen, L.J.S." + } + ], + "journal": "Applied Mathematical Modelling", + "link": "http://identifiers.org/doi/10.1016/j.apm.2019.05.028", + "month": "11", + "pages": "210-222", + "synopsis": "CD4 T cells play a fundamental role in the adaptive immune response including the stimulation of cytotoxic lymphocytes (CTLs). Human immunodeficiency virus (HIV) which infects and kills CD4 T cells causes progressive failure of the immune system. However, HIV particles are also reproduced by the infected CD4 T cells. Therefore, during HIV infection, infected and healthy CD4 T cells act in opposition to each other, reproducing virus particles and activating and stimulating cellular immune responses, respectively. In this investigation, we develop and analyze a simple system of four ordinary differential equations that accounts for these two opposing roles of CD4 T cells. The model illustrates the importance of the CTL immune response during the asymptomatic stage of HIV infection. In addition, the solution behavior exhibits the two stages of infection, asymptomatic and final AIDS stages. In the model, a weak immune response results in a short asymptomatic stage and faster development of AIDS, whereas a strong immune response illustrates the long asymptomatic stage. 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This is a mathematical model describing the treatment of tumors using oncolytic virus and chemotherapy. The model is comprised of nonlinear ordinary differential equations describing the interactions between uninfected tumor cells, infected tumor cells, an oncolytic virus, and chemotherapy.
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This a model from the article:
Modeling insulin kinetics: responses to a single oral glucose administration or ambulatory-fed conditions.
Lenbury Y, Ruktamatakul S, Amornsamarnkul S. Biosystems. 2001 Jan;59(1):15-25. 11226623 ,
Abstract:
This paper presents a nonlinear mathematical model of the glucose-insulin feedback system, which has been extended to incorporate the beta-cells' function on maintaining and regulating plasma insulin level in man. Initially, a gastrointestinal absorption term for glucose is utilized to effect the glucose absorption by the intestine and the subsequent release of glucose into the bloodstream, taking place at a given initial rate and falling off exponentially with time. An analysis of the model is carried out by the singular perturbation technique in order to derive boundary conditions on the system parameters which identify, in particular, the existence of limit cycles in our model system consistent with the oscillatory patterns often observed in clinical data. We then utilize a sinusoidal term to incorporate the temporal absorption of glucose in order to study the responses in the patients under ambulatory-fed conditions. A numerical investigation is carried out in this case to construct a bifurcation diagram to identify the ranges of parametric values for which chaotic behavior can be expected, leading to interesting biological interpretations.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: lenbury_ruktamatakul_amornsamarnkul_2001_A
The original CellML model was created by:
Catherine Lloyd
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model consists of four populations - tumor cells, dendritic cells (as an innate immune system), cytotoxic T cells, and helper T cells (as a specific immune system) - in the form of a system of ordinary differential equations. Some tumors present dendritic cell and such cells have a potential role in regulating the immune system. In this model, we assume that dendritic cells can activate cytotoxic T cells and, in turn, can clear out tumor cells. Furthermore, by adding controls as a treatment to the model, we minimize both the tumor cell population and the cost of treatment. We do this by applying the optimal control for this problem. First, Pontryagin's Principle is used to characterize the optimal control. Then, the optimal system is solved numerically using the Forward-Backward Runge- Kutta method. Finally, the effect of each treatment is investigated. 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This is a mathematical model describing the imbalance between T helper (Th1/Th2) cell types in melanome patients, together with its regulation via IL-12 treatment. The model focuses on the interactions between the two T helper cell types as mediated by their respective key cytokines, interferon gamma and IL-10.
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A mathematical model for the immunotherapeutic control of the TH1 TH2 imbalance in melanoma", + "publication": { + "accession": "10.3934/dcdsb.2013.18.1017", + "affiliation": "10 Hate'ena St., Bene Ataroth 60991, Israel", + "authors": [ + { + "name": "Kogan, Y." + }, + { + "name": "Agur, Z." + }, + { + "name": "Elishmereni, M." + } + ], + "issue": "4", + "journal": "Discrete and Continuous Dynamical Systems - Series B", + "link": "http://identifiers.org/doi/10.3934/dcdsb.2013.18.1017", + "month": "6", + "pages": "1017-1030", + "synopsis": "Aggressive cancers develop immune suppression mechanisms, allowing them to evade specific immune responses. Patients with active melanoma are polarized towards a T helper (Th) 2-type immune phenotype, which subverts effective anticancer Th1-type cellular immunity. The pro-inammatory factor, interleukin (IL)-12, can potentially restore Th1 responses in such patients, but still shows limited clinical efficacy and substantial side effects. We developed a model for the Th1/Th2 imbalance in melanoma patients and its regulation via IL-12 treatment. The model focuses on the interactions between the two Th cell types as mediated by their respective key cytokines, interferon (IFN)-\u03b3 and IL-10. Theoretical and numerical analysis showed a landscape consisting of a single, globally attracting steady state, which is stable under large ranges of relevant parameter values. Our results suggest that in melanoma, the cellular arm of the immune system cannot reverse tumor immunotolerance naturally, and that immunotherapy may be the only way to overturn tumor dominance. We have shown that given a toxicity threshold for IFN\u03b3, the maximal allowable IL-12 concentration to yield a Th1-polarized state can be estimated. Moreover, our analysis pinpoints the IL-10 secretion rate as a significant factor inuencing the Th1:Th2 balance, suggesting its use as a personal immunomarker for prognosis.", + "title": "A mathematical model for the immunotherapeutic control of the TH1/TH2 imbalance in melanoma", + "type": "DOI", + "volume": "18", + "year": 2016 + }, + "publicationId": "BIOMD0000000881", + "submissionId": "MODEL1912020001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000882": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + }, + { + "affiliation": "EMBL-EBI", + "email": "lukas@ebi.ac.uk", + "external": false, + "name": "Lukas Endler" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Munz2009 - Zombie SIZRC This is the model with an latent infection and cure for zombies described in the article. This model was originally created by libAntimony v1.4 (using libSBML 3.4.1). This model is described in the article: When zombies attack!: Mathematical modelling of an outbreak of zombie infection P. Munz, I. Hudea, J. Imad and R.J. Smith? Infectious Disease Modelling Research Progress 2009, chapter 4, pp 133-150. Editors: Jean Michel Tchuenche and C. Chiyaka; Nova Science Publishers, Inc., NY, USA. Abstract: Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. This model is hosted on BioModels Database and identified by: MODEL1008060001 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
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Unit, Department of Clinical and Molecular BioMedicine, Garibaldi-Nesima Medical Center.", + "name": "Francesco Frasca", + "orcid": "0000-0002-5556-3201" + }, + { + "institution": "Department of Drug Sciences, University of Catania, 95125, Catania, Italy.", + "name": "Francesco Pappalardo", + "orcid": "0000-0003-1668-3320" + } + ], + "issue": "13", + "journal": "Bioinformatics (Oxford, England)", + "link": "http://identifiers.org/pubmed/30481266", + "month": "7", + "pages": "2267-2275", + "synopsis": "

Motivation

Val600Glu (V600E) mutation is the most common BRAF mutation detected in thyroid cancer. Hence, recent research efforts have been performed trying to explore several inhibitors of the V600E mutation-containing BRAF kinase as potential therapeutic options in thyroid cancer refractory to standard interventions. Among them, vemurafenib is a selective BRAF inhibitor approved by Food and Drug Administration for clinical practice. Unfortunately, vemurafenib often displays limited efficacy in poorly differentiated and anaplastic thyroid carcinomas probably because of intrinsic and/or acquired resistance mechanisms. In this view, cancer stem cells (CSCs) may represent a possible mechanism of resistance to vemurafenib, due to their self-renewal and chemo resistance properties.

Results

We present a computational framework to suggest new potential targets to overcome drug resistance. It has been validated with an in\u00a0vitro model based upon a spheroid-forming method able to isolate thyroid CSCs that may mimic resistance to vemurafenib. Indeed, vemurafenib did not inhibit cell proliferation of BRAF V600E thyroid CSCs, but rather stimulated cell proliferation along with a paradoxical over-activation of ERK and AKT pathways. The computational model identified a fundamental role of mitogen-activated protein kinase 8 (MAP3K8), a serine/threonine kinase expressed in thyroid CSCs, in mediating this drug resistance. To confirm model prediction, we set a suitable in\u00a0vitro experiment revealing that the treatment with MAP3K8 inhibitor restored the effect of vemurafenib in terms of both DNA fragmentation and poly (ADP-ribose) polymerase cleavage (apoptosis) in thyroid CSCs. Moreover, MAP3K8 expression levels may be a useful marker to predict the response to vemurafenib.

Availability and implementation

The model is available in GitHub repository visiting the following URL: https://github.com/francescopappalardo/MAP3K8-Thyroid-Spheres-V-3.0.

Supplementary information

Supplementary data are available at Bioinformatics online.", + "title": "Computational modeling reveals MAP3K8 as mediator of resistance to vemurafenib in thyroid cancer stem cells.", + "type": "PubMed ID", + "volume": "35", + "year": 2019 + }, + "publicationId": "BIOMD0000000883", + "submissionId": "MODEL1902140001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000884": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Optimality of the spontaneous prophage induction rate.Cortes MG1, Krog J2, Bal\u00e1zsi G3.1 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.2 The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.3 Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: gabor.balazsi@stonybrook.edu.AbstractLysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage \u03bb and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.Copyright \u00a9 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.
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Mathematical modeling of cancer-immune system, considering the role of antibodies.Ghosh S1, Banerjee S2.Author information1    Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal, 247667, India.2    Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal, 247667, India. sandofma@iitr.ac.in.AbstractA mathematical model for the quantitative analysis of cancer-immune interaction, considering the role of antibodies has been proposed in this paper. The model is based on the clinical evidence, which states that antibodies can directly kill cancerous cells (Ivano et al. in J Clin Investig 119(8):2143-2159, 2009). The existence of transcritical bifurcation, which has been proved using Sotomayor theorem, provides strong biological implications. Through numerical simulations, it has been illustrated that under certain therapy (like monoclonal antibody therapy), which is capable of altering the parameters of the system, cancer-free state can be obtained.KEYWORDS:Antibodies; B cells; Cancer cells; Global stability; Plasma cells; Transcritical bifurcation
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MODELING THE INTERACTION BETWEEN AVASCULAR CANCEROUS CELLS AND ACQUIRED IMMUNE RESPONSEB. DUBEY, UMA S. DUBEY and SANDIP BANERJEEAbstractThis paper deals with the interaction between dispersed cancer cells and the major populations of the immune system, namely, the T helper cells, T Cytotoxic cells, B cells, and antibodies produced. The system is described by a set of five ordinary differential equations. Both local and global stability of the system has been investigated. It has been observed that under appropriate conditions this interaction is capable of controlling the growth of these cancer cells. The analytical findings are supported by numerical and computational analytical methods.
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Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug InterventionsPranav Unni 1 and Padmanabhan SeshaiyerAbstractOver the last few decades, there have been significant developments in theoretical, experimental, and clinical approaches to understand the dynamics of cancer cells and their interactions with the immune system. These have led to the development of important methods for cancer therapy including virotherapy, immunotherapy, chemotherapy, targeted drug therapy, and many others. Along with this, there have also been some developments on analytical and computational models to help provide insights into clinical observations. This work develops a new mathematical model that combines important interactions between tumor cells and cells in the immune systems including natural killer cells, dendritic cells, and cytotoxic CD8+ T cells combined with drug delivery to these cell sites. These interactions are described via a system of ordinary differential equations that are solved numerically. A stability analysis of this model is also performed to determine conditions for tumor-free equilibrium to be stable. We also study the influence of proliferation rates and drug interventions in the dynamics of all the cells involved. Another contribution is the development of a novel parameter estimation methodology to determine optimal parameters in the model that can reproduce a given dataset. Our results seem to suggest that the model employed is a robust candidate for studying the dynamics of tumor cells and it helps to provide the dynamic interactions between the tumor cells, immune system, and drug-response systems.
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Electronic address: m.tindall@reading.ac.uk.", + "name": "Marcus J Tindall", + "orcid": "0000-0001-6069-5260" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/24444765", + "month": "5", + "pages": "150-162", + "synopsis": "Cholesterol is one of the key constituents for maintaining the cellular membrane and thus the integrity of the cell itself. In contrast high levels of cholesterol in the blood are known to be a major risk factor in the development of cardiovascular disease. We formulate a deterministic nonlinear ordinary differential equation model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in a hepatocyte. The mathematical model includes a description of genetic transcription by SREBP-2 which is subsequently translated to mRNA leading to the formation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a main regulator of cholesterol synthesis. Cholesterol synthesis subsequently leads to the regulation of SREBP-2 via a negative feedback formulation. Parameterised with data from the literature, the model is used to understand how SREBP-2 transcription and regulation affects cellular cholesterol concentration. Model stability analysis shows that the only positive steady-state of the system exhibits purely oscillatory, damped oscillatory or monotic behaviour under certain parameter conditions. In light of our findings we postulate how cholesterol homeostasis is maintained within the cell and the advantages of our model formulation are discussed with respect to other models of genetic regulation within the literature.", + "title": "A mathematical model of the sterol regulatory element binding protein 2 cholesterol biosynthesis pathway.", + "type": "PubMed ID", + "volume": "349", + "year": 2014 + }, + "publicationId": "BIOMD0000000890", + "submissionId": "MODEL1912120004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000891": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Stability Analysis of a Mathematical Model for Glioma-Immune Interaction under Optimal TherapySubhas KhajanchiAbstractWe investigate a mathematical model using a system of coupled ordinary differential equations, which describes the interplay of malignant glioma cells, macrophages, glioma specific CD8+T cells and the immunotherapeutic drug Adoptive Cellular Immunotherapy (ACI). To better understand under what circumstances the glioma cells can be eliminated, we employ the theory of optimal control. We investigate the dynamics of the system by observing biologically feasible equilibrium points and their stability analysis before administration of the external therapy ACI. We solve an optimal control problem with an objective functional which minimizes the glioma cell burden as well as the side effects of the treatment. We characterize our optimal control in terms of the solutions to the optimality system, in which the state system coupled with the adjoint system. Our model simulation demonstrates that the strength of treatment u1(t) plays an important role to eliminate the glioma cells. Finally, we derive an optimal treatment strategy and then solve it numerically.Keywords: malignant gliomas; stability analysis; optimal control; adoptive cellular immunotherapy
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Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.Banerjee S1, Keval R, Gakkhar S.Author information1    Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee 247667, Uttaranchal, India. Electronic address: sandofma@iitr.ernet.in.AbstractA mathematical modeling of hepatitis C virus (HCV) dynamics and antiviral therapy has been presented in this paper. The proposed model, which involves four coupled ordinary differential equations, describes the interaction of target cells (hepatocytes), infected cells, infectious virions and non-infectious virions. The model takes into consideration the addition of ribavirin to interferon therapy and explains the dynamics regarding a biphasic and triphasic decline of viral load in the model. A critical drug efficacy parameter has been defined and it is shown that for an efficacy above this critical value, HCV is eradicated whereas for efficacy lower this critical value, a new steady state for infectious virions is reached, which is lower than the previous steady state value.Copyright \u00a9 2013 Elsevier Inc. All rights reserved.KEYWORDS:Hepatitis C virus (HCV); Infected cells; Infectious virions; Interferon; Noninfectious virions; Ribavirin; Target cells
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Noise-assisted interactions of tumor and immune cells.Bose T1, Trimper S.Author information1    Institute of Physics, Martin-Luther-University, D-06099 Halle, Germany. thomas.bose@physik.uni-halle.deAbstractWe consider a three-state model comprising tumor cells, effector cells, and tumor-detecting cells under the influence of noises. It is demonstrated that inevitable stochastic forces existing in all three cell species are able to suppress tumor cell growth completely. Whereas the deterministic model does not reveal a stable tumor-free state, the auto-correlated noise combined with cross-correlation functions can either lead to tumor-dormant states, tumor progression, as well as to an elimination of tumor cells. The auto-correlation function exhibits a finite correlation time \u03c4, while the cross-correlation functions shows a white-noise behavior. The evolution of each of the three kinds of cells leads to a multiplicative noise coupling. The model is investigated by means of a multivariate Fokker-Planck equation for small \u03c4. The different behavior of the system is, above all, determined by the variation of the correlation time and the strength of the cross-correlation between tumor and tumor-detecting cells. The theoretical model is based on a biological background discussed in detail, and the results are tested using realistic parameters from experimental observations.
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The system elements were grouped in five important classes consisting of intra- and extracellular S. Enteritidis bacteria, macrophages, CD4+, and CD8+ cells. Twelve model variables were described by ordinary differential equations, including 50 parameters. Parameter values were estimated from literature or from own immunohistochemistry data. The model described the immune development in non-infected birds with an average R\u00b2 of 0.87. The model showed less accuracy in reproducing the immune response to S. Enteritidis infection, with an average R\u00b2 of 0.51, although model response did follow observed trends in time. Evaluation of the model against independent data derived from several infection trials showed strong/significant deviations from observed values. Nevertheless, it was shown that the model could be used to simulate the effect of varying input parameters on system elements response, such as the number of immune cells at hatch. Model simulations allowed one to study the sensitivity of the model outcome for varying model inputs. The initial number of immune cells at hatch was shown to have a profound impact on the predicted development in the number of systemic S. Enteritidis bacteria after infection. The theoretical contribution of this work is the identification of responses in system elements of the developing intestinal immune system of poultry obtaining a mathematical representation which allows one to explore the relationships between these elements under contrasting environmental conditions during different stages of intestinal development.", + "title": "A mathematical model representing cellular immune development and response to Salmonella of chicken intestinal tissue.", + "type": "PubMed ID", + "volume": "330", + "year": 2013 + }, + "publicationId": "BIOMD0000000895", + "submissionId": "MODEL1912160004", + "vcsIdentifier": "aai" + }, + "BIOMD0000000896": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This is a mathematical model of heat shock protein synthesis induced by an external temperature stimulus. The model consists of a system of nine nonlinear ordinary differential equations describing the temporal evolution of key variables involved in the regulation of HSP synthesis.
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Mathematical modeling of heat shock protein synthesis in response to temperature change", + "publication": { + "accession": "19327370", + "affiliation": "ICM, University of Warsaw, Pawi\u0144skiego 5a, 02-106 Warszawa, Poland. mysz@icm.edu.pl", + "authors": [ + { + "institution": "ICM, University of Warsaw, Pawi\u0144skiego 5a, 02-106 Warszawa, Poland. mysz@icm.edu.pl", + "name": "Zuzanna Szyma\u0144ska", + "orcid": "0000-0002-2968-5627" + }, + { + "name": "Maciej Zylicz" + } + ], + "issue": "3", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/19327370", + "month": "8", + "pages": "562-569", + "synopsis": "One of the most important questions in cell biology is how cells cope with rapid changes in their environment. The range of common molecular responses includes a dramatic change in the pattern of gene expression and the elevated synthesis of so-called heat shock (or stress) proteins (HSPs). Induction of HSPs increases cell survival under stress conditions [Morimoto, R.I., 1993. Cells in stress: transcriptional activation of heat shock genes. Science 259, 1409-1410]. In this paper we propose a mathematical model of heat shock protein synthesis induced by an external temperature stimulus. Our model consists of a system of nine nonlinear ordinary differential equations describing the temporal evolution of the key variables involved in the regulation of HSP synthesis. Computational simulations of our model are carried out for different external temperature stimuli. We compare our model predictions with experimental data for three different cases-one corresponding to heat shock, the second corresponding to slow heating conditions and the third corresponding to a short heat shock (lasting about 40 min). We also present our model predictions for heat shocks carried out up to different final temperatures and finally we present a new hypothesis concerning the molecular response to stress that explains some phenomena observed in experiments.", + "title": "Mathematical modeling of heat shock protein synthesis in response to temperature change.", + "type": "PubMed ID", + "volume": "259", + "year": 2009 + }, + "publicationId": "BIOMD0000000896", + "submissionId": "MODEL1912160005", + "vcsIdentifier": "aai" + }, + "BIOMD0000000897": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
The combined effects of optimal control in cancer remissionSubhasKhajanchiDibakarGhoshAbstractWe investigate a mathematical model depicting the nonlinear dynamics of immunogenic tumors as envisioned by Kuznetsov et al. [1]. To understand the dynamics under what circumstances the cancer cells can be eliminated, we implement the theory of optimal control. We design two types of external treatment strategies, one is Adoptive Cellular Immunotherapy and another is interleukin-2. Our aim is to establish the treatment regimens that maximize the effector cell count and minimize the tumor cell burden and the deleterious effects of the total amount of drugs. We derive the existence of an optimal control by using the boundedness of solutions. We characterize the optimality system, in which the state system is coupled with co-states. The uniqueness of an optimal control of our problem is also analyzed. Finally, we demonstrate the numerical illustrations that the optimal regimens reduce the tumor burden under different scenarios.
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This is a mathematical model describing the hematopoietic lineages with leukemia lineages, as controlled by end-product negative feedback inhibition. Variables include hematopoietic stem cells, progenitor cells, terminally differentiated HSCs, leukemia stem cells, and terminally differentiated leukemia stem cells.
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Background

The haematopoietic lineages with leukaemia lineages are considered in this paper. In particular, we mainly consider that haematopoietic lineages are tightly controlled by negative feedback inhibition of end-product. Actually, leukemia has been found 100 years ago. Up to now, the exact mechanism is still unknown, and many factors are thought to be associated with the pathogenesis of leukemia. Nevertheless, it is very necessary to continue the profound study of the pathogenesis of leukemia. Here, we propose a new mathematical model which include some negative feedback inhibition from the terminally differentiated cells of haematopoietic lineages to the haematopoietic stem cells and haematopoietic progenitor cells in order to describe the regulatory mechanisms mentioned above by a set of ordinary differential equations. Afterwards, we carried out detailed dynamical bifurcation analysis of the model, and obtained some meaningful results.

Results

In this work, we mainly perform the analysis of the mathematic model by bifurcation theory and numerical simulations. We have not only incorporated some new negative feedback mechanisms to the existing model, but also constructed our own model by using the modeling method of stem cell theory with probability method. Through a series of qualitative analysis and numerical simulations, we obtain that the weak negative feedback for differentiation probability is conducive to the cure of leukemia. However, with the strengthening of negative feedback, leukemia will be more difficult to be cured, and even induce death. In contrast, strong negative feedback for differentiation rate of progenitor cells can promote healthy haematopoiesis and suppress leukaemia.

Conclusions

These results demonstrate that healthy progenitor cells are bestowed a competitive advantage over leukaemia stem cells. Weak g1, g2, and h1 enable the system stays in the healthy state. However, strong h2 can promote healthy haematopoiesis and suppress leukaemia.", + "title": "Feedback regulation in a stem cell model with acute myeloid leukaemia.", + "type": "PubMed ID", + "volume": "12", + "year": 2018 + }, + "publicationId": "BIOMD0000000898", + "submissionId": "MODEL1912170002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000899": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "johannes.p.meyer@gmail.com", + "external": false, + "name": "Johannes Meyer" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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Positive regulation of Rho GTPase activity by RhoGDIs as a result of their direct interaction with GAPs (GDI integrated)", + "publication": { + "accession": "25628036", + "affiliation": "Division of Tumor Biology, Department of Life Science, Medical Research Institute, Kanazawa Medical University, Uchinada 920-0293, Ishikawa, Japan. takahide@kanazawa-med.ac.jp.", + "authors": [ + { + "institution": "Division of Tumor Biology, Department of Life Science, Medical Research Institute, Kanazawa Medical University, Uchinada 920-0293, Ishikawa, Japan. takahide@kanazawa-med.ac.jp.", + "name": "Takahide Ota" + }, + { + "name": "Masayo Maeda" + }, + { + "name": "Mayumi Okamoto" + }, + { + "name": "Masaaki Tatsuka" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/25628036", + "month": "1", + "pages": "3", + "synopsis": "

Background

Rho GTPases function as molecular switches in many different signaling pathways and control a wide range of cellular processes. Rho GDP-dissociation inhibitors (RhoGDIs) regulate Rho GTPase signaling and can function as both negative and positive regulators. The role of RhoGDIs as negative regulators of Rho GTPase signaling has been extensively investigated; however, little is known about how RhoGDIs act as positive regulators. Furthermore, it is unclear how this opposing role of GDIs influences the Rho GTPase cycle. We constructed ordinary differential equation models of the Rho GTPase cycle in which RhoGDIs inhibit the regulatory activities of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) by interacting with them directly as well as by sequestering the Rho GTPases. Using this model, we analyzed the role of RhoGDIs in Rho GTPase signaling.

Results

The model constructed in this study showed that the functions of GEFs and GAPs are integrated into Rho GTPase signaling through the interactions of these regulators with GDIs, and that the negative role of GDIs is to suppress the overall Rho activity by inhibiting GEFs. Furthermore, the positive role of GDIs is to sustain Rho activation by inhibiting GAPs under certain conditions. The interconversion between transient and sustained Rho activation occurs mainly through changes in the affinities of GDIs to GAPs and the concentrations of GAPs.

Conclusions

RhoGDIs positively regulate Rho GTPase signaling primarily by interacting with GAPs and may participate in the switching between transient and sustained signals of the Rho GTPases. These findings enhance our understanding of the physiological roles of RhoGDIs and Rho GTPase signaling.", + "title": "Positive regulation of Rho GTPase activity by RhoGDIs as a result of their direct interaction with GAPs.", + "type": "PubMed ID", + "volume": "9", + "year": 2015 + }, + "publicationId": "BIOMD0000000899", + "submissionId": "MODEL1912170003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000900": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Persistence analysis in a Kolmogorov-type model for cancer-immune system competitionAIP Conference Proceedings 1558, 1797 (2013); https://doi.org/10.1063/1.4825874C. BiancaDipartimento di Scienze Matematiche, Politecnico di Torino, Torino, ItalyF. PappalardoDipartimento di Scienza del Farmaco, Universit\u00e0 degli Studi di Catania, Catania, ItalyM. Pennisi and M. A. RagusaDipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Catania, Catania, Italy
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Bianca" + }, + { + "institution": "Dipartimento di Scienza del Farmaco, Universit\u00e0 degli Studi di Catania, Catania, Italy", + "name": "F. Pappalardo" + }, + { + "institution": "Dipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Catania, Catania, Italy", + "name": "M. Pennisi and M. A. Ragusa" + } + ], + "issue": "1", + "journal": "AIP Conference Proceedings 1558", + "link": "http://identifiers.org/doi/10.1063/1.4825874", + "month": "10", + "synopsis": "This paper is concerned with analytical investigations on the competition between cancer cells and immune system cells. Specifically the role of the B-cells and T-cells in the evolution of cancer cells is taken into account. The mathematical model is a Kolmogorov-type system of three evolution equations where the growth rate of the cells is described by logistic law and the response of B-cells and T-cells is modeled according to Holling type-II function. 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A mathematical model describing oncolytic virotherapy with incorporation the viral lytic cycle and the virus-specific CTL response. The thresholds for viral treatment and virus-specific CTl response are also obtained.
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A fractional mathematical model of breast cancer competition modelAuthor links open overlay panelJ.E.Sol\u00eds-P\u00e9rezaJ.F.G\u00f3mez-AguilarbA.Atanganaca    Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xicob    CONACyT-Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xicoc    Institute for Groundwater Studies, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9300, South AfricaAbstractIn this paper, a mathematical model which considers population dynamics among cancer stem cells, tumor cells, healthy cells, the effects of excess estrogen and the body\u2019s natural immune response on the cell populations was considered. Fractional derivatives with power law and exponential decay law in Liouville\u2013Caputo sense were considered. Special solutions using an iterative scheme via Laplace transform were obtained. Furthermore, numerical simulations of the model considering both derivatives were obtained using the Atangana\u2013Toufik numerical method. Also, random model described by a system of random differential equations was presented. The use of fractional derivatives provides more useful information about the complexity of the dynamics of the breast cancer competition model.
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Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xico\r\n\r\nb\r\n CONACyT-Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xico\r\n\r\nc\r\n Institute for Groundwater Studies, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9300, South Africa", + "authors": [ + { + "name": "J.E.Sol\u00eds-P\u00e9rez a J.F.G\u00f3mez-Aguilar b A.Atangana c" + } + ], + "journal": "Chaos, Solitons & Fractals", + "link": "http://identifiers.org/doi/10.1016/j.chaos.2019.06.027", + "month": "10", + "pages": "38-54", + "synopsis": "In this paper, a mathematical model which considers population dynamics among cancer stem cells, tumor cells, healthy cells, the effects of excess estrogen and the body\u2019s natural immune response on the cell populations was considered. 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Modelling tumor growth with immune response and drug using ordinary differential equationsMohd Rashid Admon, Normah MaanThis is a mathematical study about tumor growth from a different perspective, with the aim of predicting and/or controlling the disease. The focus is on the effect and interaction of tumor cell with immune and drug. This paper presents a mathematical model of immune response and a cycle phase specific drug using a system of ordinary differential equations. Stability analysis is used to produce stability regions for various values of certain parameters during mitosis. The stability region of the graph shows that the curve splits the tumor decay and growth regions in the absence of immune response. However, when immune response is present, the tumor growth region is decreased. When drugs are considered in the system, the stability region remains unchanged as the system with the presence of immune response but the population of tumor cells at interphase and metaphase is reduced with percentage differences of 1.27 and 1.53 respectively. The combination of immunity and drug to fight cancer provides a better method to reduce tumor population compared to immunity alone.
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In this paper, a nonlinear mathematical model is proposed and analyzed to study theeffect of environmental toxicant on the immune response of the body. Criteria for localstability, instability and global stability are obtained. It is shown that the immuneresponse of the body decreases as the concentration of environmental toxicant increases,and certain criteria are obtained under which it settles down at its equilibrium level.In the absence of toxicant, an oscillatory behavior of immune system and pathogenicgrowth is observed. However, in the presence of toxicant, oscillatory behavior is notobserved. These studies show that the toxicant may have a grave effect on our body\u2019sdefense mechanism.Keywords: Pathogen; Immune Response; Toxicant; Stability.
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All cells and organisms exhibit stress-coping mechanisms toensure survival. Cytoplasmic protein-RNA assemblies termedstress granules are increasingly recognized to promote cellularsurvival under stress. Thus, they might represent tumor vul-nerabilities that are currently poorly explored. The translation-inhibitory eIF2\u03b1kinases are established as main drivers ofstress granule assembly. Using a systems approach, we identifythe translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regu-lator mammalian target of rapamycin complex 1 (mTORC1) topromote stress granule assembly. When highly active, PI3K is themain driver of stress granules; however, the impact of p38becomes apparent as PI3K activity declines. PI3K and p38 thusact in a hierarchical manner to drive mTORC1 activity and stressgranule assembly. Of note, this signaling hierarchy is also presentin human breast cancer tissue. Importantly, only the recognition ofthe PI3K-p38 hierarchy under stress enabled the discovery of p38\u2019srole in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as theyhierarchically promote stress granule formation
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Cytoplasmic protein-RNA assemblies termed stress granules are increasingly recognized to promote cellular survival under stress. Thus, they might represent tumor vulnerabilities that are currently poorly explored. The translation-inhibitory eIF2\u03b1 kinases are established as main drivers of stress granule assembly. Using a systems approach, we identify the translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regulator mammalian target of rapamycin complex 1 (mTORC1) to promote stress granule assembly. When highly active, PI3K is the main driver of stress granules; however, the impact of p38 becomes apparent as PI3K activity declines. PI3K and p38 thus act in a hierarchical manner to drive mTORC1 activity and stress granule assembly. Of note, this signaling hierarchy is also present in human breast cancer tissue. 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Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatmentLisette dePillis 1, , Trevor Caldwell 2, , Elizabeth Sarapata 2, and  Heather Williams 2,1. \tDepartment of Mathematics, Harvey Mudd College, Claremont, CA 917112. \tHarvey Mudd College, Claremont, CA 91711, United States, United States, United States    AbstractWe present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not.    Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response.Keywords: Renal cell carcinoma, mathematical modeling., sunitinib, immune system, regulatory T cells.
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We prove that all orbits are bounded and must converge to one of several possible equilibrium points. Therefore, the long-term behavior of an orbit is classified according to the basin of attraction in which it starts. The addition of a drug term to the system can move the solution trajectory into a desirable basin of attraction. We show that the solutions of the model with a time-varying drug term approach the solutions of the system without the drug once traatment has stopped. We present numerical experiments in which optimal control therapy is able to drive the system into a desirable basin of attraction, whereas traditional pulsed chemotherapy is not.</pre> </body> </notes>", + "files": { + "additional": [ + { + "description": "Reproduced figure(s) from original curation.", + "fileSize": "22211", + "md5sum": "fa1c7c0c99efa9f113e44cc9e89c9763", + "mimeType": "image/png", + "name": "curation_image.png", + "sha1sum": "8be2c38f55ca16b417f58beb7d42d5d658ebd4c5", + "sha256sum": "cf0619d03997abe94ff9f71d649cced604a4fa92687716fcb99242cc91f8f39a" + }, + { + "description": "Notes from original curation (describes curation_image).", + "fileSize": "99", + "md5sum": "357754109d4f9e952602243462949c14", + "mimeType": "text/plain", + "name": "curation_notes.txt", + "sha1sum": "eba98b299e6d35839efd6e013928ed9d9ec79c79", + "sha256sum": "f492589b3c4350a4d77b108391cb9fa0c7990db91e305d4a3edab94e819daeba" + }, + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "13149", + "md5sum": "3ee76c38db339d247d847ce345d7ac5a", + "mimeType": "application/rdf+xml", + "name": "dePillis2003-biopax2.owl", + "sha1sum": "b3934442606537010b8e8ea28a3a8a040895a81a", + "sha256sum": "c68ca2fe603391558dcc25d9dab96d8e4760bfd0077c033d99cb9c330ce59ec4" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "17999", + "md5sum": "f0299823aa9fc6e926cbaf6b13b19f34", + "mimeType": "application/rdf+xml", + "name": "dePillis2003-biopax3.owl", + "sha1sum": "672b659aba7401df1f5d454cf953b63f9920c54d", + "sha256sum": "bdb1634da1b3c19ecc66fa3fc50602b8ef0fb07b123cfd2542eda9fa1e729c94" + }, + { + "description": "Auto-generated Matlab file.", + "fileSize": "7130", + "md5sum": "d581147e4dce04c8b84e629137ff8766", + "mimeType": "text/x-matlab", + "name": "dePillis2003-matlab.m", + "sha1sum": "7ba2cebf5b0b020357a4646fcde419727c8c297e", + "sha256sum": "3b5b3cf17862706ecbfe39eec7eeba16d5204e371f8cc41a52f6b425eab19008" + }, + { + "description": "Auto-generated Octave file.", + "fileSize": "7130", + "md5sum": "d4d1a439b03753bf6d3fcd06d6655dfd", + "mimeType": "text/x-matlab", + "name": "dePillis2003-octave.m", + "sha1sum": "5604000cf60c666795318257af26b95b5abc2029", + "sha256sum": "bf34d938d68a943a649308115cf693f294bbaece2c7278551929f8b7c33ea12d" + }, + { + "description": "COPASI version 4.24 (Build 197) dePillis2003 - 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The dynamics of an optimally controlled tumor model: A case study", + "publication": { + "accession": "10.1016/S0895-7177(03)00133-X", + "affiliation": "Harvey Mudd College, Claremont, CA 91711, U.S.A.\r\nPomona College, Claremont, CA 91711, U.S.A.", + "authors": [ + { + "name": "L.GDe Pillis , Radunskaya" + } + ], + "issue": "11", + "journal": "Mathematical and Computer Modelling", + "link": "http://identifiers.org/doi/10.1016/S0895-7177(03)00133-X", + "month": "6", + "pages": "1221-1244", + "synopsis": "Abstract\r\n\r\nWe present a phase-space analysis of a mathematical model of tumor growth with an immune response and chemotherapy. 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We present numerical experiments in which optimal control therapy is able to drive the system into a desirable basin of attraction, whereas traditional pulsed chemotherapy is not.\r\n\r\nVolume 37, Issue 11, June 2003, Pages 1221-1244", + "title": "The dynamics of an optimally controlled tumor model: A case study", + "type": "DOI", + "volume": "37", + "year": 2003 + }, + "publicationId": "BIOMD0000000909", + "submissionId": "MODEL2001080003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000910": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "msharifshohan@ebi.ac.uk", + "external": false, + "name": "Mohammad Umer Sharif Shohan", + "orcid": "0000-0003-3985-2879" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Modelling of anti-tumour immune response: Immunocorrective effectof weak centimetre electromagnetic wavesO.G. Isaeva* and V.A. OsipovBogoliubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna,Moscow Region, RussiaWe formulate the dynamical model for the anti-tumour immune response based onintercellular cytokine-mediated interactions with the interleukin-2 (IL-2) taken intoaccount. The analysis shows that the expression level of tumour antigens on antigenpresenting cells has a distinct influence on the tumour dynamics. At low antigenpresentation, a progressive tumour growth takes place to the highest possible value.At high antigen presentation, there is a decrease in tumour size to some value when thedynamical equilibrium between the tumour and the immune system is reached. In thecase of the medium antigen presentation, both these regimes can be realized dependingon the initial tumour size and the condition of the immune system. A pronouncedimmunomodulating effect (the suppression of tumour growth and the normalization ofIL-2 concentration) is established by considering the influence of low-intensityelectromagnetic microwaves as a parametric perturbation of the dynamical system. Thisfinding is in qualitative agreement with the recent experimental results onimmunocorrective effects of centimetre electromagnetic waves in tumour-bearing mice.Keywords: carcinogenesis; interleukin-2; modelling; anti-tumour immunity;electromagnetic waves
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G. Isaeva and V. A. Osipov" + } + ], + "issue": "3", + "journal": "Computational and Mathematical Methods in Medicine", + "link": "http://identifiers.org/doi/10.1080/17486700802373540", + "month": "7", + "pages": "185-201", + "synopsis": "Abstract\r\n\r\nWe formulate the dynamical model for the anti-tumour immune response based on intercellular cytokine-mediated interactions with the interleukin-2 (IL-2) taken into account. The analysis shows that the expression level of tumour antigens on antigen presenting cells has a distinct influence on the tumour dynamics. At low antigen presentation, a progressive tumour growth takes place to the highest possible value. At high antigen presentation, there is a decrease in tumour size to some value when the dynamical equilibrium between the tumour and the immune system is reached. In the case of the medium antigen presentation, both these regimes can be realized depending on the initial tumour size and the condition of the immune system. 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An insight into tumor dormancy equilibrium via the analysis of its domain ofattractionA. Merola, C. Cosentino *, F. AmatoSchool of Computer and Biomedical Engineering, Universita` degli Studi Magna Gr\u00e6cia di Catanzaro, Campus \u2018\u2018Salvatore Venuta\u2019\u2019, 88100 Catanzaro, ItalyA B S T R A C TThe trajectories of the dynamic system which regulates the competition between the populations ofmalignant cells and immune cells may tend to an asymptotically stable equilibrium in which the sizes ofthese populations do not vary, which is called tumor dormancy. Especially for lower steady-state sizes ofthe population of malignant cells, this equilibrium represents a desirable clinical condition since thetumor growth is blocked. In this context, it is of mandatory importance to analyze the robustness of thisclinical favorable state of health in the face of perturbations. To this end, the paper presents anoptimization technique to determine whether an assigned rectangular region, which surrounds anasymptotically stable equilibrium point of a quadratic systems, is included into the domain of attractionof the equilibrium itself. The biological relevance of the application of this technique to the analysis oftumor growth dynamics is shown on the basis of a recent quadraticmodel of the tumor\u2013immune systemcompetition dynamics. Indeed the application of the proposedmethodology allows to ensure that a givensafety region, determined on the basis of clinical considerations, belongs to the domain of attraction ofthe tumor blocked equilibrium; therefore for the set of perturbed initial conditions which belong to suchregion, the convergence to the healthy steady state is guaranteed. The proposed methodology can alsoprovide an optimal strategy for cancer treatment.
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Merola, C. Cosentino *, F. Amato" + } + ], + "issue": "3", + "journal": "Biomedical Signal Processing and Control", + "link": "http://identifiers.org/doi/10.1016/j.bspc.2008.02.001", + "month": "7", + "pages": "212-219", + "synopsis": "A B S T R A C T \r\nThe trajectories of the dynamic system which regulates the competition between the populations of malignant cells and immune cells may tend to an asymptotically stable equilibrium in which the sizes of these populations do not vary, which is called tumor dormancy. Especially for lower steady-state sizes of the population of malignant cells, this equilibrium represents a desirable clinical condition since the tumor growth is blocked. In this context, it is of mandatory importance to analyze the robustness of this clinical favorable state of health in the face of perturbations. 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Tumour suppression by immune system through stochastic oscillationsGiulioCaravagnaa Albertod\u2019Onofriob PaoloMilazzoaRobertoBarbutiahttps://doi.org/10.1016/j.jtbi.2010.05.013AbstractThe well-known Kirschner\u2013Panetta model for tumour\u2013immune System interplay [Kirschner, D., Panetta, J.C., 1998. Modelling immunotherapy of the tumour\u2013immune interaction. J. Math. Biol. 37 (3), 235\u2013252] reproduces a number of features of this essential interaction, but it excludes the possibility of tumour suppression by the immune system in the absence of therapy. Here we present a hybrid\u2013stochastic version of that model. In this new framework, we show that in reality the model is also able to reproduce the suppression, through stochastic extinction after the first spike of an oscillation.
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Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies.Parra-Guillen ZP1, Berraondo P, Grenier E, Ribba B, Troconiz IF.Author informationAbstractImmunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.
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In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.", + "title": "Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies.", + "type": "PubMed ID", + "volume": "15", + "year": 2013 + }, + "publicationId": "BIOMD0000000914", + "submissionId": "MODEL2001220002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000915": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + }, + { + "affiliation": "EMBL-EBI", + "email": "sng@ebi.ac.uk", + "external": false, + "name": "Szeyi Ng" + } + ] + }, + "curationStatus": "CURATED", + "description": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <p>BackgroundCancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes.ResultsTo accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time.ConclusionsThe proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.</p> </body> </notes>", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "11803", + "md5sum": "099c7dd08428e07523488241b4a1e3d1", + "mimeType": "application/rdf+xml", + "name": "Sun2018 - Instantaneous mutation rate in cancer initiation and progression-biopax2.owl", + "sha1sum": "e99ac54b9c79a36db2e707349ba1678e4da71a94", + "sha256sum": "dce9e6aea7624e11dc2cebfd65d99deb4ff58fd76a8ec1ec351165cf8238069d" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "19229", + "md5sum": "a23eb2ca49ac854cc45d4891662e15b1", + "mimeType": "application/rdf+xml", + "name": "Sun2018 - Instantaneous mutation rate in cancer initiation and progression-biopax3.owl", + "sha1sum": 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Zhang" + }, + { + "institution": "School of Mathematical Sciences, Monash University, Melbourne, 3800, VIC, Australia. tianhai.tian@monash.edu.", + "name": "Tianhai Tian", + "orcid": "0000-0001-6191-0209" + } + ], + "issue": "Suppl 6", + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/30463617", + "month": "11", + "pages": "110", + "synopsis": "

Background

Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes.

Results

To accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time.

Conclusions

The proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.", + "title": "Instantaneous mutation rate in cancer initiation and progression.", + "type": "PubMed ID", + "volume": "12", + "year": 2018 + }, + "publicationId": "BIOMD0000000915", + "submissionId": "MODEL1909300002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000916": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
A new model is proposed to study the kinetics of [3H]cortisol metabolism by using urinary data only. The model consists of 5 pools, in which changes of the fractions of dose are given by a system of 5 ordinary differential equations. After i.v. administration of [3H]cortisol to 8 multiple pituitary deficient (MPD) patients (group I) the urines from each patient were collected in 9-15 portions during the following 3 days. From the urinary data the rate constants of cortisol metabolism were calculated. A published set of urinary data from patients with a normal cortisol metabolism (group II) was used for comparison. The overall half-life of the label in the circulation was 30 min for both groups; the half-life of the label excretion by both groups was 6 h and the time of maximal activity in the main metabolizing pool was 1.8 h in group I and 1.5 h in group II. The 20% of normal cortisol production rate (CPR) in the 8 MPD patients amounted to 7.2 + 1.9/zmol/(m2*d). Therefore, the low CPR but normal rate constants, i.e. a normal metabolic clearance rate of cortisol, in the MPD patients suggest a sensitive adjustment of the cortisol response in the target organs.
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This a model from the article:
A quantitative model of sleep-wake dynamics based on the physiology of thebrainstem ascending arousal system.
Phillips AJ, Robinson PA. J Biol Rhythms 2007 Apr;22(2):167-79 17440218 ,
Abstract:
A quantitative, physiology-based model of the ascending arousal system isdeveloped, using continuum neuronal population modeling, which involvesaveraging properties such as firing rates across neurons in each population. Themodel includes the ventrolateral preoptic area (VLPO), where circadian andhomeostatic drives enter the system, the monoaminergic and cholinergic nuclei ofthe ascending arousal system, and their interconnections. The human sleep-wakecycle is governed by the activities of these nuclei, which modulate thebehavioral state of the brain via diffuse neuromodulatory projections. The modelparameters are not free since they correspond to physiological observables.Approximate parameter bounds are obtained by requiring consistency withphysiological and behavioral measures, and the model replicates the humansleep-wake cycle, with physiologically reasonable voltages and firing rates.Mutual inhibition between the wake-promoting monoaminergic group andsleep-promoting VLPO causes ;;flip-flop'' behavior, with most time spent in 2stable steady states corresponding to wake and sleep, with transitions betweenthem on a timescale of a few minutes. The model predicts hysteresis in thesleep-wake cycle, with a region of bistability of the wake and sleep states.Reducing the monoaminergic-VLPO mutual inhibition results in a smallerhysteresis loop. This makes the model more prone to wake-sleep transitions inboth directions and makes the states less distinguishable, as in narcolepsy. Themodel behavior is robust across the constrained parameter ranges, but withsufficient flexibility to describe a wide range of observed phenomena.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Phillips AJ, Robinson PA. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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rhythms", + "link": "http://identifiers.org/pubmed/17440218", + "month": "4", + "pages": "167-179", + "synopsis": "A quantitative, physiology-based model of the ascending arousal system is developed, using continuum neuronal population modeling, which involves averaging properties such as firing rates across neurons in each population. The model includes the ventrolateral preoptic area (VLPO), where circadian and homeostatic drives enter the system, the monoaminergic and cholinergic nuclei of the ascending arousal system, and their interconnections. The human sleep-wake cycle is governed by the activities of these nuclei, which modulate the behavioral state of the brain via diffuse neuromodulatory projections. The model parameters are not free since they correspond to physiological observables. Approximate parameter bounds are obtained by requiring consistency with physiological and behavioral measures, and the model replicates the human sleep-wake cycle, with physiologically reasonable voltages and firing rates. Mutual inhibition between the wake-promoting monoaminergic group and sleep-promoting VLPO causes ;;flip-flop'' behavior, with most time spent in 2 stable steady states corresponding to wake and sleep, with transitions between them on a timescale of a few minutes. The model predicts hysteresis in the sleep-wake cycle, with a region of bistability of the wake and sleep states. Reducing the monoaminergic-VLPO mutual inhibition results in a smaller hysteresis loop. This makes the model more prone to wake-sleep transitions in both directions and makes the states less distinguishable, as in narcolepsy. The model behavior is robust across the constrained parameter ranges, but with sufficient flexibility to describe a wide range of observed phenomena.", + "title": "A quantitative model of sleep-wake dynamics based on the physiology of the brainstem ascending arousal system.", + "type": "PubMed ID", + "volume": "22", + "year": 2007 + }, + "publicationId": "BIOMD0000000917", + "submissionId": "MODEL1006230025", + "vcsIdentifier": "aaa" + }, + "BIOMD0000000918": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is passed when growth factor signaling initiates a positive feedback loop of Cdk activity. However, recent studies have cast doubt on this model by claiming R occurs prior to positive feedback activation in G1 or even before completion of the previous cell cycle. Here we reconcile these results and show that whereas many commonly used cell lines do not exhibit a G1 R, primary fibroblasts have a G1 R that is defined by a precise Cdk activity threshold and the activation of cell-cycle-dependent transcription. A simple threshold model, based solely on Cdk activity, predicted with more than 95% accuracy whether individual cells had passed R. That a single measurement accurately predicted cell fate shows that the state of complex regulatory networks can be assessed using a few critical protein activities.
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Electronic address: skotheim@stanford.edu.", + "name": "Jan M Skotheim", + "orcid": "0000-0001-8420-6820" + } + ], + "issue": "2", + "journal": "Molecular cell", + "link": "http://identifiers.org/pubmed/29351845", + "month": "1", + "pages": "253-264.e5", + "synopsis": "At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is passed when growth factor signaling initiates a positive feedback loop of Cdk activity. However, recent studies have cast doubt on this model by claiming\u00a0R occurs prior to positive feedback activation in G1 or even before completion of the previous cell cycle. Here we reconcile these results and show that whereas many commonly used cell lines do not exhibit a G1 R, primary fibroblasts have a G1\u00a0R that is defined by a precise Cdk activity threshold and the activation of cell-cycle-dependent transcription. A simple threshold model, based solely on Cdk activity, predicted with more than 95% accuracy whether individual cells\u00a0had passed R. 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In this article, we use mathematical modeling to study malaria through a combination of the Susceptible, Exposed, Infectious and Recovered (SEIR) Model for humans; Susceptible, Exposed and Infectious (SEI) Model for mosquitos; and the Four Stage Life Cycle Model of the mosquito. Due to the fact that malaria is spread to humans through the bite of a female mosquito that has been infected by the plasmodium parasite, the impacts of mosquitos are also studied in this paper using the SEI Model. Finally, the growth of the mosquito population is directly related to the spread of malaria, the Four Stage Life Cycle is incorporated to model the effects of climate change and interspecies competition within the mosquito life cycle stages of Egg, Larvae, and Pupae. The combination of these models are used to show the growth and spread of malaria.", + "title": "A comprehensive model of spread of malaria in humans and mosquitos", + "type": "DOI", + "year": 2015 + }, + "publicationId": "BIOMD0000000922", + "submissionId": "MODEL2003130001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000923": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
BackgroundThis work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure.ResultsSince both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way.ConclusionsWe present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformaticsModel is encoded by Ruby and submitted and curated to BioModels by Ahmad Zyoud
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Background

This work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure.

Results

Since both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way.

Conclusions

We present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformatics.", + "title": "Modelling osteomyelitis.", + "type": "PubMed ID", + "volume": "13 Suppl 14", + "year": 2012 + }, + "publicationId": "BIOMD0000000923", + "submissionId": "MODEL2003170001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000924": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "skananathan@ebi.ac.uk", + "external": false, + "name": "Sarubini Kananathan" + }, + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Pneumococcal pneumonia is a leading cause of death and a major source of human morbidity. The initial immune response plays a central role in determining the course and outcome of pneumococcal disease. We combine bacterial titer measurements from mice infected with Streptococcus pneumoniae with mathematical modeling to investigate the coordination of immune responses and the effects of initial inoculum on outcome. To evaluate the contributions of individual components, we systematically build a mathematical model from three subsystems that describe the succession of defensive cells in the lung: resident alveolar macrophages, neutrophils and monocyte-derived macrophages. The alveolar macrophage response, which can be modeled by a single differential equation, can by itself rapidly clear small initial numbers of pneumococci. Extending the model to include the neutrophil response required additional equations for recruitment cytokines and host cell status and damage. With these dynamics, two outcomes can be predicted: bacterial clearance or sustained bacterial growth. Finally, a model including monocyte-derived macrophage recruitment by neutrophils suggests that sustained bacterial growth is possible even in their presence. Our model quantifies the contributions of cytotoxicity and immune-mediated damage in pneumococcal pathogenesis.
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The initial immune response plays a central role in determining the course and outcome of pneumococcal disease. We combine bacterial titer measurements from mice infected with Streptococcus pneumoniae with mathematical modeling to investigate the coordination of immune responses and the effects of initial inoculum on outcome. To evaluate the contributions of individual components, we systematically build a mathematical model from three subsystems that describe the succession of defensive cells in the lung: resident alveolar macrophages, neutrophils and monocyte-derived macrophages. The alveolar macrophage response, which can be modeled by a single differential equation, can by itself rapidly clear small initial numbers of pneumococci. Extending the model to include the neutrophil response required additional equations for recruitment cytokines and host cell status and damage. With these dynamics, two outcomes can be predicted: bacterial clearance or sustained bacterial growth. Finally, a model including monocyte-derived macrophage recruitment by neutrophils suggests that sustained bacterial growth is possible even in their presence. 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We undertake a mathematical investigation of a model for the generation of thrombin, an enzyme central to haemostatic blood coagulation, as well as to thrombotic disorders, that is the end product of a complicated protein cascade with multiple feedbacks that ensures its production in the right place at the right time. In a laboratory setting, its central role is reflected in thrombin evolution over time being used as a measure of the ability of a patient's blood to clot. Here, we present a model for the generation of thrombin (based on earlier work) and analyse it using the method of matched asymptotic expansions to derive a sequence of simplified models that characterize the roles of distinct interactions over various timescales. In particular, we are able through the asymptotic analysis to provide simplified models that are an excellent substitute for the full model (capturing the explosive growth and decay of thrombin) and approximations for the key experimental measurements used to describe thrombin's characteristic evolution over time. The asymptotic results are validated against numerical simulations.
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Indeed, the pro-tumor roles of the immune system - as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example - have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed "immune-mediated" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor "education" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. 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Immune-Mediated theory of Metastasis", + "publication": { + "accession": "10.1101/565531", + "affiliation": "University of Alberta", + "authors": [ + { + "institution": "University of Alberta", + "name": "Adam Rhodes" + }, + { + "institution": "University of Alberta", + "name": "Thomas Hillen" + } + ], + "journal": "Jornal of Theoretical Biology", + "link": "http://identifiers.org/doi/10.1101/565531", + "month": "9", + "synopsis": "Accumulating experimental and clinical evidence suggest that the immune response to cancer is not exclusively anti-tumor. Indeed, the pro-tumor roles of the immune system - as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example - have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed \"immune-mediated\" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor \"education\" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. Our results suggest that further work is warranted to fully elucidate the pro-tumor effects of the immune system in metastatic cancer.", + "title": "A Mathematical Model for the Immune-Mediated Theory of Metastasis", + "type": "DOI", + "year": 2019 + }, + "publicationId": "BIOMD0000000926", + "submissionId": "MODEL1910020003", + "vcsIdentifier": "aai" + }, + "BIOMD0000000927": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "Institute of Theoretical Physics, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne", + "email": "barbara.bravi@epfl.ch", + "external": false, + "name": "Barbara Bravi" + }, + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses.
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Baker2017 - The role of cytokines, MMPs andfibronectin fragments osteoarthritis

This model is described in the article:

Baker M, Brook BS, Owen MR.
J Math Biol 2017 Feb; :

Abstract:

Osteoarthritis (OA) is a degenerative disease which causes pain and stiffness in joints. OA progresses through excessive degradation of joint cartilage, eventually leading to significant joint degeneration and loss of function. Cytokines, a group of cell signalling proteins, present in raised concentrations in OA joints, can be classified into pro-inflammatory and anti-inflammatory groups. They mediate cartilage degradation through several mechanisms, primarily the up-regulation of matrix metalloproteinases (MMPs), a group of collagen-degrading enzymes. In this paper we show that the interactions of cytokines within cartilage have a crucial role to play in OA progression and treatment. We develop a four-variable ordinary differential equation model for the interactions between pro- and anti-inflammatory cytokines, MMPs and fibronectin fragments (Fn-fs), a by-product of cartilage degradation and up-regulator of cytokines. We show that the model has four classes of dynamic behaviour: homoeostasis, bistable inflammation, tristable inflammation and persistent inflammation. We show that positive and negative feedbacks controlling cytokine production rates can determine either a pre-disposition to OA or initiation of OA. Further, we show that manipulation of cytokine, MMP and Fn-fs levels can be used to treat OA, but we suggest that multiple treatment targets may be essential to halt or slow disease progression.

This model is hosted on BioModels Database and identified by: MODEL1704120000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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michelle.baker@nottingham.ac.uk.", + "authors": [ + { + "institution": "School of Computer Science, University of Nottingham, Nottingham, UK. michelle.baker@nottingham.ac.uk.", + "name": "Michelle Baker", + "orcid": "0000-0002-8926-7689" + }, + { + "institution": "Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK.", + "name": "Bindi S Brook" + }, + { + "institution": "Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK.", + "name": "Markus R Owen" + } + ], + "issue": "4", + "journal": "Journal of mathematical biology", + "link": "http://identifiers.org/pubmed/28213682", + "month": "10", + "pages": "985-1024", + "synopsis": "Osteoarthritis (OA) is a degenerative disease which causes pain and stiffness in joints. OA progresses through excessive degradation of joint cartilage, eventually leading to significant joint degeneration and loss of function. Cytokines, a group of cell signalling proteins, present in raised concentrations in OA joints, can be classified into pro-inflammatory and anti-inflammatory groups. They mediate cartilage degradation through several mechanisms, primarily the up-regulation of matrix metalloproteinases (MMPs), a group of collagen-degrading enzymes. In this paper we show that the interactions of cytokines within cartilage have a crucial role to play in OA progression and treatment. We develop a four-variable ordinary differential equation model for the interactions between pro- and anti-inflammatory cytokines, MMPs and fibronectin fragments (Fn-fs), a by-product of cartilage degradation and up-regulator of cytokines. We show that the model has four classes of dynamic behaviour: homoeostasis, bistable inflammation, tristable inflammation and persistent inflammation. We show that positive and negative feedbacks controlling cytokine production rates can determine either a pre-disposition to OA or initiation of OA. Further, we show that manipulation of cytokine, MMP and Fn-fs levels can be used to treat OA, but we suggest that multiple treatment targets may be essential to halt or slow disease progression.", + "title": "Mathematical modelling of cytokines, MMPs and fibronectin fragments in osteoarthritic cartilage.", + "type": "PubMed ID", + "volume": "75", + "year": 2017 + }, + "publicationId": "BIOMD0000000928", + "submissionId": "MODEL1704120000", + "vcsIdentifier": "aab" + }, + "BIOMD0000000929": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.
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Singapore 117576, Singapore.", + "name": "Jian-Xin Xu" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/27338302", + "month": "10", + "pages": "42-51", + "synopsis": "Pancreatic cancer is one of the most deadly types of cancer since it typically spreads rapidly and can seldom be detected in its early stage. Pancreatic cancer therapy is thus a challenging task, and appropriate prognosis or assessment for pancreatic cancer therapy is of critical importance. In this work, based on available clinical data in Niu et al. (2013) we develop a mathematical prognosis model that can predict the overall survival of pancreatic cancer patients who receive immunotherapy. The mathematical model incorporates pancreatic cancer cells, pancreatic stellate cells, three major classes of immune effector cells CD8+ T cells, natural killer cells, helper T cells, and two major classes of cytokines interleukin-2 (IL-2) and interferon-\u03b3 (IFN-\u03b3). The proposed model describes the dynamic interaction between tumor and immune cells. In order for the model to be able to generate appropriate prognostic results for disease progression, the distribution and stability properties of equilibria in the mathematical model are computed and analysed in absence of treatments. In addition, numerical simulations for disease progression with or without treatments are performed. It turns out that the median overall survival associated with CIK immunotherapy is prolonged from 7 to 13months compared with the survival without treatment, this is consistent with the clinical data observed in Niu et al. (2013). The validity of the proposed mathematical prognosis model is thus verified. Our study confirms that immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.", + "title": "A mathematical prognosis model for pancreatic cancer patients receiving immunotherapy.", + "type": "PubMed ID", + "volume": "406", + "year": 2016 + }, + "publicationId": "BIOMD0000000929", + "submissionId": "MODEL2004060001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000930": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Its a mathematical model reflecting chemotherapy response in tumor immune interaction system. Model encoded by Sarubini Kananathan and annotated+submitted to Biomodels by Krishna Tiwari
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+ "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Liu2017 - chemotherapy targeted model of tumor immune system", + "publication": { + "accession": "10.1016/j.chaos.2017.03.002", + "affiliation": "Department of Mechanics, Tianjin University, Tianjin, 300354, PR China\r\n Tianjin Key Laboratory of Nonlinear Dynamics and Chaos Control, Tianjin, 300072, PR China", + "authors": [ + { + "institution": "Department of Mechanics, Tianjin University, Tianjin, 300354, PR China", + "name": "Peng Liu" + }, + { + "institution": "Department of Mechanics, Tianjin University, Tianjin, 300354, PR China", + "name": "Xijun Liu" + } + ], + "journal": "Chaos, Solitons and Fractals", + "link": "http://identifiers.org/doi/10.1016/j.chaos.2017.03.002", + "month": "1", + "pages": "7-13", + "synopsis": "Considering the targeted chemotherapy, a mathematical model of tumor-immune system was constructed on the basis of de Pillis\u2019s model. In this paper, we conducted qualitative analysis on the mathematical model, including the positivity and boundedness of solutions, local stability and global stability of equi- librium solutions. Some numerical simulations were given to illustrate the analytic results. Comparing the targeted chemotherapy model with regular chemotherapy model, we found that the targeted chemother- apy was benefit to kill tumor cells.", + "title": "Dynamics of a tumor-immune model considering targeted chemotherapy", + "type": "DOI", + "year": 2020 + }, + "publicationId": "BIOMD0000000930", + "submissionId": "MODEL2004060002", + "vcsIdentifier": "aai" + }, + "BIOMD0000000931": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised of hypothalamic neurons coexpressing kisspeptin, neurokoinin-B and dynorphin. Here, using mathematical modeling and in vivo optogenetics we reveal for the first time how this neural construct initiates and sustains the appropriate ultradian frequency essential for reproduction. Prompted by mathematical modeling, we show experimentally using female estrous mice that robust pulsatile release of luteinizing hormone, a proxy for GnRH, emerges abruptly as we increase the basal activity of the neuronal network using continuous low-frequency optogenetic stimulation. Further increase in basal activity markedly increases pulse frequency and eventually leads to pulse termination. Additional model predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically. Our results shed light on the long-elusive GnRH pulse generator offering new horizons for reproductive health and wellbeing.SIGNIFICANCE STATEMENT The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulationModel is encoded by Johannes and submitted to BioModels by Ahmad Zyoud.
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K.Tsaneva-Atanasova@exeter.ac.uk.The Engineering and Physical Sciences Research Council (EPSRC) Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter EX4 4QJ, United Kingdom.", + "name": "Margaritis Voliotis", + "orcid": "0000-0001-6488-7198" + }, + { + "institution": "Department of Women and Children's Health, School of Life Course Sciences, King's College London, London SE1 1UL, United Kingdom, and.", + "name": "Xiao Feng Li" + }, + { + "institution": "Department of Women and Children's Health, School of Life Course Sciences, King's College London, London SE1 1UL, United Kingdom, and.", + "name": "Ross De Burgh" + }, + { + "institution": "Department of Women and Children's Health, School of Life Course Sciences, King's College London, London SE1 1UL, United Kingdom, and.", + "name": "Geffen Lass" + }, + { + "institution": "Henry Wellcome Laboratory for Integrative Neuroscience and Endocrinology, University of Bristol, Bristol BS1 3NY, United Kingdom.", + "name": 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"synopsis": "Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised of hypothalamic neurons coexpressing kisspeptin, neurokoinin-B and dynorphin. Here, using mathematical modeling and in vivo optogenetics we reveal for the first time how this neural construct initiates and sustains the appropriate ultradian frequency essential for reproduction. Prompted by mathematical modeling, we show experimentally using female estrous mice that robust pulsatile release of luteinizing hormone, a proxy for GnRH, emerges abruptly as we increase the basal activity of the neuronal network using continuous low-frequency optogenetic stimulation. Further increase in basal activity markedly increases pulse frequency and eventually leads to pulse termination. Additional model predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically. Our results shed light on the long-elusive GnRH pulse generator offering new horizons for reproductive health and wellbeing.SIGNIFICANCE STATEMENT The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulation.", + "title": "The Origin of GnRH Pulse Generation: An Integrative Mathematical-Experimental Approach.", + "type": "PubMed ID", + "volume": "39", + "year": 2019 + }, + "publicationId": "BIOMD0000000931", + "submissionId": "MODEL2003190007", + "vcsIdentifier": "aai" + }, + "BIOMD0000000932": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Biofilms offer an excellent example of ecological interaction among bacteria. Temporal and spatial oscillations in biofilms are an emerging topic. In this paper, we describe the metabolic oscillations in Bacillus subtilis biofilms by applying the smallest theoretical chemical reaction system showing Hopf bifurcation proposed by Wilhelm and Heinrich in 1995. The system involves three differential equations and a single bilinear term. We specifically select parameters that are suitable for the biological scenario of biofilm oscillations. We perform computer simulations and a detailed analysis of the system including bifurcation analysis and quasi-steady-state approximation. We also discuss the feedback structure of the system and the correspondence of the simulations to biological observations. Our theoretical work suggests potential scenarios about the oscillatory behaviour of biofilms and also serves as an application of a previously described chemical oscillator to a biological system.
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Lyngby, Denmark.", + "name": "\u00c1kos T Kov\u00e1cs", + "orcid": "0000-0002-4465-1636" + }, + { + "institution": "Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany.", + "name": "Stefan Schuster", + "orcid": "0000-0003-2828-9355" + } + ], + "issue": "2", + "journal": "Royal Society open science", + "link": "http://identifiers.org/pubmed/32257302", + "month": "2", + "pages": "190810", + "synopsis": "Biofilms offer an excellent example of ecological interaction among bacteria. Temporal and spatial oscillations in biofilms are an emerging topic. In this paper, we describe the metabolic oscillations in Bacillus subtilis biofilms by applying the smallest theoretical chemical reaction system showing Hopf bifurcation proposed by Wilhelm and Heinrich in 1995. The system involves three differential equations and a single bilinear term. 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Autonomous biochemical oscillator in regulation of CDK1, Plk1, and APC in Xenopus Laevis cell cycle", + "publication": { + "accession": "21414480", + "affiliation": "Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305-5174, USA. james.ferrell@stanford.edu", + "authors": [ + { + "institution": "Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305-5174, USA. james.ferrell@stanford.edu", + "name": "James E Ferrell" + }, + { + "institution": "Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305-5174, USA.", + "name": "Tsai TY", + "orcid": "0000-0003-1121-9406" + }, + { + "name": "Qiong Yang", + "orcid": "0000-0002-2442-2094" + } + ], + "issue": "6", + "journal": "Cell", + "link": "http://identifiers.org/pubmed/21414480", + "month": "3", + "pages": "874-885", + "synopsis": "Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. 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The eukaryotic cell cycle is characterized by alternating oscillations in the activities of cyclin-dependent kinase (Cdk) and the anaphase-promoting complex (APC). Successful completion of the cell cycle is dependent on the precise, temporally ordered appearance of these activities. A modest level of Cdk activity is sufficient to initiate DNA replication, but mitosis and APC activation require an elevated Cdk activity. In present-day eukaryotes, this temporal order is provided by a complex network of regulatory proteins that control both Cdk and APC activities via sharp thresholds, bistability, and time delays. Using simple computational models, we show here that these dynamical features of cell-cycle organization could emerge in a control system driven by a single Cdk/cyclin complex and APC wired in a negative-feedback loop. We show that ordered phosphorylation of cellular proteins could be explained by multisite phosphorylation/dephosphorylation and competition of substrates for interconverting kinase (Cdk) and phosphatase. In addition, the competition of APC substrates for ubiquitylation can create and maintain sustained oscillations in cyclin levels. We propose a sequence of models that gets closer and closer to a realistic model of cell-cycle control in yeast. Since these models lack the elaborate control mechanisms characteristic of modern eukaryotes, they suggest that bistability and time delay
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Successful completion of the cell cycle is dependent on the precise, temporally ordered appearance of these activities. A modest level of Cdk activity is sufficient to initiate DNA replication, but mitosis and APC activation require an elevated Cdk activity. In present-day eukaryotes, this temporal order is provided by a complex network of regulatory proteins that control both Cdk and APC activities via sharp thresholds, bistability, and time delays. Using simple computational models, we show here that these dynamical features of cell-cycle organization could emerge in a control system driven by a single Cdk/cyclin complex and APC wired in a negative-feedback loop. We show that ordered phosphorylation of cellular proteins could be explained by multisite phosphorylation/dephosphorylation and competition of substrates for interconverting kinase (Cdk) and phosphatase. 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After DNA damage, cells activate p53, a tumor suppressor gene, and select a cell fate (e.g., DNA repair, cell cycle arrest, or apoptosis). Recently, a p53 oscillatory behavior was observed following DNA damage. However, the relationship between this p53 oscillation and cell-fate selection is unclear. Here, we present a novel model of the DNA damage signaling pathway that includes p53 and whole cell cycle regulation and explore the relationship between p53 oscillation and cell fate selection. The simulation run without DNA damage qualitatively realized experimentally observed data from several cell cycle regulators, indicating that our model was biologically appropriate. Moreover, the comprehensive sensitivity analysis for the proposed model was implemented by changing the values of all kinetic parameters, which revealed that the cell cycle regulation system based on the proposed model has robustness on a fluctuation of reaction rate in each process. Simulations run with four different intensities of DNA damage, i.e. Low-damage, Medium-damage, High-damage, and Excess-damage, realized cell cycle arrest in all cases. Low-damage, Medium-damage, High-damage, and Excess-damage corresponded to the DNA damage caused by 100, 200, 400, and 800 J/m(2) doses of UV-irradiation, respectively, based on expression of p21, which plays a crucial role in cell cycle arrest. In simulations run with High-damage and Excess-damage, the length of the cell cycle arrest was shortened despite the severe DNA damage, and p53 began to oscillate. Cells initiated apoptosis and were killed at 400 and 800 J/m(2) doses of UV-irradiation, corresponding to High-damage and Excess-damage, respectively. Therefore, our model indicated that the oscillatory mode of p53 profoundly affects cell fate selection.
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Aurora Kinase B and ZAK interaction model

Equivalent of the stochastic model used in \"Network pharmacology model predicts combined Aurora B and ZAK inhibition in MDA-MB-231 breast cancer cells\" by Tang et. al. 2018.The only difference is cell division and partitioning of the components, which are available in the original model for SGNS2.
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To achieve more effective and sustained clinical responses, synergistic interactions of multiple druggable targets that inhibit redundant cancer survival pathways are often required. Here, we report a systematic polypharmacology strategy to predict, test, and understand the selective drug combinations for MDA-MB-231 triple-negative breast cancer cells. We started by applying our network pharmacology model to predict synergistic drug combinations. Next, by utilizing kinome-wide drug-target profiles and gene expression data, we pinpointed a synergistic target interaction between Aurora B and ZAK kinase inhibition that led to enhanced growth inhibition and cytotoxicity, as validated by combinatorial siRNA, CRISPR/Cas9, and drug combination experiments. The mechanism of such a context-specific target interaction was elucidated using a dynamic simulation of MDA-MB-231 signaling network, suggesting a cross-talk between p53 and p38 pathways. Our results demonstrate the potential of polypharmacological modeling to systematically interrogate target interactions that may lead to clinically actionable and personalized treatment options.", + "title": "Network pharmacology modeling identifies synergistic Aurora B and ZAK interaction in triple-negative breast cancer.", + "type": "PubMed ID", + "volume": "5", + "year": 2019 + }, + "publicationId": "BIOMD0000000940", + "submissionId": "MODEL2004230001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000941": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "matt.maire@free.fr", + "external": false, + "name": "Matthieu MAIRE" + }, + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
We previously proposed a detailed, 39-variable model for the network of cyclin-dependent kinases (Cdks) that controls progression along the successive phases of the mammalian cell cycle. Here, we propose a skeleton, 5-variable model for the Cdk network that can be seen as the backbone of the more detailed model for the mammalian cell cycle. In the presence of sufficient amounts of growth factor, the skeleton model also passes from a stable steady state to sustained oscillations of the various cyclin/Cdk complexes. This transition corresponds to the switch from quiescence to cell proliferation. Sequential activation of the cyclin/Cdk complexes allows the ordered progression along the G1, S, G2 and M phases of the cell cycle. The 5-variable model can also account for the existence of a restriction point in G1, and for endoreplication. Like the detailed model, it contains multiple oscillatory circuits and can display complex oscillatory behaviour such as quasi-periodic oscillations and chaos. We compare the dynamical properties of the skeleton model with those of the more detailed model for the mammalian cell cycle.
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The p53 transcription factor is a regulator of key cellular processes including DNA repair, cell cycle arrest, and apoptosis. In this theoretical study, we investigate how the complex circuitry of the p53 network allows for stochastic yet unambiguous cell fate decision-making. The proposed Markov chain model consists of the regulatory core and two subordinated bistable modules responsible for cell cycle arrest and apoptosis. The regulatory core is controlled by two negative feedback loops (regulated by Mdm2 and Wip1) responsible for oscillations, and two antagonistic positive feedback loops (regulated by phosphatases Wip1 and PTEN) responsible for bistability. By means of bifurcation analysis of the deterministic approximation we capture the recurrent solutions (i.e., steady states and limit cycles) that delineate temporal responses of the stochastic system. Direct switching from the limit-cycle oscillations to the \"apoptotic\" steady state is enabled by the existence of a subcritical Neimark-Sacker bifurcation in which the limit cycle loses its stability by merging with an unstable invariant torus. Our analysis provides an explanation why cancer cell lines known to have vastly diverse expression levels of Wip1 and PTEN exhibit a broad spectrum of responses to DNA damage: from a fast transition to a high level of p53 killer (a p53 phosphoform which promotes commitment to apoptosis) in cells characterized by high PTEN and low Wip1 levels to long-lasting p53 level oscillations in cells having PTEN promoter methylated (as in, e.g., MCF-7 cell line).
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A model for oscillations of Cdc2 kinase in embryonic cell cycles based on Michaelis\u2013Menten phosphorylation\u2013dephosphorylation kinetics shows that the occurrence and amplitude of the oscillations strongly depend on the ultrasensitivity of the enzymatic cascade that controls the activity of the cyclin-dependent kinase.
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A two compartment mathematical model of the antineoplastic compound topotecan

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Cell based mathematical model of topotecan", + "publication": { + "accession": "15094319", + "affiliation": "School of Engineering, University of Warwick, Coventry CV4 7AL, UK.", + "authors": [ + { + "institution": "School of Engineering, University of Warwick, Coventry CV4 7AL, UK.", + "name": "Neil D Evans", + "orcid": "0000-0003-2450-445X" + }, + { + "name": "Rachel J Errington" + }, + { + "name": "Michael Shelley" + }, + { + "name": "Graham P Feeney" + }, + { + "name": "Michael J Chapman" + }, + { + "name": "Keith R Godfrey" + }, + { + "name": "Paul J Smith" + }, + { + "name": "Michael J Chappell" + } + ], + "issue": "2", + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/15094319", + "month": "6", + "pages": "185-217", + "synopsis": "In this paper a compartmental modelling approach is applied to provide a mathematical description of the activity of the anti-cancer agent topotecan, and delivery to its nuclear DNA target following administration. 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Parameter estimation is performed using experimental data which offers concentrations of active and inactive forms of topotecan from high performance liquid chromatography methods.", + "title": "A mathematical model for the in vitro kinetics of the anti-cancer agent topotecan.", + "type": "PubMed ID", + "volume": "189", + "year": 2004 + }, + "publicationId": "BIOMD0000000945", + "submissionId": "MODEL1805170002", + "vcsIdentifier": "aag" + }, + "BIOMD0000000946": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mroberts@ebi.ac.uk", + "external": false, + "name": "Matthew Roberts" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Compartment model for the antineoplastic drug topotecan. Modelling drug in its active lactone and inactive hydroxy acid forms in the medium, extracellular, cytoplasm and nucleus.

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Our study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.", + "title": "3D gut-liver chip with a PK model for prediction of first-pass metabolism.", + "type": "PubMed ID", + "volume": "19", + "year": 2017 + }, + "publicationId": "BIOMD0000000947", + "submissionId": "MODEL1803050002", + "vcsIdentifier": "aag" + }, + "BIOMD0000000948": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mroberts@ebi.ac.uk", + "external": false, + "name": "Matthew Roberts" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "Pharmacokinetic model of alkylresorcinols. 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Background

Alkylresorcinols (ARs), phenolic lipids almost exclusively present in the outer parts of wheat and rye grains in commonly consumed foods, have been proposed as specific dietary biomarkers of whole-grain wheat and rye intakes.

Objective

The objective was to assess the dose response of plasma ARs and the excretion of 2 recently discovered AR metabolites in 24-h urine samples in relation to AR intake and to establish a pharmacokinetic model for predicting plasma AR concentration.

Design

Sixteen subjects were given rye bran flakes containing 11, 22, or 44 mg total ARs 3 times daily during week-long intervention periods separated by 1-wk washout periods in a nonblinded randomized crossover design. Blood samples were collected at baseline, after the 1-wk run-in period, and after each treatment and washout period. Two 24-h urine samples were collected at baseline and after each treatment period.

Results

Plasma AR concentrations and daily excretion of 2 urinary AR metabolites increased with increasing AR dose (P < 0.001). Recovery of urinary metabolites in 24-h samples decreased with increasing doses from approximately 90% to approximately 45% in the range tested. A one-compartment model with 2 absorption compartments with different lag times and absorption rate constants adequately predicted plasma AR concentrations at the end of each intervention period.

Conclusion

Both plasma AR concentrations and urinary metabolites in 24-h samples showed a dose-response relation to increased AR intake, which strongly supports the hypothesis that ARs and their metabolites may be useful as biomarkers of whole-grain wheat and rye intakes.", + "title": "Dose response of whole-grain biomarkers: alkylresorcinols in human plasma and their metabolites in urine in relation to intake.", + "type": "PubMed ID", + "volume": "89", + "year": 2009 + }, + "publicationId": "BIOMD0000000948", + "submissionId": "MODEL1803050001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000949": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mroberts@ebi.ac.uk", + "external": false, + "name": "Matthew Roberts" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + 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Detrick, MD; and Departments of \u2021Clinical Epidemiology and \u00a7Thrombosis and Haemostasis, Leiden University Medical Center, Leiden, The Netherlands.", + "authors": [ + { + "institution": "DoD Biotechnology High-Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command, ATTN: MCMR-TT, 504 Scott Street, Ft Detrick, MD 21702, USA. alex@bioanalysis.org", + "name": "Alexander Y Mitrophanov", + "orcid": "0000-0002-9019-4034" + }, + { + "name": "Frits R Rosendaal" + }, + { + "name": "Jaques Reifman" + } + ], + "issue": "2", + "journal": "Anesthesia and analgesia", + "link": "http://identifiers.org/pubmed/25839182", + "month": "8", + "pages": "278-288", + "synopsis": "

Background

Acidosis, a frequent complication of trauma and complex surgery, results from tissue hypoperfusion and IV resuscitation with acidic fluids. While acidosis is known to inhibit the function of distinct enzymatic reactions, its cumulative effect on the blood coagulation system is not fully understood. Here, we use computational modeling to test the hypothesis that acidosis delays and reduces the amount of thrombin generation in human blood plasma. Moreover, we investigate the sensitivity of different thrombin generation parameters to acidosis, both at the individual and population level.

Methods

We used a kinetic model to simulate and analyze the generation of thrombin and thrombin-antithrombin complexes (TAT), which were the end points of this study. Large groups of temporal thrombin and TAT trajectories were simulated and used to calculate quantitative parameters, such as clotting time (CT), thrombin peak time, maximum slope of the thrombin curve, thrombin peak height, area under the thrombin trajectory (AUC), and prothrombin time. The resulting samples of parameter values at different pH levels were compared to assess the acidosis-induced effects. To investigate intersubject variability, we parameterized the computational model using the data on clotting factor composition for 472 subjects from the Leiden Thrombophilia Study. To compare acidosis-induced relative parameter changes in individual (\"virtual\") subjects, we estimated the probabilities of relative change patterns by counting the pattern occurrences in our virtual subjects. Distribution overlaps for thrombin generation parameters at distinct pH levels were quantified using the Bhattacharyya coefficient.

Results

Acidosis in the range of pH 6.9 to 7.3 progressively increased CT, thrombin peak time, AUC, and prothrombin time, while decreasing maximum slope of the thrombin curve and thrombin peak height (P < 10). Acidosis delayed the onset and decreased the amount of TAT generation (P < 10). As a measure of intrasubject variability, maximum slope of the thrombin curve and CT displayed the largest and second-largest acidosis-induced relative changes, and AUC displayed the smallest relative changes among all thrombin generation parameters in our virtual subject group (1-sided 95% lower confidence limit on the fraction of subjects displaying the patterns, 0.99). As a measure of intersubject variability, the overlaps between the maximum slope of the thrombin curve distributions at acidotic pH levels with the maximum slope of the thrombin curve distribution at physiological pH level systematically exceeded analogous distribution overlaps for CT, thrombin peak time, and prothrombin time.

Conclusions

Acidosis affected all quantitative parameters of thrombin and TAT generation. While maximum slope of the thrombin curve showed the highest sensitivity to acidosis at the individual-subject level, it may be outperformed by CT, thrombin peak time, and prothrombin time as an indicator of acidosis at the subject-group level.", + "title": "Mechanistic Modeling of the Effects of Acidosis on Thrombin Generation.", + "type": "PubMed ID", + "volume": "121", + "year": 2015 + }, + "publicationId": "BIOMD0000000951", + "submissionId": "MODEL1806270001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000952": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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Mathematical model of mitotic exit in budding yeast.

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At the restriction point (R-point), cells become irreversibly committed to the completion of the cell cycle independent of mitogen. The mechanism involving hyper-phosphorylation of retinoblastoma (Rb) and activation of transcription factor E2F is linked to the R-point passage. However, stress stimuli trigger exit from the cell cycle back to the mitogen-sensitive quiescent state after Rb hyper-phosphorylation but only until APC/CCdh1 inactivation. In this study, we developed a mathematical model to investigate the reversible transition between quiescence and proliferation in mammalian cells with respect to mitogen and stress signals. The model integrates the current mechanistic knowledge and accounts for the recent experimental observations with cells exiting quiescence and proliferating cells. We show that Cyclin E:Cdk2 couples Rb-E2F and APC/CCdh1 bistable switches and temporally segregates the R-point and the G1/S transition. 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Our study builds a coherent framework and generates hypotheses that can be further explored by experiments.", + "title": "Mathematical modelling of reversible transition between quiescence and proliferation.", + "type": "PubMed ID", + "volume": "13", + "year": 2018 + }, + "publicationId": "BIOMD0000000954", + "submissionId": "MODEL2006220001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000955": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. 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Heidelberg University, Heidelberg, Germany.", + "name": "Fr\u00e9d\u00e9rique Kok", + "orcid": "0000-0002-7855-1422" + }, + { + "institution": "University of Freiburg", + "name": "Marcus Rosenblatt", + "orcid": "0000-0002-7234-1328" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.", + "name": "Melissa Teusel" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.", + "name": "Tamar Nizharadze", + "orcid": "0000-0001-8292-6867" + }, + { + "institution": "Research Group \"Dynamics of Early Viral Infection and the Innate Antiviral Response\", Division Virus-Associated Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Vladimir Gon\u00e7alves Magalh\u00e3es", + "orcid": "0000-0002-4593-3467" + }, + { + "institution": "Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.Research Group \"Dynamics of Early Viral Infection and the Innate Antiviral Response\", Division Virus-Associated Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Christopher D\u00e4chert", + "orcid": "0000-0001-6441-3099" + }, + { + "institution": "Institute of Physics, University of Freiburg, Freiburg, Germany.", + "name": "Tim Maiwald" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.", + "name": "Artyom Vlasov" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marvin W\u00e4sch" + }, + { + "institution": "Department of General, Visceral and Transplantation Surgery, Ruprecht Karls University Heidelberg, Heidelberg, Germany.", + "name": "Silvana Tyufekchieva" + }, + { + "institution": "Department of General, Visceral and Transplantation Surgery, Ruprecht Karls University Heidelberg, Heidelberg, Germany.", + "name": "Katrin Hoffmann" + }, + { + "institution": "Department of Hepatobiliary Surgery and Visceral Transplantation, University of Leipzig, Leipzig, Germany.", + "name": "Georg Damm" + }, + { + "institution": "Department of Hepatobiliary Surgery and Visceral Transplantation, University of Leipzig, Leipzig, Germany.", + "name": "Daniel Seehofer" + }, + { + "institution": "Department of Medicine II, University Hospital Freiburg-Faculty of Medicine, University of Freiburg, Freiburg, Germany.", + "name": "Tobias Boettler", + "orcid": "0000-0002-1195-055X" + }, + { + "name": "Binder M", + "orcid": "0000-0002-5805-6109" + }, + { + "institution": "Institute of Physics, University of Freiburg, 79104 Freiburg, Germany.FDM-Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany.Signalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany.", + "name": "Jens Timmer", + "orcid": "0000-0003-4517-1383" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Ursula Klingm\u00fcller", + "orcid": "0000-0001-9845-3099" + } + ], + "issue": "7", + "journal": "Molecular systems biology", + "link": "http://identifiers.org/pubmed/32696599", + "month": "7", + "pages": "e8955", + "synopsis": "Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFN\u03b1) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFN\u03b1 doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation\u00a0model for IFN\u03b1 signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFN\u03b1 dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFN\u03b1 leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFN\u03b1 signal response.", + "title": "Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction.", + "type": "PubMed ID", + "volume": "16", + "year": 2020 + }, + "publicationId": "BIOMD0000000959", + "submissionId": "MODEL2005110001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000960": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. 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"name": "Igor Luppi de Oliveira" + }, + { + "institution": "Institute of Science and Technology (ICT), Federal University of S\u00e3o Paulo (UNIFESP), S\u00e3o Jos\u00e9 dos Campos, SP, Brazil.", + "name": "Gabriele Fernandes Garcia", + "orcid": "0000-0001-5816-0312" + } + ], + "issue": "7", + "journal": "PloS one", + "link": "http://identifiers.org/pubmed/32735581", + "month": "0", + "pages": "e0236386", + "synopsis": "This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.", + "title": "A data-driven model to describe and forecast the dynamics of COVID-19 transmission.", + "type": "PubMed ID", + "volume": "15", + "year": 2020 + }, + "publicationId": "BIOMD0000000960", + "submissionId": "MODEL2008200001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000961": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "ETH Z\u00fcrich", + "email": "mgra@ethz.ch", + "external": false, + "name": "Matthias Grass" + }, + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, our model enables prediction of the cell\u2019s condition up to the moment of reperfusion. The simulations revealed a distinct transition between energetically sustainable and unsustainable ATP concentrations for various energetic demands. Our model illustrates how even low oxygen concentrations allow the cell to perform essential functions. We found that the oxygen level required for a sustainable level of ATP increases roughly linearly with the ATP consumption rate. An extracellular O2 concentration of ~0.007 mM could supply basic energy needs in non-beating cardiomyocytes, suggesting that increased collateral circulation may provide an important source of oxygen to sustain the cardiomyocyte during extended ischemia. 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of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.", + "authors": [ + { + "institution": "Departments of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139.", + "name": "Anthony D McDougal", + "orcid": "0000-0002-9980-4193" + }, + { + "institution": "Departments of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139. Electronic address: cfdewey@mit.edu.", + "name": "C Forbes Dewey" + } + ], + "issue": "28", + "journal": "The Journal of biological chemistry", + "link": "http://identifiers.org/pubmed/28487363", + "month": "7", + "pages": "11760-11776", + "synopsis": "Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. 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Our model provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion.", + "title": "Modeling oxygen requirements in ischemic cardiomyocytes.", + "type": "PubMed ID", + "volume": "292", + "year": 2017 + }, + "publicationId": "BIOMD0000000961", + "submissionId": "MODEL1912160001", + "vcsIdentifier": "aai" + }, + "BIOMD0000000962": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Background - The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures. Methods - We develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models. Results - The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R &amp;gt; 1, and after January 30, all regions had a reproductive number R lesser than 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty. Conclusions - We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "10763", + "md5sum": "bc4d1cb5a44e46a9b1ffab0fa076a59a", + "mimeType": "application/rdf+xml", + "name": "Zhao2020-biopax2.owl", + "sha1sum": "e8c110cf384a1888f441f717f8cd5a6aeeddec60", + "sha256sum": "fb78ca3f5e043d090a330be5102340d22493ea316aa4c928f2b6cbd1f94606bb" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "14595", + "md5sum": "21d85e21c9ffd82165dbdc4f9267326f", + "mimeType": "application/rdf+xml", + "name": "Zhao2020-biopax3.owl", + "sha1sum": "e52f16aaa92cf4710e6d90961f28113ee33e83ea", + "sha256sum": "762a8bc739f6714d74f05e8473a7fa3b02deb4e52bee3f6fdca9892e7dcfc246" + }, + { + "description": "Auto-generated Matlab file.", + "fileSize": "13002", + "md5sum": 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Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101 China.China National Center for Bioinformation, Beijing, 100101 China.School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100049 China.", + "name": "Shilei Zhao" + }, + { + "institution": "CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101 China.China National Center for Bioinformation, Beijing, 100101 China.School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100049 China.CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223 China.", + "name": "Hua Chen", + "orcid": "0000-0002-9829-6561" + } + ], + "issue": "1", + "journal": "Quantitative biology (Beijing, China)", + "link": "http://identifiers.org/pubmed/32219006", + "month": "0", + "pages": "11-19", + "synopsis": "

Background

The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures.

Methods

We develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models.

Results

The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R > 1, and after January 30, all regions had a reproductive number R < 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty.

Conclusions

We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.

Supplementary materials

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Atlanta, GA, USA. jsweitz@gatech.edu.", + "authors": [ + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. jsweitz@gatech.edu.School of Physics, Georgia Institute of Technology, Atlanta, GA, USA. jsweitz@gatech.edu.Center for Microbial Dynamics and Infection, Georgia Institute of Technology, Atlanta, GA, USA. jsweitz@gatech.edu.", + "name": "Joshua S Weitz", + "orcid": "0000-0002-3433-8312" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Stephen J Beckett", + "orcid": "0000-0002-4410-2960" + }, + { + "institution": "School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Ashley R Coenen" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "David Demory" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Marian Dominguez-Mirazo", + "orcid": "0000-0001-8653-3091" + }, + { + "institution": "Department of Biology, McMaster University, Hamilton, Ontario, Canada.DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada.", + "name": "Jonathan Dushoff", + "orcid": "0000-0003-0506-4794" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Chung-Yin Leung", + "orcid": "0000-0003-0319-4974" + }, + { + "institution": "School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Guanlin Li" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Andreea M\u0103g\u0103lie" + }, + { + "institution": "Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.", + "name": "Sang Woo Park" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Rogelio Rodriguez-Gonzalez", + "orcid": "0000-0002-5460-4999" + }, + { + "institution": "School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Shashwat Shivam" + }, + { + "institution": "School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.Interdisciplinary Graduate Program in Quantitative Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.", + "name": "Conan Y Zhao", + "orcid": "0000-0001-5714-2286" + } + ], + "issue": "6", + "journal": "Nature medicine", + "link": "http://identifiers.org/pubmed/32382154", + "month": "6", + "pages": "849-854", + "synopsis": "The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.", + "title": "Modeling shield immunity to reduce COVID-19 epidemic spread.", + "type": "PubMed ID", + "volume": "26", + "year": 2020 + }, + "publicationId": "BIOMD0000000963", + "submissionId": "MODEL2009160002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000964": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Objective: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number R0. The model equations are solved numerically using fourth and ffth order Runge\u2013Kutta methods. Results: We found an R0 of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. 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"School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. smusili@jkuat.ac.ke.", + "authors": [ + { + "institution": "School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya. smusili@jkuat.ac.ke.Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya. smusili@jkuat.ac.ke.", + "name": "Samuel Mwalili", + "orcid": "0000-0002-9703-6514" + }, + { + "institution": "Mathematics & Statistics Department, Machakos University, Machakos, Kenya.", + "name": "Mark Kimathi", + "orcid": "0000-0002-5822-8687" + }, + { + "institution": "School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.", + "name": "Viona Ojiambo" + }, + { + "institution": "School of Mathematical Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya.", + "name": "Duncan Gathungu" + }, + { + "institution": "Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya.", + "name": "Rachel Mbogo", + "orcid": "0000-0003-1639-1360" + } + ], + "issue": "1", + "journal": "BMC research notes", + "link": "http://identifiers.org/pubmed/32703315", + "month": "7", + "pages": "352", + "synopsis": "

Objective

Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number [Formula: see text]. The model equations are solved numerically using fourth and fifth order Runge-Kutta methods.

Results

We found an [Formula: see text] of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus.", + "title": "SEIR model for COVID-19 dynamics incorporating the environment and social distancing.", + "type": "PubMed ID", + "volume": "13", + "year": 2020 + }, + "publicationId": "BIOMD0000000964", + "submissionId": "MODEL2009210004", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000965": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "California Institute of Technology", + "email": "hdharuri@cds.caltech.edu", + "external": false, + "name": "Harish Dharuri" + }, + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "The properties of inositol 1,4,5-trisphosphate (IP3)-dependent intracellular calcium oscillations in pancreatic acinar cells depend crucially on the agonist used to stimulate them. Acetylcholine or carbachol (CCh) cause high-frequency (10\u201312-s period) calcium oscillations that are superimposed on a raised baseline, while cholecystokinin (CCK) causes long-period (.100-s period) baseline spiking. We show that physiological concentrations of CCK induce rapid phosphorylation of the IP3 receptor, which is not true of physiological concentrations of CCh. Based on this and other experimental data, we construct a mathematical model of agonist-specific intracellular calcium oscillations in pancreatic acinar cells. Model simulations agree with previous experimental work on the rates of activation and inactivation of the IP3 receptor by calcium (DuFour, J.-F., I.M. Arias, and T.J. Turner. 1997. J. Biol. Chem. 272:2675\u20132681), and reproduce both short-period, raised baseline oscillations, and long-period baseline spiking. The steady state open probability curve of the model IP3 receptor is an increasing function of calcium concentration, as found for type-III IP3 receptors by Hagar et al. (Hagar, R.E., A.D. Burgstahler, M.H. Nathanson, and B.E. Ehrlich. 1998. Nature. 396:81\u201384). We use the model to predict the effect of the removal of external calcium, and this prediction is confirmed experimentally. We also predict that, for type-III IP3 receptors, the steady state open probability curve will shift to lower calcium concentrations as the background IP3 concentration increases. We conclude that the differences between CCh- and CCK-induced calcium oscillations in pancreatic acinar cells can be explained by two principal mechanisms: (a) CCK causes more phosphorylation of the IP3 receptor than does CCh, and the phosphorylated receptor cannot pass calcium current; and (b) the rate of calcium ATPase pumping and the rate of calcium influx from the outside the cell are greater in the presence of CCh than in the presence of CCK.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "15902", + "md5sum": "5533bd246c6589f803961bd6f099650f", + "mimeType": "application/rdf+xml", + "name": "LeBeau1999_Phosphorylation_CCK-biopax2.owl", + "sha1sum": "f1940a8b4806882a68fa37377f2f59031415a1e9", + "sha256sum": "c8ae56afe0395b12fdbcb9f681d4e688a8a58212c7c814b5098a79932411f276" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "21997", + "md5sum": "4dafe17c74ff028f11d07fc16a6064a9", + 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Acetylcholine or carbachol (CCh) cause high-frequency (10-12-s period) calcium oscillations that are superimposed on a raised baseline, while cholecystokinin (CCK) causes long-period (>100-s period) baseline spiking. We show that physiological concentrations of CCK induce rapid phosphorylation of the IP3 receptor, which is not true of physiological concentrations of CCh. Based on this and other experimental data, we construct a mathematical model of agonist-specific intracellular calcium oscillations in pancreatic acinar cells. Model simulations agree with previous experimental work on the rates of activation and inactivation of the IP3 receptor by calcium (DuFour, J.-F., I.M. Arias, and T.J. Turner. 1997. J. Biol. Chem. 272:2675-2681), and reproduce both short-period, raised baseline oscillations, and long-period baseline spiking. The steady state open probability curve of the model IP3 receptor is an increasing function of calcium concentration, as found for type-III IP3 receptors by Hagar et al. (Hagar, R.E., A.D. Burgstahler, M.H. Nathanson, and B.E. Ehrlich. 1998. Nature. 396:81-84). We use the model to predict the effect of the removal of external calcium, and this prediction is confirmed experimentally. We also predict that, for type-III IP3 receptors, the steady state open probability curve will shift to lower calcium concentrations as the background IP3 concentration increases. We conclude that the differences between CCh- and CCK-induced calcium oscillations in pancreatic acinar cells can be explained by two principal mechanisms: (a) CCK causes more phosphorylation of the IP3 receptor than does CCh, and the phosphorylated receptor cannot pass calcium current; and (b) the rate of calcium ATPase pumping and the rate of calcium influx from the outside the cell are greater in the presence of CCh than in the presence of CCK.", + "title": "Agonist-dependent phosphorylation of the inositol 1,4,5-trisphosphate receptor: A possible mechanism for agonist-specific calcium oscillations in pancreatic acinar cells.", + "type": "PubMed ID", + "volume": "113", + "year": 1999 + }, + "publicationId": "BIOMD0000000965", + "submissionId": "MODEL9200487367", + "vcsIdentifier": "aab" + }, + "BIOMD0000000966": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + }, + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "BACKGROUND: The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc homeostasis systems. Its underlying regulatory machine consists of repression on zinc influx through ZnuABC by Zur (Zn2+ uptake regulator) and activation on zinc efflux via ZntA by ZntR (a zinc-responsive regulator). Although these transcriptional regulations seem to be well characterized, and there is an abundance of detailed in vitro experimental data available, as yet there is no mathematical model to help interpret these data. To our knowledge, the work described here is the first attempt to use a mathematical model to simulate these regulatory relations and to help explain the in vitro experimental data. RESULTS: We develop a unified mathematical model consisting of 14 reactions to simulate the in vitro transcriptional response of the zinc homeostasis system in E. coli. Firstly, we simulate the in vitro Zur-DNA interaction by using two of these reactions, which are expressed as 4 ordinary differential equations (ODEs). By imposing the conservation restraints and solving the relevant steady state equations, we find that the simulated sigmoidal curve matches the corresponding experimental data. Secondly, by numerically solving the ODEs for simulating the Zur and ZntR run-off transcription experiments, and depicting the simulated concentrations of zntA and znuC transcripts as a function of free zinc concentration, we find that the simulated curves fit the corresponding in vitro experimental data. Moreover, we also perform simulations, after taking into consideration the competitive effects of ZntR with the zinc buffer, and depict the simulated concentration of zntA transcripts as a function of the total ZntR concentration, both in the presence and absence of Zn(II). The obtained simulation results are in general agreement with the corresponding experimental data. CONCLUSION: Simulation results show that our model can quantitatively reproduce the results of several of the in vitro experiments conducted by Outten CE and her colleagues. 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J.Cui@uva.nl", + "authors": [ + { + "institution": "Section Computational Science, Faculty of Science, University of Amsterdam, Amsterdam, The Netherlands. J.Cui@uva.nl", + "name": "Jiangjun Cui" + }, + { + "name": "Jaap A Kaandorp" + }, + { + "name": "Catherine M Lloyd" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/18950480", + "month": "10", + "pages": "89", + "synopsis": "

Background

The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc homeostasis systems. Its underlying regulatory machine consists of repression on zinc influx through ZnuABC by Zur (Zn2+ uptake regulator) and activation on zinc efflux via ZntA by ZntR (a zinc-responsive regulator). Although these transcriptional regulations seem to be well characterized, and there is an abundance of detailed in vitro experimental data available, as yet there is no mathematical model to help interpret these data. To our knowledge, the work described here is the first attempt to use a mathematical model to simulate these regulatory relations and to help explain the in vitro experimental data.

Results

We develop a unified mathematical model consisting of 14 reactions to simulate the in vitro transcriptional response of the zinc homeostasis system in E. coli. Firstly, we simulate the in vitro Zur-DNA interaction by using two of these reactions, which are expressed as 4 ordinary differential equations (ODEs). By imposing the conservation restraints and solving the relevant steady state equations, we find that the simulated sigmoidal curve matches the corresponding experimental data. Secondly, by numerically solving the ODEs for simulating the Zur and ZntR run-off transcription experiments, and depicting the simulated concentrations of zntA and znuC transcripts as a function of free zinc concentration, we find that the simulated curves fit the corresponding in vitro experimental data. Moreover, we also perform simulations, after taking into consideration the competitive effects of ZntR with the zinc buffer, and depict the simulated concentration of zntA transcripts as a function of the total ZntR concentration, both in the presence and absence of Zn(II). The obtained simulation results are in general agreement with the corresponding experimental data.

Conclusion

Simulation results show that our model can quantitatively reproduce the results of several of the in vitro experiments conducted by Outten CE and her colleagues. Our model provides a detailed insight into the dynamics of the regulatory system and also provides a general framework for simulating in vitro metal-binding and transcription experiments and interpreting the relevant experimental data.", + "title": "Simulating in vitro transcriptional response of zinc homeostasis system in Escherichia coli.", + "type": "PubMed ID", + "volume": "2", + "year": 2008 + }, + "publicationId": "BIOMD0000000966", + "submissionId": "MODEL1172501439", + "vcsIdentifier": "aab" + }, + "BIOMD0000000967": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + }, + { + "affiliation": "Indian Institute of Technology Madras", + "email": "sathvikanantakrishnan@smail.iitm.ac.in", + "external": false, + "name": "Sathvik Anantakrishnan" + } + ] + }, + "curationStatus": "CURATED", + "description": "A new mechanism is proposed for the apparent breakthrough of HIV that occurs approximately 6 months after the commencement of therapy with zidovudine (AZT). 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medicine", + "link": "http://identifiers.org/pubmed/32046137", + "month": "2", + "pages": "E462", + "synopsis": "Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. 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109029 Moscow, Russia.", + "name": "Aleksandra A Filkova", + "orcid": "0000-0002-0448-3981" + }, + { + "institution": "Faculty of Physics, Lomonosov Moscow State University, 1/2 Leninskie gory, 119991 Moscow, Russia.National Medical Research Cent\u0435r of Pediatric Hematology, Oncology and Immunology named after Dmitry Rogachev, 1 Samory Mashela St, 117198 Moscow, Russia.Center for Theoretical Problems of Physico-\u0421hemical Pharmacology, Russian Academy of Sciences, 30 Srednyaya Kalitnikovskaya str., 109029 Moscow, Russia.Faculty of Biological and Medical Physics, Moscow Institute of Physics and Technology, 9 Institutskii per., 141700 Dolgoprudnyi, Russia.", + "name": "Mikhail A Panteleev" + }, + { + "institution": "Faculty of Physics, Lomonosov Moscow State University, 1/2 Leninskie gory, 119991 Moscow, Russia.National Medical Research Cent\u0435r of Pediatric Hematology, Oncology and Immunology named after Dmitry Rogachev, 1 Samory Mashela St, 117198 Moscow, Russia.Center for Theoretical Problems of Physico-\u0421hemical Pharmacology, Russian Academy of Sciences, 30 Srednyaya Kalitnikovskaya str., 109029 Moscow, Russia.Department of Normal Physiology, Sechenov First Moscow State Medical University, 8/2 Trubetskaya St., 119991 Moscow, Russia.", + "name": "Anastasia N Sveshnikova", + "orcid": "0000-0003-4720-7319" + } + ], + "issue": "6", + "journal": "Life (Basel, Switzerland)", + "link": "http://identifiers.org/pubmed/32604803", + "month": "6", + "pages": "E97", + "synopsis": "The process of clustering of plasma membrane receptors in response to their agonist is the first step in signal transduction. The rate of the clustering process and the size of the clusters determine further cell responses. Here we aim to demonstrate that a simple 2-differential equation mathematical model is capable of quantitative description of the kinetics of 2D or 3D cluster formation in various processes. Three mathematical models based on mass action kinetics were considered and compared with each other by their ability to describe experimental data on GPVI or CR3 receptor clustering (2D) and albumin or platelet aggregation (3D) in response to activation. The models were able to successfully describe experimental data without losing accuracy after switching between complex and simple models. However, additional restrictions on parameter values are required to match a single set of parameters for the given experimental data. The extended clustering model captured several properties of the kinetics of cluster formation, such as the existence of only three typical steady states for this system: unclustered receptors, receptor dimers, and clusters. Therefore, a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering in response to activation both in 2D and in 3D.", + "title": "Development of a Simple Kinetic Mathematical Model of Aggregation of Particles or Clustering of Receptors.", + "type": "PubMed ID", + "volume": "10", + "year": 2020 + }, + "publicationId": "BIOMD0000000973", + "submissionId": "MODEL2011030002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000974": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. 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Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. 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Sciences and Engineering, Hohai University, Nanjing, China.", + "name": "Jing Ba" + } + ], + "journal": "Frontiers in public health", + "link": "http://identifiers.org/pubmed/32574303", + "month": "0", + "pages": "230", + "synopsis": "An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.", + "title": "A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model.", + "type": "PubMed ID", + "volume": "8", + "year": 2020 + }, + "publicationId": "BIOMD0000000974", + "submissionId": "MODEL2011030005", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000975": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "viji@ebi.ac.uk", + "external": false, + "name": "Vijayalakshmi Chelliah", + "orcid": "0000-0002-2618-283X" + }, + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "We extend the study of a computational model recently proposed for the mammalian circadian clock (Proc. 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Bifurcation diagrams and computer simulations nevertheless indicate the possible existence of a second source of oscillatory behavior. Thus, sustained oscillations might arise from the sole negative autoregulation of Bmal1 expression. This second oscillatory mechanism may not be functional in physiological conditions, and its period need not necessarily be circadian. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark (LD) cycles. Long-term suppression of circadian oscillations by a single light pulse can occur in the model when a stable steady state coexists with a stable limit cycle. The phase of the oscillations upon entrainment in LD critically depends on the parameters that govern the level of CRY protein. Small changes in the parameters governing CRY levels can shift the peak in Per mRNA from the L to the D phase, or can prevent entrainment. The results are discussed in relation to physiological disorders of the sleep-wake cycle linked to perturbations of the human circadian clock, such as the familial advanced sleep phase syndrome or the non-24h sleep-wake syndrome.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "56788", + "md5sum": "ba81cee7ab4428ba73ceba4839fe5113", + "mimeType": "application/rdf+xml", + "name": "Leloup2004_CircadianRhythmsSet4-biopax2.owl", + "sha1sum": "9f251b1a5b257fb847b4cf5334f854cdb8ea049b", + "sha256sum": "17d2bc6fd8c8c3adb7e0dbca008e7f9853ce5087624cf3024c6e35e5840a8c33" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "96017", + "md5sum": "7d48ba84f60d5f3daf50ee0221489e17", + "mimeType": "application/rdf+xml", + "name": "Leloup2004_CircadianRhythmsSet4-biopax3.owl", + "sha1sum": "1774cf95f365948190a713f629aab70a7f53d3d6", + "sha256sum": "edb2a1428dde8173436b4155f5074e8b3020f6c104eca8f7eaacb9325330b057" 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P. 231, B-1050 Brussels, Belgium.", + "name": "Jean-Christophe Leloup", + "orcid": "0000-0002-5488-9381" + }, + { + "name": "Albert Goldbeter" + } + ], + "issue": "4", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/15363675", + "month": "10", + "pages": "541-562", + "synopsis": "We extend the study of a computational model recently proposed for the mammalian circadian clock (Proc. Natl Acad. Sci. USA 100 (2003) 7051). The model, based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, and Clock genes, can give rise to sustained circadian oscillations in conditions of continuous darkness. These limit cycle oscillations correspond to circadian rhythms autonomously generated by suprachiasmatic nuclei and by some peripheral tissues. By using different sets of parameter values producing circadian oscillations, we compare the effect of the various parameters and show that both the occurrence and the period of the oscillations are generally most sensitive to parameters related to synthesis or degradation of Bmal1 mRNA and BMAL1 protein. The mechanism of circadian oscillations relies on the formation of an inactive complex between PER and CRY and the activators CLOCK and BMAL1 that enhance Per and Cry expression. Bifurcation diagrams and computer simulations nevertheless indicate the possible existence of a second source of oscillatory behavior. Thus, sustained oscillations might arise from the sole negative autoregulation of Bmal1 expression. This second oscillatory mechanism may not be functional in physiological conditions, and its period need not necessarily be circadian. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark (LD) cycles. Long-term suppression of circadian oscillations by a single light pulse can occur in the model when a stable steady state coexists with a stable limit cycle. The phase of the oscillations upon entrainment in LD critically depends on the parameters that govern the level of CRY protein. Small changes in the parameters governing CRY levels can shift the peak in Per mRNA from the L to the D phase, or can prevent entrainment. The results are discussed in relation to physiological disorders of the sleep-wake cycle linked to perturbations of the human circadian clock, such as the familial advanced sleep phase syndrome or the non-24h sleep-wake syndrome.", + "title": "Modeling the mammalian circadian clock: sensitivity analysis and multiplicity of oscillatory mechanisms.", + "type": "PubMed ID", + "volume": "230", + "year": 2004 + }, + "publicationId": "BIOMD0000000975", + "submissionId": "MODEL0478895291", + "vcsIdentifier": "aab" + }, + "BIOMD0000000976": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + }, + { + "affiliation": "Institute of Biochemistry, University Medicine Charit\u00e9 Berlin, 10117 Berlin, Germany. matthias.koenig@charite.de", + "email": "konigmatt@googlemail.com", + "external": false, + "name": "Matthias K\u00f6nig", + "orcid": "0000-0003-1725-179X" + } + ] + }, + "curationStatus": "CURATED", + "description": "One of the common misconceptions about COVID-19 disease is to assume that we will not see a recurrence after the first wave of the disease has subsided. This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. 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This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. 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Our predictions are based on real data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced.", + "title": "Modeling and forecasting the COVID-19 pandemic in India.", + "type": "PubMed ID", + "volume": "139", + "year": 2020 + }, + "publicationId": "BIOMD0000000977", + "submissionId": "MODEL2011300001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000978": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. 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These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. 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These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. 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Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.", + "title": "Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection.", + "type": "PubMed ID", + "volume": "139", + "year": 2020 + }, + "publicationId": "BIOMD0000000979", + "submissionId": "MODEL2012040001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000980": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully pro- tected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious- Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are in- distinguishable before the infection peak. 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To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious-Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are indistinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.", + "title": "Simulation of coronavirus disease 2019 (COVID-19) scenarios with possibility of reinfection.", + "type": "PubMed ID", + "volume": "139", + "year": 2020 + }, + "publicationId": "BIOMD0000000980", + "submissionId": "MODEL2012070001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000981": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Background: In December 2019, an outbreak of coronavirus disease (later named as COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.Methods: A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of the mainland of China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number (Rc), as well as the effective daily reproduction ratio- Re(t), of the disease transmission in the mainland of China excluding Hubei province.Results: The estimation outcomes indicate that Rc is 3.36 (95% CI: 3.20-3.64) and Re(t) has dropped below 1 since 31 January 2020, which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected, and may even cause a second wave of epidemic or outbreaks. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020.Conclusions: To ensure the pandemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, keeping social distance, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.", + "files": { + "additional": [ + { + "description": "Auto-generated BioPAX (Level 2) file.", + "fileSize": "26156", + "md5sum": "23cad7e827ac859b18a278a722ea46a4", + "mimeType": "application/rdf+xml", + "name": "Wan2020-biopax2.owl", + "sha1sum": "a5b6445c891c63ba2aeaae01622b4b1f33a16bce", + "sha256sum": "8ad30136dddfdf318b79db815a00e4bad086b8aaeab37e502895c7f98f50cf6b" + }, + { + "description": "Auto-generated BioPAX (Level 3) file.", + "fileSize": "41365", + "md5sum": "de80b305e4276f76f81d9a9ac93d718e", + "mimeType": "application/rdf+xml", + "name": "Wan2020-biopax3.owl", + "sha1sum": "d8c57cefacb2958285c16e5de354bddee9ceb5f8", + "sha256sum": "5d1f9e1269c748f8c3f38fe89780a6cf0a4f465e4de954b4e471202e63ee0446" + }, + { + "description": "Auto-generated Matlab file.", + "fileSize": "9979", + "md5sum": "35d778ae20da69eca9d4cd948ec5a898", + "mimeType": 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"institution": "Department of Mathematics, School of Science, Beijing University of Civil Engineering and Architecture, Beijing, China.", + "name": "Jing-An Cui" + }, + { + "institution": "Hainan Medical University, Haikou, 571199, China. guojingyang@hotmail.com.Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel, CH-4002, Switzerland. guojingyang@hotmail.com.University of Basel, Basel, Switzerland. guojingyang@hotmail.com.", + "name": "Guo-Jing Yang", + "orcid": "0000-0001-5102-7869" + } + ], + "issue": "1", + "journal": "Infectious diseases of poverty", + "link": "http://identifiers.org/pubmed/32831142", + "month": "8", + "pages": "116", + "synopsis": "

Background

In December 2019, an outbreak of coronavirus disease (later named as COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.

Methods

A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of the mainland of China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number (Rc), as well as the effective daily reproduction ratio- Re(t), of the disease transmission in the mainland of China excluding Hubei province.

Results

The estimation outcomes indicate that Rc is 3.36 (95% CI: 3.20-3.64) and Re(t) has dropped below 1 since 31 January 2020, which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected, and may even cause a second wave of epidemic or outbreaks. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020.

Conclusions

To ensure the pandemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, keeping social distance, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.", + "title": "Risk estimation and prediction of the transmission of coronavirus disease-2019 (COVID-19) in the mainland of China excluding Hubei province.", + "type": "PubMed ID", + "volume": "9", + "year": 2020 + }, + "publicationId": "BIOMD0000000981", + "submissionId": "MODEL2012090001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000982": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. 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Projection generated based on observed data is useful for future planning and control of COVID-19.", + "title": "Tracking the early depleting transmission dynamics of COVID-19 with a time-varying SIR model.", + "type": "PubMed ID", + "volume": "10", + "year": 2020 + }, + "publicationId": "BIOMD0000000982", + "submissionId": "MODEL2012240001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000983": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "The main objective of this paper is to address the following question: are the containment measures imposed by most of the world governments effective and sufficient to stop the epidemic of COVID-19 beyond the lock-down period? 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In this paper, we propose a mathematical model which allows us to investigate and analyse this problem. We show by means of the reproductive number, ${\\cal R}_0$ that the containment measures appear to have slowed the growth of the outbreak. Nevertheless, these measures remain only effective as long as a very large fraction of population, p, greater than the critical value $1-1/{\\cal R}_0$ remains confined. Using French current data, we give some simulation experiments with five scenarios including: (i) the validation of model with p estimated to 93%, (ii) the study of the effectiveness of containment measures, (iii) the study of the effectiveness of the large-scale testing, (iv) the study of the social distancing and wearing masks measures and (v) the study taking into account the combination of the large-scale test of detection of infected individuals and the social distancing with linear progressive easing of restrictions. 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We also noticed that if the measures studied in our five scenarios are taken separately then the second wave might occur at least as far as the parameter values remain unchanged.", + "title": "A model of COVID-19 transmission to understand the effectiveness of the containment measures: application to data from France.", + "type": "PubMed ID", + "volume": "148", + "year": 2020 + }, + "publicationId": "BIOMD0000000983", + "submissionId": "MODEL2101150002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000000984": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Using the parameterized susceptible\u2010exposed\u2010infectious\u2010recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID\u201019) outbreak and impact of different control measures, conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the datafitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS\u2010CoV\u20102) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. 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Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.

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Alterations in the cellular response to TGFb are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFb is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.", + "title": "Cell-specific responses to the cytokine TGFb are determined by variability in protein levels.", + "type": "PubMed ID", + "volume": "14", + "year": 2018 + }, + "publicationId": "BIOMD0000000989", + "submissionId": "MODEL1712050001", + "vcsIdentifier": "aag" + }, + "BIOMD0000000990": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "UCB \u2013 Inspired by patients", + "email": "udi.tgfb.2015@gmail.com", + "external": false, + "name": "Uddipan Sarma" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + }, + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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"26578352", + "affiliation": "Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. jukka.intosalmi@aalto.fi.", + "authors": [ + { + "institution": "Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. jukka.intosalmi@aalto.fi.", + "name": "Jukka Intosalmi" + }, + { + "institution": "The Francis Crick Institute, Mill Hill Laboratory, Mill HillLondon, UK. helena.ahlfors@gmail.com.Current affiliation: Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, UK. helena.ahlfors@gmail.com.", + "name": "Helena Ahlfors", + "orcid": "0000-0002-8183-0895" + }, + { + "institution": "Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. sini.rautio@aalto.fi.", + "name": "Sini Rautio", + "orcid": "0000-0001-5215-4456" + }, + { + "institution": "Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. henrik.mannerstrom@aalto.fi.", + "name": "Henrik Mannerst\u00f6m" + }, + { + "institution": "Turku Centre for Biotechnology, University of Turku and \u00c5bo Akademi, Turku, Finland. zchen@btk.fi.", + "name": "Zhi Jane Chen", + "orcid": "0000-0002-4658-7463" + }, + { + "institution": "Turku Centre for Biotechnology, University of Turku and \u00c5bo Akademi, Turku, Finland. riitta.lahesmaa@btk.fi.", + "name": "Riitta Lahesmaa" + }, + { + "institution": "The Francis Crick Institute, Mill Hill Laboratory, Mill HillLondon, UK. brigitta.stockinger@crick.ac.uk.", + "name": "Brigitta Stockinger", + "orcid": "0000-0001-8781-336X" + }, + { + "institution": "Department of Computer Science, Aalto University, Aalto, FI-00076, Finland. harri.lahdesmaki@aalto.fi.Turku Centre for Biotechnology, University of Turku and \u00c5bo Akademi, Turku, Finland. harri.lahdesmaki@aalto.fi.", + "name": "Harri L\u00e4hdesm\u00e4ki" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/26578352", + "month": "11", + "pages": "81", + "synopsis": "

Background

The differentiation of naive CD 4(+) helper T (Th) cells into effector Th17 cells is steered by extracellular cytokines that activate and control the lineage specific transcriptional program. While the inducing cytokine signals and core transcription factors driving the differentiation towards Th17 lineage are well known, detailed mechanistic interactions between the key components are poorly understood.

Results

We develop an integrative modeling framework which combines RNA sequencing data with mathematical modeling and enables us to construct a mechanistic model for the core Th17 regulatory network in a data-driven manner.

Conclusions

Our results show significant evidence, for instance, for inhibitory mechanisms between the transcription factors and reveal a previously unknown dependency between the dosage of the inducing cytokine TGF \u03b2 and the expression of the master regulator of competing (induced) regulatory T cell lineage. Further, our experimental validation approves this dependency in Th17 polarizing conditions.", + "title": "Analyzing Th17 cell differentiation dynamics using a novel integrative modeling framework for time-course RNA sequencing data.", + "type": "PubMed ID", + "volume": "9", + "year": 2015 + }, + "publicationId": "BIOMD0000001004", + "submissionId": "MODEL2102260001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001005": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ], + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + }, + { + "affiliation": "SASTRA Deemed University", + "email": "apalania@scbt.sastra.edu", + "external": false, + "name": "Ashok Palaniappan", + "orcid": "0000-0003-2841-9527" + } + ] + }, + "curationStatus": "CURATED", + "description": "

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host's health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver.

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Recent data show that p53 activity does not increase steadily with genetic damage but rather fluctuates in an oscillatory fashion. Theoretical studies suggest that oscillations can arise from a combination of positive and negative feedbacks or from a long negative feedback loop alone. Both negative and positive feedbacks are present in the p53/Mdm2 network, but it is not known what roles they play in the oscillatory response to DNA damage. We developed a mathematical model of p53 oscillations based on positive and negative feedbacks in the p53/Mdm2 network. According to the model, the system reacts to DNA damage by moving from a stable steady state into a region of stable limit cycles. Oscillations in the model are born with large amplitude, which guarantees an all-or-none response to damage. As p53 oscillates, damage is repaired and the system moves back to a stable steady state with low p53 activity. The model reproduces experimental data in quantitative detail. 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The proposal accounts for basic features of p53-mediated responses to DNA damage and suggests new experiments to probe the dynamics of p53 signaling.", + "title": "Exploring mechanisms of the DNA-damage response: p53 pulses and their possible relevance to apoptosis.", + "type": "PubMed ID", + "volume": "6", + "year": 2007 + }, + "publicationId": "BIOMD0000001010", + "submissionId": "MODEL2106230002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001011": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model is based on the publication:\"CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models\".Odelaisy Le\u00f3n-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Mar\u00eda Rosa, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ramirez-Orellana, V\u00edctor M. P\u00e9rez-Garc\u00edadoi: 10.1016/j.cnsns.2020.105570 Comment:This model is based on equations (4a)-(4c).Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.
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Haemato-Oncology, Haematopoietic Stem Cell Transplantation and Cell Therapy, Hospital Universitario La Paz, Madrid, Spain\ne Paediatric Haemato-Oncology Department, Hospital Universitario La Paz, Madrid, Spain\nf Department of Paediatric Haematology and Oncology, Hospital Infantil Universitario Ni\u00f1o Jes\u00fas, Universidad Aut\u00f3noma de Madrid, Madrid, Spain", + "authors": [ + { + "name": "Odelaisy Leon-Triana" + }, + { + "name": "Soukaina Sabir" + }, + { + "name": "Gabriel F. Calvo" + }, + { + "name": "Juan Belmonte-Beitia" + }, + { + "name": "Salvador Chulian" + }, + { + "name": "Alvaro Martinez-Rubio" + }, + { + "name": "Maria Rosa" + }, + { + "name": "Antonio Perez-Martinez" + }, + { + "name": "Manuel Ramirez-Orellana" + }, + { + "name": "Victor M. Perez-Garcia" + } + ], + "journal": "Communications in Nonlinear Science and Numerical Simulation", + "link": "http://identifiers.org/doi/10.1016/j.cnsns.2020.105570", + "month": "3", + "pages": "105570", + "synopsis": "Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.", + "title": "CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models", + "type": "DOI", + "volume": "94", + "year": 2020 + }, + "publicationId": "BIOMD0000001011", + "submissionId": "MODEL2107120001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001012": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model is based on the publication:\"CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models\".Odelaisy Le\u00f3n-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Mar\u00eda Rosa, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ramirez-Orellana, V\u00edctor M. P\u00e9rez-Garc\u00edadoi: 10.1016/j.cnsns.2020.105570 Comment:This model is based on equations (3a)-(3c) from the paper.Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.
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del Mar, C\u00e1diz, Spain\nd Translational Research Unit in Paediatric Haemato-Oncology, Haematopoietic Stem Cell Transplantation and Cell Therapy, Hospital Universitario La Paz, Madrid, Spain\ne Paediatric Haemato-Oncology Department, Hospital Universitario La Paz, Madrid, Spain\nf Department of Paediatric Haematology and Oncology, Hospital Infantil Universitario Ni\u00f1o Jes\u00fas, Universidad Aut\u00f3noma de Madrid, Madrid, Spain", + "authors": [ + { + "name": "Odelaisy Leon-Triana" + }, + { + "name": "Soukaina Sabir" + }, + { + "name": "Gabriel F. Calvo" + }, + { + "name": "Juan Belmonte-Beitia" + }, + { + "name": "Salvador Chulian" + }, + { + "name": "Alvaro Martinez-Rubio" + }, + { + "name": "Maria Rosa" + }, + { + "name": "Antonio Perez-Martinez" + }, + { + "name": "Manuel Ramirez-Orellana" + }, + { + "name": "Victor M. Perez-Garcia" + } + ], + "journal": "Communications in Nonlinear Science and Numerical Simulation", + "link": "http://identifiers.org/doi/10.1016/j.cnsns.2020.105570", + "month": "3", + "pages": "105570", + "synopsis": "Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.", + "title": "CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models", + "type": "DOI", + "volume": "94", + "year": 2020 + }, + "publicationId": "BIOMD0000001012", + "submissionId": "MODEL2107120002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001013": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article:\"Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept\"Odelaisy Le\u00f3n-Triana, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ram\u00edrez-Orellana and V\u00edctor M. P\u00e9rez-Garc\u00edaCancers 2021, 13, 703.; doi: 10.3390/cancers13040703Comment:This is the first mathematical model, derived from equations 1 and 2, used in the paper.Reproduction of Fig. 5a was achieved by setting alpha_1 = 0.04, different to the value quoted in the article caption for Fig. 5.Abstract:Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
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Competition between tumour cells and single-target CAR T-cells", + "publication": { + "accession": "33572301", + "affiliation": "Mathematical Oncology Laboratory (MOLAB), Department of Mathematics, Instituto de Matem\u00e1tica Aplicada a la Ciencia y la Ingenier\u00eda, Universidad de Castilla-La Mancha, Avda. Camilo Jos\u00e9 Cela, 3, 13071 Ciudad Real, Spain.", + "authors": [ + { + "institution": "Mathematical Oncology Laboratory (MOLAB), Department of Mathematics, Instituto de Matem\u00e1tica Aplicada a la Ciencia y la Ingenier\u00eda, Universidad de Castilla-La Mancha, Avda. Camilo Jos\u00e9 Cela, 3, 13071 Ciudad Real, Spain.", + "name": "Odelaisy Le\u00f3n-Triana", + "orcid": "0000-0002-7509-7754" + }, + { + "institution": "Paediatric Haemato-Oncology Department, Hospital Universitario La Paz, 28046 Madrid, Spain.", + "name": "Antonio P\u00e9rez-Mart\u00ednez", + "orcid": "0000-0002-6436-9195" + }, + { + "institution": "Translational Research Unit in Paediatric Haemato-Oncology, Haematopoietic Stem Cell Transplantation and Cell Therapy, Hospital Infantil Universitario Ni\u00f1o Jes\u00fas, 28009 Madrid, Spain.", + "name": "Manuel Ram\u00edrez-Orellana", + "orcid": "0000-0003-0332-6973" + }, + { + "institution": "Mathematical Oncology Laboratory (MOLAB), Department of Mathematics, Instituto de Matem\u00e1tica Aplicada a la Ciencia y la Ingenier\u00eda, Universidad de Castilla-La Mancha, Avda. Camilo Jos\u00e9 Cela, 3, 13071 Ciudad Real, Spain.", + "name": "V\u00edctor M P\u00e9rez-Garc\u00eda", + "orcid": "0000-0002-6575-495X" + } + ], + "issue": "4", + "journal": "Cancers", + "link": "http://identifiers.org/pubmed/33572301", + "month": "2", + "pages": "703", + "synopsis": "Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.", + "title": "Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept.", + "type": "PubMed ID", + "volume": "13", + "year": 2021 + }, + "publicationId": "BIOMD0000001013", + "submissionId": "MODEL2107120003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001014": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article:\"Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept\"Odelaisy Le\u00f3n-Triana, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ram\u00edrez-Orellana and V\u00edctor M. P\u00e9rez-Garc\u00edaCancers 2021, 13, 703.; doi: 10.3390/cancers13040703Comment:This is the second mathematical model, derived from equations 3 to 6, used in the paper.Reproduction of Figure 5b was achieved by setting alpha_1 = 0.183, in substitution for alpha_1 = 0.2 as quoted in the article.Abstract:Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
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Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.", + "title": "Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept.", + "type": "PubMed ID", + "volume": "13", + "year": 2021 + }, + "publicationId": "BIOMD0000001014", + "submissionId": "MODEL2107120004", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001015": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "rkausthubh@gmail.com", + "external": false, + "name": "Kausthubh Ramachandran", + "orcid": "0000-0002-6221-8166" + } + ] + }, + "curationStatus": "CURATED", + "description": "Duchenne muscular dystrophy (DMD) is a genetic disease that results in the death of affected boys by early adulthood.The genetic defect responsible for DMD has been known for over 25 years, yet at present there is neither cure nor effective treatment for DMD. 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Box 9502, 2300 RA, Leiden, The Netherlands. suruchi.bakshi@certara.com.", + "authors": [ + { + "institution": "Division of Systems Biomedicine and Pharmacology, LACDR, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands. suruchi.bakshi@certara.com.Certara QSP, 4818 SJ, Breda, The Netherlands. suruchi.bakshi@certara.com.", + "name": "Suruchi Bakshi", + "orcid": "0000-0002-7767-1705" + }, + { + "institution": "Cytokine, Chemokine and Complement DPU, Immunoinflammation TA Unit, GSK, Stevenage, UK.", + "name": "Fraser Cunningham", + "orcid": "0000-0001-6436-7555" + }, + { + "institution": "Cytokine, Chemokine and Complement DPU, Immunoinflammation TA Unit, GSK, Stevenage, UK.", + "name": "Eva-Maria Nichols" + }, + { + "institution": "Cytokine, Chemokine and Complement DPU, Immunoinflammation TA Unit, GSK, Stevenage, UK.", + "name": "Marta Biedzka-Sarek" + }, + { + "institution": "Cytokine, Chemokine and Complement DPU, Immunoinflammation TA Unit, GSK, Stevenage, UK.", + "name": "Jessica Neisen" + }, + { + "institution": "Cytokine, Chemokine and Complement DPU, Immunoinflammation TA Unit, GSK, Stevenage, UK.", + "name": "Sebastien Petit-Frere" + }, + { + "institution": "Cytokine, Chemokine and Complement DPU, Immunoinflammation TA Unit, GSK, Stevenage, UK.", + "name": "Christina Bessant" + }, + { + "institution": "Computational and Modelling Sciences, Platform Technology Sciences, GSK, Collegeville, Pennsylvania, USA.", + "name": "Loveleena Bansal" + }, + { + "institution": "Mathematical Institute, Leiden University, P.O. Box 9512, 2300 RA, Leiden, The Netherlands.", + "name": "Lambertus A Peletier" + }, + { + "institution": "Clinical Pharmacology, Modelling and Simulation, GSK, Stevenage, UK.", + "name": "Stefano Zamuner" + }, + { + "institution": "Division of Systems Biomedicine and Pharmacology, LACDR, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.Certara QSP, Canterbury, CT2 7FG, UK.", + "name": "Piet H van der Graaf" + } + ], + "issue": "2", + "journal": "Bulletin of mathematical biology", + "link": "http://identifiers.org/pubmed/32062771", + "month": "2", + "pages": "33", + "synopsis": "The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). 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In recent years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate in patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable for use as mathematical models. In this work, we develop a three-population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities are considered uncertainties in the model parameters. 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Hospital das Cl\u00ednicas da Faculdade de Medicina ds Universidade de S\u00e3o Paulo, S\u00e3o Paulo 01246-000, Brazil.", + "authors": [ + { + "institution": "Center for Translational Research in Oncology, Instituto do C\u00e2ncer do Estado de S\u00e3o Paulo, Hospital das Cl\u00ednicas da Faculdade de Medicina ds Universidade de S\u00e3o Paulo, S\u00e3o Paulo 01246-000, Brazil.", + "name": "Luciana R C Barros", + "orcid": "0000-0002-8633-716X" + }, + { + "institution": "Graduate Program, Laborat\u00f3rio Nacional de Computa\u00e7\u00e3o Cient\u00edfica, Petr\u00f3polis 25651-075, Brazil.", + "name": "Emanuelle A Paix\u00e3o", + "orcid": "0000-0003-1496-5324" + }, + { + "institution": "Computer Science Department, Universidade Federal do Esp\u00edrito Santo, Vit\u00f3ria 29075-910, Brazil.", + "name": "Andrea M P Valli", + "orcid": "0000-0003-2575-2578" + }, + { + "institution": "Graduate Program, Laborat\u00f3rio Nacional de Computa\u00e7\u00e3o Cient\u00edfica, Petr\u00f3polis 25651-075, Brazil.", + "name": "Gustavo T Naozuka", + "orcid": "0000-0002-7331-9763" + }, + { + "institution": "Institute for Mathematics and Computer Science, Universidade Federal de Itajub\u00e1, Itajub\u00e1 37500-903, Brazil.", + "name": "Artur C Fassoni", + "orcid": "0000-0002-3634-0061" + }, + { + "institution": "Computational Modeling Department, Laborat\u00f3rio Nacional de Computa\u00e7\u00e3o Cient\u00edfica, Petr\u00f3polis 25651-075, Brazil.", + "name": "Regina C Almeida", + "orcid": "0000-0002-9756-8131" + } + ], + "issue": "12", + "journal": "Cancers", + "link": "http://identifiers.org/pubmed/34208323", + "month": "6", + "pages": "2941", + "synopsis": "Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient's T lymphocytes are genetically manipulated to recognize tumor-specific antigens, increasing tumor elimination efficiency. In recent years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate in patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable for use as mathematical models. In this work, we develop a three-population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities are considered uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reducing and optimizing the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such an in silico laboratory is an easy-to-run open-source simulator, built on a Shiny R-based platform called CARTmath. It contains the results of this manuscript as examples and documentation. The developed model together with the CARTmath platform have potential use in assessing different CAR-T cell immunotherapy protocols and its associated efficacy, becoming an accessory for in silico trials.", + "title": "CARTmath-A Mathematical Model of CAR-T Immunotherapy in Preclinical Studies of Hematological Cancers.", + "type": "PubMed ID", + "volume": "13", + "year": 2021 + }, + "publicationId": "BIOMD0000001020", + "submissionId": "MODEL2107220002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001021": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model is described in the following article:\"Autocrine and paracrine interferon signalling as \u2018ring vaccination\u2019 and \u2018contact tracing\u2019 strategies to suppress virus infection in a host\"G. Michael Lavigne, Hayley Russell, Barbara Sherry and Ruian KeDOI: 10.1098/rspb.2020.3002Comment:This model is based on the ordinary differential equations of the non-spatial model of well-mixed viral infection stated in the manuscript (Eq. 2.1 in the article).Abstract:The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as \u2018contact tracing\u2019 or \u2018ring vaccination\u2019. Thus, our results here may have implications for the outbreak control at the population scale more broadly.
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Non-spatial model of viral infection dynamics and interferon response of well-mixed viral infection", + "publication": { + "accession": "33622135", + "affiliation": "Department of Mathematics, North Carolina State University, Raleigh, NC 27606, USA.", + "authors": [ + { + "institution": "Department of Mathematics, North Carolina State University, Raleigh, NC 27606, USA.", + "name": "G Michael Lavigne" + }, + { + "institution": "Department of Mathematics, North Carolina State University, Raleigh, NC 27606, USA.", + "name": "Hayley Russell" + }, + { + "institution": "School of Veterinary Medicine, North Carolina State University, Raleigh, NC 27606, USA.", + "name": "Barbara Sherry" + }, + { + "institution": "Department of Mathematics, North Carolina State University, Raleigh, NC 27606, USA.T-6, Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.", + "name": "Ruian Ke", + "orcid": "0000-0001-5307-8934" + } + ], + "issue": "1945", + "journal": "Proceedings. Biological sciences", + "link": "http://identifiers.org/pubmed/33622135", + "month": "2", + "pages": "20203002", + "synopsis": "The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as 'contact tracing' or 'ring vaccination'. Thus, our results here may have implications for the outbreak control at the population scale more broadly.", + "title": "Autocrine and paracrine interferon signalling as 'ring vaccination' and 'contact tracing' strategies to suppress virus infection in a host.", + "type": "PubMed ID", + "volume": "288", + "year": 2021 + }, + "publicationId": "BIOMD0000001021", + "submissionId": "MODEL2107220003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001022": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication:Creemers JHA, Lesterhuis WJ, Mehra N, et al.\"A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.\"Journal for ImmunoTherapy of Cancer 2021;9:e002032.doi:10.1136/jitc-2020-002032Comment:Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used:Reproduction of Fig. 1(C), xi = 0.0005Reproduction of Fig. 1(D), xi = 0.00025Abstract:Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.Results: Our model shows that a tipping point\u2014a sharp state transition between immune control and immune evasion\u2014induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient\u2019s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.
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"authors": [ + { + "institution": "Department of Tumor Immunology, Radboudumc, Nijmegen, The Netherlands.Oncode Institute, Nijmegen, The Netherlands.", + "name": "Jeroen H A Creemers", + "orcid": "0000-0003-0371-8416" + }, + { + "institution": "School of Biomedical Sciences and Telethon Kids Institute, University of Western Australia, Perth, Western Australia, Australia.", + "name": "W Joost Lesterhuis", + "orcid": "0000-0002-2718-276X" + }, + { + "institution": "Department of Medical Oncology, Radboudumc, Nijmegen, The Netherlands.", + "name": "Niven Mehra", + "orcid": "0000-0002-4794-1831" + }, + { + "institution": "Department of Medical Oncology, Radboudumc, Nijmegen, The Netherlands.", + "name": "Winald R Gerritsen", + "orcid": "0000-0003-1110-9119" + }, + { + "institution": "Department of Tumor Immunology, Radboudumc, Nijmegen, The Netherlands.Oncode Institute, Nijmegen, The Netherlands.", + "name": "Carl G Figdor", + "orcid": "0000-0002-2366-9212" + }, + { + "institution": "Department of Tumor Immunology, Radboudumc, Nijmegen, The Netherlands.", + "name": "I Jolanda M de Vries", + "orcid": "0000-0002-8653-4040" + }, + { + "institution": "Department of Tumor Immunology, Radboudumc, Nijmegen, The Netherlands johannes.textor@radboudumc.nl.Data Science Department, Radboud University Institute for Computing and Information Sciences, Nijmegen, The Netherlands.", + "name": "Johannes Textor", + "orcid": "0000-0002-0459-9458" + } + ], + "issue": "5", + "journal": "Journal for immunotherapy of cancer", + "link": "http://identifiers.org/pubmed/34059522", + "month": "5", + "pages": "e002032", + "synopsis": "

Background

Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.

Methods

A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.

Results

Our model shows that a tipping point-a sharp state transition between immune control and immune evasion-induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.

Conclusion

These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient's distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.", + "title": "A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.", + "type": "PubMed ID", + "volume": "9", + "year": 2021 + }, + "publicationId": "BIOMD0000001022", + "submissionId": "MODEL2107290001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001023": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model of tumor cell growth, called the normal-tumor-immune-unhealthy diet model (NTIUNHDM), is described by the publication:Alharbi, S.A.; Rambely, A.S.\"A New ODE-Based Model for Tumor Cells and Immune System Competition\"Mathematics 2020, 8, 1285.doi:10.3390/math8081285Abstract:Changes in diet are heavily associated with high mortality rates in several types of cancer. In this paper, a new mathematical model of tumor cells growth is established to dynamically demonstrate the effects of abnormal cell progression on the cells affected by the tumor in terms of the immune system\u2019s functionality and normal cells\u2019 dynamic growth. This model is called the normal-tumor-immune-unhealthy diet model (NTIUNHDM) and governed by a system of ordinary differential equations. In the NTIUNHDM, there are three main populations normal cells, tumor cell and immune cells. The model is discussed analytically and numerically by utilizing a fourth-order Runge\u2013Kutta method. The dynamic behavior of the NTIUNHDM is discussed by analyzing the stability of the system at various equilibrium points and the Mathematica software is used to simulate the model. From analysis and simulation of the NTIUNHDM, it can be deduced that instability of the response stage, due to a weak immune system, is classified as one of the main reasons for the coexistence of abnormal cells and normal cells. Additionally, it is obvious that the NTIUNHDM has only one stable case when abnormal cells begin progressing into early stages of tumor cells such that the immune cells are generated once. Thus, early boosting of the immune system might contribute to reducing the risk of cancer.
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An ODE-based model of the dynamics of tumor cell progression and its effects on normal cell growth and immune system functionality", + "publication": { + "accession": "10.3390/math8081285", + "affiliation": "Department of Mathematics & Statistics, College of Science, Taibah University, Yanbu 41911, Almadinah Almunawarah, Saudi Arabia\nDepartment of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Selangor, Malaysia", + "authors": [ + { + "name": "Sana Abdulkream Alharbi" + }, + { + "name": "Azmin Sham Rambely" + } + ], + "issue": "8", + "journal": "Mathematics", + "link": "http://identifiers.org/doi/10.3390/math8081285", + "month": "8", + "pages": "1285", + "synopsis": "Changes in diet are heavily associated with high mortality rates in several types of cancer. In this paper, a new mathematical model of tumor cells growth is established to dynamically demonstrate the effects of abnormal cell progression on the cells affected by the tumor in terms of the immune system\u2019s functionality and normal cells\u2019 dynamic growth. This model is called the normal-tumor-immune-unhealthy diet model (NTIUNHDM) and governed by a system of ordinary differential equations. In the NTIUNHDM, there are three main populations normal cells, tumor cell and immune cells. The model is discussed analytically and numerically by utilizing a fourth-order Runge\u2013Kutta method. The dynamic behavior of the NTIUNHDM is discussed by analyzing the stability of the system at various equilibrium points and the Mathematica software is used to simulate the model. From analysis and simulation of the NTIUNHDM, it can be deduced that instability of the response stage, due to a weak immune system, is classified as one of the main reasons for the coexistence of abnormal cells and normal cells. Additionally, it is obvious that the NTIUNHDM has only one stable case when abnormal cells begin progressing into early stages of tumor cells such that the immune cells are generated once. 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This ordinary differential equation model of the cellular kinetics and pharmacodynamics of CAR-T cell therapy is described in the publication:Chaudhury, A., Zhu, X., Chu, L., Goliaei, A., June, C., Kearns, J. and Stein, A., 2020. Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic\u2010Pharmacodynamic Modeling Approaches. The Journal of Clinical Pharmacology, 60(S1).DOI: 10.1002/jcph.1691Comment:This model is based on equations 4-5 from the manuscript.Abstract:Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.
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There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.", + "title": "Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic-Pharmacodynamic Modeling Approaches.", + "type": "PubMed ID", + "volume": "60 Suppl 1", + "year": 2020 + }, + "publicationId": "BIOMD0000001025", + "submissionId": "MODEL2108050002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001026": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biosystems Group, LAtvia University of Agriculture", + "email": "egils.stalidzans@gmail.com", + "external": false, + "name": "Egils Stalidzans" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
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Background

Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs).

Objective

We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies.

Methods

An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline.

Results

A single passive transport coefficient, k\u00a0=\u00a00.044\u00a0\u00b1\u00a00.014 (h-1), can be applied, describing the uptake and release transport rate versus the linear equation v\u00a0=\u00a0k \u00d7 (Mpl\u00a0-\u00a0MRBC), where Mpl is the metformin concentration in plasma and MRBC is the metformin concentration in RBCs.

Conclusions

Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs.", + "title": "Metformin Transport Rates Between Plasma and Red Blood Cells in Humans.", + "type": "DOI", + "year": 2021 + }, + "publicationId": "BIOMD0000001026", + "submissionId": "MODEL2103170002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001027": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biosystems Group, LAtvia University of Agriculture", + "email": "egils.stalidzans@gmail.com", + "external": false, + "name": "Egils Stalidzans" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, Janis Kurlovics, Vitalijs Komasilovs, Janis Klovins and Egils Stalidzans.The model is pre-set for simulation of a single peroral dose.This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments. The model consists of 20 compartments (\u201cCompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 20 species (\u201cSpecies\u201d in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 33 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. 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Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. 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Stalidzans" + } + ], + "issue": "4", + "journal": "PLOS ONE", + "link": "http://identifiers.org/doi/10.1371/journal.pone.0249594", + "month": "4", + "pages": "e0249594", + "synopsis": "Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans\u2019 known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.", + "title": "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues", + "type": "DOI", + "volume": "16", + "year": 2021 + }, + "publicationId": "BIOMD0000001027", + "submissionId": "MODEL2103020001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001028": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biosystems Group, LAtvia University of Agriculture", + "email": "egils.stalidzans@gmail.com", + "external": false, + "name": "Egils Stalidzans" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments. The model consists of 21 compartments (\u201ccompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 21 species (\u201cspecies\u201d in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 35 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 62 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).2.Parameter describing human physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Parameters involved in the scale-up of the model\u2022Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.\u2022Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions \u201c13.4. KidneyPlasma -> KidneyTissue\u201d and \u201c13.5. KidneyTissue -> KidneyTubular\u201d. This parameter was determined using parameter estimation. \u2022Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine. 4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). 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+ "issue": "4", + "journal": "PLOS ONE", + "link": "http://identifiers.org/doi/10.1371/journal.pone.0249594", + "month": "4", + "pages": "e0249594", + "synopsis": "Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans\u2019 known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.", + "title": "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues", + "type": "DOI", + "volume": "16", + "year": 2021 + }, + "publicationId": "BIOMD0000001028", + "submissionId": "MODEL2103020003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001029": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biosystems Group, LAtvia University of Agriculture", + "email": "egils.stalidzans@gmail.com", + "external": false, + "name": "Egils Stalidzans" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments. The model consists of 21 compartments (\u201ccompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 21 species (\u201cspecies\u201d in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 35 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 62 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).2.Parameter describing human physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Parameters involved in the scale-up of the model\u2022Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.\u2022Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions \u201c13.4. KidneyPlasma -> KidneyTissue\u201d and \u201c13.5. KidneyTissue -> KidneyTubular\u201d. This parameter was determined using parameter estimation. \u2022Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine. 4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). 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"name": "Janis Klovins" + }, + { + "name": "Egils Stalidzans" + } + ], + "issue": "4", + "journal": "PLOS ONE", + "link": "http://identifiers.org/doi/10.1371/journal.pone.0249594", + "month": "4", + "pages": "e0249594", + "synopsis": "Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans\u2019 known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. 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This model of the immune system response to antigen presentation is based on the publication:Eduardo D.Sontag (2017) 'A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination', Cell Systems, 4(2)DOI: 10.1016/j.cels.2016.12.003Comment:This model is based on the \"toy model\" as described by Equations 1A-1C from the manuscript and the system represented by Equation 2, which exhibits a type of incoherent feedforward loop (IFFL).Abstract:The immune system must discriminate between agents of disease and an organism\u2019s healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system\u2019s response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed \u201ctwo-zone tolerance.\u201d Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T cells.
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Electronic address: eduardo.sontag@rutgers.edu.", + "authors": [ + { + "institution": "Department of Mathematics and Center for Quantitative Biology, Rutgers University, New Brunswick, NJ 08903, USA. Electronic address: eduardo.sontag@rutgers.edu.", + "name": "Eduardo D Sontag", + "orcid": "0000-0001-8020-5783" + } + ], + "issue": "2", + "journal": "Cell systems", + "link": "http://identifiers.org/pubmed/28131824", + "month": "2", + "pages": "231-241.e11", + "synopsis": "The immune system must discriminate between agents of disease and an organism's healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system's response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in\u00a0vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed \"two-zone tolerance.\" Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T\u00a0cells.", + "title": "A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination.", + "type": "PubMed ID", + "volume": "4", + "year": 2017 + }, + "publicationId": "BIOMD0000001030", + "submissionId": "MODEL2108160001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001031": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "email": "sheriff@ebi.ac.uk", + "external": false, + "name": "Rahuman S Malik-Sheriff", + "orcid": "0000-0003-0705-9809" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model of cancer virotherapy dynamics is described in the publication:Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,\"Modeling dynamics of cancer radiovirotherapy\",Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,DOI: 10.1016/j.jtbi.2020.110405.Comment:This model is represented by the equations in system (2) of the publication manuscript and describes the cancer-virus interactions for the Phase I virotherapy treatment.Abstract:Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
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This ordinary differential equation model of cancer radiovirotherapy dynamics is described in the publication:Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,\"Modeling dynamics of cancer radiovirotherapy\",Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,DOI: 10.1016/j.jtbi.2020.110405.Comment:This model is represented by the equations in system (14) of the publication manuscript and describes the cancer-virus interactions for the Phase II radiovirotherapy treatment.Abstract:Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
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This mathematical model of the role of the innate immune responses generated by macrophages in the context of anti-tumour oncolytic viral therapies is described in the publication:Nada Almuallem, Dumitru Trucu, Raluca Eftimie. \"Oncolytic viral therapies and the delicate balance between virus-macrophage-tumour interactions: A mathematical approach\". Mathematical Biosciences and Engineering, 2021, 18(1): 764-799.doi: 10.3934/mbe.2021041Comment:This model is represented by Equations 2.1a-f of the publication manuscript.Abstract:The success of oncolytic virotherapies depends on the tumour microenvironment, which contains a large number of infiltrating immune cells. In this theoretical study, we derive an ODE model to investigate the interactions between breast cancer tumour cells, an oncolytic virus (Vesicular Stomatitis Virus), and tumour-infiltrating macrophages with different phenotypes which can impact the dynamics of oncolytic viruses. The complexity of the model requires a combined analytical-numerical approach to understand the transient and asymptotic dynamics of this model. We use this model to propose new biological hypotheses regarding the impact on tumour elimination/relapse/persistence of: (i) different macrophage polarisation/re-polarisation rates; (ii) different infection rates of macrophages and tumour cells with the oncolytic virus; (iii) different viral burst sizes for macrophages and tumour cells. We show that increasing the rate at which the oncolytic virus infects the tumour cells can delay tumour relapse and even eliminate tumour. Increasing the rate at which the oncolytic virus particles infect the macrophages can trigger transitions between steady-state dynamics and oscillatory dynamics, but it does not lead to tumour elimination unless the tumour infection rate is also very large. Moreover, we confirm numerically that a large tumour-induced M1\u2192M2 polarisation leads to fast tumour growth and fast relapse (if the tumour was reduced before by a strong anti-tumour immune and viral response). The increase in viral-induced M2\u2192M1 re-polarisation reduces temporarily the tumour size, but does not lead to tumour elimination. Finally, we show numerically that the tumour size is more sensitive to the production of viruses by the infected macrophages.
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Virus-macrophage-tumour interactions in oncolytic viral therapies", + "publication": { + "accession": "33525118", + "affiliation": "Department of Mathematics, University of Dundee, Dundee, DD1 4HN, UK.", + "authors": [ + { + "institution": "Department of Mathematics, University of Dundee, Dundee, DD1 4HN, UK.", + "name": "Nada Almuallem" + }, + { + "institution": "Department of Mathematics, University of Dundee, Dundee, DD1 4HN, UK.", + "name": "Dumitru Trucu" + }, + { + "institution": "Division of Mathematics, University of Dundee, Dundee, DD1 4HN, UK. r.a.eftimie@dundee.ac.uk.", + "name": "R Eftimie", + "orcid": "0000-0002-9726-1498" + } + ], + "issue": "1", + "journal": "Mathematical biosciences and engineering : MBE", + "link": "http://identifiers.org/pubmed/33525118", + "month": "12", + "pages": "764-799", + "synopsis": "The success of oncolytic virotherapies depends on the tumour microenvironment, which contains a large number of infiltrating immune cells. In this theoretical study, we derive an ODE model to investigate the interactions between breast cancer tumour cells, an oncolytic virus (Vesicular Stomatitis Virus), and tumour-infiltrating macrophages with different phenotypes which can impact the dynamics of oncolytic viruses. The complexity of the model requires a combined analytical-numerical approach to understand the transient and asymptotic dynamics of this model. We use this model to propose new biological hypotheses regarding the impact on tumour elimination/relapse/persistence of: (i) different macrophage polarisation/re-polarisation rates; (ii) different infection rates of macrophages and tumour cells with the oncolytic virus; (iii) different viral burst sizes for macrophages and tumour cells. We show that increasing the rate at which the oncolytic virus infects the tumour cells can delay tumour relapse and even eliminate tumour. Increasing the rate at which the oncolytic virus particles infect the macrophages can trigger transitions between steady-state dynamics and oscillatory dynamics, but it does not lead to tumour elimination unless the tumour infection rate is also very large. Moreover, we confirm numerically that a large tumour-induced M1\u2192M2 polarisation leads to fast tumour growth and fast relapse (if the tumour was reduced before by a strong anti-tumour immune and viral response). The increase in viral-induced M2\u2192M1 re-polarisation reduces temporarily the tumour size, but does not lead to tumour elimination. Finally, we show numerically that the tumour size is more sensitive to the production of viruses by the infected macrophages.", + "title": "Oncolytic viral therapies and the delicate balance between virus-macrophage-tumour interactions: A mathematical approach.", + "type": "PubMed ID", + "volume": "18", + "year": 2020 + }, + "publicationId": "BIOMD0000001033", + "submissionId": "MODEL2108190002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001034": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model, simulating the tumor-immune interactions involved in BCG immunotherapy to treat superficial bladder cancer, is described by the publication:Bunimovich-Mendrazitsky, S., Shochat, E., Stone, L. \"Mathematical Model of BCG Immunotherapy in Superficial Bladder Cancer\". Bull. Math. Biol. 69, 1847\u20131870 (2007). DOI: 10.1007/s11538-007-9195-zComment:This model is based on the system of ODEs given in Equation 4 of the publication manuscript.Reproduction of Figure 4 was achieved by setting p4 = 0.085.Abstract:Immunotherapy with Bacillus Calmette-Gu\u00e9rin (BCG)-an attenuated strain of Mycobacterium bovis (M. bovis) used for anti tuberculosis immunization-is a clinically established procedure for the treatment of superficial bladder cancer. However, the mode of action has not yet been fully elucidated, despite much extensive biological experience. The purpose of this paper is to develop a first mathematical model that describes tumor-immune interactions in the bladder as a result of BCG therapy. A mathematical analysis of the ODE model identifies multiple equilibrium points, their stability properties, and bifurcation points. Intriguing regimes of bistability are identified in which treatment has potential to result in a tumor-free equilibrium or a full-blown tumor depending only on initial conditions. Attention is given to estimating parameters and validating the model using published data taken from in vitro, mouse and human studies. The model makes clear that intensity of immunotherapy must be kept in limited bounds. While small treatment levels may fail to clear the tumor, a treatment that is too large can lead to an over-stimulated immune system having dangerous side effects for the patient.
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However, the mode of action has not yet been fully elucidated, despite much extensive biological experience. The purpose of this paper is to develop a first mathematical model that describes tumor-immune interactions in the bladder as a result of BCG therapy. A mathematical analysis of the ODE model identifies multiple equilibrium points, their stability properties, and bifurcation points. Intriguing regimes of bistability are identified in which treatment has potential to result in a tumor-free equilibrium or a full-blown tumor depending only on initial conditions. Attention is given to estimating parameters and validating the model using published data taken from in vitro, mouse and human studies. The model makes clear that intensity of immunotherapy must be kept in limited bounds. 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This ordinary differential equation model of the role of the immune response in cancer virotherapy dynamics is described in the publication:Al-Tuwairqi, S.M., Al-Johani, N.O.,  Simbawa, E.A. \"Modeling dynamics of cancer virotherapy with immune response.\" Adv Differ Equ 2020, 438 (2020).DOI: 10.1186/s13662-020-02893-6Comment:This model is represented by the system described in Equation 2 of the publication manuscript.Abstract:Virotherapy is a therapeutic treatment for cancer. It uses genetically engineered viruses to selectively infect, replicate in, and destroy cancer cells without damaging normal cells. In this paper, we present a modified model to include, within the dynamics of virotherapy, the interaction between uninfected tumor cells and immune response. The model is analyzed qualitatively to produce five equilibrium points. One of these equilibriums demonstrates the effect observed in virotherapy, where the immune system demolishes infected cells as well as viruses. Moreover, the existence and stability of the equilibrium points are established under certain criteria. Numerical simulations are performed to display the agreement with the analytical results. Finally, parameter analysis is carried out to illustrate which parameters in the model affect the outcome of virotherapy.
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Dynamics of cancer virotherapy with immune response", + "publication": { + "accession": "10.1186/s13662-020-02893-6", + "affiliation": "Mathematics Department, King Abdulaziz University, Jeddah, Saudi Arabia", + "authors": [ + { + "name": "Salma M. Al-Tuwairqi" + }, + { + "name": "Najwa O. Al-Johani" + }, + { + "name": "Eman A. Simbawa" + } + ], + "issue": "1", + "journal": "Advances in Difference Equations", + "link": "http://identifiers.org/doi/10.1186/s13662-020-02893-6", + "month": "8", + "synopsis": "Virotherapy is a therapeutic treatment for cancer. It uses genetically engineered viruses to selectively infect, replicate in, and destroy cancer cells without damaging normal cells. In this paper, we present a modified model to include, within the dynamics of virotherapy, the interaction between uninfected tumor cells and immune response. The model is analyzed qualitatively to produce five equilibrium points. One of these equilibriums demonstrates the effect observed in virotherapy, where the immune system demolishes infected cells as well as viruses. Moreover, the existence and stability of the equilibrium points are established under certain criteria. Numerical simulations are performed to display the agreement with the analytical results. Finally, parameter analysis is carried out to illustrate which parameters in the model affect the outcome of virotherapy.", + "title": "Modeling dynamics of cancer virotherapy with immune response", + "type": "DOI", + "volume": "2020", + "year": 2020 + }, + "publicationId": "BIOMD0000001035", + "submissionId": "MODEL2108190004", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001036": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model is the Panetta-Kirschner model of tumor-immune interactions is described in the publication:Cappuccio A, Castiglione F, Piccoli B.\" Determination of the optimal therapeutic protocols in cancer immunotherapy.\" Math Biosci. 2007 Sep;209(1):1-13.doi: 10.1016/j.mbs.2007.02.009. Comment:The model represented in Equations 1-3 is the Panetta-Kirschner model and was used to reproduce Fig. 1.N.B.: Labelling of Fig. 1 graphs in manuscript mismatches with simulation results - CTL and tumor cell plots swapped.Abstract:Cancer immunotherapy aims at eliciting an immune system response against the tumor. However, it is often characterized by toxic side-effects. Limiting the tumor growth and, concurrently, avoiding the toxicity of a drug, is the problem of protocol design. We formulate this question as an optimization problem and derive an algorithm for its solution. Unlike the standard optimal control approach, the algorithm simulates impulse-like drug administrations. It relies on an exact computation of the gradient of the cost function with respect to any protocol by means of the variational equations, that can be solved in parallel with the system. In comparison with previous versions of this method [F. Castiglione, B. Piccoli, Optimal control in a model of dendritic cell transfection cancer immunotherapy, Bull. Math. Biol. 68 (2006) 255-274; B. Piccoli, F. Castiglione, Optimal vaccine scheduling in cancer immunotherapy, Physica A. 370 (2) (2007) 672-680], we optimize both the timing and the dosage of each administration and introduce a penalty term to avoid clustering of subsequent injections, a requirement consistent with the clinical practice. In addition, we implement the optimization scheme to simulate the case of multi-therapies. The procedure works for any ODE system describing the pharmacokinetics and pharmacodynamics of an arbitrary number of therapeutic agents. In this work, it was tested for a well known model of the tumor-immune system interaction [D. Kirschner, J.C. Panetta, Modeling immunotherapy of tumor-immune interaction, J. Math. Biol. 37 (1998) 235-252]. Exploring three immunotherapeutic scenarios (CTL therapy, IL-2 therapy and combined therapy), we display the stability and efficacy of the optimization method, obtaining protocols that are successful compromises between various clinical requirements.
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Tumor-immune system interactions and determination of the optimal therapeutic protocol in immunotherapy", + "publication": { + "accession": "17416392", + "affiliation": "Istituto Applicazioni Calcolo - CNR, V.le del Policlinico 137, 00161 Roma, Italy. a.cappuccio@iac.cnr.it", + "authors": [ + { + "institution": "Istituto Applicazioni Calcolo - CNR, V.le del Policlinico 137, 00161 Roma, Italy. a.cappuccio@iac.cnr.it", + "name": "Antonio Cappuccio", + "orcid": "0000-0002-8266-2344" + }, + { + "institution": "Institute for Applied Mathematics, National Research Council of Italy, 00185 Rome, Italy.", + "name": "Castiglione F", + "orcid": "0000-0002-1442-3552" + }, + { + "name": "Benedetto Piccoli", + "orcid": "0000-0002-6000-5394" + } + ], + "issue": "1", + "journal": "Mathematical biosciences", + "link": "http://identifiers.org/pubmed/17416392", + "month": "9", + "pages": "1-13", + "synopsis": "Cancer immunotherapy aims at eliciting an immune system response against the tumor. However, it is often characterized by toxic side-effects. Limiting the tumor growth and, concurrently, avoiding the toxicity of a drug, is the problem of protocol design. We formulate this question as an optimization problem and derive an algorithm for its solution. Unlike the standard optimal control approach, the algorithm simulates impulse-like drug administrations. It relies on an exact computation of the gradient of the cost function with respect to any protocol by means of the variational equations, that can be solved in parallel with the system. In comparison with previous versions of this method [F. Castiglione, B. Piccoli, Optimal control in a model of dendritic cell transfection cancer immunotherapy, Bull. Math. Biol. 68 (2006) 255-274; B. Piccoli, F. Castiglione, Optimal vaccine scheduling in cancer immunotherapy, Physica A. 370 (2) (2007) 672-680], we optimize both the timing and the dosage of each administration and introduce a penalty term to avoid clustering of subsequent injections, a requirement consistent with the clinical practice. In addition, we implement the optimization scheme to simulate the case of multi-therapies. The procedure works for any ODE system describing the pharmacokinetics and pharmacodynamics of an arbitrary number of therapeutic agents. In this work, it was tested for a well known model of the tumor-immune system interaction [D. Kirschner, J.C. Panetta, Modeling immunotherapy of tumor-immune interaction, J. Math. Biol. 37 (1998) 235-252]. Exploring three immunotherapeutic scenarios (CTL therapy, IL-2 therapy and combined therapy), we display the stability and efficacy of the optimization method, obtaining protocols that are successful compromises between various clinical requirements.", + "title": "Determination of the optimal therapeutic protocols in cancer immunotherapy.", + "type": "PubMed ID", + "volume": "209", + "year": 2007 + }, + "publicationId": "BIOMD0000001036", + "submissionId": "MODEL2108260001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001037": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model of the interactions between tumor and normal cells is based on the publication:S. A. Alharbi and A. S. Rambely, \"Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells,\" in IEEE Access, vol. 7, pp. 128816-128827, 2019doi: 10.1109/ACCESS.2019.2940060.Comment:This TNM model is described by the system of equations presented in (2), in the publication manuscript.Abstract:The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.
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Tumor-normal model (TNM) of the development of tumor cells and their impact on normal cell dynamics", + "publication": { + "accession": "10.1109/ACCESS.2019.2940060", + "affiliation": "Mathematics Program, Faculty of Science and Technology, University Kebangsaan Malaysia, Bangi, Malaysia", + "authors": [ + { + "name": "Sana Abdulkream Alharbi" + }, + { + "name": "Azmin Sham Rambely" + } + ], + "journal": "IEEE Access", + "link": "http://identifiers.org/doi/10.1109/ACCESS.2019.2940060", + "month": "7", + "pages": "128816--128827", + "synopsis": "The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.", + "title": "Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells", + "type": "DOI", + "volume": "7", + "year": 2019 + }, + "publicationId": "BIOMD0000001037", + "submissionId": "MODEL2108260002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001038": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model of the interactions between tumor and normal cells, in the presence of a regular rate of vitamins, is based on the publication:S. A. Alharbi and A. S. Rambely, \"Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells,\" in IEEE Access, vol. 7, pp. 128816-128827, 2019doi: 10.1109/ACCESS.2019.2940060.Comment:This TNVM model is described by the system of equations presented in (10), in the publication manuscript.Reproduction of Fig. 11 was achieved by swapping the values of gamma and beta2 stated in the manuscript, i.e. gamma = 0.9817 and beta2 = 0.2291.Abstract:The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.
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Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. 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This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.", + "title": "Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells", + "type": "DOI", + "volume": "7", + "year": 2019 + }, + "publicationId": "BIOMD0000001038", + "submissionId": "MODEL2108260003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001039": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biosystems Group, LAtvia University of Agriculture", + "email": "egils.stalidzans@gmail.com", + "external": false, + "name": "Egils Stalidzans" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments. The model consists of 20 compartments (\u201cCompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 20 species (\u201cSpecies\u201d in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 33 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 52 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing metformin dose \u2013 either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg). 2.Parameter describing mice physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). 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"synopsis": "Metformin is the primary drug for type 2 diabetes treatment and a promising candidate for other disease treatment. It has significant deviations between individuals in therapy efficiency and pharmacokinetics, leading to the administration of an unnecessary overdose or an insufficient dose. There is a lack of data regarding the concentration-time profiles in various human tissues that limits the understanding of pharmacokinetics and hinders the development of precision therapies for individual patients. The physiologically based pharmacokinetic (PBPK) model developed in this study is based on humans\u2019 known physiological parameters (blood flow, tissue volume, and others). The missing tissue-specific pharmacokinetics parameters are estimated by developing a PBPK model of metformin in mice where the concentration time series in various tissues have been measured. Some parameters are adapted from human intestine cell culture experiments. The resulting PBPK model for metformin in humans includes 21 tissues and body fluids compartments and can simulate metformin concentration in the stomach, small intestine, liver, kidney, heart, skeletal muscle adipose, and brain depending on the body weight, dose, and administration regimen. Simulations for humans with a bodyweight of 70kg have been analyzed for doses in the range of 500-1500mg. Most tissues have a half-life (T1/2) similar to plasma (3.7h) except for the liver and intestine with shorter T1/2 and muscle, kidney, and red blood cells that have longer T1/2. The highest maximal concentrations (Cmax) turned out to be in the intestine (absorption process) and kidney (excretion process), followed by the liver. The developed metformin PBPK model for mice does not have a compartment for red blood cells and consists of 20 compartments. The developed human model can be personalized by adapting measurable values (tissue volumes, blood flow) and measuring metformin concentration time-course in blood and urine after a single dose of metformin. The personalized model can be used as a decision support tool for precision therapy development for individuals.", + "title": "Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues", + "type": "DOI", + "volume": "16", + "year": 2021 + }, + "publicationId": "BIOMD0000001039", + "submissionId": "MODEL2103020002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001040": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Biosystems Group, LAtvia University of Agriculture", + "email": "egils.stalidzans@gmail.com", + "external": false, + "name": "Egils Stalidzans" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This model is a supplementary material of a manuscript\"Diffusion driven metformin exchange transport rates between plasma and red blood cells\"by Janis Kurlovics, Darta Maija Zake, Linda Zaharenko, Kristaps Berzins, Janis Klovins, Egils StalidzansThe setting of the model correspond to Fig.2 for the case with a single coefficient for experimental values of average concentration curve. Parameter estimation can be executed using experimental data file \"average_exp_data.txt\".A=B if metformin concentration is 0 at t=0.
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Background

Metformin has been used for the treatment of type 2 diabetes for over 60 years; however, its mechanism of pharmacological action is not fully clear. Different hypotheses exist regarding metformin distribution and redistribution mechanisms between plasma and erythrocytes/red blood cells (RBCs).

Objective

We aimed to test the hypothesis that the metformin distribution between plasma and RBC occurs via concentration difference-driven passive transport and estimated transport rate coefficient values based on metformin concentration time series in plasma and RBCs from in vivo studies.

Methods

An ordinary differential equation (ODE) system with two compartments was used to describe diffusion-based passive transport between plasma and RBCs. Metformin concentration time series in plasma and RBCs of 35 individuals were used for metformin transport parametrization. Plasma concentration has been approximated by biexponential decline.

Results

A single passive transport coefficient, k\u00a0=\u00a00.044\u00a0\u00b1\u00a00.014 (h-1), can be applied, describing the uptake and release transport rate versus the linear equation v\u00a0=\u00a0k \u00d7 (Mpl\u00a0-\u00a0MRBC), where Mpl is the metformin concentration in plasma and MRBC is the metformin concentration in RBCs.

Conclusions

Our research suggests that passive transport can explain metformin distribution dynamics between plasma and RBCs because transport speed is proportional to the metformin concentration difference and independent of the transport direction. Concentration difference-driven passive transport can explain the mechanism of faster metformin distribution to RBCs the first few hours after administration, and faster release and domination of the redistribution transport rate after metformin concentration in plasma becomes smaller than in RBCs.", + "title": "Metformin Transport Rates Between Plasma and Red Blood Cells in Humans.", + "type": "DOI", + "year": 2021 + }, + "publicationId": "BIOMD0000001040", + "submissionId": "MODEL2103170001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001041": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This mathematical model of T cell-tumour interactions considering the roles of T cell competition and stochastic extinction events in CAR T cell therapy is described by the publication:Kimmel GJ, Locke FL, Altrock PM. \"The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy.\" Proc Biol Sci. 2021 Mar 31;288(1947):20210229.doi: 10.1098/rspb.2021.0229Comment:Reproduction of Fig. 2(a) and (b) was simulated by using the fitted model parameter set given in Table 1 of the manuscript's Supplementary Material, however substituting the values of r_N and rho_C for those stated in Table 1 of the publication manuscript, i.e. r_N = 0.17 and rho_C = 0.0251.Abstract:Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this expansion and subsequent tumour eradication remain unknown. We develop a mathematical model of T cell\u2013tumour cell interactions and demonstrate that expansion can be explained by immune reconstitution dynamics after LD and competition among T cells. CAR T cells rapidly grow and engage tumour cells but experience an emerging growth rate disadvantage compared to normal T cells. Since tumour eradication is deterministically unstable in our model, we define cure as a stochastic event, which, even when likely, can occur at variable times. However, we show that variability in timing is largely determined by patient variability. While cure events impacted by these fluctuations occur early and are narrowly distributed, progression events occur late and are more widely distributed in time. We parameterized our model using population-level CAR T cell and tumour data over time and compare our predictions with progression-free survival rates. We find that therapy could be improved by optimizing the tumour-killing rate and the CAR T cells' ability to adapt, as quantified by their carrying capacity. Our tumour extinction model can be leveraged to examine why therapy works in some patients but not others, and to better understand the interplay of deterministic and stochastic effects on outcomes. For example, our model implies that LD before a second CAR T injection is necessary.
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Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.", + "name": "Philipp M Altrock", + "orcid": "0000-0001-7731-3345" + } + ], + "issue": "1947", + "journal": "Proceedings. Biological sciences", + "link": "http://identifiers.org/pubmed/33757357", + "month": "3", + "pages": "20210229", + "synopsis": "Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this expansion and subsequent tumour eradication remain unknown. We develop a mathematical model of T cell-tumour cell interactions and demonstrate that expansion can be explained by immune reconstitution dynamics after LD and competition among T cells. CAR T cells rapidly grow and engage tumour cells but experience an emerging growth rate disadvantage compared to normal T cells. Since tumour eradication is deterministically unstable in our model, we define cure as a stochastic event, which, even when likely, can occur at variable times. However, we show that variability in timing is largely determined by patient variability. While cure events impacted by these fluctuations occur early and are narrowly distributed, progression events occur late and are more widely distributed in time. We parameterized our model using population-level CAR T cell and tumour data over time and compare our predictions with progression-free survival rates. We find that therapy could be improved by optimizing the tumour-killing rate and the CAR T cells' ability to adapt, as quantified by their carrying capacity. Our tumour extinction model can be leveraged to examine why therapy works in some patients but not others, and to better understand the interplay of deterministic and stochastic effects on outcomes. For example, our model implies that LD before a second CAR T injection is necessary.", + "title": "The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy.", + "type": "PubMed ID", + "volume": "288", + "year": 2021 + }, + "publicationId": "BIOMD0000001041", + "submissionId": "MODEL2109010001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001042": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This ordinary differential equation model simulating the interactions between tumor and immune cells is detailed in the publication:Ahmed M. Makhlouf, Lamiaa El-Shennawy, Hesham A. Elkaranshawy, \"Mathematical Modelling for the Role of CD4+T Cells in Tumor-Immune Interactions\", Comput Math Methods Med. 2020 Feb 19;2020:7187602.doi: 10.1155/2020/7187602Comment:This no treatment model is described by equations 1-7 of the publication manuscript. Abstract:Mathematical modelling has been used to study tumor-immune cell interaction. Some models were proposed to examine the effect of circulating lymphocytes, natural killer cells, and CD8+T cells, but they neglected the role of CD4+T cells. Other models were constructed to study the role of CD4+T cells but did not consider the role of other immune cells. In this study, we propose a mathematical model, in the form of a system of nonlinear ordinary differential equations, that predicts the interaction between tumor cells and natural killer cells, CD4+T cells, CD8+T cells, and circulating lymphocytes with or without immunotherapy and/or chemotherapy. This system is stiff, and the Runge\u2013Kutta method failed to solve it. Consequently, the \u201cAdams predictor-corrector\u201d method is used. The results reveal that the patient\u2019s immune system can overcome small tumors; however, if the tumor is large, adoptive therapy with CD4+T cells can be an alternative to both CD8+T cell therapy and cytokines in some cases. Moreover, CD4+T cell therapy could replace chemotherapy depending upon tumor size. Even if a combination of chemotherapy and immunotherapy is necessary, using CD4+T cell therapy can better reduce the dose of the associated chemotherapy compared to using combined CD8+T cells and cytokine therapy. Stability analysis is performed for the studied patients. It has been found that all equilibrium points are unstable, and a condition for preventing tumor recurrence after treatment has been deduced. Finally, a bifurcation analysis is performed to study the effect of varying system parameters on the stability, and bifurcation points are specified. New equilibrium points are created or demolished at some bifurcation points, and stability is changed at some others. Hence, for systems turning to be stable, tumors can be eradicated without the possibility of recurrence. The proposed mathematical model provides a valuable tool for designing patients\u2019 treatment intervention strategies.
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Moreover, CD4+T cell therapy could replace chemotherapy depending upon tumor size. Even if a combination of chemotherapy and immunotherapy is necessary, using CD4+T cell therapy can better reduce the dose of the associated chemotherapy compared to using combined CD8+T cells and cytokine therapy. Stability analysis is performed for the studied patients. It has been found that all equilibrium points are unstable, and a condition for preventing tumor recurrence after treatment has been deduced. Finally, a bifurcation analysis is performed to study the effect of varying system parameters on the stability, and bifurcation points are specified. New equilibrium points are created or demolished at some bifurcation points, and stability is changed at some others. Hence, for systems turning to be stable, tumors can be eradicated without the possibility of recurrence. The proposed mathematical model provides a valuable tool for designing patients' treatment intervention strategies.", + "title": "Mathematical Modelling for the Role of CD4+T Cells in Tumor-Immune Interactions.", + "type": "PubMed ID", + "volume": "2020", + "year": 2020 + }, + "publicationId": "BIOMD0000001042", + "submissionId": "MODEL2109060001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001043": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "University of Cambridge", + "email": "emiliachen1@gmail.com", + "external": false, + "name": "Emilia Chen" + } + ] + }, + "curationStatus": "CURATED", + "description": "
This mathematical model of the dynamics between tumor, virus and virus-specific CTL populations is described by the publication:Wodarz D. \"Viruses as antitumor weapons: defining conditions for tumor remission\". Cancer Res. 2001 Apr 15;61(8):3501-7.PMID: 11309314Comment:Reproduction of Fig. 3A was achieved by using the initial conditions infected_tumor_cells = 0.01, uninfected_tumor_cells = 0.0001, virus-specific_CTLs = 0, and with modified parameter sets.For Fig. 3A non-cytotoxic virus, k = 17 and beta= 0.5.For Fig. 3A cytotoxic virus, beta = 0.5.These simulation conditions yield plots similar to Fig. 3A in the manuscript.Abstract:Recent research has indicated that viruses specifically infecting tumor cells could be used as an alternative therapeutic approach in cancer patients. A particular example is the adenovirus ONYX-015, which has entered clinical trials in the context of head and neck cancer. Successful therapy crucially requires an understanding about how viral and host parameters influence tumor load. The interactions between the growing tumor, the replicating virus, and possible immune responses are multifactorial and nonlinear. Hence, a complete understanding of how virus and host characteristics influence the outcome of therapy requires mathematical models. In this study, such mathematical models are presented and analyzed. The study investigates three possible scenarios that could be relevant for therapy: (a) viral cytotoxicity alone kills tumor cells; (b) a virus-specific lytic CTL response contributes to killing of infected tumor cells; (c) the virus elicits immunostimulatory signals within the tumor that promote the development of tumor-specific CTL. The models precisely define conditions required for successful therapy. They identify the parameters that need to be measured and modulated to evaluate and refine the existing therapy regimes.
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Viruses as antitumor weapons", + "publication": { + "accession": "11309314", + "affiliation": "Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540. wodarz@ias.edu", + "authors": [ + { + "institution": "Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540. wodarz@ias.edu", + "name": "D Wodarz" + } + ], + "issue": "8", + "journal": "Cancer research", + "link": "http://identifiers.org/pubmed/11309314", + "month": "4", + "pages": "3501-3507", + "synopsis": "Recent research has indicated that viruses specifically infecting tumor cells could be used as an alternative therapeutic approach in cancer patients. A particular example is the adenovirus ONYX-015, which has entered clinical trials in the context of head and neck cancer. Successful therapy crucially requires an understanding about how viral and host parameters influence tumor load. The interactions between the growing tumor, the replicating virus, and possible immune responses are multifactorial and nonlinear. Hence, a complete understanding of how virus and host characteristics influence the outcome of therapy requires mathematical models. In this study, such mathematical models are presented and analyzed. The study investigates three possible scenarios that could be relevant for therapy: (a) viral cytotoxicity alone kills tumor cells; (b) a virus-specific lytic CTL response contributes to killing of infected tumor cells; (c) the virus elicits immunostimulatory signals within the tumor that promote the development of tumor-specific CTL. The models precisely define conditions required for successful therapy. They identify the parameters that need to be measured and modulated to evaluate and refine the existing therapy regimes.", + "title": "Viruses as antitumor weapons: defining conditions for tumor remission.", + "type": "PubMed ID", + "volume": "61", + "year": 2001 + }, + "publicationId": "BIOMD0000001043", + "submissionId": "MODEL2109090001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001044": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EBML-EBI", + "email": "lenov@ebi.ac.uk", + "external": false, + "name": "Nicolas Le Nov\u00e8re", + "orcid": "0000-0002-6309-7327" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from the Cell Cycle Database . It is described in:
Analysis of a generic model of eukaryotic cell-cycle regulation. Csik\u00e1sz-Nagy A , Battogtokh D , Chen KC , Nov\u00e1k B , Tyson JJ Biophys. J. [2006 Jun],90(12 ):4361-79
PMID: 16581849
Abstract:
We propose a protein interaction network for the regulation of DNA synthesis and mitosis that emphasizes the universality of the regulatory system among eukaryotic cells. The idiosyncrasies of cell cycle regulation in particular organisms can be attributed, we claim, to specific settings of rate constants in the dynamic network of chemical reactions. The values of these rate constants are determined ultimately by the genetic makeup of an organism. To support these claims, we convert the reaction mechanism into a set of governing kinetic equations and provide parameter values (specific to budding yeast, fission yeast, frog eggs, and mammalian cells) that account for many curious features of cell cycle regulation in these organisms. Using one-parameter bifurcation diagrams, we show how overall cell growth drives progression through the cell cycle, how cell-size homeostasis can be achieved by two different strategies, and how mutations remodel bifurcation diagrams and create unusual cell-division phenotypes. The relation between gene dosage and phenotype can be summarized compactly in two-parameter bifurcation diagrams. Our approach provides a theoretical framework in which to understand both the universality and particularity of cell cycle regulation, and to construct, in modular fashion, increasingly complex models of the networks controlling cell growth and division.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Mycobacteria owe many of their unique qualities to mycolic acids, which are known to be important for their growth, survival, and pathogenicity. Mycolic acid biosynthesis has therefore been the focus of a number of biochemical and genetic studies. It also turns out to be the pathway inhibited by front-line anti-tubercular drugs such as isoniazid and ethionamide. Recent years have seen the emergence of systems-based methodologies that can be used to study microbial metabolism. Here, we seek to apply insights from flux balance analyses of the mycolic acid pathway (MAP) for the identification of anti-tubercular drug targets. We present a comprehensive model of mycolic acid synthesis in the pathogen M. tuberculosis involving 197 metabolites participating in 219 reactions catalysed by 28 proteins. Flux balance analysis (FBA) has been performed on the MAP model, which has provided insights into the metabolic capabilities of the pathway. In silico systematic gene deletions and inhibition of InhA by isoniazid, studied here, provide clues about proteins essential for the pathway and hence lead to a rational identification of possible drug targets. Feasibility studies using sequence analysis of the M. tuberculosis H37Rv and human proteomes indicate that, apart from the known InhA, potential targets for anti-tubercular drug design are AccD3, Fas, FabH, Pks13, DesA1/2, and DesA3. Proteins identified as essential by FBA correlate well with those previously identified experimentally through transposon site hybridisation mutagenesis. This study demonstrates the application of FBA for rational identification of potential anti-tubercular drug targets, which can indeed be a general strategy in drug design. The targets, chosen based on the critical points in the pathway, form a ready shortlist for experimental testing.", + "title": "Flux balance analysis of mycolic acid pathway: targets for anti-tubercular drugs.", + "type": "PubMed ID", + "volume": "1", + "year": 2005 + }, + "publicationId": "BIOMD0000001046", + "submissionId": "MODEL8568434338", + "vcsIdentifier": "aaa" + }, + "BIOMD0000001047": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "
ODE model describing how slight variations in Notch and Delta cellular concentrations, through lateral inhibition, lead to cells with different states of differentiation. Lateral inhibition is a process whereby a given cell adopting a given fate prevents its immediate neighbouring cells from doing likewise. Notch and Delta are interacting transmembrane proteins and according to this model, lateral inhibition is due to a process where the inhibited cells (where notch has been activated by Delta) loose their ability to inhibit other cells (by synthesizing Delta). This process creates a feedback loop where cells with more delta proteins on their surface inhibit their immediate neighbours and adopt a different cell fate than those neighbours.
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+ "submitted": 1724293235, + "submitter": "Lucian Smith", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MAMO_0000046", + "name": "Ordinary differential equation model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000046" + }, + { + "accession": "MODEL2204050001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL2204050001" + }, + { + "accession": "BIOMD0000001047", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/BIOMD0000001047" + }, + { + "accession": "9015458", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "https://identifiers.org/pubmed:9015458" + }, + { + "accession": "BTO:0006223", + "name": "neural ectoderm", + "qualifier": "bqbiol:occursIn", + "resource": "Brenda Tissue Ontology", + "uri": "http://identifiers.org/bto/BTO:0006223" + }, + { + "accession": "7215", + "name": "Drosophila", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/7215" + }, + { + "accession": "9015458", + "qualifier": "bqbiol:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/9015458" + }, + { + "accession": "GO:0007219", + "name": "Notch signaling pathway", + "qualifier": "bqbiol:hasProperty", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0007219" + }, + { + "accession": "GO:0046331", + "name": "lateral inhibition", + "qualifier": "bqbiol:hasProperty", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0046331" + }, + { + "accession": "GO:0009888", + "name": "tissue development", + "qualifier": "bqbiol:hasProperty", + "resource": "Gene Ontology", + "uri": "http://identifiers.org/go/GO:0009888" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Collier1996 - Delta Notch intercellular signalling and lateral inhibition", + "publication": { + "accession": "9015458", + "affiliation": "Centre for Mathematical Biology, University of Oxford, UK.", + "authors": [ + { + "institution": "Centre for Mathematical Biology, University of Oxford, UK.", + "name": "J R Collier" + }, + { + "name": "N A Monk" + }, + { + "name": "P K Maini" + }, + { + "name": "J H Lewis" + } + ], + "issue": "4", + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/pubmed/9015458", + "month": "12", + "pages": "429-446", + "synopsis": "In many developing tissues, adjacent cells diverge in character so as to create a fine-grained pattern of cells in contrasting states of differentiation. It has been proposed that such patterns can be generated through lateral inhibition--a type of cell-cell interaction whereby a cell that adopts a particular fate inhibits its immediate neighbors from doing likewise. Lateral inhibition is well documented in flies, worms and vertebrates. In all of these organisms, the transmembrane proteins Notch and Delta (or their homologues) have been identified as mediators of the interaction--Notch as receptor, Delta as its ligand on adjacent cells. However, it is not clear under precisely what conditions the Delta-Notch mechanism of lateral inhibition can generate the observed types of pattern, or indeed whether this mechanism is capable of generating such patterns by itself. Here we construct and analyse a simple and general mathematical model of such contact-mediated lateral inhibition. In accordance with experimental data, the model postulates that receipt of inhibition (i.e. activation of Notch) diminished the ability to deliver inhibition (i.e. to produce active Delta). This gives rise to a feedback loop that can amplify differences between adjacent cells. We investigate the pattern-forming potential and temporal behaviour of this model both analytically and through numerical simulation. Inhomogeneities are self-amplifying and develop without need of any other machinery, provided the feedback is sufficiently strong. 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As this cascade culminates, activation of the protease thrombin occurs and soluble fibrinogen is converted into an insoluble polymerized fibrin network. Fibrin polymerization is critical for bleeding cessation and subsequent stages of wound healing. We develop a cooperative enzyme kinetics model for in vitro fibrin matrix polymerization capturing dynamic interactions among fibrinogen, thrombin, fibrin, and intermediate complexes. A tailored parameter subset selection technique is also developed to evaluate parameter identifiability for a representative data curve for fibrin accumulation in a short-duration in vitro polymerization experiment. Our approach is based on systematic analysis of eigenvalues and eigenvectors of the classical information matrix for simulations of accumulating fibrin matrix via optimization based on a least squares objective function. Results demonstrate robustness of our approach in that a significant reduction in objective function cost is achieved relative to a more ad hoc curve-fitting procedure. Capabilities of this approach to integrate non-overlapping subsets of the data to enhance the evaluation of parameter identifiability are also demonstrated. Unidentifiable reaction rate parameters are screened to determine whether individual reactions can be eliminated from the overall system while preserving the low objective cost. These findings demonstrate the high degree of information within a single fibrin accumulation curve, and a tailored model and parameter subset selection approach for improving optimization and reducing model complexity in the context of polymerization experiments.", + "title": "Modeling and Parameter Subset Selection for Fibrin Polymerization Kinetics with Applications to Wound Healing.", + "type": "PubMed ID", + "volume": "83", + "year": 2021 + }, + "publicationId": "BIOMD0000001054", + "submissionId": "MODEL2204200001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001055": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "Miji Jeon, Hye-Won Kang & Songon An. A Mathematical Model for Enzyme Clustering in Glucose Metabolism. Scientific Reports 8, 1 (2018).

We have recently demonstrated that the rate-limiting enzymes in human glucose metabolism organize into cytoplasmic clusters to form a multienzyme complex, the glucosome, in at least three different sizes. Quantitative high-content imaging data support a hypothesis that the glucosome clusters regulate the direction of glucose flux between energy metabolism and building block biosynthesis in a cluster size-dependent manner. However, direct measurement of their functional contributions to cellular metabolism at subcellular levels has remained challenging. In this work, we develop a mathematical model using a system of ordinary differential equations, in which the association of the rate-limiting enzymes into multienzyme complexes is included as an essential element. We then demonstrate that our mathematical model provides a quantitative principle to simulate glucose flux at both subcellular and population levels in human cancer cells. 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Collectively, we introduce a new mathematical model for human glucose metabolism, which promotes our understanding of functional roles of differently sized multienzyme complexes in both single-cell and population levels.", + "title": "A Mathematical Model for Enzyme Clustering in Glucose Metabolism.", + "type": "DOI", + "volume": "8", + "year": 2018 + }, + "publicationId": "BIOMD0000001055", + "submissionId": "MODEL2204290002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001056": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Anna Marciniak-Czochra, Thomas Stiehl, Cristina Bl\u00e1zquez Go\u00f1i, Juan Francisco Rodr\u00edguez Guti\u00e9rrez, Manuel Ram\u00edrez Orellana, Ana Castillo Robleda, V\u00edctor M. P\u00e9rez-Garc\u00eda & Mar\u00eda Rosa. Dynamical properties of feedback signalling in B lymphopoiesis: A mathematical modelling approach. Journal of Theoretical Biology 522 (2021).

Haematopoiesis is the process of generation of blood cells. Lymphopoiesis generates lymphocytes, the cells in charge of the adaptive immune response. Disruptions of this process are associated with diseases like leukaemia, which is especially incident in children. The characteristics of self-regulation of this process make them suitable for a mathematical study.

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Electronic address: salvador.chulian@uca.es.", + "authors": [ + { + "name": "Chuli\u00e1n S" + }, + { + "name": "Mart\u00ednez-Rubio \u00c1" + }, + { + "name": "Marciniak-Czochra A" + }, + { + "name": "Stiehl T" + }, + { + "name": "Go\u00f1i CB" + }, + { + "name": "Rodr\u00edguez Guti\u00e9rrez JF" + }, + { + "name": "Ram\u00edrez Orellana M" + }, + { + "name": "Castillo Robleda A" + }, + { + "institution": "Mathematical Oncology Laboratory (MOLAB), Department of Mathematics, Instituto de Matem\u00e1tica Aplicada a la Ciencia y la Ingenier\u00eda, Universidad de Castilla-La Mancha, Avda. Camilo Jos\u00e9 Cela, 3, 13071 Ciudad Real, Spain.", + "name": "P\u00e9rez-Garc\u00eda VM", + "orcid": "0000-0002-6575-495X" + }, + { + "name": "Rosa M" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/doi/10.1016/j.jtbi.2021.110685", + "month": "8", + "pages": "110685", + "synopsis": "Haematopoiesis is the process of generation of blood cells. 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Research into haematological disorders could benefit from a precise dynamical description of B lymphopoiesis.", + "title": "Dynamical properties of feedback signalling in B lymphopoiesis: A mathematical modelling approach.", + "type": "DOI", + "volume": "522", + "year": 2021 + }, + "publicationId": "BIOMD0000001056", + "submissionId": "MODEL2205170001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001057": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "Svetoslav Nikolov, Olaf Wolkenhauer, Julio Vera & Momchil Nenov. The role of cooperativity in a p53-miR34 dynamical mathematical model. Journal of Theoretical Biology 495 (2020).

The objective of this study is to evaluate the role of cooperativity, captured by the Hill coefficient, in a minimal mathematical model describing the interactions between p53 and miR-34a. The model equations are analyzed for negative, none and normal cooperativity using a specific version of bifurcation theory and they are solved numerically. Special attention is paid to the sign of so-called first Lyapunov value. Interpretations of the results are given, both according to dynamic theory and in biological terms. In terms of cell signaling, we propose the hypothesis that when the outgoing signal of a system spends a physiologically significant amount of time outside of its equilibrium state, then the value of that signal can be sampled at any point along the trajectory towards that equilibrium and indeed, at multiple points. Coupled with non-linear behavior, such as that caused by cooperativity, this feature can account for a complex and varied response, which p53 is known for. 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G. Bonchev Str., Bl. 4, 1113 Sofia, Bulgaria; University of Transport, Geo Milev Str., 158, 1574 Sofia, Bulgaria; Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Erlangen, Germany. Electronic address: s.nikolov@imbm.bas.bg.", + "authors": [ + { + "name": "Nikolov S" + }, + { + "name": "Wolkenhauer O" + }, + { + "institution": "Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Germany.", + "name": "Vera J", + "orcid": "0000-0002-3076-5122" + }, + { + "name": "Nenov M" + } + ], + "journal": "Journal of theoretical biology", + "link": "http://identifiers.org/doi/10.1016/j.jtbi.2020.110252", + "month": "6", + "pages": "110252", + "synopsis": "The objective of this study is to evaluate the role of cooperativity, captured by the Hill coefficient, in a minimal mathematical model describing the interactions between p53 and miR-34a. The model equations are analyzed for negative, none and normal cooperativity using a specific version of bifurcation theory and they are solved numerically. Special attention is paid to the sign of so-called first Lyapunov value. Interpretations of the results are given, both according to dynamic theory and in biological terms. In terms of cell signaling, we propose the hypothesis that when the outgoing signal of a system spends a physiologically significant amount of time outside of its equilibrium state, then the value of that signal can be sampled at any point along the trajectory towards that equilibrium and indeed, at multiple points. Coupled with non-linear behavior, such as that caused by cooperativity, this feature can account for a complex and varied response, which p53 is known for. From dynamical point of view, we found that when cooperativity is negative, the system has only one stable equilibrium point. In the absence of cooperativity, there is a single unstable equilibrium point with a critical boundary of stability. In the case with normal cooperativity, the system can have one, two, or three steady states with both, bi-stability and bi-instability occurring.", + "title": "The role of cooperativity in a p53-miR34 dynamical mathematical model.", + "type": "DOI", + "volume": "495", + "year": 2020 + }, + "publicationId": "BIOMD0000001057", + "submissionId": "MODEL2205180001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001058": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "Bela Novak & John J. Tyson. Mitotic kinase oscillation governs the latching of cell cycle switches. Current Biology 32, 12 (2022).

In 1996, Kim Nasmyth1 proposed that the eukaryotic cell cycle is an alternating sequence of transitions from G1 to S-G2-M and back again. These two phases correlate to high activity of cyclin-dependent kinases (CDKs) that trigger S-G2-M events and CDK antagonists that stabilize G1 phase. We associated these \u201calternative phases\u201d with the coexistence of two stable steady states of the biochemical reactions among CDKs and their antagonists. Transitions between these steady states (G1-to-S and M-to-G1) are driven by \u201chelper\u201d proteins. The fact that the transitions are irreversible is guaranteed by a \u201clatching\u201d property of the molecular switches, as we have argued in previous publications. Here, we show that if the latch is broken, then the biochemical reactions can swing back-and-forth across the transitions; either G1-S-G1-S \u2026 (periodic DNA replication without mitosis or cell division) or M-(G1)-M-(G1) \u2026 (periodic Cdc14 release, without fully exiting mitosis). 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Mitotic kinase oscillation", + "publication": { + "accession": "10.1016/j.cub.2022.04.016", + "affiliation": "Department of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, UK. Electronic address: bela.novak@bioch.ox.ac.uk.", + "authors": [ + { + "name": "Novak B" + }, + { + "name": "Tyson JJ" + } + ], + "journal": "Current biology : CB", + "link": "http://identifiers.org/doi/10.1016/j.cub.2022.04.016", + "month": "4", + "pages": "S0960-9822(22)00585-1", + "synopsis": "In 1996, Kim Nasmyth1 proposed that the eukaryotic cell cycle is an alternating sequence of transitions from G1 to S-G2-M and back again. These two phases correlate to high activity of cyclin-dependent kinases (CDKs) that trigger S-G2-M events and CDK antagonists that stabilize G1 phase. We associated these \"alternative phases\" with the coexistence of two stable steady states of the biochemical reactions among CDKs and their antagonists.2,3 Transitions between these steady states (G1-to-S and M-to-G1) are driven by \"helper\" proteins. The fact that the transitions are irreversible is guaranteed by a \"latching\" property of the molecular switches, as we have argued in previous publications.4,5 Here, we show that if the latch is broken, then the biochemical reactions can swing back-and-forth across the transitions; either G1-S-G1-S \u2026 (periodic DNA replication without mitosis or cell division) or M-(G1)-M-(G1) \u2026 (periodic Cdc14 release, without fully exiting mitosis). Using mathematical modeling of the molecular control circuit in budding yeast, we provide a fresh account of aberrant cell cycles in mutant strains: endoreplication in the clb1-5\u0394 strain6 and periodic release and resequestration of Cdc14 (an \"exit\" phosphatase) in the CLB2kd\u0394 strain.7,8 In our opinion, these \"endocycles\" are not autonomous oscillatory modules that must be entrained by the CDK oscillator6,7 but rather inadvertent and deleterious oscillations that are normally suppressed by the CDK latching-gate mechanism.8.", + "title": "Mitotic kinase oscillation governs the latching of cell cycle switches.", + "type": "DOI", + "year": 2022 + }, + "publicationId": "BIOMD0000001058", + "submissionId": "MODEL2205190001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001059": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "J\u00f6rg W. Stucki & Hans-Uwe Simon. Mathematical modeling of the regulation of caspase-3 activation and degradation. Journal of Theoretical Biology 234, 1 (2005).

Caspases are thought to be important players in the execution process of apoptosis. Inhibitors of apoptosis (IAPs) are able to block caspases and therefore apoptosis. The fact that a subgroup of the IAP family inhibits active caspases implies that not each caspase activation necessarily leads to apoptosis. In such a scenario, however, processed and enzymically active caspases should somehow be removed. Indeed, IAP\u2013caspase complexes covalently bind ubiquitin, resulting in degradation by the 26S proteasome. Following release from mitochondria, IAP antagonists (e.g. second mitochondrial activator of caspases (Smac)) inactivate IAPs. Moreover, although pro-apoptotic factors such as irradiation or anti-cancer drugs may release Smac from mitochondria in tumor cells, high cytoplasmic survivin and ML-IAP levels might be able to neutralize it and, consequently, IAPs would further be able to bind activated caspases. 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University of California San Diego, La Jolla, California, United States of America.", + "name": "Zengler K", + "orcid": "0000-0002-8062-3296" + }, + { + "name": "Zepeda A", + "orcid": "0000-0002-0587-5039" + }, + { + "name": "Zu\u00f1iga C", + "orcid": "0000-0002-0135-7429" + } + ], + "issue": "2", + "journal": "PLoS computational biology", + "link": "http://identifiers.org/doi/10.1371/journal.pcbi.1009828", + "month": "2", + "pages": "e1009828", + "synopsis": "The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants (e.g., aromatic hydrocarbons such as benzene and toluene) enable it to thrive under various harsh environmental conditions. Constraint-based metabolic models constructed from genome sequences enable quantitative insight into the central and specialized metabolism within a target organism. These genome-scale models have been utilized to understand, optimize, and design new strategies for improved bioprocesses. Reduced modeling approaches have been used to elucidate Nitrosomonas europaea metabolism at a pathway level. However, genome-scale knowledge about the simultaneous oxidation of ammonia and pollutant metabolism of N. europaea remains limited. Here, we describe the reconstruction, manual curation, and validation of the genome-scale metabolic model for N. europaea, iGC535. This reconstruction is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. Calculated flux distributions under different trophic conditions show that several key pathways are affected by the type of carbon source available, including central carbon metabolism and energy production.", + "title": "Flux balance analysis of the ammonia-oxidizing bacterium Nitrosomonas europaea ATCC19718 unravels specific metabolic activities while degrading toxic compounds.", + "type": "DOI", + "volume": "18", + "year": 2022 + }, + "publicationId": "BIOMD0000001061", + "submissionId": "MODEL2204270001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001062": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Northwestern University", + "email": "aswathy.raghu@gmail.com", + "external": true, + "name": "Aswathy K. 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EcN is a representative Gram-negative probiotic in biomedical research and is an intensively studied probiotic. However, to date, its genome-wide metabolic network model has not been developed. Here, we developed a comprehensive and highly curated EcN metabolic model, referred to as iDK1463, based on genome comparison and phenome analysis. The model was improved and validated by comparing the simulation results with experimental results from phenotype microarray tests. iDK1463 comprises 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions. Phenome data of EcN were compared with those of Escherichia coli intestinal commensal K-12 MG1655. iDK1463 was simulated to identify the genetic determinants responsible for the observed phenotypic differences between EcN and K-12. Further, the model was simulated for gene essentiality analysis and utilization of nutrient sources under anaerobic growth conditions. 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These analyses provided insights into the metabolic mechanisms by which EcN colonizes and persists in the gut. iDK1463 will contribute to the system-level understanding of the functional capacity of gut microbes and their interactions with microbiota and human hosts, as well as the development of live microbial therapeutics.", + "title": "Development of a Genome-Scale Metabolic Model and Phenome Analysis of the Probiotic Escherichia coli Strain Nissle 1917.", + "type": "PubMed ID", + "volume": "22", + "year": 2021 + }, + "publicationId": "BIOMD0000001062", + "submissionId": "MODEL2204180001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001063": { + "contributors": { + "Curator": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Chalmers University", + "email": "feiranl@chalmers.se", + "external": false, + "name": "feiran" + }, + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "yeast-GEM: The consensus genome-scale metabolic model of Saccharomyces cerevisiae. 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Through integration with enzyme constraints (ecYeast8) and protein 3D structures (proYeast8DB), Yeast8 further facilitates the exploration of yeast metabolism at a multi-scale level, enabling prediction of how single nucleotide variations translate to phenotypic traits.", + "title": "A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism.", + "type": "PubMed ID", + "volume": "10", + "year": 2019 + }, + "publicationId": "BIOMD0000001063", + "submissionId": "MODEL2204280003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001064": { + "contributors": { + "Curator": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + }, + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ], + "Modeller": [ + { + "affiliation": "KAIST", + "email": "ehukim@kaist.ac.kr", + "external": false, + "name": "Hyun Uk Kim" + }, + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This is a model of the genome scale reconstruction of the Vibrio vulnificus metabolic network, VvuMBEL943, described in the article:
Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery
Hyun Uk Kim, Soo Young Kim, Haeyoung Jeong, Tae Yong Kim, Jae Jong Kim, Hyon E Choy, Kyu Yang Yi, Joon Haeng Rhee, and Sang Yup Lee. Molecular Systems Biology 7:460 Jan 2011 doi: 10.1038/msb.2010.115

Abstract:
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.

This metabolic network model has been thoroughly validated by the authors. VvuMBEL943 is a stoichiometric model that contains the metabolic information of the microbial pathogen, Vibrio vulnificus CMCP6, at genome-scale. The SBML version was generated by Hyun Uk Kim using MetaFluxNet.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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USA.Department of Oncological Sciences, The Precision Immunology Institute, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.", + "name": "Diego Chowell" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.", + "name": "Seong-Keun Yoo", + "orcid": "0000-0002-8749-7050" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Cristina Valero", + "orcid": "0000-0003-3397-2888" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Alessandro Pastore", + "orcid": "0000-0002-3621-4807" + }, + { + "institution": "Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Chirag Krishna" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Mark Lee" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.", + "name": "Douglas Hoen" + }, + { + "institution": "Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Hongyu Shi" + }, + { + "institution": "Information Systems, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Daniel W Kelly" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Neal Patel", + "orcid": "0000-0002-4352-4723" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.", + "name": "Vladimir Makarov" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.", + "name": "Xiaoxiao Ma", + "orcid": "0000-0001-8661-5660" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Lynda Vuong" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Erich Y Sabio" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Kate Weiss" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Fengshen Kuo", + "orcid": "0000-0003-1797-2896" + }, + { + "institution": "Research Unit for Evolutionary Immunogenomics, Department of Biology, Universit\u00e4t Hamburg, Hamburg, Germany.", + "name": "Tobias L Lenz", + "orcid": "0000-0002-7203-0044" + }, + { + "institution": "Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.", + "name": "Robert M Samstein" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Nadeem Riaz", + "orcid": "0000-0001-9873-5862" + }, + { + "institution": "Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Prasad S Adusumilli", + "orcid": "0000-0002-1699-2046" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Vinod P Balachandran", + "orcid": "0000-0002-2956-223X" + }, + { + "institution": "Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "George Plitas" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA.Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "A Ari Hakimi" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Omar Abdel-Wahab", + "orcid": "0000-0002-3907-6171" + }, + { + "institution": "Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Alexander N Shoushtari" + }, + { + "institution": "Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Michael A Postow" + }, + { + "institution": "Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Robert J Motzer" + }, + { + "institution": "Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Marc Ladanyi" + }, + { + "institution": "Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Ahmet Zehir", + "orcid": "0000-0001-5406-4104" + }, + { + "institution": "Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Michael F Berger", + "orcid": "0000-0003-3882-5000" + }, + { + "institution": "Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.", + "name": "Mithat G\u00f6nen" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org.Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA. morrisl@mskcc.org.", + "name": "Luc G T Morris", + "orcid": "0000-0002-4417-2280" + }, + { + "institution": "Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA. weinholn@mskcc.org.Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. weinholn@mskcc.org.", + "name": "Nils Weinhold", + "orcid": "0000-0003-3290-6192" + }, + { + "institution": "Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA. chant2@ccf.org.Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA. chant2@ccf.org.Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA. chant2@ccf.org.Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA. chant2@ccf.org.Weill Cornell School of Medicine, New York, NY, USA. chant2@ccf.org.", + "name": "Timothy A Chan", + "orcid": "0000-0002-9265-0283" + } + ], + "issue": "4", + "journal": "Nature biotechnology", + "link": "http://identifiers.org/pubmed/34725502", + "month": "4", + "pages": "499-506", + "synopsis": "Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose1. Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions.", + "title": "Improved prediction of immune checkpoint blockade efficacy across multiple cancer types.", + "type": "PubMed ID", + "volume": "40", + "year": 2022 + }, + "publicationId": "BIOMD0000001066", + "submissionId": "MODEL2304250001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001067": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + } + ], + "Modeller": [ + { + "affiliation": "Cardiff University", + "email": "deshpandes1@cardiff.ac.uk", + "external": false, + "name": "Sumukh Deshpande" + }, + { + "affiliation": "Forschungszentrum J\u00fclich", + "email": "n.hoffmann@fz-juelich.de", + "external": false, + "name": "Nils Hoffmann", + "orcid": "0000-0002-6540-6875" + } + ] + }, + "curationStatus": "CURATED", + "description": "This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. 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1683889903, + "submitter": "Sumukh Deshpande", + "version": 3 + }, + { + "comment": "Added scripts for extracting features from training and test set sequences", + "submitted": 1698748163, + "submitter": "Sumukh Deshpande", + "version": 4 + }, + { + "comment": "Correct the annotations file using the identifiers.org based URIs", + "submitted": 1733771106, + "submitter": "Tung Nguyen", + "version": 5 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL2304280002", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2304280002" + }, + { + "accession": "BIOMD0000001067", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001067" + }, + { + "accession": "30665012", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "https://identifiers.org/pubmed:30665012" + }, + { + "accession": "SIO_010073", + "name": "open reading frame", + "qualifier": "bqmodel:hasProperty", + "resource": "Semanticscience Integrated Ontology", + "uri": "https://identifiers.org/sio:SIO_010073" + }, + { + "accession": "NCRO_0004004", + "name": "long non-coding RNA", + "qualifier": "bqmodel:hasOutput", + "resource": "Non-Coding RNA Ontology", + "uri": "http://purl.obolibrary.org/obo/NCRO_0004004" + }, + { + "accession": "NCIT:C48691", + "name": "Coding Region", + "qualifier": "bqmodel:hasOutput", + "resource": "NCIt", + "uri": "https://identifiers.org/ncit:C48691" + } + ], + "modelTags": [ + "FAIR-AIML", + "Machine Learning Model" + ], + "modellingApproach": { + "accession": "EDAM:3474", + "name": "machine learning", + "resource": "https://identifiers.org/bptl/edam:topic_3474" + }, + "name": "Deshpande2019 - Random Forest model to predict long non-coding RNAs from coding RNAs in Zea Mays plant transcriptomic data", + "publication": { + "accession": "30665012", + "affiliation": "School of Computing, Electronics and Mathematics, 1 Gulson Road Coventry University, Coventry, Warwickshire, CV1 2JH, United Kingdom. Electronic address: deshpan4@uni.coventry.ac.uk.", + "authors": [ + { + "institution": "School of Computing, Electronics and Mathematics, 1 Gulson Road Coventry University, Coventry, Warwickshire, CV1 2JH, United Kingdom. Electronic address: deshpan4@uni.coventry.ac.uk.", + "name": "Sumukh Deshpande" + }, + { + "institution": "School of Computing, Electronics and Mathematics, 1 Gulson Road Coventry University, Coventry, Warwickshire, CV1 2JH, United Kingdom.", + "name": "James Shuttleworth", + "orcid": "0000-0003-4345-9412" + }, + { + "institution": "School of Computing, Electronics and Mathematics, 1 Gulson Road Coventry University, Coventry, Warwickshire, CV1 2JH, United Kingdom.", + "name": "Jianhua Yang" + }, + { + "institution": "School of Computing, Electronics and Mathematics, 1 Gulson Road Coventry University, Coventry, Warwickshire, CV1 2JH, United Kingdom.", + "name": "Sandy Taramonli", + "orcid": "0000-0002-0751-7446" + }, + { + "institution": "School of Computing, Electronics and Mathematics, 1 Gulson Road Coventry University, Coventry, Warwickshire, CV1 2JH, United Kingdom.", + "name": "Matthew England", + "orcid": "0000-0001-5729-3420" + } + ], + "journal": "Computers in biology and medicine", + "link": "http://identifiers.org/pubmed/30665012", + "month": "2", + "pages": "169-181", + "synopsis": "Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs which play a significant role in several biological processes. RNA-seq based transcriptome sequencing has been extensively used for identification of lncRNAs. However, accurate identification of lncRNAs in RNA-seq datasets is crucial for exploring their characteristic functions in the genome as most coding potential computation (CPC) tools fail to accurately identify them in transcriptomic data. Well-known CPC tools such as CPC2, lncScore, CPAT are primarily designed for prediction of lncRNAs based on the GENCODE, NONCODE and CANTATAdb databases. The prediction accuracy of these tools often drops when tested on transcriptomic datasets. This leads to higher false positive results and inaccuracy in the function annotation process. In this study, we present a novel tool, PLIT, for the identification of lncRNAs in plants RNA-seq datasets. PLIT implements a feature selection method based on L1 regularization and iterative Random Forests (iRF) classification for selection of optimal features. Based on sequence and codon-bias features, it classifies the RNA-seq derived FASTA sequences into coding or long non-coding transcripts. Using L1 regularization, 31 optimal features were obtained based on lncRNA and protein-coding transcripts from 8 plant species. The performance of the tool was evaluated on 7 plant RNA-seq datasets using 10-fold cross-validation. The analysis exhibited superior accuracy when evaluated against currently available state-of-the-art CPC tools.", + "title": "PLIT: An alignment-free computational tool for identification of long non-coding RNAs in plant transcriptomic datasets.", + "type": "PubMed ID", + "volume": "105", + "year": 2019 + }, + "publicationId": "BIOMD0000001067", + "submissionId": "MODEL2304280002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001068": { + "contributors": { + "Modeller": [ + { + "affiliation": "Heidelberg Institute for Theoretical Studies", + "email": "sucheta.ghosh@ieee.org", + "external": false, + "name": "Sucheta Ghosh", + "orcid": "0000-0002-2896-4552" + } + ] + }, + "curationStatus": "CURATED", + "description": "This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image. This conversion ensures interoperability and open access. The ONNX format utility can perform the following essential tasks: model conversion, inference, inspection, and optimization. Reference: 1) https://github.com/elixir-europe/biohackathon-projects-2022/tree/main/9 2) https://www.ebi.ac.uk/biomodels/search?query=Maaly+Nassar&domain=biomodels 3) https://gitlab.com/maaly7/emerald_metagenomics_annotations 4) This model is built upon the model of the following publication: Maaly Nassar, Alexander B Rogers, Francesco Talo', Santiago Sanchez, Zunaira Shafique, Robert D Finn, Johanna McEntyre, A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications, GigaScience, Volume 11, 2022, giac077, https://doi.org/10.1093/gigascience/giac077", + "files": { + "additional": [ + { + "description": "The compressed zipfile consists of code and files required to reproduce the result", + "fileSize": "293272", + "md5sum": "a7dc4720d5322c3709c66843bcd5ab8e", + "mimeType": "application/zip", + "name": "onnx-metagene-text-classifier.zip", + "sha1sum": "baffdaace02d5bbeae12c51930c0181e2525a085", + "sha256sum": "83e30a4683be2525244b8feb577e16b7e3059c8402d63979020fe7a074628391" + } + ], + "main": [ + { + "description": "This is the best performing model in ONNX format", + "fileSize": "865713", + "md5sum": "f4445063ececaa2b205953b24a9ea812", + "mimeType": "application/octet-stream", + "name": "doc2vec-model.onnx", + "sha1sum": "79acbbbcfbe7db64178595f61647a833f4408db7", + "sha256sum": "49a6e8a84cd957a10f5840d7d2a4008849bfbb280b3200746a4bcc4a5d86ebcd" + } + ] + }, + "firstPublished": 1685026548, + "format": { + "identifier": "ONNX", + "name": "Open Neural Network Exchange", + "version": "*" + }, + "history": { + "revisions": [ + { + "comment": "minor revision", + "submitted": 1685025645, + "submitter": "Sucheta Ghosh", + "version": 2 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.1093/gigascience/giac077", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi/10.1093/gigascience/giac077" + }, + { + "accession": "MODEL2304210001", + "qualifier": "bqbiol:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db/MODEL2304210001" + } + ], + "modelTags": [ + "FAIR-AIML", + "Machine Learning Model" + ], + "modellingApproach": { + "accession": "MAMO_0000023", + "name": "computational model", + "resource": "http://identifiers.org/mamo/MAMO_0000023" + }, + "name": "Nassar2022 - Metagenomics Classification Task for Scientific Literature Text", + "publication": { + "accession": "10.1093/gigascience/giac077", + "affiliation": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "authors": [ + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.Current affiliation: SciBite - an Elsevier Company, Wellcome Genome Campus, Hinxton, Cambridge CB10 1DR, UK.", + "name": "Maaly Nassar", + "orcid": "0000-0003-3362-5202" + }, + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "name": "Rogers AB", + "orcid": "0000-0002-4283-6135" + }, + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "name": "Talo' F", + "orcid": "0000-0002-5306-4329" + }, + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "name": "Sanchez S", + "orcid": "0000-0001-7018-3891" + }, + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "name": "Shafique Z", + "orcid": "0000-0002-2389-4225" + }, + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "name": "Finn RD", + "orcid": "0000-0001-8626-2148" + }, + { + "institution": "European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.", + "name": "McEntyre J", + "orcid": "0000-0002-1611-6935" + } + ], + "journal": "GigaScience", + "link": "http://identifiers.org/doi/10.1093/gigascience/giac077", + "month": "8", + "pages": "giac077", + "synopsis": "Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API.", + "title": "A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications.", + "type": "DOI", + "volume": "11", + "year": 2022 + }, + "publicationId": "BIOMD0000001068", + "submissionId": "MODEL2304210001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001069": { + "contributors": { + "Curator": [ + { + "affiliation": "Forschungszentrum J\u00fclich", + "email": "n.hoffmann@fz-juelich.de", + "external": false, + "name": "Nils Hoffmann", + "orcid": "0000-0002-6540-6875" + } + ], + "Modeller": [ + { + "affiliation": "Forschungszentrum J\u00fclich", + "email": "n.hoffmann@fz-juelich.de", + "external": false, + "name": "Nils Hoffmann", + "orcid": "0000-0002-6540-6875" + } + ] + }, + "curationStatus": "CURATED", + "description": "This model is used for automatic identification and counting of three types of blood cells: Red Blood Cells (RBC), White Blood Cells (WBC) and Platelet (Platelets) using the \u2018you only look once\u2019 (YOLO) object detection and classification algorithm with some additions to remove overannotation. The YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Postprocessing with k-nearest neighbor (KNN) and intersection over union (IOU) approach reduces issues with multiple annotation of platelets. The original code was extended to save the trained YoloV2 network state into the protobuf format. This is then used to generate the ONNX model, containing the weigths. Additional code was added to implement the inference step for image annotation based on the ONNX model, as well as the post-processing logic as used on the original model output. 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Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.", + "title": "Machine learning approach of automatic identification and counting of blood cells.", + "type": "DOI", + "volume": "6", + "year": 2019 + }, + "publicationId": "BIOMD0000001069", + "submissionId": "MODEL2305050001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001070": { + "contributors": { + "Curator": [ + { + "affiliation": "Cambridge University", + "email": "kieran.didi@gmail.com", + "external": false, + "name": "Kieran Didi", + "orcid": "0000-0001-6839-3320" + } + ] + }, + "curationStatus": "CURATED", + "description": "Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. 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Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches. Our method significantly surpasses state-of-the-art models in sequence and structure modeling, antigen-binding CDR design, and binding affinity optimization. Specifically, the relative improvement to baselines is about 23% in antigen-binding CDR design and 34% for affinity optimization.", + "title": "Conditional Antibody Design as 3D Equivariant Graph Translation", + "type": "DOI", + "year": 2022 + }, + "publicationId": "BIOMD0000001070", + "submissionId": "MODEL2304300002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001071": { + "contributors": { + "Curator": [ + { + "affiliation": "Cambridge University", + "email": "kieran.didi@gmail.com", + "external": false, + "name": "Kieran Didi", + "orcid": "0000-0001-6839-3320" + } + ] + }, + "curationStatus": "CURATED", + "description": "Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. 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The second approach, \u201cinpainting,\u201d starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network.", + "files": { + "main": [ + { + "description": "Dockerfile to build the image and run the model", + "fileSize": "2612", + "md5sum": "517acd5631b701de2296a0f8c0a39c99", + "mimeType": "text/plain", + "name": "Dockerfile.txt", + "sha1sum": "4a75e6bf40316f58dc212e86a45cfba17f6def94", + "sha256sum": "af417f79d4c22c5e34446a102ac004a5ea40e3ed3ddf7ac6146bb89b1ae3371c" + } + ] + }, + "firstPublished": 1683732221, + "format": { + "identifier": "UNKNOWN", + "name": "Other", + "version": "*" + }, + "history": { + "revisions": [ + { + "comment": "Model revised without commit message", + "submitted": 1685106241, + "submitter": "Kieran Didi", + "version": 1 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.1126/science.abn2100", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi/10.1126/science.abn2100" + }, + { + "accession": "MODEL2304300001", + "qualifier": "bqbiol:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db/MODEL2304300001" + } + ], + "modelTags": [ + "FAIR-AIML", + "Machine Learning Model" + ], + "modellingApproach": { + "accession": "OTHER", + "name": "Other", + "resource": "Unknown" + }, + "name": "Wang2022 - Scaffolding protein functional sites using deep learning", + "publication": { + "accession": "10.1126/science.abn2100", + "affiliation": "Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.", + "authors": [ + { + "name": "Wang J", + "orcid": "0000-0002-1139-6640" + }, + { + "name": "Lisanza S", + "orcid": "0000-0003-2706-8606" + }, + { + "name": "Juergens D", + "orcid": "0000-0001-6425-8391" + }, + { + "name": "Tischer D", + "orcid": "0000-0003-3633-5542" + }, + { + "name": "Watson JL", + "orcid": "0000-0001-5492-0249" + }, + { + "name": "Castro KM" + }, + { + "name": "Ragotte R", + "orcid": "0000-0002-6463-1595" + }, + { + "name": "Saragovi A", + "orcid": "0000-0002-4376-1390" + }, + { + "name": "Milles LF", + "orcid": "0000-0001-8417-3205" + }, + { + "name": "Baek M", + "orcid": "0000-0003-3414-9404" + }, + { + "name": "Anishchenko I", + "orcid": "0000-0003-3645-2044" + }, + { + "name": "Yang W" + }, + { + "name": "Hicks DR", + "orcid": "0000-0003-2950-1848" + }, + { + "name": "Exp\u00f2sit M", + "orcid": "0000-0002-2980-303X" + }, + { + "name": "Schlichthaerle T", + "orcid": "0000-0003-4444-2575" + }, + { + "name": "Chun JH", + "orcid": "0000-0003-4778-4175" + }, + { + "name": "Dauparas J", + "orcid": "0000-0002-0030-144X" + }, + { + "name": "Bennett N", + "orcid": "0000-0001-8590-1454" + }, + { + "name": "Wicky BIM", + "orcid": "0000-0002-2501-7875" + }, + { + "name": "Muenks A" + }, + { + "name": "DiMaio F", + "orcid": "0000-0002-7524-8938" + }, + { + "name": "Correia B", + "orcid": "0000-0002-7377-8636" + }, + { + "name": "Ovchinnikov S", + "orcid": "0000-0003-2774-2744" + }, + { + "name": "Baker D", + "orcid": "0000-0001-7896-6217" + } + ], + "issue": "6604", + "journal": "Science (New York, N.Y.)", + "link": "http://identifiers.org/doi/10.1126/science.abn2100", + "month": "7", + "pages": "387-394", + "synopsis": "The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. 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We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.", + "title": "Scaffolding protein functional sites using deep learning.", + "type": "DOI", + "volume": "377", + "year": 2022 + }, + "publicationId": "BIOMD0000001071", + "submissionId": "MODEL2304300001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001072": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "Max Delbr\u00fcck Center", + "email": "zsofi.bujtar@gmail.com", + "external": false, + "name": "Zs\u00f3fia Bujt\u00e1r", + "orcid": "0000-0002-9236-7955" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": 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In mammals, these rhythms are generated by pacemaker neurons in the suprachiasmatic nucleus (SCN) of the hypothalamus. The SCN is remarkably consistent in structure and function between species, yet mammalian rest/activity patterns are extremely diverse, including diurnal, nocturnal, and crepuscular behaviors. Two mechanisms have been proposed to account for this diversity: (i) modulation of SCN output by downstream nuclei, and (ii) direct effects of light on activity. These two mechanisms are difficult to disentangle experimentally and their respective roles remain unknown. To address this, we developed a computational model to simulate the two mechanisms and their influence on temporal niche. In our model, SCN output is relayed via the subparaventricular zone (SPZ) to the dorsomedial hypothalamus (DMH), and thence to ventrolateral preoptic nuclei (VLPO) and lateral hypothalamus (LHA). Using this model, we generated rich phenotypes that closely resemble experimental data. 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Given the ubiquitous effects of temporal organization on all aspects of animal behavior and physiology, this study sheds light on the physiological changes required to orchestrate adaptation to various temporal niches.", + "title": "Mammalian rest/activity patterns explained by physiologically based modeling.", + "type": "PubMed ID", + "volume": "9", + "year": 2013 + }, + "publicationId": "BIOMD0000001072", + "submissionId": "MODEL2003080001", + "vcsIdentifier": "aai" + }, + "BIOMD0000001073": { + "contributors": { + "Curator": [ + { + "affiliation": "EMBL-EBI", + "email": "dtiwari@ebi.ac.uk", + "external": false, + "name": "Divyang Deep Tiwari" + } + ] + }, + "curationStatus": "CURATED", + "description": "A Random Forest model is developed to incorporate tumor mutation data within the context of the biological process known as leukocyte proliferation regulation. 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Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB.", + "title": "Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.", + "type": "PubMed ID", + "volume": "13", + "year": 2022 + }, + "publicationId": "BIOMD0000001073", + "submissionId": "MODEL2306280001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001074": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Cambridge", + "email": "haoxuewang@ebi.ac.uk", + "external": false, + "name": "Haoxue Wang" + } + ] + }, + "curationStatus": "CURATED", + "description": "Immunotherapy has improved the prognosis of patients with advanced non-small cell lungcancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. 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"resource": "NCI Thesaurus OBO Edition", + "uri": "http://purl.obolibrary.org/obo/NCIT_C143250" + }, + { + "accession": "BTO_0002058", + "name": "NSCLC Cell", + "qualifier": "bqmodel:occursIn", + "resource": "BTO (BRENDA Tissue Ontology)", + "uri": "http://purl.obolibrary.org/obo/BTO_0002058" + } + ], + "modelTags": [ + "FAIR-AIML", + "Machine Learning Model" + ], + "modellingApproach": { + "accession": "MAMO_0000023", + "name": "computational model", + "resource": "http://identifiers.org/mamo/MAMO_0000023" + }, + "name": "Liu2023 - Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data", + "publication": { + "accession": "36812231", + "affiliation": "Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.", + "authors": [ + { + "institution": "Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.", + "name": "Na Liu" + }, + { + "institution": "Shanghai Artificial Intelligence Laboratory, Shanghai, China.", + "name": "Bi-Lin Liang" + }, + { + "institution": "Shanghai Artificial Intelligence Laboratory, Shanghai, China.", + "name": "Lu Lu" + }, + { + "institution": "Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.", + "name": "Bing-Qian Zhang" + }, + { + "institution": "Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.", + "name": "Jing-Jing Sun" + }, + { + "institution": "Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.", + "name": "Jian-Tao Yang" + }, + { + "institution": "Shanghai Artificial Intelligence Laboratory, Shanghai, China.", + "name": "Jie Xu" + }, + { + "institution": "Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.Department of Clinical Trial, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.", + "name": "Zheng-Bo Song" + }, + { + "institution": "Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.", + "name": "Lei Shi" + } + ], + "issue": "2", + "journal": "Neoplasma", + "link": "http://identifiers.org/pubmed/36812231", + "month": "4", + "pages": "300-310", + "synopsis": "Immunotherapy has improved the prognosis of patients with advanced non-small cell lung cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC. We retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, a combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic curve. Survival analysis was performed to determine the difference in progression-free survival (PFS) between the two groups with the prediction label generated by the combined model. The radiomic model based on the combination of precontrast and postcontrast CT radiomic features and the clinical model produced an AUC of 0.92\u00b10.04 and 0.89\u00b10.03, respectively. By integrating radiomic and clinical features together, the combined model had the best performance with an AUC of 0.94\u00b10.02. The survival analysis showed that the two groups had significantly different PFS times (p<0.0001). The baseline multidimensional data including CT radiomic and multiple clinical features were valuable in predicting the efficacy of ICIs monotherapy in patients with advanced NSCLC.", + "title": "Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data.", + "type": "PubMed ID", + "volume": "70", + "year": 2023 + }, + "publicationId": "BIOMD0000001074", + "submissionId": "MODEL2307100001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001075": { + "contributors": { + "Curator": [ + { + "affiliation": "EMBL-EBI", + "email": "dtiwari@ebi.ac.uk", + "external": false, + "name": "Divyang Deep Tiwari" + } + ] + }, + "curationStatus": "CURATED", + "description": "In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. 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UK.", + "name": "Stephen-John Sammut", + "orcid": "0000-0003-4472-904X" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.", + "name": "Mireia Crispin-Ortuzar" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.", + "name": "Suet-Feung Chin", + "orcid": "0000-0001-5697-1082" + }, + { + "institution": "CRUK Cambridge Centre, Cambridge Experimental Cancer Medicine Centre (ECMC) and NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.", + "name": "Elena Provenzano", + "orcid": "0000-0003-3345-3965" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.", + "name": "Helen A Bardwell" + }, + { + "institution": "School of Clinical Medicine, University of Cambridge, Cambridge, UK.", + "name": "Wenxin Ma" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.", + "name": "Wei Cope" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.Institute of Astronomy, University of Cambridge, Cambridge, UK.", + "name": "Ali Dariush" + }, + { + "institution": "Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.Centre of Cancer Research and Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.", + "name": "Sarah-Jane Dawson", + "orcid": "0000-0002-8276-0374" + }, + { + "institution": "Department of Oncology, University of Cambridge, Cambridge, UK.CRUK Cambridge Centre, Cambridge Experimental Cancer Medicine Centre (ECMC) and NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.", + "name": "Jean E Abraham" + }, + { + "institution": "Warwick Clinical Trials Unit, University of Warwick, Coventry, UK.", + "name": "Janet Dunn", + "orcid": "0000-0001-7313-4446" + }, + { + "institution": "Warwick Clinical Trials Unit, University of Warwick, Coventry, UK.", + "name": "Louise Hiller", + "orcid": "0000-0001-8538-9163" + }, + { + "institution": "Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, UK.Q2 Laboratory Solutions, Livingston, UK.", + "name": "Jeremy Thomas", + "orcid": "0000-0002-4652-610X" + }, + { + "institution": "Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, UK.", + "name": "David A Cameron", + "orcid": "0000-0002-2717-7979" + }, + { + "institution": "Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, UK.Ontario Institute for Cancer Research, Toronto, Ontario, Canada.Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.", + "name": "John M S Bartlett" + }, + { + "institution": "Edinburgh Cancer Research Centre, Western General Hospital, Edinburgh, UK.", + "name": "Larry Hayward" + }, + { + "institution": "CRUK Cambridge Centre, Cambridge Experimental Cancer Medicine Centre (ECMC) and NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.Strangeways Research Laboratory, University of Cambridge, Cambridge, UK.", + "name": "Paul D Pharoah", + "orcid": "0000-0001-8494-732X" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.", + "name": "Florian Markowetz", + "orcid": "0000-0002-2784-5308" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK.MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.", + "name": "Oscar M Rueda", + "orcid": "0000-0003-0008-4884" + }, + { + "institution": "Department of Oncology, University of Cambridge, Cambridge, UK.CRUK Cambridge Centre, Cambridge Experimental Cancer Medicine Centre (ECMC) and NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.", + "name": "Helena M Earl", + "orcid": "0000-0003-1549-8094" + }, + { + "institution": "Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK. carlos.caldas@cruk.cam.ac.uk.Department of Oncology, University of Cambridge, Cambridge, UK. carlos.caldas@cruk.cam.ac.uk.CRUK Cambridge Centre, Cambridge Experimental Cancer Medicine Centre (ECMC) and NIHR Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. carlos.caldas@cruk.cam.ac.uk.", + "name": "Carlos Caldas", + "orcid": "0000-0003-3547-1489" + } + ], + "issue": "7894", + "journal": "Nature", + "link": "http://identifiers.org/pubmed/34875674", + "month": "1", + "pages": "623-629", + "synopsis": "Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to\u00a0cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a\u00a0pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.", + "title": "Multi-omic machine learning predictor of breast cancer therapy response.", + "type": "PubMed ID", + "volume": "601", + "year": 2022 + }, + "publicationId": "BIOMD0000001075", + "submissionId": "MODEL2308250001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001076": { + "contributors": { + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "dtiwari@ebi.ac.uk", + "external": false, + "name": "Divyang Deep Tiwari" + } + ] + }, + "curationStatus": "CURATED", + "description": "This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins", + "files": { + "additional": [ + { + "description": "Model annotation file in csv format for the MLP-BC model.", + "fileSize": "4335", + "md5sum": "ba38e8b6f9b5b0fd84a4fbe3441f682d", + "mimeType": "text/plain", + "name": "MLP_BC_annotation.csv", + "sha1sum": "d64a63d0466f35c6579fdb18cb8f88bba9270bd2", + "sha256sum": "bcc86ddb323211ca96e1a80d53fcd12f4079ef5e11c8a94931df3cc71cba7766" + }, + { + "description": "A ReadMe file to provide further comments on the MLP-BC model.", + "fileSize": "1256", + "md5sum": "f9a164b91778f09b8e75651bb23b73f8", + "mimeType": "application/rtf", + "name": "ReadMe_MLP-BC.rtf", + "sha1sum": "4216171bbf00becf9f537eca4f0a08f4db66f0cd", + "sha256sum": "32dbb6e3bff44193f7ae58ac5954c3cc6d695e77ce5840bc5c4dfaeabf561c87" + }, + { + "description": "Reproduced table of AUROC scores for the 3 cross validation runs of the 13 machine learning algorithms", + "fileSize": "586", + "md5sum": "083df3faf20a0949cd0fd2555d7a33df", + "mimeType": "text/plain", + "name": "Reproduced_AUROC_scores.csv", + "sha1sum": "15a8e48b1d0c0c28bb1f0e123922bb95a47a18c9", + "sha256sum": "fb298c571b480696006601b071947c5e5389566a62d9aff6ca0cdee56a778d44" + }, + { + "description": "Reproduced Figure depicting the performance of 3 cross validation runs for all the 13 machine learning algorithms", + "fileSize": "105621", + "md5sum": "759c5b7d310ed6693a6799e7a40189af", + "mimeType": "application/pdf", + "name": "Reproduced_Figure.pdf", + "sha1sum": "06b7215c5b8ad63be3c173d965f2348a250026e9", + "sha256sum": "9b4c011fd286a4a7b7b779ce8910082e5f40f104d42db88d07218e74209645c1" + }, + { + "description": "Docker file to load the 'MLP_model3.onnx' model and make predictions on the cancer immunotherapy proteins dataset to identify Breast Cancer (BC) proteins.", + "fileSize": "296162", + "md5sum": "73090e57c5b5288b159814da1d593837", + "mimeType": "application/zip", + "name": "docker_predict.zip", + "sha1sum": "9d6b07b7f6fdd167f395098d0f820d01fc3d8388", + "sha256sum": "03ac16f95b08c5368247b7dc7d62ebd8bba87a1d5743dfb469c5bcefaa4788e6" + }, + { + "description": "Docker file and dependencies to train the 'MLP-BC' model using 'train.py' script.", + "fileSize": "1448180", + "md5sum": "9be124252c479e4e77380dfc3a6a4bd8", + "mimeType": "application/zip", + "name": "docker_train.zip", + "sha1sum": "d1003c1eb72b5659458288a0e0a69657878b582f", + "sha256sum": "b2defac369abe439748ba809baad85f8a6e6229bf8eb63db25bf009e90982ec3" + }, + { + "description": "This folder contains all the necessary code files and dataset that is required to train the MLP-BC model.", + "fileSize": "1446767", + "md5sum": "9d93aaba8177baf1868bde90800d860c", + "mimeType": "application/zip", + "name": "training_files.zip", + "sha1sum": "349561819b3bdf5942ee3fcf04e1a19eb8f1764b", + "sha256sum": "1bf39c8fbb4028d5a56e3c04f7fa075cf71f5f5b631744d4afefd5d7055a400d" + } + ], + "main": [ + { + "description": "ONNX (Open Neural Network Exchange) file of the best Multi Layer Perceptron (MLP-BC) model for Breast Cancer protein classification.", + "fileSize": "25518", + "md5sum": "24e3db3160c59dfcd9721a85b49d8567", + "mimeType": "application/octet-stream", + "name": "MLP_model3.onnx", + "sha1sum": "f3ec2574c7f06b509d832369522cb3e43171c41a", + "sha256sum": "18f73d169aef6df49618942e1d0c03044aad4ac92ea266b28b2bb72a0725f6d0" + } + ] + }, + "firstPublished": 1696845107, + "format": { + "identifier": "ONNX", + "name": "Open Neural Network Exchange", + "version": "*" + }, + "history": { + "revisions": [ + { + "comment": "Import of L\u00f3pez-Cort\u00e9s2020 - Prediction of Breast Cancer (BC) proteins involved in cancer immunotherapy using molecular descriptors and Multi Layer Perceptron (MLP) neural network", + "submitted": 1695383213, + "submitter": "Divyang Deep Tiwari", + "version": 1 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL2309220001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db/MODEL2309220001" + }, + { + "accession": "32444848", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "https://identifiers.org/pubmed/32444848" + }, + { + "accession": "BIOMD0000001076", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db/BIOMD0000001076" + } + ], + "modelTags": [ + "FAIR-AIML" + ], + "modellingApproach": { + "accession": "MAMO_0000023", + "name": "computational model", + "resource": "http://identifiers.org/mamo/MAMO_0000023" + }, + "name": "L\u00f3pez-Cort\u00e9s2020 - Prediction of Breast Cancer (BC) proteins involved in cancer immunotherapy using molecular descriptors and Multi Layer Perceptron (MLP) neural network", + "publication": { + "accession": "32444848", + "affiliation": "Centro de Investigaci\u00f3n Gen\u00e9tica y Gen\u00f3mica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador. aalc84@gmail.com.", + "authors": [ + { + "institution": "Centro de Investigaci\u00f3n Gen\u00e9tica y Gen\u00f3mica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador. aalc84@gmail.com.RNASA-IMEDIR, Computer Science Faculty, University of Coruna, Coruna, 15071, Spain. aalc84@gmail.com.Red Latinoamericana de Implementaci\u00f3n y Validaci\u00f3n de Gu\u00edas Cl\u00ednicas Farmacogen\u00f3micas (RELIVAF-CYTED), Quito, Ecuador. aalc84@gmail.com.", + "name": "Andr\u00e9s L\u00f3pez-Cort\u00e9s", + "orcid": "0000-0003-1503-1929" + }, + { + "institution": "RNASA-IMEDIR, Computer Science Faculty, University of Coruna, Coruna, 15071, Spain.Grupo de Bio-Quimioinform\u00e1tica, Universidad de Las Am\u00e9ricas, Avenue de los Granados, Quito, 170125, Ecuador.Carrera de Enfermer\u00eda, Facultad de Ciencias de la Salud, Universidad de Las Am\u00e9ricas, Avenue de los Granados, Quito, 170125, Ecuador.", + "name": "Alejandro Cabrera-Andrade", + "orcid": "0000-0001-9702-6618" + }, + { + "institution": "RNASA-IMEDIR, Computer Science Faculty, University of Coruna, Coruna, 15071, Spain.Centro de Investigaci\u00f3n en Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones (CITIC), Campus de Elvi\u00f1a s/n 15071, A Coru\u00f1a, Spain.Biomedical Research Institute of A Coru\u00f1a (INIBIC), University Hospital Complex of A Coru\u00f1a (CHUAC), 15006, A Coru\u00f1a, Spain.", + "name": "Jos\u00e9 M V\u00e1zquez-Naya", + "orcid": "0000-0002-6194-5329" + }, + { + "institution": "RNASA-IMEDIR, Computer Science Faculty, University of Coruna, Coruna, 15071, Spain.Centro de Investigaci\u00f3n en Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones (CITIC), Campus de Elvi\u00f1a s/n 15071, A Coru\u00f1a, Spain.Biomedical Research Institute of A Coru\u00f1a (INIBIC), University Hospital Complex of A Coru\u00f1a (CHUAC), 15006, A Coru\u00f1a, Spain.", + "name": "Alejandro Pazos", + "orcid": "0000-0003-2324-238X" + }, + { + "institution": "Department of Organic Chemistry II, University of the Basque Country UPV/EHU, Leioa 48940, Biscay, Spain.IKERBASQUE, Basque Foundation for Science, Bilbao, 48011, Biscay, Spain.", + "name": "Humberto Gonz\u00e1les-D\u00edaz", + "orcid": "0000-0002-9392-2797" + }, + { + "institution": "Centro de Investigaci\u00f3n Gen\u00e9tica y Gen\u00f3mica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador.", + "name": "C\u00e9sar Paz-Y-Mi\u00f1o", + "orcid": "0000-0002-6693-7344" + }, + { + "institution": "Centro de Investigaci\u00f3n Gen\u00e9tica y Gen\u00f3mica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, Quito, 170129, Ecuador.", + "name": "Santiago Guerrero", + "orcid": "0000-0003-3473-7214" + }, + { + "institution": "Grupo de Bio-Quimioinform\u00e1tica, Universidad de Las Am\u00e9ricas, Avenue de los Granados, Quito, 170125, Ecuador.Escuela de Ciencias F\u00edsicas y Matem\u00e1ticas, Universidad de Las Am\u00e9ricas, Avenue de los Granados, Quito, 170125, Ecuador.", + "name": "Yunierkis P\u00e9rez-Castillo", + "orcid": "0000-0002-3710-0035" + }, + { + "institution": "Grupo de Bio-Quimioinform\u00e1tica, Universidad de Las Am\u00e9ricas, Avenue de los Granados, Quito, 170125, Ecuador.Facultad de Ingenier\u00eda y Ciencias Agropecuarias, Universidad de Las Am\u00e9ricas, Avenue de los Granados, Quito, 170125, Ecuador.", + "name": "Eduardo Tejera", + "orcid": "0000-0002-1377-0413" + }, + { + "institution": "RNASA-IMEDIR, Computer Science Faculty, University of Coruna, Coruna, 15071, Spain.Centro de Investigaci\u00f3n en Tecnolog\u00edas de la Informaci\u00f3n y las Comunicaciones (CITIC), Campus de Elvi\u00f1a s/n 15071, A Coru\u00f1a, Spain.Biomedical Research Institute of A Coru\u00f1a (INIBIC), University Hospital Complex of A Coru\u00f1a (CHUAC), 15006, A Coru\u00f1a, Spain.", + "name": "Cristian R Munteanu", + "orcid": "0000-0002-5628-2268" + } + ], + "issue": "1", + "journal": "Scientific reports", + "link": "http://identifiers.org/pubmed/32444848", + "month": "5", + "pages": "8515", + "synopsis": "Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980\u2009\u00b1\u20090.0037, and accuracy of 0.936\u2009\u00b1\u20090.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to find new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.", + "title": "Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.", + "type": "PubMed ID", + "volume": "10", + "year": 2020 + }, + "publicationId": "BIOMD0000001076", + "submissionId": "MODEL2309220001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001077": { + "contributors": { + "Curator": [ + { + "affiliation": "University of Washington", + "email": "lpsmith@uw.edu", + "external": false, + "name": "Lucian Smith", + "orcid": "0000-0001-7002-6386" + } + ], + "Modeller": [ + { + "affiliation": "German Cancer Research Center", + "email": "m.schilling@dkfz.de", + "external": false, + "name": "Marcel Schilling" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "

A mathematical model for cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

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"affiliation": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Department of Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Hamburg Center for Translational Immunology (HCTI), University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.", + "authors": [ + { + "name": "Lorenz Adlung", + "orcid": "0000-0002-0971-2309" + }, + { + "institution": "Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health, Institute of Computational Biology, 85764 Neuherberg, Germany; Technische Universit\u00e4t M\u00fcnchen, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748 Garching, Germany.", + "name": "Paul Stapor" + }, + { + "institution": "Institute of Physics and Freiburg Center for Data Analysis and Modelling (FDM), University of Freiburg, 79104 Freiburg, Germany; CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany.", + "name": "Christian T\u00f6nsing", + "orcid": "0000-0003-2822-5191" + }, + { + "institution": "Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health, Institute of Computational Biology, 85764 Neuherberg, Germany; Technische Universit\u00e4t M\u00fcnchen, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748 Garching, Germany.", + "name": "Leonard Schmiester", + "orcid": "0000-0001-7946-3232" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.", + "name": "Luisa E Schwarzm\u00fcller" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.", + "name": "Lena Postawa" + }, + { + "institution": "Helmholtz Zentrum M\u00fcnchen - German Research Center for Environmental Health, Institute of Computational Biology, 85764 Neuherberg, Germany; Technische Universit\u00e4t M\u00fcnchen, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748 Garching, Germany.", + "name": "Dantong Wang", + "orcid": "0000-0002-3277-5033" + }, + { + "institution": "Institute of Physics, University of Freiburg, 79104 Freiburg, Germany.FDM-Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany.Signalling Research Centres BIOSS and CIBSS, University of Freiburg, 79104 Freiburg, Germany.", + "name": "Jens Timmer", + "orcid": "0000-0003-4517-1383" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Translational Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), 69120 Heidelberg, Germany. Electronic address: u.klingmueller@dkfz.de.", + "name": "Ursula Klingm\u00fcller" + }, + { + "name": "J Hasenauer", + "orcid": "0000-0002-4935-3312" + }, + { + "institution": "Division Systems Biology of Signal Transduction, German Cancer Research Center (DKFZ), Heidelberg, Germany.", + "name": "Marcel Schilling", + "orcid": "0000-0002-9517-5166" + } + ], + "issue": "6", + "journal": "Cell reports", + "link": "http://identifiers.org/pubmed/34380040", + "month": "8", + "pages": "109507", + "synopsis": "Survival or apoptosis is a binary decision in individual cells. However, at the cell-population level, a graded increase in survival of colony-forming unit-erythroid (CFU-E) cells is observed upon stimulation with erythropoietin (Epo). To identify components of Janus kinase 2/signal transducer and activator of transcription 5 (JAK2/STAT5) signal transduction that contribute to the graded population response, we extended a cell-population-level model calibrated with experimental data to study the behavior in single cells. The single-cell model shows that the high cell-to-cell variability in nuclear phosphorylated STAT5 is caused by variability in the amount of Epo receptor (EpoR):JAK2 complexes and of SHP1, as well as the extent of nuclear import because of the large variance in the cytoplasmic volume of CFU-E cells. 24-118 pSTAT5 molecules in the nucleus for 120\u00a0min are sufficient to ensure cell survival. 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As this might complicate the interpretation and generalization of the obtained results, it is often desirable to reduce the complexity of the model. One way to accomplish this is by replacing the detailed reaction mechanisms of certain modules in the model by a mathematical expression that qualitatively describes the dynamical behavior of these modules. Such an approach has been widely adopted for ultrasensitive responses, for which underlying reaction mechanisms are often replaced by a single Hill function. Also time delays are usually accounted for by using an explicit delay in delay differential equations. In contrast, however, S-shaped response curves, which by definition have multiple output values for certain input values and are often encountered in bistable systems, are not easily modeled in such an explicit way. Here, we extend the classical Hill function into a mathematical expression that can be used to describe both ultrasensitive and S-shaped responses. We show how three ubiquitous modules (ultrasensitive responses, S-shaped responses and time delays) can be combined in different configurations and explore the dynamics of these systems. As an example, we apply our strategy to set up a model of the cell cycle consisting of multiple bistable switches, which can incorporate events such as DNA damage and coupling to the circadian clock in a phenomenological way.", + "title": "A modular approach for modeling the cell cycle based on functional response curves.", + "type": "DOI", + "volume": "17", + "year": 2021 + }, + "publicationId": "BIOMD0000001080", + "submissionId": "MODEL2212060002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001081": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Northwestern University", + "email": "aswathy.raghu@gmail.com", + "external": true, + "name": "Aswathy K. raghu" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "FROG and miniFROG reports compiled for the metabolic model, iMsOB3b. The model can be found in the Supplementary Section of the Naizabekov et al, 2020 paper cited here.", + "files": { + "additional": [ + { + "description": "miniFROG Report", + "fileSize": "5882", + "md5sum": "a2e8a7bba07f450e88903953ff317138", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "miniFROG_iMsOB3b.xlsx", + "sha1sum": "de648fb6388f33ae869fabe5518e110bc6caf358", + "sha256sum": "53099cae33ec2502b0d1b0a8434a06abeae9751aae5665ba5f6e4ee80ee13f24" + } + ], + "main": [ + { + "description": "FROG Report", + "fileSize": "95260", + "md5sum": "248cf68a3fd2701f810ff618238d52d1", + "mimeType": "application/zip", + "name": "iMsOB3b.omex", + "sha1sum": "3ff15c815e8e4edbbd7a58a1e88273c2b3f7b9c9", + "sha256sum": "688c2db3863a048d8c68adb442680d622f683ea2ffaf9d59a59e8bd39fee07b4" + } + ] + }, + "firstPublished": 1729014395, + "format": { + "identifier": "OMEX", + "name": "COMBINE archive", + "version": "0.1" + }, + "history": { + "revisions": [ + { + "comment": "Import of Naizabekov2020 - Methylosinus trichosporium OB3b - iMsOB3b", + "submitted": 1724233028, + "submitter": "Tanisha Malpani", + "version": 1 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.3390/microorganisms8030437", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi:10.3390/microorganisms8030437" + }, + { + "accession": "MODEL2408210001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2408210001" + }, + { + "accession": "BIOMD0000001081", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001081" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "Naizabekov2020 - Methylosinus trichosporium OB3b - iMsOB3b", + "publication": { + "accession": "10.3390/microorganisms8030437", + "affiliation": "Department of Chemical Engineering, Kyung Hee University, Gyeonggi-do 17104, Korea.", + "authors": [ + { + "institution": "Department of Chemical Engineering, Kyung Hee University, Gyeonggi-do 17104, Korea.", + "name": "Sanzhar Naizabekov", + "orcid": "0000-0001-5833-6883" + }, + { + "institution": "Department of Chemical Engineering, Kyung Hee University, Gyeonggi-do 17104, Korea.", + "name": "Eun Yeol Lee", + "orcid": "0000-0002-6974-3262" + } + ], + "issue": "3", + "journal": "Microorganisms", + "link": "http://identifiers.org/doi/10.3390/microorganisms8030437", + "month": "3", + "pages": "E437", + "synopsis": "Methylosinus trichosporium OB3b is an obligate aerobic methane-utilizing alpha-proteobacterium. Since its isolation, M. trichosporium OB3b has been established as a model organism to study methane metabolism in type II methanotrophs. M. trichosporium OB3b utilizes soluble and particulate methane monooxygenase (sMMO and pMMO respectively) for methane oxidation. While the source of electrons is known for sMMO, there is less consensus regarding electron donor to pMMO. To investigate this and other questions regarding methane metabolism, the genome-scale metabolic model for M. trichosporium OB3b (model ID: iMsOB3b) was reconstructed. The model accurately predicted oxygen: methane molar uptake ratios and specific growth rates on nitrate-supplemented medium with methane as carbon and energy source. The redox-arm mechanism which links methane oxidation with complex I of electron transport chain has been found to be the most optimal mode of electron transfer. The model was also qualitatively validated on ammonium-supplemented medium indicating its potential to accurately predict methane metabolism in different environmental conditions. Finally, in silico investigations regarding flux distribution in central carbon metabolism of M. trichosporium OB3b were performed. Overall, iMsOB3b can be used as an organism-specific knowledgebase and a platform for hypothesis-driven theoretical investigations of methane metabolism.", + "title": "Genome-Scale Metabolic Model Reconstruction and in Silico Investigations of Methane Metabolism in Methylosinus trichosporium OB3b.", + "type": "DOI", + "volume": "8", + "year": 2020 + }, + "publicationId": "BIOMD0000001081", + "submissionId": "MODEL2408210001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001082": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + }, + { + "affiliation": "Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India", + "email": "bt19m012@alumni.iitm.ac.in", + "external": true, + "name": "Sanjaay Balakrishnan" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "

FROG and miniFROG Reports for the organism Yersinia pestis.
These models originate from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

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Background

Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas.

Results

Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain.

Conclusions

Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.", + "title": "An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92.", + "type": "DOI", + "volume": "5", + "year": 2011 + }, + "publicationId": "BIOMD0000001082", + "submissionId": "MODEL2408040007", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001083": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology Madras", + "email": "pratyay.iitm@gmail.com", + "external": false, + "name": "Pratyay Sengupta", + "orcid": "0000-0002-0184-9335" + }, + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "

FROG and miniFROG Reports for the organism Synechococcus elongatus PCC 7942.
These models originate from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

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Craig Venter Institute, La Jolla, CA 92037.", + "name": "Niu Du" + }, + { + "institution": "Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093.", + "name": "Nathan Mih", + "orcid": "0000-0002-4302-9052" + }, + { + "institution": "Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093.", + "name": "Spencer Diamond", + "orcid": "0000-0003-4131-341X" + }, + { + "institution": "Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093.", + "name": "Jenny J Lee" + }, + { + "institution": "Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093; sgolden@ucsd.edu.Center for Circadian Biology, University of California, San Diego, La Jolla, CA 92093.", + "name": "Susan S Golden" + }, + { + "institution": "Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093.Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093.", + "name": "Bernhard O Palsson" + } + ], + "issue": "51", + "journal": "Proceedings of the National Academy of Sciences of the United States of America", + "link": "http://identifiers.org/doi/10.1073/pnas.1613446113", + "month": "12", + "pages": "E8344-E8353", + "synopsis": "The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.", + "title": "Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis.", + "type": "DOI", + "volume": "113", + "year": 2016 + }, + "publicationId": "BIOMD0000001083", + "submissionId": "MODEL2408040006", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001084": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Northwestern University", + "email": "aswathy.raghu@gmail.com", + "external": true, + "name": "Aswathy K. Raghu" + }, + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "FROG and miniFROG reports are provided for p_thermo genome-scale metabolic model. p_thermo is the model for Parageobacillus thermoglucosidasius NCIMB 11955. An analysis of the metabolism and internal fluxes of P. thermoglucosidasius is done in this study and the model can be found in the Supplementary data of Mol et al, 2021.", + "files": { + "additional": [ + { + "description": "miniFROG Report", + "fileSize": "6141", + "md5sum": "6c36a79e9705e68f88696ba3efb821db", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "miniFROG_p-thermo.xlsx", + "sha1sum": "5f18fb174418e7eb392d10693d072683edf3a7a7", + "sha256sum": "111d2e1e3d8dc548f3369d340e99a48620d137de50a9c43a97cb737c6aa2ba9e" + } + ], + "main": [ + { + "description": "FROG Report", + "fileSize": "292158", + "md5sum": "3d5ea1925e0b7155e562dad469f145b5", + "mimeType": "application/zip", + "name": "p_thermo.omex", + "sha1sum": "905d5e779aa3f531ccdb090161cececa700c410c", + "sha256sum": "f1a1c41aeab24ffc358a77d8eb7a9b59c49239e97a6277842c642d5f3e1cf8da" + } + ] + }, + "firstPublished": 1729015719, + "format": { + "identifier": "OMEX", + "name": "COMBINE archive", + "version": "0.1" + }, + "history": { + "revisions": [ + { + "comment": "Import of Mol2021 - P. thermoglucosidasius NCIMB 11955 (p_thermo)", + "submitted": 1724149291, + "submitter": "Tanisha Malpani", + "version": 5 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.1016/j.ymben.2021.03.002", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi:10.1016/j.ymben.2021.03.002" + }, + { + "accession": "MODEL2408030003", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2408030003" + }, + { + "accession": "BIOMD0000001084", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001084" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "Mol2021 - P. thermoglucosidasius NCIMB 11955 (p_thermo)", + "publication": { + "accession": "10.1016/j.ymben.2021.03.002", + "affiliation": "The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.", + "authors": [ + { + "institution": "The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.", + "name": "Vivi\u00ebnne Mol" + }, + { + "institution": "The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom.", + "name": "Martyn Bennett" + }, + { + "institution": "The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.", + "name": "Benjam\u00edn J S\u00e1nchez" + }, + { + "institution": "The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom.", + "name": "Beata K Lisowska" + }, + { + "institution": "The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark; BioInnovation Institute, Copenhagen N, Denmark.", + "name": "Markus J Herrg\u00e5rd" + }, + { + "institution": "The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark. Electronic address: atn@biosustain.dtu.dk.", + "name": "Alex Toftgaard Nielsen", + "orcid": "0000-0001-6616-0187" + }, + { + "institution": "The Department of Biology & Biochemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; The Centre for Sustainable Chemical Technologies (CSCT), University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom. Electronic address: djl36@bath.ac.uk.", + "name": "David J Leak" + }, + { + "institution": "The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs, Lyngby, Denmark.", + "name": "Nikolaus Sonnenschein", + "orcid": "0000-0002-7581-4936" + } + ], + "journal": "Metabolic engineering", + "link": "http://identifiers.org/doi/10.1016/j.ymben.2021.03.002", + "month": "5", + "pages": "123-134", + "synopsis": "Parageobacillus thermoglucosidasius represents a thermophilic, facultative anaerobic bacterial chassis, with several desirable traits for metabolic engineering and industrial production. To further optimize strain productivity, a systems level understanding of its metabolism is needed, which can be facilitated by a genome-scale metabolic model. Here, we present p-thermo, the most complete, curated and validated genome-scale model (to date) of Parageobacillus thermoglucosidasius NCIMB 11955. It spans a total of 890 metabolites, 1175 reactions and 917 metabolic genes, forming an extensive knowledge base for P. thermoglucosidasius NCIMB 11955 metabolism. The model accurately predicts aerobic utilization of 22 carbon sources, and the predictive quality of internal fluxes was validated with previously published 13C-fluxomics data. In an application case, p-thermo was used to facilitate more in-depth analysis of reported metabolic engineering efforts, giving additional insight into fermentative metabolism. Finally, p-thermo was used to resolve a previously uncharacterised bottleneck in anaerobic metabolism, by identifying the minimal required supplemented nutrients (thiamin, biotin and iron(III)) needed to sustain anaerobic growth. This highlights the usefulness of p-thermo for guiding the generation of experimental hypotheses and for facilitating data-driven metabolic engineering, expanding the use of P. thermoglucosidasius as a high yield production platform.", + "title": "Genome-scale metabolic modeling of P. thermoglucosidasius NCIMB 11955 reveals metabolic bottlenecks in anaerobic metabolism.", + "type": "DOI", + "volume": "65", + "year": 2021 + }, + "publicationId": "BIOMD0000001084", + "submissionId": "MODEL2408030003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001085": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + }, + { + "affiliation": "Indian Institute of Technology Madras", + "email": "indumathi2223@gmail.com", + "external": false, + "name": "Indumathi", + "orcid": "0009-0009-9237-5996" + } + ], + "Modeller": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "

FROG and miniFROG Reports for the organism A. baumannii.
The model (iCN718) originates from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

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There is thus a significant need to discover new therapeutic targets in this organism. One means for doing so is through the use of high-quality genome-scale reconstructions. Well-curated and accurate genome-scale models (GEMs) of A. baumannii would be useful for improving treatment options. We present an updated and improved genome-scale reconstruction of A. baumannii AYE, named iCN718, that improves and standardizes previous A. baumannii AYE reconstructions. iCN718 has 80% accuracy for predicting gene essentiality data and additionally can predict large-scale phenotypic data with as much as 89% accuracy, a new capability for an A. baumannii reconstruction. We further demonstrate that iCN718 can be used to analyze conserved metabolic functions in the A. baumannii core genome and to build strain-specific GEMs of 74 other A. baumannii strains from genome sequence alone. iCN718 will serve as a resource to integrate and synthesize new experimental data being generated for this urgent threat pathogen.", + "title": "iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE.", + "type": "DOI", + "volume": "9", + "year": 2018 + }, + "publicationId": "BIOMD0000001085", + "submissionId": "MODEL2406270001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001086": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + }, + { + "affiliation": "Indian Institute of Technology Madras", + "email": "indumathi2223@gmail.com", + "external": false, + "name": "Indumathi", + "orcid": "0009-0009-9237-5996" + } + ], + "Curator": [ + { + "affiliation": "Indian Institute of Technology Madras", + "email": "indumathi2223@gmail.com", + "external": false, + "name": "Indumathi", + "orcid": "0009-0009-9237-5996" + } + ], + "Modeller": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "We have presented FROG and miniFROG reports for the first genome-scale model, iCJ415, for Streptococcus oralis SK141. The model can be found in the Supplementary Material of the publication by Jensen et al, 2020 cited here.", + "files": { + "additional": [ + { + "description": "miniFROG Report", + "fileSize": "7711", + "md5sum": "c1a3c4e050f5c07b8d2b349692e8ec7f", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "iCJ415_miniFROG.xlsx", + "sha1sum": "345a1fa618c1bc3889348178fb247f55330e4d1b", + "sha256sum": "d939e12f732a03809a448d6edc9ca01b791ba25048d808aeeb82644f4bfafa21" + } + ], + "main": [ + { + "description": "FROG Report", + "fileSize": "57071", + "md5sum": "3e693b1fa3b73e5d0039cd9fdf0a7aff", + "mimeType": "application/zip", + "name": "suppl_datasheet_s2_iCJ415.omex", + "sha1sum": "014490fb286595ee48df44e6cac576a5e8f469d2", + "sha256sum": "9ca782d10ee23f8b3b6c1904630a3f6ecfe28d2349f15289ef4c92fe63dc4664" + } + ] + }, + "firstPublished": 1729015985, + "format": { + "identifier": "OMEX", + "name": "COMBINE archive", + "version": "0.1" + }, + "history": { + "revisions": [ + { + "comment": "Import of Jensen2020- Streptococcus oralis (iCJ415)", + "submitted": 1723554866, + "submitter": "Tanisha Malpani", + "version": 4 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.3389/fgene.2020.00116", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi:10.3389/fgene.2020.00116" + }, + { + "accession": "MODEL2406260001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2406260001" + }, + { + "accession": "BIOMD0000001086", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001086" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "Jensen2020 - Streptococcus oralis (iCJ415)", + "publication": { + "accession": "10.3389/fgene.2020.00116", + "affiliation": "The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark.", + "authors": [ + { + "institution": "The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark.", + "name": "Christian S Jensen", + "orcid": "0000-0002-9530-1919" + }, + { + "institution": "Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.", + "name": "Charles J Norsigian" + }, + { + "institution": "Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.", + "name": "Xin Fang" + }, + { + "institution": "The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark.", + "name": "Xiaohui C Nielsen" + }, + { + "institution": "The Regional Department of Clinical Microbiology, Region Zealand, Slagelse, Denmark.Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.", + "name": "Jens J\u00f8rgen Christensen", + "orcid": "0000-0001-6721-4917" + }, + { + "institution": "Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.", + "name": "Bernhard O Palsson" + }, + { + "institution": "Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.", + "name": "Jonathan M Monk" + } + ], + "journal": "Frontiers in genetics", + "link": "http://identifiers.org/doi/10.3389/fgene.2020.00116", + "month": "6", + "pages": "116", + "synopsis": "The mitis group of streptococci (MGS) is a member of the healthy human microbiome in the oral cavity and upper respiratory tract. Troublingly, some MGS are able to escape this niche and cause infective endocarditis, a severe and devastating disease. Genome-scale models have been shown to be valuable in investigating metabolism of bacteria. Here we present the first genome-scale model, iCJ415, for Streptococcus oralis SK141. We validated the model using gene essentiality and amino acid auxotrophy data from closely related species. iCJ415 has 71-76% accuracy in predicting gene essentiality and 85% accuracy in predicting amino acid auxotrophy. Further, the phenotype of S. oralis was tested using the Biolog Phenotype microarrays, giving iCJ415 a 82% accuracy in predicting carbon sources. iCJ415 can be used to explore the metabolic differences within the MGS, and to explore the complicated metabolic interactions between different species in the human oral cavity.", + "title": "Reconstruction and Validation of a Genome-Scale Metabolic Model of Streptococcus oralis (iCJ415), a Human Commensal and Opportunistic Pathogen.", + "type": "DOI", + "volume": "11", + "year": 2020 + }, + "publicationId": "BIOMD0000001086", + "submissionId": "MODEL2406260001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001087": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + }, + { + "affiliation": "Indian Institute of Technology Madras", + "email": "maziya.ibrahim@gmail.com", + "external": false, + "name": "Maziya Ibrahim" + } + ], + "Curator": [ + { + "affiliation": "Indian Institute of Technology Madras", + "email": "maziya.ibrahim@gmail.com", + "external": false, + "name": "Maziya Ibrahim" + } + ], + "Modeller": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "FROG and miniFROG reports compiled for iKS1317, a reconstructed genome-scale metabolic model (GEM) for Streptomyces coelicolor. The model reconstruction is available in the Supporting Information of Kumelj et al, 2019 cited here.", + "files": { + "additional": [ + { + "description": "miniFROG Report", + "fileSize": "11496", + "md5sum": "73c936686945aea49cc1621c1ec4756d", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "iKS1317_miniFROG.xlsx", + "sha1sum": "d7a7c3a4d562f2b9891728546bf0eb6911fbb4aa", + "sha256sum": "dc1b6f09f52b82ccfeb193099951c1fb37a52682400eaae6d0644a3837cb922d" + } + ], + "main": [ + { + "description": "FROG Report", + "fileSize": "305903", + "md5sum": "11037c4ba21a14c6676f0c041b004bcd", + "mimeType": "application/zip", + "name": "iKS1317.omex", + "sha1sum": "a48317efd6733a3acce9242633b6bd343f057a39", + "sha256sum": "9c38749e287b3a7909f4c00946a8a9c1aae5028b4f023e8d31a01c93dd6a59ef" + } + ] + }, + "firstPublished": 1729016018, + "format": { + "identifier": "OMEX", + "name": "COMBINE archive", + "version": "0.1" + }, + "history": { + "revisions": [ + { + "comment": "Import of Kumelj2019- Streptomyces coelicolor (iKS1317)", + "submitted": 1722804788, + "submitter": "Tanisha Malpani", + "version": 6 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.1002/biot.201800180", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi:10.1002/biot.201800180" + }, + { + "accession": "MODEL2406250004", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2406250004" + }, + { + "accession": "BIOMD0000001087", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001087" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "Kumelj2019 - Streptomyces coelicolor (iKS1317)", + "publication": { + "accession": "10.1002/biot.201800180", + "affiliation": "Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.", + "authors": [ + { + "institution": "Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.", + "name": "Tja\u0161a Kumelj" + }, + { + "institution": "Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.SINTEF Industry, Department of Biotechnology and Nanomedicine, Trondheim, Norway.", + "name": "Snorre Sulheim", + "orcid": "0000-0002-3967-1683" + }, + { + "institution": "SINTEF Industry, Department of Biotechnology and Nanomedicine, Trondheim, Norway.", + "name": "Alexander Wentzel", + "orcid": "0000-0002-9420-1959" + }, + { + "institution": "Department of Biotechnology and Food Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.K.G. Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.", + "name": "Eivind Almaas", + "orcid": "0000-0002-9125-326X" + } + ], + "issue": "4", + "journal": "Biotechnology journal", + "link": "http://identifiers.org/doi/10.1002/biot.201800180", + "month": "4", + "pages": "e1800180", + "synopsis": "Streptomyces coelicolor is a model organism for the Actinobacteria, a phylum known to produce an extensive range of different bioactive compounds that include antibiotics currently used in the clinic. Biosynthetic gene clusters discovered in genomes of other Actinobacteria can be transferred to and expressed in S. coelicolor, making it a factory for heterologous production of secondary metabolites. Genome-scale metabolic reconstructions have successfully been used in several biotechnology applications to facilitate the over-production of target metabolites. Here, the authors present iKS1317, the most comprehensive and accurate reconstructed genome-scale metabolic model (GEM) for S. coelicolor. The model reconstruction is based on previous models, publicly available databases, and published literature and includes 1317 genes, 2119 reactions, and 1581 metabolites. It correctly predicts wild-type growth in 96.5% of the evaluated growth environments and gene knockout predictions in 78.4% when comparing with observed mutant growth phenotypes, with a total accuracy of 83.3%. However, using a minimal nutrient environment for the gene knockout predictions, iKS1317 has an accuracy of 87.1% in predicting mutant growth phenotypes. Furthermore, we used iKS1317 and existing strain design algorithms to suggest robust gene-knockout strategies to increase the production of acetyl-CoA. Since acetyl-CoA is the most important precursor for polyketide antibiotics, the suggested strategies may be implemented in vivo to improve the function of S. coelicolor as a heterologous expression host.", + "title": "Predicting Strain Engineering Strategies Using iKS1317: A Genome-Scale Metabolic Model of Streptomyces coelicolor.", + "type": "DOI", + "volume": "14", + "year": 2019 + }, + "publicationId": "BIOMD0000001087", + "submissionId": "MODEL2406250004", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001088": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India", + "email": "lavanyaraajaraam@gmail.com", + "external": true, + "name": "Lavanya Raajaraam" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "FROG and miniFROG reports are compiled for the reconstructed genome-scale metabolic model (iHN446) for Z. mobilis. The model can be found in the article Nouri et al, 2020 cited here.", + "files": { + "additional": [ + { + "description": "miniFROG Report", + "fileSize": "11751", + "md5sum": "e92d949fb925455b52889bc09b7ce87b", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "iHN446_miniFROG.xlsx", + "sha1sum": "2fc5f787690a7530fa2cbf9001256d1180366b81", + "sha256sum": "35592e1f8a9f6efc214ca008ce8a9ec25ef9964eb62d0f9a0a396675437286ad" + } + ], + "main": [ + { + "description": "FROG Report", + "fileSize": "171070", + "md5sum": "b155a3ca74157599c02f4e2a5a0d4e2f", + "mimeType": "application/zip", + "name": "iHN446.omex", + "sha1sum": "f964d0bdf71548218a2c9f480cee77ef64d9d7aa", + "sha256sum": "7d4ac800d6f2cd8592ccc0edc9bf8216df72908f400e865adbcd68b9864006a1" + } + ] + }, + "firstPublished": 1729016047, + "format": { + "identifier": "OMEX", + "name": "COMBINE archive", + "version": "0.1" + }, + "history": { + "revisions": [ + { + "comment": "Import of Nouri2020- Zymomonas mobilis (iHN446)", + "submitted": 1722805028, + "submitter": "Tanisha Malpani", + "version": 2 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.1038/s41598-020-64721-x", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi:10.1038/s41598-020-64721-x" + }, + { + "accession": "MODEL2406250003", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2406250003" + }, + { + "accession": "BIOMD0000001088", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001088" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "Nouri2020 - Zymomonas mobilis (iHN446)", + "publication": { + "accession": "10.1038/s41598-020-64721-x", + "affiliation": "Department of Microbial Biotechnology, School of Biology, College of Science, University of Tehran, Tehran, Iran.", + "authors": [ + { + "institution": "Department of Microbial Biotechnology, School of Biology, College of Science, University of Tehran, Tehran, Iran.", + "name": "Hoda Nouri" + }, + { + "institution": "Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.", + "name": "Hamideh Fouladiha" + }, + { + "institution": "Department of Microbial Biotechnology, School of Biology, College of Science, University of Tehran, Tehran, Iran. hmoghimi@ut.ac.ir.", + "name": "Hamid Moghimi", + "orcid": "0000-0002-9454-7474" + }, + { + "institution": "Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran. marashi@ut.ac.ir.", + "name": "Sayed-Amir Marashi", + "orcid": "0000-0001-9801-7449" + } + ], + "issue": "1", + "journal": "Scientific reports", + "link": "http://identifiers.org/doi/10.1038/s41598-020-64721-x", + "month": "5", + "pages": "7782", + "synopsis": "Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z. mobilis, which involves 446 genes, 859 reactions, and 894 metabolites. We started by first reconciling the existing GEMs previously constructed for Z. mobilis to obtain a draft network. Next, recent gene annotations, up-to-date literature, physiological data and biochemical databases were used to upgrade the network. Afterward, the draft network went through a curative and iterative process of gap-filling by computational tools and manual refinement. The final model was evaluated using experimental data and literature information. We next applied this model as a platform for analyzing the links between transcriptome-flux and transcriptome-metabolome. We found that experimental observations were in agreement with the predicted results from our final GEM. Taken together, this comprehensive model (iHN446) can be utilized for studying metabolism in Z. mobilis and finding rational targets for metabolic engineering applications.", + "title": "A reconciliation of genome-scale metabolic network model of Zymomonas mobilis ZM4.", + "type": "DOI", + "volume": "10", + "year": 2020 + }, + "publicationId": "BIOMD0000001088", + "submissionId": "MODEL2406250003", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001089": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology Madras", + "email": "maziya.ibrahim@gmail.com", + "external": false, + "name": "Maziya Ibrahim" + } + ], + "Curator": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + }, + { + "affiliation": "Indian Institute of Technology Madras", + "email": "maziya.ibrahim@gmail.com", + "external": false, + "name": "Maziya Ibrahim" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "FROG and miniFROG reports have been compiled for iLM.c559, which is a GSM of the bacterium Leuconostoc mesenteroides subsp. cremoris. The model can be found in the \u00d6zcan et al, 2019 paper cited here.", + "files": { + "additional": [ + { + "description": "miniFROG Report", + "fileSize": "7057", + "md5sum": "78372035590bd1f5e1dab88d563c94b6", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "253_2019_9630_MOESM3_ESM_miniFROG.xlsx", + "sha1sum": "2429f91c83cba40331afd47a5df6789d3a354372", + "sha256sum": "e026e96a3cbad8cf209062e8d0aeea2572ca7586298286cd09b8ec425a3afb8d" + } + ], + "main": [ + { + "description": "FROG Report", + "fileSize": "106359", + "md5sum": "6ffb38e997bc1987cb86a9a368a46460", + "mimeType": "application/zip", + "name": "253_2019_9630_MOESM3_ESM.omex", + "sha1sum": "ef5da03f8a82f888f3f521332c9131935547e850", + "sha256sum": "2cceb7c18bbb756fa0b233cb77f6f6a9468142e21b18cb1bfe3ef28f2e890ad9" + } + ] + }, + "firstPublished": 1729016078, + "format": { + "identifier": "OMEX", + "name": "COMBINE archive", + "version": "0.1" + }, + "history": { + "revisions": [ + { + "comment": "Import of \u00d6zcan2019 - GSM of Leuconostoc mesenteroides subsp. cremoris", + "submitted": 1722806725, + "submitter": "Tanisha Malpani", + "version": 3 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "30712128", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "https://identifiers.org/pubmed:30712128" + }, + { + "accession": "MODEL2405210001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2405210001" + }, + { + "accession": "BIOMD0000001089", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001089" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "\u00d6zcan2019 - Leuconostoc mesenteroides subsp. cremoris (iLM.c559)", + "publication": { + "accession": "30712128", + "affiliation": "Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.", + "authors": [ + { + "institution": "Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.IBSB, Department of Bioengineering, Marmara University, Istanbul, Turkey.Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU Amsterdam, Amsterdam, The Netherlands.", + "name": "Emrah \u00d6zcan", + "orcid": "0000-0001-9036-9909" + }, + { + "institution": "IBSB, Department of Bioengineering, Marmara University, Istanbul, Turkey.", + "name": "S Selvin Selvi" + }, + { + "institution": "Genetics and Bioengineering Department, Yeditepe University, Istanbul, Turkey.", + "name": "Emrah Nikerel", + "orcid": "0000-0002-9157-8662" + }, + { + "institution": "Systems Bioinformatics, Amsterdam Institute for Molecules, Medicines and Systems, VU Amsterdam, Amsterdam, The Netherlands.", + "name": "Bas Teusink", + "orcid": "0000-0003-3929-0423" + }, + { + "institution": "IBSB, Department of Bioengineering, Marmara University, Istanbul, Turkey.", + "name": "Ebru Toksoy \u00d6ner", + "orcid": "0000-0001-6054-8842" + }, + { + "institution": "Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey. tcakir@gtu.edu.tr.", + "name": "Tunahan \u00c7ak\u0131r", + "orcid": "0000-0001-8262-4420" + } + ], + "issue": "7", + "journal": "Applied microbiology and biotechnology", + "link": "http://identifiers.org/pubmed/30712128", + "month": "4", + "pages": "3153-3165", + "synopsis": "Leuconostoc mesenteroides subsp. cremoris is an obligate heterolactic fermentative lactic acid bacterium that is mostly used in industrial dairy fermentations. The phosphoketolase pathway (PKP) is a unique feature of the obligate heterolactic fermentation, which leads to the production of lactate, ethanol, and/or acetate, and the final product profile of PKP highly depends on the energetics and redox state of the organism. Another characteristic of the L. mesenteroides subsp. cremoris is the production of aroma compounds in dairy fermentation, such as in cheese production, through the utilization of citrate. Considering its importance in dairy fermentation, a detailed metabolic characterization of the organism is necessary for its more efficient use in the industry. To this aim, a genome-scale metabolic model of dairy-origin L. mesenteroides subsp. cremoris ATCC 19254 (iLM.c559) was reconstructed to explain the energetics and redox state mechanisms of the organism in full detail. The model includes 559 genes governing 1088 reactions between 1129 metabolites, and the reactions cover citrate utilization and citrate-related flavor metabolism. The model was validated by simulating co-metabolism of glucose and citrate and comparing the in silico results to our experimental results. Model simulations further showed that, in co-metabolism of citrate and glucose, no flavor compounds were produced when citrate could stimulate the formation of biomass. Significant amounts of flavor metabolites (e.g., diacetyl and acetoin) were only produced when citrate could not enhance growth, which suggests that flavor formation only occurs under carbon and ATP excess. The effects of aerobic conditions and different carbon sources on product profiles and growth were also investigated using the reconstructed model. The analyses provided further insights for the growth stimulation and flavor formation mechanisms of the organism.", + "title": "A genome-scale metabolic network of the aroma bacterium Leuconostoc mesenteroides subsp. cremoris.", + "type": "PubMed ID", + "volume": "103", + "year": 2019 + }, + "publicationId": "BIOMD0000001089", + "submissionId": "MODEL2405210001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001090": { + "contributors": { + "Curator": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "Indian Institute of Technology Madras", + "email": "indumathi2223@gmail.com", + "external": false, + "name": "Indumathi", + "orcid": "0009-0009-9237-5996" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ], + "Modeller": [ + { + "affiliation": "Centre of Biological Engineering, University of Minho, Braga, Portugal.", + "email": "odias@deb.uminho.pt", + "external": false, + "name": "Oscar Dias", + "orcid": "0000-0002-1765-7178" + } + ], + "Other": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "EMBL-EBI", + "email": "nvntung@gmail.com", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "Phenotypically Reconciled Genome-scale Metabolic Model for the Metabolism of Streptococcus pneumoniae Strain R6.", + "files": { + "additional": [ + { + "description": "FROG Report", + "fileSize": "95598", + "md5sum": "c404730e4ab34597eb59b52fd3e8166f", + "mimeType": "application/zip", + "name": "FROG.omex", + "sha1sum": "748c043bfe82cd4cb79f6a642cf19f7d974a0b96", + "sha256sum": "f309091f7db24d39530c8f3a295b29a1039b96f4e36fbaa530fa7899d3e8444d" + }, + { + "description": "MEMOTE Report", + "fileSize": "3885918", + "md5sum": "399b75261ebcd18dacea179236fb9f30", + "mimeType": "text/html", + "name": "MemoteReport.html", + "sha1sum": "c8a1ef16a91e04919821e8d1424d884ce101f11d", + "sha256sum": "3c9ba9a27f133f077c0addb20f9eed1f563294bebfc851841ac60413c03e0077" + }, + { + "description": "MiniFROG report", + "fileSize": "58409", + "md5sum": "f8f92b59ed3e18b6cc0d672b218e3906", + "mimeType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", + "name": "miniFROG Report_Dias2019.xlsx", + "sha1sum": "a034adeb79a3e6f2880e9460f7ca5034213f6954", + "sha256sum": "ba5f1e62e2b832f2818c8c1601a60c39f63cb2724886e43c6e860857b38959d4" + } + ], + "main": [ + { + "description": "SBML L3V1 Genome-scale Metabolic model of Streptococcus pneumoniae R6", + "fileSize": "1545277", + "md5sum": "6c527b6703cdbc4c49da14e6efe1814a", + "mimeType": "application/xml", + "name": "Dias2019_iDS372.xml", + "sha1sum": "ed01bae67c44ff2c88ca38f15a2d39f2d5cb7f15", + "sha256sum": "f8bc8a9c718af7bb595a632e87f53b562a16c6088685ff79333a37bc811e0e8a" + } + ] + }, + "firstPublished": 1729016255, + "format": { + "identifier": "SBML", + "name": "SBML", + "version": "L3V1" + }, + "history": { + "revisions": [ + { + "comment": "Import of Dias2019 - GSM model of S. pneumoniae R6", + "submitted": 1651488223, + "submitter": "Oscar Dias", + "version": 1 + }, + { + "comment": "Updated the model with the curated FROG and MEMOTE report.", + "submitted": 1701092490, + "submitter": "Tung Nguyen", + "version": 2 + }, + { + "comment": "Updated the model with the curated FROG and MEMOTE report.", + "submitted": 1723456830, + "submitter": "Tanisha Malpani", + "version": 3 + }, + { + "comment": "Automatically added model identifier BIOMD0000001090", + "submitted": 1739361728, + "submitter": "Tanisha Malpani", + "version": 4 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "MODEL2205020001", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "http://identifiers.org/biomodels.db/MODEL2205020001" + }, + { + "accession": "31293525", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed:31293525" + }, + { + "accession": "31293525", + "qualifier": "bqmodel:isDescribedBy", + "resource": "PubMed", + "uri": "http://identifiers.org/pubmed/31293525" + }, + { + "accession": "171101", + "name": "Streptococcus pneumoniae (strain ATCC BAA-255 / R6)", + "qualifier": "bqbiol:hasTaxon", + "resource": "Taxonomy", + "uri": "http://identifiers.org/taxonomy/171101" + }, + { + "accession": "SBO_0000629", + "name": "biomass production\n", + "qualifier": "bqbiol:hasProperty", + "resource": "Systems Biology Ontology", + "uri": "http://biomodels.net/SBO/SBO_0000629" + }, + { + "accession": "MAMO_0000009", + "name": "Constraint-based model", + "qualifier": "bqbiol:hasProperty", + "resource": "Mathematical Modelling Ontology", + "uri": "http://identifiers.org/mamo/MAMO_0000009" + }, + { + "accession": "BIOMD0000001090", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001090" + } + ], + "modelTags": [ + "FROG" + ], + "modellingApproach": { + "accession": "MAMO_0000009", + "name": "constraint-based model", + "resource": "http://identifiers.org/mamo/MAMO_0000009" + }, + "name": "Dias2019 - GSM model of S. pneumoniae R6", + "publication": { + "accession": "31293525", + "affiliation": "Centre of Biological Engineering, University of Minho, Braga, Portugal.", + "authors": [ + { + "institution": "Centre of Biological Engineering, University of Minho, Braga, Portugal.", + "name": "Oscar Dias", + "orcid": "0000-0002-1765-7178" + }, + { + "institution": "Centre of Biological Engineering, University of Minho, Braga, Portugal.", + "name": "Jo\u00e3o Saraiva", + "orcid": "0000-0001-8251-1960" + }, + { + "institution": "Centre of Biological Engineering, University of Minho, Braga, Portugal.", + "name": "Cristiana Faria" + }, + { + "institution": "Instituto de Microbiologia, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal.", + "name": "Mario Ramirez", + "orcid": "0000-0002-4084-6233" + }, + { + "institution": "BioISI - Biosystems & Integrative Sciences Institute, Faculdade de Ci\u00eancias, Universidade de Lisboa, Lisbon, Portugal.", + "name": "Francisco Pinto", + "orcid": "0000-0002-4217-0054" + }, + { + "institution": "Centre of Biological Engineering, University of Minho, Braga, Portugal.Instituto de Tecnologia Qu\u00edmica e Biol\u00f3gica Ant\u00f3nio Xavier, Universidade Nova de Lisboa (ITQB-NOVA), Oeiras, Portugal.", + "name": "Isabel Rocha", + "orcid": "0000-0001-9494-3410" + } + ], + "journal": "Frontiers in microbiology", + "link": "http://identifiers.org/pubmed/31293525", + "month": "1", + "pages": "1283", + "synopsis": "A high-quality GSM model for Streptococcus pneumoniae R6 model strain (iDS372), comprising 372 genes and 529 reactions, was developed. The construction of this model involved performing a genome-wide reannotation to identify the metabolic capacity of the bacterium. A reaction representing the abstraction of the biomass composition was reconciled from several studies reported in the literature and previous models, and included in the model. The final model comprises two compartments and manifold automatically generated gene rules. The validation was performed with experimental data from recent studies, regarding the usability of carbon sources, the effect of the presence of oxygen, and the requirement of amino acids for growth. This model can be used to better understand the metabolism of this major pathogen, provide clues regarding new drug targets, and eventually design strategies for fighting infections by these bacteria.", + "title": "iDS372, a Phenotypically Reconciled Model for the Metabolism of Streptococcus pneumoniae Strain R6.", + "type": "PubMed ID", + "volume": "10", + "year": 2019 + }, + "publicationId": "BIOMD0000001090", + "submissionId": "MODEL2205020001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001091": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology Madras", + "email": "maziya.ibrahim@gmail.com", + "external": false, + "name": "Maziya Ibrahim" + } + ], + "Curator": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ], + "Modeller": [ + { + "affiliation": "Biosoft.ru, 630058 Novosibirsk, Russia.Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia.Department of Molecular Biotechnology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia.", + "email": "akberdinir@gmail.com", + "external": false, + "name": "Ilya R Akberdin", + "orcid": "0000-0003-0010-8620" + } + ], + "Other": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ], + "Submitter": [ + { + "affiliation": "EMBL-EBI", + "email": "nvntung@gmail.com", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ] + }, + "curationStatus": "CURATED", + "description": "Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. 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Liao2011 - Genome-scale metabolicreconstruction of Klebsiella pneumoniae (iYL1228)

This model is described in the article:

Liao YC, Huang TW, Chen FC, Charusanti P, Hong JS, Chang HY, Tsai SF, Palsson BO, Hsiung CA.
J. Bacteriol. 2011 Apr; 193(7): 1710-1717

Abstract:

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.

This model is hosted on BioModels Database and identified by: MODEL1507180054.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228)", + "publication": { + "accession": "21296962", + "affiliation": "Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan.", + "authors": [ + { + "institution": "Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan 350, Taiwan.", + "name": "Yu-Chieh Liao", + "orcid": "0000-0002-4360-7932" + }, + { + "name": "Tzu-Wen Huang" + }, + { + "name": "Feng-Chi Chen" + }, + { + "name": "Pep Charusanti", + "orcid": "0000-0003-0009-6615" + }, + { + "name": "Jay S J Hong" + }, + { + "name": "Hwan-You Chang" + }, + { + "name": "Shih-Feng Tsai" + }, + { + "name": "Bernhard O Palsson" + }, + { + "name": "Chao A Hsiung" + } + ], + "issue": "7", + "journal": "Journal of bacteriology", + "link": "http://identifiers.org/pubmed/21296962", + "month": "4", + "pages": "1710-1717", + "synopsis": "Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.", + "title": "An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228.", + "type": "PubMed ID", + "volume": "193", + "year": 2011 + }, + "publicationId": "BIOMD0000001094", + "submissionId": "MODEL1507180054", + "vcsIdentifier": "aab" + }, + "BIOMD0000001095": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology Madras", + "email": "pratyay.iitm@gmail.com", + "external": false, + "name": "Pratyay Sengupta", + "orcid": "0000-0002-0184-9335" + } + ], + "Modeller": [ + { + "affiliation": "The Babraham Institute", + "email": "n.lenovere@gmail.com", + "external": false, + "name": "Nicolas Le Nov\u00e8re" + } + ], + "Other": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Jamshidi2007 - Genome-scale metabolic networkof Mycobacterium tuberculosis (iNJ661)

This model is described in the article:

Jamshidi N, Palsson B\u00d8.
BMC Syst Biol 2007; 1: 26

Abstract:

BACKGROUND: Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. RESULTS: We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints. CONCLUSION: Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.

This model is hosted on BioModels Database and identified by: MODEL1507180001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Genome-scale metabolic network of Mycobacterium tuberculosis (iNJ661)", + "publication": { + "accession": "17555602", + "affiliation": "Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093-0412, USA. neema@ucsd.edu", + "authors": [ + { + "name": "Neema Jamshidi", + "orcid": "0000-0003-3857-9735" + }, + { + "name": "Bernhard \u00d8 Palsson" + } + ], + "journal": "BMC systems biology", + "link": "http://identifiers.org/pubmed/17555602", + "month": "6", + "pages": "26", + "synopsis": "

Background

Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them.

Results

We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints.

Conclusion

Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.", + "title": "Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets.", + "type": "PubMed ID", + "volume": "1", + "year": 2007 + }, + "publicationId": "BIOMD0000001095", + "submissionId": "MODEL1507180001", + "vcsIdentifier": "aab" + }, + "BIOMD0000001096": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Northwestern University", + "email": "aswathy.raghu@gmail.com", + "external": true, + "name": "Aswathy K. Raghu" + } + ], + "Modeller": [ + { + "affiliation": "Chalmers University of Technology", + "email": "eduardk@chalmers.se", + "external": false, + "name": "Eduard Kerkhoven" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Irani2015 - Genome-scale metabolic model ofP.pastoris N-glycosylation

This model is described in the article:

Irani ZA, Kerkhoven E, Shojaosadati SA, Nielsen J.
Biotechnol. Bioeng. 2015 Oct;

Abstract:

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better predictions of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. This article is protected by copyright. All rights reserved.

This model is hosted on BioModels Database and identified by: MODEL1510220000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Benedict2011 - Genome-scale metoblic networkof Methanosarcina acetivorans (iMB745)

This model is described in the article:

Benedict MN, Gonnerman MC, Metcalf WW, Price ND.
J. Bacteriol. 2012 Feb; 194(4): 855-865

Abstract:

Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis.

This model is hosted on BioModels Database and identified by: MODEL1507180040.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis.", + "title": "Genome-scale metabolic reconstruction and hypothesis testing in the methanogenic archaeon Methanosarcina acetivorans C2A.", + "type": "PubMed ID", + "volume": "194", + "year": 2012 + }, + "publicationId": "BIOMD0000001097", + "submissionId": "MODEL1507180040", + "vcsIdentifier": "aab" + }, + "BIOMD0000001098": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + }, + { + "affiliation": "Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras, Chennai 600 036, India", + "email": "bt19m012@alumni.iitm.ac.in", + "external": true, + "name": "Sanjaay Balakrishnan" + } + ], + "Modeller": [ + { + "affiliation": "Nature Publishing Group", + "email": "msbforum@embo.org", + "external": false, + "name": "Molecular Systems Biology" + } + ], + "Submitter": [ + { + "affiliation": "Indian Institute of Technology, Madras", + "email": "tanishamalpani4@gmail.com", + "external": false, + "name": "Tanisha Malpani" + } + ] + }, + "curationStatus": "CURATED", + "description": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
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In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Richards2016 - Genome-scale metabolicreconstruction of Methanococcus maripaludis (iMR539)

This model is described in the article:

Richards MA, Lie TJ, Zhang J, Ragsdale SW, Leigh JA, Price ND.
J. Bacteriol. 2016 Dec; 198(24): 3379-3390

Abstract:

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

This model is hosted on BioModels Database and identified by: MODEL1607200000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Importance

Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.", + "title": "Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis.", + "type": "PubMed ID", + "volume": "198", + "year": 2016 + }, + "publicationId": "BIOMD0000001099", + "submissionId": "MODEL1607200000", + "vcsIdentifier": "aaa" + }, + "BIOMD0000001100": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ], + "Modeller": [ + { + "affiliation": "Sanofi", + "email": "marcel.mohr@sanofi.com", + "external": false, + "name": "Marcel Mohr", + "orcid": "0000-0001-6553-8651" + } + ] + }, + "curationStatus": "CURATED", + "description": "we performed a comparative benchmark and investigated a variety of mathematical cardiac AP models, including a newly developed minimalistic model specifically tailored to the AP of rabbit Purkinje cells, for their ability to substitute experiments. The simulated changes in AP duration (dAPD90) at increasing drug concentrations were compared to experimental results from 588 internal Purkinje fiber studies covering 555 different drugs with diverse modes of action. 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Traditionally, a cascade of in vitro/in vivo assays has been applied in pharmaceutical research to screen out molecules at risk for cardiac side effects and prevent subsequent risk for patients. Drug cardiosafety assessments typically employ early mechanistic, hazard-oriented in silico/in vitro assays for compound inhibition of cardiac ion channels, followed by induced pluripotent stem cells (iPSCs) or tissue-based models such as the rabbit Purkinje fiber assay, which includes the major mechanisms contributing to action potential (AP) genesis. Additionally, multiscale simulation techniques based on mathematical models have become available, which are performed in silico 'at the heart' of compound triage to substitute Purkinje tests and increase translatability through mechanistic interpretability. To adhere to the 3R principle and reduce animal experiments, we performed a comparative benchmark and investigated a variety of mathematical cardiac AP models, including a newly developed minimalistic model specifically tailored to the AP of rabbit Purkinje cells, for their ability to substitute experiments. The simulated changes in AP duration (dAPD90) at increasing drug concentrations were compared to experimental results from 588 internal Purkinje fiber studies covering 555 different drugs with diverse modes of action. Using our minimalistic model, 80% of the Purkinje experiments could be quantitatively reproduced. This result allows for significant saving of experimental effort in early research and justifies the embedding of electrophysiological simulations into the DMTA (Design, Make, Test, Analyze) cycle in pharmaceutical compound optimization.", + "title": "Accurate in silico simulation of the rabbit Purkinje fiber electrophysiological assay to facilitate early pharmaceutical cardiosafety assessment: Dream or reality?", + "type": "DOI", + "year": 2022 + }, + "publicationId": "BIOMD0000001100", + "submissionId": "MODEL2204250001", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001101": { + "contributors": { + "AnnotationCurator": [ + { + "email": "xin.zhou.joy@hotmail.com", + "external": false, + "name": "XIN ZHOU", + "orcid": "0000-0003-2814-3399" + } + ], + "Modeller": [ + { + "email": "xin.zhou.joy@hotmail.com", + "external": false, + "name": "XIN ZHOU", + "orcid": "0000-0003-2814-3399" + } + ] + }, + "curationStatus": "CURATED", + "description": "A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets. 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"Import of Zhou2024PostInfarctionMyocyteElectromechanics", + "submitted": 1709212744, + "submitter": "XIN ZHOU", + "version": 1 + }, + { + "comment": "Updated model file reproducing results and also added annotation file", + "submitted": 1741090397, + "submitter": "Krishna Kumar Tiwari", + "version": 4 + } + ] + }, + "modelLevelAnnotations": [ + { + "accession": "10.7554/eLife.93002.2", + "qualifier": "bqmodel:isDescribedBy", + "resource": "DOI", + "uri": "https://identifiers.org/doi:10.7554/eLife.93002.2" + }, + { + "accession": "MODEL2402290004", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:MODEL2402290004" + }, + { + "accession": "BIOMD0000001101", + "qualifier": "bqmodel:is", + "resource": "BioModels Database", + "uri": "https://identifiers.org/biomodels.db:BIOMD0000001101" + } + ], + "modellingApproach": { + "accession": "MAMO_0000046", + "name": "ordinary differential equation model", + "resource": "http://identifiers.org/mamo/MAMO_0000046" + }, + "name": "Zhou2024 - Post Infarction Myocyte Electromechanics", + "publication": { + "accession": "10.7554/eLife.93002.2", + "affiliation": "University of Oxford", + "authors": [ + { + "name": "Xin Zhou" + }, + { + "name": "Zhinuo Jenny Wang" + }, + { + "name": "Julia Camps" + }, + { + "name": "Jakub Tomek" + }, + { + "name": "Alfonso Santiago" + }, + { + "name": "Adria Quintanas" + }, + { + "name": "Mariano Vazquez" + }, + { + "name": "Marmar Vaseghi" + }, + { + "name": "Blanca Rodriguez" + } + ], + "journal": "eLife", + "link": "http://identifiers.org/doi/10.7554/eLife.93002.2", + "month": "10", + "synopsis": "Aims\nSudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic remodelling, which are reflected as variable phenotypes. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with the post-MI pro-arrhythmic heterogeneities are unknown. We aim to provide a mechanistic explanation of clinical phenotypes in acute and chronic MI, from ionic remodeling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations.\n\nMethods and Results\nA human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets. Abnormalities caused by scar and border zone ionic remodeling are introduced in varying degrees as reported in experimental data obtained in acute and chronic infarction. Simulations enabled reproducing and explaining clinical phenotypes post-MI, from ionic remodelling to ECGs and pressure-volume loops. In acute MI, T-wave inversion and Brugada phenocopy were explained by up to 57 ms of local APD prolongation and activation failure due to the inhibition of potassium, sodium and calcium channels in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion caused by uneven potassium channel expression in border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI ionic remodelling reduced EF by up to 10% through inhibition of calcium transient amplitude due to weaker calcium currents or SERCA activity, but the EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing.\n\nConclusions\nMulti-scale modelling and simulation coherently integrates experimental and clinical data at subcellular, tissue, and organ scales to unravel electromechanical disease mechanisms in MI. In acute post-MI, ionic remodelling and its effect on refractoriness and propagation failure in the BZ have a strong impact on phenotypic ECG variability, whereas in chronic post-MI, the repolarisation dispersion across the BZ is crucial. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.", + "title": "Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations", + "type": "DOI", + "year": 2024 + }, + "publicationId": "BIOMD0000001101", + "submissionId": "MODEL2402290004", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001102": { + "contributors": { + "AnnotationCurator": [ + { + "affiliation": "EMBL-EBI", + "email": "ktiwari@ebi.ac.uk", + "external": false, + "name": "Krishna Kumar Tiwari", + "orcid": "0000-0002-3699-0937" + } + ], + "Modeller": [ + { + "affiliation": "Institute of Physics, University of Freiburg, 79104 Freiburg, Germany.FDM-Freiburg Center for Data Analysis and Modeling, University of Freiburg, 79104 Freiburg, Germany.", + "email": "svenja.kemmer@fdm.uni-freiburg.de", + "external": false, + "name": "Svenja Kemmer", + "orcid": "0000-0001-7708-0545" + } + ] + }, + "curationStatus": "CURATED", + "description": "

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, non-alcoholic fatty liver disease (NAFLD) is gaining importance. As the disease progresses it can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in NAFLD development, but the molecular mechanisms responsible for disease development remained unresolved. Here, we uncover a strong downregulation of the PI3K-AKT pathway and an upregulation of the MAPK pathway in primary hepatocytes from a preclinical model fed with a Western diet (WD). Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveals a patient-specific variability in basal MET phosphorylation, which correlates with the outcome of patients after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

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Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.", + "title": "Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.", + "type": "DOI", + "year": 2024 + }, + "publicationId": "BIOMD0000001102", + "submissionId": "MODEL2306280002", + "vcsIdentifier": "aaj" + }, + "BIOMD0000001103": { + "contributors": { + "Modeller": [ + { + "affiliation": "SASTRA Deemed University", + "email": "apalania@scbt.sastra.edu", + "external": false, + "name": "Ashok Palaniappan", + "orcid": "0000-0003-2841-9527" + }, + { + "affiliation": "EMBL-EBI", + "email": "mdaou@ebi.ac.uk", + "external": false, + "name": "Mikal Daou" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Poor awareness and access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative approach for a new kind of diagnosis has been developed, comprising identification of optimal biomarkers, design of the sensor, and simulation of the diagnostic circuit. Using miRNA expression data in the public domain, we identified circulating miRNA biomarkers specific to cervical cancer using multi-tier screening. Synthetic riboregulators called toehold switches specific for the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we developed a generic grammar of these switches, and built a multivariate linear regression model using thermodynamic features derived from RNA secondary structure and interaction. The model yielded predictions of toehold efficacy with an adjusted R2 = 0.59. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10nM and 100nM before saturation. Our study demonstrates an end-to-end workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic signatures that would be increasingly important in today\u2019s world. The approach has the potential to direct experimental efforts and minimise costs. All resources are provided open-source (https://github.com/igem2019) under GNU GPLv3 licence.
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Cell free modelling of second generation Toehold switches", + "publication": { + "accession": "35475253", + "affiliation": "Department of Bioengineering, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613 401, India.", + "authors": [ + { + "institution": "Department of Bioengineering, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613 401, India.School of Advanced Materials Science and Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.", + "name": "Priyannth Ramasami S Baabu" + }, + { + "institution": "Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu 613401, India.Life Sciences Division, TCS Research, Tata Consultancy Services, Chennai, Tamil Nadu, 600042, India.", + "name": "Shivaramakrishna Srinivasan" + }, + { + "institution": "Department of Bioengineering, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613 401, India.", + "name": "Swetha Nagarajan" + }, + { + "institution": "Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu 613401, India.", + "name": "Sangeetha Muthamilselvan", + "orcid": "0000-0002-2269-5216" + }, + { + "institution": "Department of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu 613401, India.", + "name": "Thamarai Selvi" + }, + { + "institution": "Department of Bioengineering, School of Chemical and Biotechnology, SASTRA Deemed University, Thanjavur, Tamil Nadu, 613 401, India.", + "name": "Raghavv R Suresh" + }, + { + "institution": "SASTRA Deemed University", + "name": "Ashok Palaniappan", + "orcid": "0000-0003-2841-9527" + } + ], + "issue": "2", + "journal": "Synthetic and systems biotechnology", + "link": "http://identifiers.org/pubmed/35475253", + "month": "6", + "pages": "802-814", + "synopsis": "Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Relatively scant awareness and limited access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative unified pipeline for diagnosis has been developed, beginning with identification of optimal biomarkers, concurrent design of toehold switch sensors, and finally simulation of the designed diagnostic circuits to assess performance. Using miRNA expression data in the public domain, we identified miR-21-5p and miR-20a-5p as blood-based miRNA biomarkers specific to early-stage cervical cancer employing a multi-tier algorithmic screening. Synthetic riboregulators called toehold switches specific to the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we used a generic grammar of these switches, and built a neural network model of dynamic range using thermodynamic features derived from mRNA secondary structure and interaction. Second-generation toehold switches were used to overcome the design challenges associated with miRNA biomarkers. The resultant model yielded an adj. R2 \u223c0.71, outperforming earlier models of toehold-switch dynamic range. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10\u00a0nM and 100\u00a0nM before saturation. Our study demonstrates an end-to-end computational workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic/nucleic-acid signatures. The approach has the potential to replace iterative experimental trial and error, and focus time, money, and efforts. All software including the toehold grammar parser, neural network model and reaction kinetics simulation are available as open-source software (https://github.com/SASTRA-iGEM2019) under GNU GPLv3 licence.", + "title": "End-to-end computational approach to the design of RNA biosensors for detecting miRNA biomarkers of cervical cancer.", + "type": "PubMed ID", + "volume": "7", + "year": 2022 + }, + "publicationId": "BIOMD0000001103", + "submissionId": "MODEL2107150001", + "vcsIdentifier": "aaj" + }, + "MODEL0318407497": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL0318407497: 'curationStatus'" + }, + "MODEL0911139400": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL0911139400: 'curationStatus'" + }, + "MODEL0911270009": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL0911270009: 'curationStatus'" + }, + "MODEL0912040001": { + "error": "Error retrieving model at 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}, + "MODEL1805090001": { + "contributors": { + "Modeller": [ + { + "affiliation": "EMBL-EBI", + "email": "tnguyen@ebi.ac.uk", + "external": false, + "name": "Tung Nguyen", + "orcid": "0000-0002-2876-6046" + }, + { + "affiliation": "EMBL-EBI", + "email": "mroberts@ebi.ac.uk", + "external": false, + "name": "Matthew Roberts" + }, + { + "affiliation": "EMBL-EBI", + "email": "azyoud@ebi.ac.uk", + "external": false, + "name": "Ahmad Zyoud", + "orcid": "0000-0002-5937-8564" + } + ] + }, + "curationStatus": "CURATED", + "description": "
Bridging systems biology and pharmacokinetics\u2013pharmacodynamics has resulted in models that are highly complex and complicated. They usually contain large numbers of states and parameters and describe multiple input\u2013output relationships. Based on any given data set relating to a specific input\u2013output process, it is possible that some states of the system are either less important or have no influence at all. In this study, we explore a simplification of a systems pharmacology model of the coagulation network for use in describing the time course of fibrinogen recovery after a brown snake bite. The technique of proper lumping is used to simplify the 62-state systems model to a 5-state model that describes the brown snake venom\u2013fibrinogen relationship while maintaining an appropriate mechanistic relationship. The simplified 5-state model explains the observed decline and recovery in fibrinogen concentrations well. The techniques used in this study can be applied to other multiscale models.
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They usually contain large numbers of states and parameters and describe multiple input-output relationships. Based on any given data set relating to a specific input-output process, it is possible that some states of the system are either less important or have no influence at all. In this study, we explore a simplification of a systems pharmacology model of the coagulation network for use in describing the time course of fibrinogen recovery after a brown snake bite. The technique of proper lumping is used to simplify the 62-state systems model to a 5-state model that describes the brown snake venom-fibrinogen relationship while maintaining an appropriate mechanistic relationship. The simplified 5-state model explains the observed decline and recovery in fibrinogen concentrations well. The techniques used in this study can be applied to other multiscale models.", + "title": "Scale reduction of a systems coagulation model with an application to modeling pharmacokinetic-pharmacodynamic data.", + "type": "PubMed ID", + "volume": "3", + "year": 2014 + }, + "submissionId": "MODEL1805090001", + "vcsIdentifier": "aag" + }, + "MODEL1805300001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL1805300001: 'curationStatus'" + }, + "MODEL1806100001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL1806100001: 'curationStatus'" + }, + "MODEL1806100002": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL1806100002: 'curationStatus'" + }, + "MODEL1806110001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL1806110001: 'curationStatus'" + }, + "MODEL1806110002": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL1806110002: 'curationStatus'" + 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}, + "MODEL2407200001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2407200001: 'curationStatus'" + }, + "MODEL2407240001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2407240001: 'curationStatus'" + }, + "MODEL2407310001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2407310001: 'curationStatus'" + }, + "MODEL2408030001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2408030001: 'curationStatus'" + }, + "MODEL2409300001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2409300001: 'curationStatus'" + }, + "MODEL2410030001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2410030001: 'curationStatus'" + }, + "MODEL2411210001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2411210001: 'curationStatus'" + }, + "MODEL2412050001": { + "error": "Error retrieving model at 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}, + "MODEL2502220001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2502220001: 'curationStatus'" + }, + "MODEL2502270001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2502270001: 'curationStatus'" + }, + "MODEL2502270002": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2502270002: 'curationStatus'" + }, + "MODEL2503190002": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2503190002: 'curationStatus'" + }, + "MODEL2503240001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2503240001: 'curationStatus'" + }, + "MODEL2503240002": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2503240002: 'curationStatus'" + }, + "MODEL2503250001": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL2503250001: 'curationStatus'" + }, + "MODEL2503270001": { + "error": "Error retrieving model at 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}, + "MODEL8074238178": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL8074238178: 'curationStatus'" + }, + "MODEL8686689662": { + "error": "Error retrieving model at https://www.ebi.ac.uk/biomodels/MODEL8686689662: 'curationStatus'" } } \ No newline at end of file diff --git a/libraries/client/cached_info/modeldb.json b/libraries/client/cached_info/modeldb.json index 8cdba0677..17db794f2 100644 --- a/libraries/client/cached_info/modeldb.json +++ b/libraries/client/cached_info/modeldb.json @@ -1,4 +1,26 @@ { + "0": { + "errors": [ + "Problem with model: 2016219 (1857/1891) An integrated deep learning-based model of spatial cells that combines self-motion with sensory information (Aziz et al., 2022)", + "Problem with model: 2016662 (1874/1891) Impact of extracellular current flow on action potential propagation in myelinated axons", + "Problem with model: 2017005 (1875/1891) Reverse Engineering MN Pool (Chardon et al, 2023)", + "Problem with model: 2018003 (1879/1891) Axonal ectopic burst induced by repetitive stimuli of hippocampal mossy fiber", + "Problem with model: 2018022 (1881/1891) Isolated pacemaker neuron from rodent respiratory CPG under voltage-clamp" + ], + "to_be_forked": [ + "Yet to be forked: 2014825, order 1826; TD2Q reinforcement learning (Blackwell and Doya, 2023)", + "Yet to be forked: 2018247, order 1880; Virtual Human Retina: Simulating Neural Signalling, Degeneration, and Responses to Electrical Stimulation (Ly et al 2025)", + "Yet to be forked: 2018262, order 1882; KV1 Channels Enable Myelinated Axons to Transmit Spikes Reliably without Spiking Ectopically (Abdollahi et al., 2025)", + "Yet to be forked: 2018260, order 1883; L2/3 Dendritic Morphology (Landau et al., 2022)", + "Yet to be forked: 2018006, order 1884; Synchronization in a realistic model of CA1 hippocampal neurons (Fiasconaro and Migliore, 2024)", + "Yet to be forked: 2017402, order 1885; Substantia Nigra Par Compacta (Ortner et al., 2017)", + "Yet to be forked: 267752, order 1886; Intrinsic motivation in cognitive architecture (Nagashima et al., 2024)", + "Yet to be forked: 2018263, order 1887; Network models of single compartment neurons for investigating downmodulation of voltage-gated potassium channels (Ho et al., 2025)", + "Yet to be forked: 2019342, order 1888; Differential roles of NaV1.2 and NaV1.6 in neocortical pyramidal cell excitability (Garcia et al. 2025)", + "Yet to be forked: 2019444, order 1889; SCN1A variants of uncertain significance (Knox et al, 2025)", + "Yet to be forked: 2018259, order 1890; Octopus neuron (Kreeger et al., 2025)" + ] + }, "279": { "class_id": 19, "created": "2001-01-01T00:00:00", @@ -20382,6 +20404,8 @@ "attr_id": 24, "value": "We computed the steady-state activity of a large-scale model of the granular layer of the rat cerebellum. Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. ... The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. See paper for more and details." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/50219", "receptors": { "attr_id": 26, "value": [ @@ -112721,6 +112745,8 @@ "attr_id": 24, "value": "Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength." }, + "osbv2_gh_branch": "master", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/155727", "other_implementer": { "attr_id": 308, "value": "Trousdale, James [jamest212 at gmail.com]" @@ -210689,6 +210715,8 @@ "attr_id": 24, "value": "Model code for: 'Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment', provisionally accepted at PLOS Computational Biology. " }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267088", "public_submitter_email": { "attr_id": 309, "value": "BBarlow@uOttawa.ca" @@ -213989,6 +214017,8 @@ "attr_id": 24, "value": "Our work aims to understand the cellular and synaptic mechanisms underlying eupnea and sigh rhythms in the brain. Part of our work benefited from a mathematical model that made predictions about key components of both rhythm generators. We show a network a neurons can generate two oscillations simultaneously via different mechanisms." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267252", "other_implementer": { "attr_id": 308, "value": "Daniel S. Borrus\r\nGregory D. Conradi-Smith\r\nChristopher A. Del Negro\r\nCameron Grover" @@ -214441,6 +214471,8 @@ "attr_id": 24, "value": "" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267267", "public_submitter_email": { "attr_id": 309, "value": "mpetroccione@albany.edu" @@ -219803,7 +219835,7 @@ } ] }, - "name": "Decorrelation in the developing visual thalamus (Tikidji-Hamburyan et al, accepted)", + "name": "Decorrelation in the developing visual thalamus (Tikidji-Hamburyan et al, 2023)", "notes": { "attr_id": 24, "value": "The developing visual thalamus and cortex extract positional information encoded in\r\nthe correlated activity of retinal ganglion cells by synaptic plasticity, allowing for the refinement of\r\nconnectivity. Here, we use a biophysical model of the visual thalamus during the initial visual circuit\r\nrefinement period to explore the role of synaptic and circuit properties in the regulation of such\r\nneural correlations. We find that the NMDA receptor dominance, combined with weak recurrent\r\nexcitation and inhibition characteristic of this age, prevents the emergence of spike-\u00adcorrelations\r\nbetween thalamocortical neurons on the millisecond timescale. Such precise correlations, which\r\nwould emerge due to the broad, unrefined connections from the retina to the thalamus, reduce the\r\nspatial information contained by thalamic spikes, and therefore we term them \"parasitic\" correlations.\r\nOur results suggest that developing synapses and circuits evolved mechanisms to compensate for\r\nsuch detrimental parasitic correlations arising from the unrefined and immature circuit." @@ -219852,8 +219884,8 @@ } ] }, - "ver_date": "2023-05-10T13:11:56.053", - "ver_number": 13 + "ver_date": "2025-03-04T01:57:36.269676", + "ver_number": 14 }, "267591": { "class_id": 19, @@ -223776,6 +223808,8 @@ "attr_id": 24, "value": "" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/267741", "public_submitter_email": { "attr_id": 309, "value": "tuomo.maki-marttunen@tuni.fi" @@ -223958,6 +223992,62 @@ "ver_date": "2023-06-27T01:12:04.62", "ver_number": 4 }, + "267752": { + "_citation_text": null, + "_implementers_text": null, + "class_id": 19, + "created": "2023-06-05T00:47:28.427", + "gitrepo": false, + "has_modelview": { + "attr_id": 502, + "value": false + }, + "hide_autolaunch_button": { + "attr_id": 582, + "value": false + }, + "id": 267752, + "model_paper": { + "attr_id": 155, + "value": [ + { + "object_id": 2019037, + "object_name": "Nagashima K, Morita J, Takeuchi Y (2024)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 114646, + "object_name": "Python" + }, + { + "object_id": 267753, + "object_name": "Lisp" + } + ] + }, + "name": "Intrinsic motivation in cognitive architecture (Nagashima et al., 2024)", + "notes": { + "attr_id": 24, + "value": "These models are designed for a maze continuation task that specifically emphasizes curiosity. The act-r directory represents a model implemented using ACT-R, a cognitive architecture. On the other hand, the icm directory represents a model implemented using the Intrinsic Curiosity Module (ICM) for deep reinforcement learning. Despite the difference in implementation, both models are intended to perform the same task." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nagashima.kazuma.16@shizuoka.ac.jp" + }, + "public_submitter_name": { + "attr_id": 621, + "value": "Source Code And Data Archive" + }, + "value": { + "object_id": 2019037 + }, + "ver_date": "2025-03-04T02:38:59.086464", + "ver_number": 4 + }, "267754": { "citation": { "attr_id": 391, @@ -224774,6 +224864,8 @@ "attr_id": 24, "value": "This study aims to quantify the effects of specific myelin dystrophies, such as demyelination and remyelination, which have been observed in the aging rhesus dlPFC, on the propagation of action potentials (APs) in layer 3 pyramidal neurons. Furthermore, this study sheds light on how such age-related myelin changes affect a core cognitive function: spatial working memory." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014821", "public_submitter_email": { "attr_id": 309, "value": "nil@bu.edu" @@ -224944,8 +225036,8 @@ "attr_id": 155, "value": [ { - "object_id": 267785, - "object_name": "Blackwell K, Doya K (2023)" + "object_id": 2019180, + "object_name": "Blackwell KT, Doya K (2023)" } ] }, @@ -224962,7 +225054,7 @@ } ] }, - "name": "TD2Q reinforcement learning (Blackwell and Doya, in press)", + "name": "TD2Q reinforcement learning (Blackwell and Doya, 2023)", "neurons": { "attr_id": 25, "value": [] @@ -225001,8 +225093,8 @@ } ] }, - "ver_date": "2023-07-25T14:42:15.499288+00:00", - "ver_number": 1 + "ver_date": "2025-03-04T15:21:35.145622", + "ver_number": 2 }, "2014830": { "_citation_text": "S\u00e6tra MJ, Ellingsrud AJ, Rognes ME (2023) Neural activity induces strongly coupled electro-chemo-mechanical interactions and fluid flow in astrocyte networks and extracellular space\u2014A computational study. PLoS Comput Biol 19(7): e1010996. https://doi.org/10.1371/journal.pcbi.1010996", @@ -225249,6 +225341,8 @@ "attr_id": 24, "value": "\"We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.\"" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2014832", "public_submitter_email": { "attr_id": 309, "value": "salvadordura@gmail.com" @@ -225813,6 +225907,8 @@ "attr_id": 24, "value": "The back propagation of an action potential (AP) from the axon/soma to the dendrites plays a central role in dendritic integration. This process involves the intricate orchestration of various ion channels, yet a comprehensive understanding of the contribution of each channel type remains elusive. In this study, we leverage ultrafast membrane potential recordings (Vm) and Ca2+ imaging techniques to shed light on the involvement of N-type voltage-gated Ca2+ channels (VGCCs) in layer-5 neocortical pyramidal neurons' apical dendrites.\r\nOur findings reveal a selective interaction between N-type VGCCs and large-conductance Ca2+-activated K+ channels (BK CAKCs). Remarkably, we observe that BK CAKCs are activated within a mere 500 \u00b5s after the AP peak, preceding the peak of the Ca2+ current triggered by the AP. Consequently, when N-type VGCCs are inhibited, the early broadening of the AP shape amplifies the activity of other VGCCs, leading to an augmented total Ca2+ influx. Our NEURON model, constructed to replicate and support these experimental results, underscores the critical coupling between N-type and BK channels.\r\nThis study not only redefines the conventional role of N-type VGCCs as primarily involved in presynaptic neurotransmitter release but also establishes their distinct and essential function as activators of BK CAKCs in neuronal dendrites. Furthermore, our results provide original functional validation of a physical interaction between Ca2+ and K+ channels, elucidated through ultrafast kinetic reconstruction. This insight enhances our understanding of the intricate mechanisms governing neuronal signaling and may have far-reaching implications in the field.\r\n" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015410", "public_submitter_email": { "attr_id": 309, "value": "elisabetta.giacalone@ibf.cnr.it" @@ -225914,6 +226010,8 @@ "attr_id": 24, "value": "A modified motor network model (from Moraud et al. 2016 and Formento et al. 2018) with added cutaneous input and dPV neuron model." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015411", "public_submitter_email": { "attr_id": 309, "value": "laura.medlock@mail.utoronto.ca" @@ -226164,7 +226262,12 @@ "value": [] }, "model_paper": { - "value": [] + "value": [ + { + "object_id": 2018411, + "object_name": "Clements KN et al. (2023)" + } + ] }, "model_type": { "attr_id": 112, @@ -226184,7 +226287,7 @@ } ] }, - "name": "Zebrafish Mauthner escape circuit with dopamine, gaba, and glycine (Clements et al., accepted)", + "name": "Zebrafish Mauthner escape circuit with dopamine, gaba, and glycine (Clements et al., 2023)", "neurons": { "attr_id": 25, "value": [ @@ -226234,8 +226337,8 @@ "attr_id": 471, "value": [] }, - "ver_date": "2023-10-24T16:43:08.470034+00:00", - "ver_number": 1 + "ver_date": "2025-03-04T01:05:52.130969", + "ver_number": 2 }, "2015420": { "_citation_text": "F\u00fczesi, T., Rasiah, N.P., Rosenegger, D.G. et al. Hypothalamic CRH neurons represent physiological memory of positive and negative experience. Nat Commun 14, 8522 (2023). https://doi.org/10.1038/s41467-023-44163-5", @@ -226684,6 +226787,8 @@ "attr_id": 24, "value": "" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015423", "public_submitter_email": { "attr_id": 309, "value": "carmen.lupascu@ibf.cnr.it" @@ -226794,6 +226899,8 @@ "attr_id": 24, "value": "Simulation of ectopic burst firings from distal axons induced by local inhibition of axonal K channels on the hippocampal mossy fibers." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015571", "public_submitter_email": { "attr_id": 309, "value": "kamiya@med.hokudai.ac.jp" @@ -226853,6 +226960,8 @@ "attr_id": 24, "value": "Sensory signals are processed by the cerebellum to coordinate movements. Unipolar brush cells (UBCs) are excitatory interneurons that project to granule cells and transform their input into prolonged increases or decreases in firing, depending on their ON or OFF UBC subtype. Further extension and diversification of the input signal could be produced by UBCs that project to one another, but whether this circuitry exists was unclear. In this work we showed that UBCs innervate one another using transgenic mice and immunohistochemistry. This NEURON model explores how these feed-forward networks of UBCs could extend the length of bursts or pauses and introduce delays\u2014transformations that may be necessary for cerebellar functions from modulation of eye movements to adaptive learning across time scales." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015953", "public_submitter_email": { "attr_id": 309, "value": "timothy.balmer@asu.edu" @@ -226989,6 +227098,8 @@ "attr_id": 24, "value": "This is an updated version of a closed-loop respiratory control model incorporating a central pattern generator (CPG), the Butera-Rinzel-Smith (BRS) model, together with lung mechanics, oxygen handling, and chemosensory components (see accession number 229640). We explored model parameters consistent with the silent hypoxemia phenomenon observed in some COVID-19 patients." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2015954", "public_submitter_email": { "attr_id": 309, "value": "diekman@njit.edu" @@ -227106,6 +227217,8 @@ "attr_id": 24, "value": "" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016137", "public_submitter_email": { "attr_id": 309, "value": "tar.luca@koki.hu" @@ -227199,7 +227312,12 @@ ] }, "model_paper": { - "value": [] + "value": [ + { + "object_id": 2018867, + "object_name": "Cirtala G, De Schutter E (2024)" + } + ] }, "model_type": { "attr_id": 112, @@ -227223,7 +227341,7 @@ } ] }, - "name": "Heterogeneous Purkinje Cell model (Cirtala et al., accepted)", + "name": "Heterogeneous Purkinje Cell model (Cirtala and De Schutter, 2024)", "neurons": { "attr_id": 25, "value": [ @@ -227241,6 +227359,8 @@ "attr_id": 24, "value": "We propose the first computational model of cerebellar Purkinje cell with dendritic heterogeneity. Each branch in our model is an individual unit and is characterized by its own set ion channel conductance density. We model clustered parallel fiber input and we measure the peak amplitude of a response. We observe how changes in P-type calcium conductance density changes the peak amplitude response from linear to bimodal step-plateau and vice-versa for each branch of the dendritic tree. We show how the dendritic calcium spikes propagate and how Kv4 channels block spreading depolarization to nearby branches. " }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016138", "public_submitter_email": { "attr_id": 309, "value": "gabriela-cirtala@oist.jp" @@ -227258,8 +227378,8 @@ } ] }, - "ver_date": "2024-07-09T17:43:18.882727", - "ver_number": 2 + "ver_date": "2025-03-04T02:02:29.675124", + "ver_number": 3 }, "2016216": { "_citation_text": "", @@ -227308,7 +227428,12 @@ "value": [] }, "model_paper": { - "value": [] + "value": [ + { + "object_id": 2018433, + "object_name": "John SR, Phillips RS, Rubin JE (2024)" + } + ] }, "model_type": { "attr_id": 112, @@ -227332,7 +227457,7 @@ } ] }, - "name": "A novel mechanism for ramping bursts based on slow negative feedback in model respiratory neurons (John et al., accepted)", + "name": "A novel mechanism for ramping bursts based on slow negative feedback in model respiratory neurons (John et al., 2024)", "neurons": { "attr_id": 25, "value": [ @@ -227354,6 +227479,8 @@ "attr_id": 24, "value": "Recordings from pre-Botzinger complex neurons responsible for the inspiratory phase of the respiratory rhythm reveal a ramping burst pattern, starting around the time that the transition from expiration to inspiration begins, in which the spike rate gradually rises until a transition into a high-frequency burst occurs. The spike rate increase along the burst is accompanied by a gradual depolarization of the plateau potential that underlies the spikes. These effects may be functionally important for inducing the onset of inspiration and hence maintaining effective respiration; however, most mathematical models for inspiratory bursting do not capture this activity pattern. Here, we study how the modulation of spike height and afterhyperpolarization via the slow inactivation of an inward current can support various activity patterns including ramping bursts. We use dynamical systems methods designed for multiple timescale systems, such as bifurcation analysis based on timescale decomposition and averaging over fast oscillations, to generate an understanding of and predictions about the specific dynamic effects that lead to ramping bursts. We also analyze how transitions between ramping and other activity patterns may occur with parameter changes, which could be associated with experimental manipulations, environmental conditions and/or development." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016216", "public_submitter_email": { "attr_id": 309, "value": "srj35@pitt.edu" @@ -227371,8 +227498,8 @@ } ] }, - "ver_date": "2024-06-09T16:57:10.818448", - "ver_number": 4 + "ver_date": "2025-03-04T01:19:36.588380", + "ver_number": 5 }, "2016218": { "_citation_text": "", @@ -227447,6 +227574,8 @@ "attr_id": 24, "value": "\"... unlike the dichotomous frequency preference characteristic of RA1 and RA2/Pacinian afferents in other species, rodent RAs fell along a continuum. Fitting generalized linear models (GLMs) to experimental data reproduced the reliability and precision of rodent RAs. The resulting model parameters highlight key mechanistic differences across the RA spectrum; specifically, the integration window of different RAs transitions from wide to narrow as tuning preferences across the population move from low to high frequencies...\"" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016218", "public_submitter_email": { "attr_id": 309, "value": "laura.medlock@mail.utoronto.ca" @@ -227655,6 +227784,8 @@ "attr_id": 24, "value": "While many theoretical models of neural dynamics during sleep exist, few include the effects of neuromodulators on sleep oscillations and describe transitions between different states of vigilance. Here, we ported an established thalamocortical network model (https://doi.org/10.7554/eLife.18607) from C++ to NEURON. This model, which includes a biophysically realistic description of intrinsic and synaptic channels, allows for testing the effects of different neuromodulators, intrinsic cell properties, and synaptic connectivity on neural dynamics during sleep. " }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016601", "public_submitter_email": { "attr_id": 309, "value": "finkt@gonzaga.edu" @@ -227761,6 +227892,8 @@ "attr_id": 24, "value": "" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016658", "public_submitter_email": { "attr_id": 309, "value": "ccanav@lsuhsc.edu" @@ -227849,6 +227982,8 @@ "attr_id": 24, "value": "The model illustrates how the plastic integration of spatially stable grid-cell inputs could contribute to hippocampal place fields' generation and dynamic character. Theoretically, the grid-to-place transformation is possible if a place cell can respond selectively to a combination of suitably aligned grids. A synaptic plasticity rule whereby postsynaptic activation gates synaptic change while presynaptic activation determines its direction can accomplish this task during rat foraging behavior. The synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time when the model is run in larger environments and the topographical/modular organization of grid inputs is considered. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization - ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016659", "public_submitter_email": { "attr_id": 309, "value": "fsavelli.research@gmail.com" @@ -228025,6 +228160,8 @@ "attr_id": 24, "value": "Abstract: Nociceptive sensory neurons convey pain signals to the CNS using action potentials. Loss-of-function mutations in the voltage-gated sodium channel NaV1.7 cause insensitivity to pain (presumably by reducing nociceptor excitability) but efforts to treat pain by inhibiting NaV1.7 pharmacologically have largely failed. This may reflect the variable contribution of NaV1.7 to nociceptor excitability. Contrary to claims that NaV1.7 is necessary for nociceptors to initiate action potentials, we show that nociceptors can achieve equivalent excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8. Selectively blocking one of those NaV subtypes reduces nociceptor excitability only if the other two subtypes are weakly expressed. For example, excitability relies on NaV1.8 in acutely dissociated nociceptors but responsibility shifts to NaV1.7 and NaV1.3 by the fourth day in culture. A similar shift in NaV dependence occurs in vivo after inflammation, impacting ability of the NaV1.7-selective inhibitor PF-05089771 to reduce pain in behavioral tests. Flexible use of different NaV subtypes \u2013 an example of degeneracy \u2013 compromises the reliable modulation of nociceptor excitability by subtype-selective inhibitors. Identifying the dominant NaV subtype to predict drug efficacy is not trivial. Degeneracy at the cellular level must be considered when choosing drug targets at the molecular level." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016663", "public_submitter_email": { "attr_id": 309, "value": "strawcup@gmail.com" @@ -228121,6 +228258,8 @@ "attr_id": 24, "value": "An electrochromic dye was used to make fast, high signal/noise ratio, linear scale optical measurements of membrane potential from spines and basal dendrites of mouse somatosensory cortex layer 5 pyramidal cells. These experiments revealed close electrical coupling, with little signal loss, for membrane potential spreading from the spine head to the adjacent dendritic shaft. Modeling was used to explore the relationship between cellular properties (anatomy and biophysics) and the degree of electrical coupling between spines and dendrites. For usage instructions, see readme.pdf" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016666", "public_submitter_email": { "attr_id": 309, "value": "ted.carnevale@yale.edu" @@ -228233,7 +228372,12 @@ ] }, "model_paper": { - "value": [] + "value": [ + { + "object_id": 2018741, + "object_name": "Sanda P et al. (2024)" + } + ] }, "model_type": { "attr_id": 112, @@ -228253,7 +228397,7 @@ } ] }, - "name": "Cholinergic modulation of resting state networks (Sanda et al., accepted)", + "name": "Cholinergic modulation of resting state networks (Sanda et al., 2024)", "neurons": { "attr_id": 25, "value": [ @@ -228271,6 +228415,8 @@ "attr_id": 24, "value": "Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions\r\nbetween the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.\r\n" }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016670", "public_submitter_email": { "attr_id": 309, "value": "sanda@cs.cas.cz" @@ -228296,8 +228442,8 @@ "attr_id": 471, "value": [] }, - "ver_date": "2024-05-21T20:48:40.713936", - "ver_number": 4 + "ver_date": "2025-03-04T01:29:52.087021", + "ver_number": 5 }, "2016995": { "_citation_text": "https://pubmed.ncbi.nlm.nih.gov/38761846/", @@ -228398,6 +228544,8 @@ "attr_id": 24, "value": "Capsazepine (CPZ) was recognized as a synthetic inhibitor of capsaicin activation of TRPV1 channel. TRPV1 has been demonstrated to be widely distributed in endocrine or neuroendocrine cells, and different types of central neurons. However, whether and how this compound might produce any perturbations on varying types of ionic currents, other than block of capsaicin-induced TRPV1 or activation of epithelial Na+ current, remain largely unclear. In this study, we aimed to clarify the effect of CPZ on hyperpolarization-activated cationic current (Ih, or HCN-encoded current) and voltage-gated Na+ current (INa) in pituitary GH3 cells. By use of whole-cell patch-clamp recordings, the CPZ application caused a concentration-dependent inhibition of Ih amplitude or slowing in activation time course of the current with the measured IC50 or KD value of 3.1 or 3.16 \u03bcM, respectively. The steady-state activation curve of Ih during the exposure to 3 \u03bcM CPZ was shifted toward more hyperpolarized potential by approximately 20 mV; however, no change in the gating charge of the current was noticed. In this work, a modified Markovian model designed for Ih was implemented to evaluate the plausible modifications of CPZ on the hysteresis of the current; and the model was well suited to predict CPZ-mediated decrease in hysteretic strength of Ih. The INa identified in GH3 cells was also suppressed by CPZ, despite the activation or inactivation time course of the current was changed. Moreover, under cell-attached current measurements, cell exposure to CPZ resulted in a reduction of spontaneous firing. Collectively, finding the current observations suggest that CPZ-perturbed inhibition of Ih or INa appears to be direct and independent of its action on vanilloid receptor(s); hence, such actions would be a yet unidentified but important ionic mechanism underlying perturbed intrinsic membrane excitability in the in-vivo endocrine or neuroendocrine cells, or neurons." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016995", "public_submitter_email": { "attr_id": 309, "value": "071320@tool.caaumed.org.tw" @@ -228533,6 +228681,8 @@ "attr_id": 24, "value": "What are the implications for the existence of subthreshold ion channels, their localization profiles, and plasticity on local field potentials (LFPs)? Here, we assessed the role of hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels in altering hippocampal theta-frequency LFPs and the associated spike phase. We presented spatiotemporally randomized, balanced theta-modulated excitatory and inhibitory inputs to somatically aligned, morphologically realistic pyramidal neuron models spread across a cylindrical neuropil. We computed LFPs from seven electrode sites and found that the insertion of an experimentally constrained HCN-conductance gradient into these neurons introduced a location-dependent lead in the LFP phase without significantly altering its amplitude. Further, neurons fired action potentials at a specific theta phase of the LFP, and the insertion of HCN channels introduced large lags in this spike phase and a striking enhancement in neuronal spike-phase coherence. Importantly, graded changes in either HCN conductance or its half-maximal activation voltage resulted in graded changes in LFP and spike phases. Our conclusions on the impact of HCN channels on LFPs and spike phase were invariant to changes in neuropil size, to morphological heterogeneity, to excitatory or inhibitory synaptic scaling, and to shifts in the onset phase of inhibitory inputs. Finally, we selectively abolished the inductive lead in the impedance phase introduced by HCN channels without altering neuronal excitability and found that this inductive phase lead contributed significantly to changes in LFP and spike phase. Our results uncover specific roles for HCN channels and their plasticity in phase-coding schemas and in the formation and dynamic reconfiguration of neuronal cell assemblies. " }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2016996", "public_submitter_email": { "attr_id": 309, "value": "manishas@alum.iisc.ac.in" @@ -228693,6 +228843,8 @@ "attr_id": 24, "value": "This model simulates the afferent neural encoding in response to mechanical colorectal stretching. A custom-built mechano-sensitive ion channel, gated by membrane tension induced by circumferential colorectal stretch, is incorporated. A lumped parametric model has been developed to calculate membrane tension from the overall colorectal stretch." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2017006", "public_submitter_email": { "attr_id": 309, "value": "fengb@uconn.edu" @@ -228834,6 +228986,8 @@ "attr_id": 24, "value": "Plateau potentials in models of striatal projection neurons were studied. Plateau potentials are supralinear dendritic voltage elevations which have: i) high somatic voltage amplitude and ii) are threshold, or all-or-none, events (like somatic action potentials). While the high somatic amplitude can sometimes be captured in models of striatal projection neurons, their all-or-none property is not usually not. In this study, we found that including glutamate spillover consistently and robustly provides all-or-none plateaus in addition to a high somatic amplitude. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to parameters in the model for the Mg2+ block of the NMDA receptors, which is the mechanism for generating plateaus. These results suggest a potentially significant role of glutamate spillover in plateau potential generation. See the related article for more details." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2017143", "public_submitter_email": { "attr_id": 309, "value": "trpevski@kth.se" @@ -228863,33 +229017,49 @@ "ver_date": "2024-07-11T02:55:00.077311", "ver_number": 4 }, - "2017403": { - "_citation_text": "Nicoletti, M., Chiodo, L., Loppini, A., Liu, Q., Folli, V., Ruocco, G., & Filippi, S. (2024). Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families. PLoS One, 19(3), e0298105.\r\nDOI: 10.1371/journal.pone.0298105\r\nPMID: 38551921", - "_implementers_text": "M. Nicoletti", + "2017402": { + "_citation_text": "Ortner NJ, Bock G, Dougalis A, Kharitonova M, Duda J, Hess S, Tuluc P, Pomberger T, Stefanova N, Pitterl F, Ciossek T, Oberacher H, Draheim HJ, Kloppenburg P, Liss B, Striessnig J. Lower Affinity of Isradipine for L-Type Ca2+ Channels during Substantia Nigra Dopamine Neuron-Like Activity: Implications for Neuroprotection in Parkinson's Disease. J Neurosci. 2017 Jul 12;37(28):6761-6777. doi: 10.1523/JNEUROSCI.2946-16.2017. Epub 2017 Jun 7. PMID: 28592699; PMCID: PMC6596555.", + "_implementers_text": "Antonios Dougalis", "class_id": 19, - "created": "2024-07-03T12:40:45.293833+00:00", + "created": "2024-07-02T12:35:36.627675+00:00", "currents": { "attr_id": 27, "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, { "object_id": 2396, "object_name": "I Calcium" }, { - "object_id": 2405, - "object_name": "I Potassium" + "object_id": 139267, + "object_name": "I Cl, leak" }, { - "object_id": 243504, - "object_name": "IK Bkca" + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 251, + "object_name": "I h" }, { "object_id": 243506, "object_name": "IK Skca" }, { - "object_id": 167500, - "object_name": "Kir" + "object_id": 143445, + "object_name": "I_KD" } ] }, @@ -228898,26 +229068,39 @@ "attr_id": 476, "value": [] }, - "id": 2017403, + "id": 2017402, "implemented_by": { "attr_id": 299, "value": [ { - "object_id": 267244, - "object_name": "Nicoletti, Martina" + "object_id": 226011, + "object_name": "Dougalis, Antonios [antoniosdougalis at hotmail.com]" } ] }, - "license": "MIT", + "license": "GPL", "model_concept": { "attr_id": 113, - "value": [] + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3630, + "object_name": "Bursting" + }, + { + "object_id": 98012, + "object_name": "Calcium dynamics" + } + ] }, "model_paper": { "value": [ { - "object_id": 2017519, - "object_name": "Nicoletti M et al. (2024)" + "object_id": 2018989, + "object_name": "Ortner NJ et al. (2017)" } ] }, @@ -228936,121 +229119,247 @@ { "object_id": 1882, "object_name": "NEURON" - }, - { - "object_id": 114646, - "object_name": "Python" } ] }, - "name": "C.elegans motor and interneurons (Nicoletti at al. 2024)", + "name": "Substantia Nigra Par Compacta (Ortner et al., 2017)", "neurons": { "attr_id": 25, "value": [ { - "object_id": 156124, - "object_name": "Abstract single compartment conductance based cell" - }, - { - "object_id": 114737, - "object_name": "Hodgkin-Huxley neuron" + "object_id": 267559, + "object_name": "Dopaminergic substantia nigra neuron" } ] }, "neurotransmitters": { "attr_id": 28, - "value": [] + "value": [ + { + "object_id": 233, + "object_name": "Glutamate" + } + ] }, "notes": { "attr_id": 24, - "value": "Biophysically accurate models of six classes of C. elegans neurons: AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. The implemented codes reproduce current-clamp and voltage-clamp experiments reported in literature and simulate knockout neurons, with the aim to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. These models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system." + "value": "The model contains three different isoforms of the calcium L- type channels (Cav1.2 \u03b11-subunit, long and short Cav1.3 \u03b11-subunit splice variants) according to the data and the descriptions of Ortner et al., 2017 J Neurosci. \r\nSince calcium overload confers the susceptibility of SNc neurons to neurodegeneration. our work described the impact of the those different L- type calcium isoforms in calcium accumulation, action potential firing and burst firing (NMDA-induced) of the SNc neurons. " }, "public_submitter_email": { "attr_id": 309, - "value": "a.loppini@unicampus.it" + "value": "antoniosdougalis@gmail.com" }, "receptors": { "attr_id": 26, - "value": [] + "value": [ + { + "object_id": 206, + "object_name": "NMDA" + } + ] }, "region": { "attr_id": 471, - "value": [] + "value": [ + { + "object_id": 115952, + "object_name": "Basal ganglia" + } + ] }, - "ver_date": "2024-07-11T01:54:59.243820", + "ver_date": "2025-03-04T02:21:34.105603", "ver_number": 2 }, - "2017405": { - "_citation_text": "Fleming, J.E., Senneff, S. and Lowery, M.M., 2023. Multivariable closed-loop control of deep brain stimulation for Parkinson\u2019s disease. Journal of Neural Engineering, 20(5), p.056029.", - "_implementers_text": "John E Fleming", + "2017403": { + "_citation_text": "Nicoletti, M., Chiodo, L., Loppini, A., Liu, Q., Folli, V., Ruocco, G., & Filippi, S. (2024). Biophysical modeling of the whole-cell dynamics of C. elegans motor and interneurons families. PLoS One, 19(3), e0298105.\r\nDOI: 10.1371/journal.pone.0298105\r\nPMID: 38551921", + "_implementers_text": "M. Nicoletti", "class_id": 19, - "created": "2024-07-08T16:23:30.934977+00:00", + "created": "2024-07-03T12:40:45.293833+00:00", "currents": { "attr_id": 27, "value": [ - { - "object_id": 248, - "object_name": "I K" - }, - { - "object_id": 2395, - "object_name": "I Sodium" - }, { "object_id": 2396, "object_name": "I Calcium" }, { - "object_id": 127083, - "object_name": "I_AHP" + "object_id": 2405, + "object_name": "I Potassium" }, { - "object_id": 243, - "object_name": "I L high threshold" + "object_id": 243504, + "object_name": "IK Bkca" }, { - "object_id": 245, - "object_name": "I T low threshold" + "object_id": 243506, + "object_name": "IK Skca" + }, + { + "object_id": 167500, + "object_name": "Kir" } ] }, "expmotivation": "", - "id": 2017405, + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2017403, "implemented_by": { + "attr_id": 299, "value": [ { - "object_id": 263054, - "object_name": "Fleming, John E" + "object_id": 267244, + "object_name": "Nicoletti, Martina" } ] }, "license": "MIT", "model_concept": { "attr_id": 113, - "value": [ - { - "object_id": 3543, - "object_name": "Activity Patterns" - }, - { - "object_id": 116944, - "object_name": "Deep brain stimulation" - }, - { - "object_id": 3634, - "object_name": "Oscillations" - }, - { - "object_id": 65417, - "object_name": "Parkinson's" - } - ] + "value": [] }, "model_paper": { "value": [ { - "object_id": 2017408, - "object_name": "Fleming JE, Senneff S, Lowery MM (2023)" + "object_id": 2017519, + "object_name": "Nicoletti M et al. (2024)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 114646, + "object_name": "Python" + } + ] + }, + "name": "C.elegans motor and interneurons (Nicoletti at al. 2024)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 156124, + "object_name": "Abstract single compartment conductance based cell" + }, + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Biophysically accurate models of six classes of C. elegans neurons: AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. The implemented codes reproduce current-clamp and voltage-clamp experiments reported in literature and simulate knockout neurons, with the aim to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. These models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system." + }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2017403", + "public_submitter_email": { + "attr_id": 309, + "value": "a.loppini@unicampus.it" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [] + }, + "ver_date": "2024-07-11T01:54:59.243820", + "ver_number": 2 + }, + "2017405": { + "_citation_text": "Fleming, J.E., Senneff, S. and Lowery, M.M., 2023. Multivariable closed-loop control of deep brain stimulation for Parkinson\u2019s disease. Journal of Neural Engineering, 20(5), p.056029.", + "_implementers_text": "John E Fleming", + "class_id": 19, + "created": "2024-07-08T16:23:30.934977+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + } + ] + }, + "expmotivation": "", + "id": 2017405, + "implemented_by": { + "value": [ + { + "object_id": 263054, + "object_name": "Fleming, John E" + } + ] + }, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3543, + "object_name": "Activity Patterns" + }, + { + "object_id": 116944, + "object_name": "Deep brain stimulation" + }, + { + "object_id": 3634, + "object_name": "Oscillations" + }, + { + "object_id": 65417, + "object_name": "Parkinson's" + } + ] + }, + "model_paper": { + "value": [ + { + "object_id": 2017408, + "object_name": "Fleming JE, Senneff S, Lowery MM (2023)" } ] }, @@ -229111,6 +229420,8 @@ "attr_id": 24, "value": "We developed a computational model of the parkinsonian motor network to investigate multivariable closed-loop control strategies of deep brain stimulation (DBS) for Parkinson\u2019s disease (PD). The motor network model includes a model of the cortical basal ganglia coupled to a model of the motoneuron pool. The cortical basal ganglia model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode, while (iv) the motoneuron pool model includes a model of electromyography and (v) force generated due to the activation of motoneurons in the pool. The model simulates periods of elevated beta- and tremor-band activity to facilitate investigation of tremor- and beta-based closed-loop DBS control strategies, modulating DBS amplitude, pulse duration or frequency, using clinically accessible measures of tremor- (based on the measured force signal) and beta-band activity (based on the local field potential)." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2017405", "public_submitter_email": { "attr_id": 309, "value": "john.fleming@ndcn.ox.ac.uk" @@ -229253,6 +229564,152 @@ "ver_date": "2024-10-02T12:58:18.637602+00:00", "ver_number": 1 }, + "2018006": { + "_citation_text": "", + "_implementers_text": "Alessandro Fiasconaro; Michele Migliore", + "class_id": 19, + "created": "2024-10-16T18:13:20.245590+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 146507, + "object_name": "Ca pump" + }, + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 255, + "object_name": "I CAN" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 243, + "object_name": "I L high threshold" + }, + { + "object_id": 250, + "object_name": "I M" + }, + { + "object_id": 242, + "object_name": "I Na,t" + }, + { + "object_id": 245, + "object_name": "I T low threshold" + }, + { + "object_id": 251, + "object_name": "I h" + }, + { + "object_id": 143445, + "object_name": "I_KD" + } + ] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2018006, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 2018010, + "object_name": "Fiasconaro, Alessandro" + }, + { + "object_id": 33737, + "object_name": "Migliore, Michele [Michele.Migliore at Yale.edu]" + } + ] + }, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [] + }, + "model_paper": { + "value": [ + { + "object_id": 2018743, + "object_name": "Fiasconaro A, Migliore M (2024)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Synchronization in a realistic model of CA1 hippocampal neurons (Fiasconaro and Migliore, 2024)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 258, + "object_name": "Hippocampus CA1 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "We study the synchronisation of neurons in a realistic model under the Hodgkin-Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. Synchronisation is studied using phase spiking correlation as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay.\r\n\r\nWe found that the neurons' spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibitory weight, whereas the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "afiascon@unizar.es" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2025-03-04T01:39:48.903995", + "ver_number": 3 + }, "2018009": { "_citation_text": "Effects of synapse location, delay and background stochastic activity on synchronising hippocampal CA1 neurons. Chaos, Solitons and fractals X, 2024", "_implementers_text": "Alessandro Fiasconaro; Michele Migliore", @@ -229387,6 +229844,8 @@ "attr_id": 24, "value": "We study the synchronisation of neurons in a realistic model under the Hodgkin-Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. Synchronisation is studied using phase spiking correlation as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay.\r\nWe found that the neurons' spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibitory weight, whereas the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2018009", "public_submitter_email": { "attr_id": 309, "value": "afiascon@unizar.es" @@ -229475,6 +229934,8 @@ "attr_id": 24, "value": "This code reproduces the computational model of cortical circuits presented in Beerendonk, Mejias et al., PNAS 2024. The model describes a simplified population-level cortical circuit with pyramidal neurons and multiple interneuron types (PV, SST, VIP) which performs a simple perceptual decision making task. Notably, the model incorporates the effect of arousal signals on the circuit to reproduce the inverted-U relationship between task performance and arousal level found experimentally (and the corresponding U-shape between reaction times and arousal levels)." }, + "osbv2_gh_branch": "main", + "osbv2_gh_repo": "https://github.com/OpenSourceBrain/2018021", "public_submitter_email": { "attr_id": 309, "value": "j.f.mejias@uva.nl" @@ -229494,5 +229955,907 @@ }, "ver_date": "2024-12-05T01:32:16.253511", "ver_number": 2 + }, + "2018022": { + "_citation_text": "Parker, J., & Ramirez, J. M. (2025). Differentiating the contributions of Na+/K+ pump current and persistent Na+ current in simulated voltage-clamp experiments. Journal of neurophysiology, Accepted. 10.1152/jn.00060.2024", + "_implementers_text": "", + "class_id": 19, + "created": "2024-12-01T21:04:32.297493+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + }, + { + "object_id": 143445, + "object_name": "I_KD" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "expmotivation": "Paper Abstract:\r\n\r\nThe persistent Na+ current (INaP) is thought to play important roles in many brain regions including the generation of inspiration in the ventral respiratory column (VRC) of mammals. The characterization of the slow inactivation of INaP requires long-lasting voltage steps (>1 s), which will increase intracellular Na+ and activate the Na+/K+-ATPase pump current (IPump). Thus, IPump may contribute to the previously measured slow inactivation of INaP and the generation of the inspiratory bursting rhythm.\r\n\r\nTo test this hypothesis, we computationally modeled a respiratory pacemaker neuron that included a non-inactivating INaP and IPump in addition to other basic spike-generating currents. This model produces an inspiration-like bursting rhythm, in which the dynamics of [Na+]i account for burst initiation and termination. We simulated a voltage-clamp experiment measuring the INaP inactivation kinetics using our model of non-inactivating INaP and IPump. Consistent with prior measurements in the VRC, we found a sigmoidal inactivation curve and a current that only partially inactivated reaching a minimum inactivation of 0.37. The biexponential time course of inactivation had decay rate constants of 0.45 s and 2.33 s with contributions of 49% and 51% respectively. The time constant of inactivation was 2.16 s. This decay was caused by the slow growth of IPump and the slow hyperpolarization of the Na+ reversal potential in response to the growing [Na+]i. \r\n\r\nWe conclude that important biophysical properties previously attributed to the INaP may be caused by IPump. This has important implications for understanding respiratory rhythmogenesis and other neuronal functions.\r\n", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2018022, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 185343, + "object_name": "Persistent activity" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 3529, + "object_name": "C or C++ program" + }, + { + "object_id": 36835, + "object_name": "MATLAB" + } + ] + }, + "name": "Isolated pacemaker neuron from rodent respiratory CPG under voltage-clamp", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 125611, + "object_name": "Respiratory column neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "This is the model used throughout the paper, \r\n\r\nParker, J., & Ramirez, J. M. (2024). Differentiating the contributions of Na+/K+ pump current and persistent Na+ current in simulated voltage-clamp experiments. Journal of neurophysiology, In Review.\r\n\r\nIncluded in this code is a few different versions of the model. Firstly, there is the full canonical model of a single neuron producing inspiration-like bursting or the associated bursting seen when rhythmic cells are recorded in vitro, which is much slower than actual inspiration. Second, there is a reduced version of the model where the intracellular Na+ concentration is held constant as a parameter and modified. Third, there is a model where the cell is voltage-clamped and voltage protocols are applied to measure the inactivation kinetics of persistent sodium. This part gets confusing, because the model does not actually contain an hNaP variable. You really need to read the paper to understand what is going on here. But in short, we are looking at whether the presence of Na+/K+ pump current can make the cell INaP appear to slowly inactivate. There are then 2 more versions of the voltage-clamped model, Model A and Model B, which are slight variations of the original.\r\n" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "jessiraeparker2@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2024-12-01T21:04:32.297493+00:00", + "ver_number": 1 + }, + "2018247": { + "_citation_text": "Ly K, Italiano ML, Shivdasani MN, Tsai D, Zhang JY, Jiang C, Lovell NH, Dokos S, Guo T. Virtual Human Retina: Simulating Neural Signalling, Degeneration, and Responses to Electrical Stimulation. Brain Stimul. 2025 Jan 17:S1935-861X(25)00015-4. doi: 10.1016/j.brs.2025.01.013. Epub ahead of print. PMID: 39827982.", + "_implementers_text": "Keith Ly", + "class_id": 19, + "created": "2025-01-26T19:15:25.204197+00:00", + "expmotivation": "The results suggest that compared to direct RGC activation, network-mediated stimulation produces neural activation mosaics more akin to that of a naturalistic spotlight pulse. It was found that the rectification of the OFF pathway was preserved in subretinal, intraretinal INL, and intraretinal IPL stimulation, resulting in activation of ON RGCs alongside inhibition of OFF RGCs. These results suggests that emerging strategies for prosthetic vision should a) incorporate inhibitory circuitry mechanisms into consideration when devising electrical stimulation strategies; b) better preserve naturalistic firing mosaics by avoiding indiscriminate RGC activation, ideally sending less conflicting information to the visual cortex; and c) further consider the impacts of functional remodelling and degeneration in the retina on the efficacy of artificial stimulation.", + "id": 2018247, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 2018265, + "object_name": "Ly, Keith" + } + ] + }, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3641, + "object_name": "Detailed Neuronal Models" + } + ] + }, + "model_paper": { + "value": [ + { + "object_id": 2018264, + "object_name": "Ly K et al. (2025)" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Virtual Human Retina: Simulating Neural Signalling, Degeneration, and Responses to Electrical Stimulation (Ly et al 2025)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 270, + "object_name": "Retina ganglion GLU cell" + }, + { + "object_id": 223953, + "object_name": "Retina amacrine cell" + }, + { + "object_id": 7020, + "object_name": "Retina bipolar GLU cell" + }, + { + "object_id": 225100, + "object_name": "Retina horizontal cell" + }, + { + "object_id": 1966, + "object_name": "Retina photoreceptor cone GLU cell" + }, + { + "object_id": 153361, + "object_name": "Retina photoreceptor rod GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 214, + "object_name": "Glycine" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Abstract\r\nIntroduction: Current brain-based visual prostheses pose significant challenges impeding adoption such as the necessarily complex surgeries and occurrence of more substantial side effects due to the sensitivity of the brain. This has led to much effort toward vision restoration being focused on the more approachable part of the brain - the retina. Here we introduce a novel, parameterized simulation platform that enables study of human retinal degeneration and optimization of stimulation strategies. The platform bears immense potential for patient-specific tailoring and serves to enhance artificial vision solutions for individuals with visual impairments.\r\n\r\nMaterial and method: Our virtual retina is developed using the software package, NEURON. This virtual retina platform supports large-scale simulations of over 10,000 neurons whilst upholding strong biological plausibility with multiple important visual pathways and detailed network properties. The comprehensive three-dimensional model includes photoreceptors, horizontal cells, bipolar cells, amacrine cells, and midget and parasol retinal ganglion cells, with comprehensive network connectivity across various eccentricities (1 mm to 5 mm from the fovea) in the human retina. The model is constructed using electrophysiology, immunohistology, and optical coherence tomography imaging data from healthy and degenerate human retinas. We validated our model by replicating numerous experimental observations from human and primate retina, with a particular focus on retinal degeneration.\r\n\r\nResult: We simulated interactions between diseased retinas and state-of-the-art retinal implants, shedding light on the limitations of commercial retinal prostheses. Our results suggested that appropriate stimulation settings with intraretinal prototype devices could leverage network-mediated activation to achieve activation mosaics more alike that of the retina's response to natural light, promoting the prospect of more naturalistic vision. Our study additionally highlights the importance of controlling inhibitory circuits in the retinal network to induce functionally relevant retinal activity.\r\n\r\nConclusion: This study demonstrates the potential of this software package and highlights its utility as a valuable tool for engineers, scientists, and clinicians in the design and optimisation of retinal stimulation devices for both research and educational applications.\r\n\r\nKeywords: Virtual retina; bionic vision; data-driven model; discrete neuronal network model; electrical stimulation; human retina; mechanistic model; retinal degeneration." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "keithly@stanford.edu" + }, + "receptors": { + "attr_id": 26, + "value": [ + { + "object_id": 213, + "object_name": "Gaba" + }, + { + "object_id": 207, + "object_name": "mGluR" + }, + { + "object_id": 1361, + "object_name": "Glycine" + }, + { + "object_id": 206, + "object_name": "NMDA" + }, + { + "object_id": 210, + "object_name": "Kainate" + } + ] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 118529, + "object_name": "Retina" + } + ] + }, + "ver_date": "2025-02-07T21:57:29.887504", + "ver_number": 2 + }, + "2018259": { + "_citation_text": "PMC10925315", + "_implementers_text": "Suraj Honnuraiah", + "class_id": 19, + "created": "2025-02-04T18:35:08.347735+00:00", + "currents": { + "attr_id": 27, + "value": [] + }, + "expmotivation": "Biophysical model of Octopus neuron in auditory brain stem", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2018259, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 260965, + "object_name": "Honnuraiah, Suraj [hs at ini.phys.ethz.ch]" + } + ] + }, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3632, + "object_name": "Coincidence Detection" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "value": [] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3537, + "object_name": "Neuron or other electrically excitable cell" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "Octopus neuron (Kreeger et al., 2025)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 277, + "object_name": "Cochlear nucleus octopus GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Animals navigate the auditory world by recognizing complex sounds, from the rustle of a predator to the call of a potential mate. This ability depends in part on the octopus cells of the auditory brainstem, which respond to multiple frequencies that change over time, as occurs in natural stimuli. Unlike the average neuron, which integrates inputs over time on the order of tens of milliseconds, octopus cells must detect momentary coincidence of excitatory inputs from the cochlea during an ongoing sound on both the millisecond and submillisecond time scale. Here, we show that octopus cells receive inhibitory inputs on their dendrites that enhance opportunities for coincidence detection in the cell body, thereby allowing for responses both to rapid onsets at the beginning of a sound and to frequency modulations during the sound. This mechanism is crucial for the fundamental process of integrating the synchronized frequencies of natural auditory signals over time." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "suraj_honnuraiah@hms.harvard.edu" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 143145, + "object_name": "Auditory brainstem" + }, + { + "object_id": 183076, + "object_name": "Brainstem" + } + ] + }, + "ver_date": "2025-04-11T21:43:39.219941", + "ver_number": 2 + }, + "2018260": { + "_citation_text": "Andrew T Landau, Pojeong Park, J David Wong-Campos, He Tian, Adam E Cohen, Bernardo L Sabatini (2022) Dendritic branch structure compartmentalizes voltage-dependent calcium influx in cortical layer 2/3 pyramidal cells eLife 11:e76993", + "_implementers_text": "", + "class_id": 19, + "created": "2025-02-04T21:08:46.762689+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 247, + "object_name": "I A" + }, + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 248, + "object_name": "I K" + } + ] + }, + "expmotivation": "Understand how cable properties affect AP amplitude and calcium conductances. ", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2018260, + "license": "GPL", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 3639, + "object_name": "Active Dendrites" + }, + { + "object_id": 3640, + "object_name": "Influence of Dendritic Geometry" + } + ] + }, + "model_paper": { + "value": [ + { + "object_id": 2018305, + "object_name": "Landau AT et al. (2022)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 8114, + "object_name": "Dendrite" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + } + ] + }, + "name": "L2/3 Dendritic Morphology (Landau et al., 2022)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 266, + "object_name": "Neocortex L2/3 pyramidal GLU cell" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "This code has some accurate morphological models of L2/3 neurons recorded in slices from mouse cortex. The paper it's related to aimed to understand how dendritic morphology (branching structure) affected action potential backpropagation and calcium signaling in dendrites, and that's what this code allows you to study. \r\n\r\nhttps://github.com/landoskape/landau-2022" + }, + "public_submitter_email": { + "attr_id": 309, + "value": "andrewtylerlandau@gmail.com" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 228598, + "object_name": "Mouse" + }, + { + "object_id": 115945, + "object_name": "Neocortex" + } + ] + }, + "ver_date": "2025-02-04T21:08:46.762689+00:00", + "ver_number": 1 + }, + "2018262": { + "_citation_text": "Abdollahi, N., Xie, Y. F., Ratt\u00e9, S., & Prescott, S. A. (2025). KV1 channels enable myelinated axons to transmit spikes reliably without spiking ectopically. Journal of Neuroscience.", + "_implementers_text": "Nooshin Abdollahi", + "class_id": 19, + "created": "2025-02-06T00:03:23.088333+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 151550, + "object_name": "Channelrhodopsin (ChR)" + }, + { + "object_id": 248, + "object_name": "I K" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 222730, + "object_name": "I Na, slow inactivation" + }, + { + "object_id": 116966, + "object_name": "I_K,Na" + } + ] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2018262, + "implemented_by": { + "attr_id": 299, + "value": [ + { + "object_id": 2015408, + "object_name": "Abdollahi, Nooshin [nooshin.abdollahi at mail.utoronto.ca]" + } + ] + }, + "license": "other", + "model_concept": { + "attr_id": 113, + "value": [] + }, + "model_paper": { + "value": [ + { + "object_id": 2018270, + "object_name": "Abdollahi N, Xie YF, Ratt\u00e9 S, Prescott SA (2025)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3538, + "object_name": "Axon" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [] + }, + "name": "KV1 Channels Enable Myelinated Axons to Transmit Spikes Reliably without Spiking Ectopically (Abdollahi et al., 2025)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 52425, + "object_name": "Myelinated neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [] + }, + "notes": { + "attr_id": 24, + "value": "Action potentials (spikes) are regenerated at each node of Ranvier during saltatory transmission along a myelinated axon. The high density of voltage-gated sodium channels required by nodes to reliably transmit spikes increases the risk of ectopic spike generation in the axon. Here we show that ectopic spiking is avoided because KV1 channels prevent nodes from responding to slow depolarization; instead, axons respond selectively to rapid depolarization because KV1 channels implement a high-pass filter. To characterize this filter, we compared spike initiation properties in the soma and axon of CA1 pyramidal neurons from mice of both sexes, using spatially restricted photoactivation of channelrhodopsin-2 (ChR2) to evoke spikes in either region while simultaneously recording at the soma. Somatic photostimulation evoked repetitive spiking whereas axonal photostimulation evoked transient spiking. Blocking KV1channels converted the axon photostimulation response to repetitive spiking and encouraged spontaneous ectopic spike initiation in the axon. According to computational modeling, the high-pass filter implemented by KV1 channels matches the axial current waveform associated with saltatory conduction, enabling axons to faithfully transmit digital signals by maximizing their signal-to-noise ratio for this task. Specifically, a node generates a single spike only when rapidly depolarized, which is precisely what occurs during saltatory conduction when a pulse of axial current(triggered by a spike occurring at the upstream node) reaches the next node. The soma and axon use distinct spike initiation mechanisms (filters) appropriate for the task required of each region, namely, analog-to-digital transduction in the soma versus digital signal transmission in the axon." + }, + "public_submitter_email": { + "attr_id": 309, + "value": "nooshin.abdollahi@mail.utoronto.ca" + }, + "receptors": { + "attr_id": 26, + "value": [] + }, + "region": { + "attr_id": 471, + "value": [ + { + "object_id": 115946, + "object_name": "Hippocampus" + } + ] + }, + "ver_date": "2025-03-03T21:01:40.561100", + "ver_number": 2 + }, + "2018263": { + "_citation_text": "ECY Ho, AJH Newton, E Urdapilleta, S Dura-Bernal and W Truccolo \"Downmodulation of Potassium Conductances Induces Epileptic Seizures in Cortical Network Models Via Multiple Synergistic Factors\" J. Neurosci. 45(13):e1909232025, 2025.", + "_implementers_text": "", + "class_id": 19, + "created": "2025-02-07T13:45:38.231651+00:00", + "currents": { + "attr_id": 27, + "value": [ + { + "object_id": 2396, + "object_name": "I Calcium" + }, + { + "object_id": 139267, + "object_name": "I Cl, leak" + }, + { + "object_id": 253, + "object_name": "I K,Ca" + }, + { + "object_id": 249, + "object_name": "I K,leak" + }, + { + "object_id": 139268, + "object_name": "I Na, leak" + }, + { + "object_id": 241, + "object_name": "I Na,p" + }, + { + "object_id": 2405, + "object_name": "I Potassium" + }, + { + "object_id": 2395, + "object_name": "I Sodium" + }, + { + "object_id": 127083, + "object_name": "I_AHP" + }, + { + "object_id": 120693, + "object_name": "Na/K pump" + } + ] + }, + "expmotivation": "", + "gene": { + "attr_id": 476, + "value": [] + }, + "id": 2018263, + "license": "MIT", + "model_concept": { + "attr_id": 113, + "value": [ + { + "object_id": 3649, + "object_name": "Action Potentials" + }, + { + "object_id": 52405, + "object_name": "Epilepsy" + }, + { + "object_id": 238958, + "object_name": "Stochastic simulation" + }, + { + "object_id": 183719, + "object_name": "Synaptic noise" + }, + { + "object_id": 3635, + "object_name": "Synchronization" + } + ] + }, + "model_paper": { + "value": [ + { + "object_id": 2019439, + "object_name": "Ho \u4f55\u93ae\u5b87 ECY, Newton AJH, Urdapilleta E, Dura-Bernal S, Truccolo W (2025)" + } + ] + }, + "model_type": { + "attr_id": 112, + "value": [ + { + "object_id": 3536, + "object_name": "Realistic Network" + } + ] + }, + "modeling_application": { + "attr_id": 114, + "value": [ + { + "object_id": 1882, + "object_name": "NEURON" + }, + { + "object_id": 195884, + "object_name": "NetPyNE" + } + ] + }, + "name": "Network models of single compartment neurons for investigating downmodulation of voltage-gated potassium channels (Ho et al., 2025)", + "neurons": { + "attr_id": 25, + "value": [ + { + "object_id": 114737, + "object_name": "Hodgkin-Huxley neuron" + } + ] + }, + "neurotransmitters": { + "attr_id": 28, + "value": [ + { + "object_id": 232, + "object_name": "Gaba" + }, + { + "object_id": 233, + "object_name": "Glutamate" + } + ] + }, + "notes": { + "attr_id": 24, + "value": "Voltage-gated potassium conductances gK play a critical role not only in normal neural function, but also in many neurological disorders and related therapeutic interventions. In particular, in an important animal model of epileptic seizures, 4-aminopyridine (4-AP) administration is thought to induce seizures by reducing gK in cortex and other brain areas. Interestingly, 4-AP has also been useful in the treatment of neurological disorders such as multiple sclerosis (MS) and spinal cord injury, where it is thought to improve action potential propagation in axonal fibers. Here, we examined gK downmodulation in biophysical models of cortical networks that included different neuron types organized in layers, potassium diffusion in interstitial and larger extracellular spaces, and glial buffering. Our findings are fourfold. First, gK downmodulation in pyramidal and fast-spiking inhibitory interneurons led to\r\ndifferential effects, making the latter much more likely to enter depolarization block. Second, both neuron types showed an increase in the duration and amplitude of action potentials, with more pronounced effects in pyramidal neurons. Third, a sufficiently strong gK reduction dramatically increased network synchrony, resulting in seizure-like dynamics. Fourth, we hypothesized that broader action potentials were likely to not only improve their propagation, as in 4-AP therapeutic uses, but also to increase synaptic coupling. Notably, graded-synapses incorporating this effect further amplified network synchronization and seizure-like dynamics. 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support": "0", + "NeuroML v2.x support": "3", + "NeuroML version": "v2.x", + "Original format": "C++", + "PSICS support": "-1", + "PyNN support": "-1", + "Specie": "C. elegans", + "Spine classification": "Invertebrate", + "Status info": "In development", + "Tags": "OpenWorm", + "description": "github:README.md", + "id": 296, + "identifier": "worm2d", + "main_branch": "main", + "name": "Worm2D" } } \ No newline at end of file diff --git a/libraries/client/cached_info/repos_v2.json b/libraries/client/cached_info/repos_v2.json index ef93f9e9c..ed155b3ab 100644 --- a/libraries/client/cached_info/repos_v2.json +++ b/libraries/client/cached_info/repos_v2.json @@ -127622,7 +127622,7 @@ ], "default_context": "main", "id": 2216, - "name": "Role of synchrony in sensation and the basis for paresthesia-free spinal cord stimulation", + "name": "Role of synchrony in sensation and the basis for paresthesia-free spinal cord stimulation (Sagalajev et al., 2024)", "repository_type": "github", "summary": "We showed that DC axons are activated synchronously during c-SCS (50 Hz) and asynchronously during kf-SCS. 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The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. See paper for more and details.", + "tags": [ + { + "id": 826, + "tag": "GENESIS (web link to model)" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3024, + "tag": "ModelDB:50219" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2025-01-27 14:34:58.923477+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50219", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2267": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2267, + "name": "Vertical System (VS) tangential cells network model (Trousdale et al. 2014)", + "repository_type": "github", + "summary": "Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 935, + "tag": "Connectivity matrix" + }, + { + "id": 805, + "tag": "Invertebrate" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3025, + "tag": "ModelDB:155727" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 776, + "tag": "Simplified Models" + }, + { + "id": 788, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2025-01-27 14:53:42.575259+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/155727", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2268": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2268, + "name": "Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment (Barlow et al., 2024)", + "repository_type": "github", + "summary": "Model code for: 'Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment', provisionally accepted at PLOS Computational Biology. 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Part of our work benefited from a mathematical model that made predictions about key components of both rhythm generators. We show a network a neurons can generate two oscillations simultaneously via different mechanisms.", + "tags": [ + { + "id": 1603, + "tag": "Brain Rhythms" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3027, + "tag": "ModelDB:267252" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 1612, + "tag": "Respiratory control" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2025-01-27 15:32:49.556325+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267252", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2270": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2270, + "name": "D1-MSN: The effect of EAAC1 on firing frequency (Petroccione et al., 2023)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3028, + "tag": "ModelDB:267267" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:32:50.280882+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267267", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2271": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2271, + "name": "Biochemically detailed model of post-synaptic plasticity for computational analyses of schizophrenia (Maki-Marttunen et al. in press)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 825, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3029, + "tag": "ModelDB:267741" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 885, + "tag": "STDP" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2025-01-27 15:32:53.205006+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267741", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2272": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2272, + "name": "Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex (Iba\u00f1ez, Sengupta et al., 2024)", + "repository_type": "github", + "summary": "This study aims to quantify the effects of specific myelin dystrophies, such as demyelination and remyelination, which have been observed in the aging rhesus dlPFC, on the propagation of action potentials (APs) in layer 3 pyramidal neurons. Furthermore, this study sheds light on how such age-related myelin changes affect a core cognitive function: spatial working memory.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 2330, + "tag": "Brian 2" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3030, + "tag": "ModelDB:2014821" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + }, + { + "id": 877, + "tag": "Working memory" + } + ], + "timestamp_created": "2025-01-27 15:32:54.728697+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2014821", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2273": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2273, + "name": "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics (Dura-Bernal et al., 2023)", + "repository_type": "github", + "summary": "\"We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.\"", + "tags": [ + { + "id": 849, + "tag": "Audition" + }, + { + "id": 1622, + "tag": "Evoked LFP" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 796, + "tag": "I N" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 936, + "tag": "Laminar Connectivity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3031, + "tag": "ModelDB:2014832" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 709, + "tag": "NetPyNE" + }, + { + "id": 787, + "tag": "Oscillations" + } + ], + "timestamp_created": "2025-01-27 15:32:55.996116+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2014832", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2274": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2274, + "name": "Kinetics and functional consequences of BK Channels activation by N-type Ca2+ channels in the dendrite of mouse neocortical layer-5 pyramidal neurons (Bl\u00f6mer et al., 2024)", + "repository_type": "github", + "summary": "The back propagation of an action potential (AP) from the axon/soma to the dendrites plays a central role in dendritic integration. This process involves the intricate orchestration of various ion channels, yet a comprehensive understanding of the contribution of each channel type remains elusive. In this study, we leverage ultrafast membrane potential recordings (Vm) and Ca2+ imaging techniques to shed light on the involvement of N-type voltage-gated Ca2+ channels (VGCCs) in layer-5 neocortical pyramidal neurons' apical dendrites.\r\nOur findings reveal a selective interaction between N-type VGCCs and large-conductance Ca2+-activated K+ channels (BK CAKCs). Remarkably, we observe that BK CAKCs are activated within a mere 500 \u00b5s after the AP peak, preceding the peak of the Ca2+ current triggered by the AP. Consequently, when N-type VGCCs are inhibited, the early broadening of the AP shape amplifies the activity of other VGCCs, leading to an augmented total Ca2+ influx. Our NEURON model, constructed to replicate and support these experimental results, underscores the critical coupling between N-type and BK channels.\r\nThis study not only redefines the conventional role of N-type VGCCs as primarily involved in presynaptic neurotransmitter release but also establishes their distinct and essential function as activators of BK CAKCs in neuronal dendrites. Furthermore, our results provide original functional validation of a physical interaction between Ca2+ and K+ channels, elucidated through ultrafast kinetic reconstruction. This insight enhances our understanding of the intricate mechanisms governing neuronal signaling and may have far-reaching implications in the field.\r\n", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 2237, + "tag": "I Na, slow inactivation" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 2473, + "tag": "IK Bkca" + }, + { + "id": 2474, + "tag": "IK Skca" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3032, + "tag": "ModelDB:2015410" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:32:57.954430+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015410", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2275": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2275, + "name": "Touch-Motor Circuit (Gradwell et al., 2024)", + "repository_type": "github", + "summary": "A modified motor network model (from Moraud et al. 2016 and Formento et al. 2018) with added cutaneous input and dPV neuron model.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3033, + "tag": "ModelDB:2015411" + }, + { + "id": 709, + "tag": "NetPyNE" + } + ], + "timestamp_created": "2025-01-27 15:32:58.461691+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015411", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2276": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2276, + "name": "Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models (Marascoa et al., 2024)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3034, + "tag": "ModelDB:2015423" + }, + { + "id": 661, + "tag": "NEST" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:33:01.183219+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015423", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2277": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2277, + "name": "Axonal K channel inhibition promotes ectopic burst of hippocampal mossy fiber (Kamiya 2024)", + "repository_type": "github", + "summary": "Simulation of ectopic burst firings from distal axons induced by local inhibition of axonal K channels on the hippocampal mossy fibers.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 804, + "tag": "Axonal Action Potentials" + }, + { + "id": 2829, + "tag": "Excitability" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3035, + "tag": "ModelDB:2015571" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:01.831306+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015571", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2278": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2278, + "name": "Unipolar brush cell circuits extend and diversify spiking patterns (Hariani et al., 2023)", + "repository_type": "github", + "summary": "Sensory signals are processed by the cerebellum to coordinate movements. Unipolar brush cells (UBCs) are excitatory interneurons that project to granule cells and transform their input into prolonged increases or decreases in firing, depending on their ON or OFF UBC subtype. Further extension and diversification of the input signal could be produced by UBCs that project to one another, but whether this circuitry exists was unclear. In this work we showed that UBCs innervate one another using transgenic mice and immunohistochemistry. This NEURON model explores how these feed-forward networks of UBCs could extend the length of bursts or pauses and introduce delays\u2014transformations that may be necessary for cerebellar functions from modulation of eye movements to adaptive learning across time scales.", + "tags": [ + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3036, + "tag": "ModelDB:2015953" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:02.566224+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015953", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2279": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2279, + "name": "Respiratory control model with brainstem CPG and sensory feedback adapted for COVID-19 associated silent hypoxemia (Diekman et al., 2024)", + "repository_type": "github", + "summary": "This is an updated version of a closed-loop respiratory control model incorporating a central pattern generator (CPG), the Butera-Rinzel-Smith (BRS) model, together with lung mechanics, oxygen handling, and chemosensory components (see accession number 229640). We explored model parameters consistent with the silent hypoxemia phenomenon observed in some COVID-19 patients.", + "tags": [ + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3037, + "tag": "ModelDB:2015954" + }, + { + "id": 2311, + "tag": "Pacemaking mechanism" + }, + { + "id": 1612, + "tag": "Respiratory control" + } + ], + "timestamp_created": "2025-01-27 15:33:03.400051+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015954", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2280": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2280, + "name": "Hippocampal O-LM interneurons and hippocampo-septal neurons with simplified and detailed biophysics (Tak\u00e1cs et al, 2024)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 1607, + "tag": "I Cl, leak" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 908, + "tag": "Learning" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3038, + "tag": "ModelDB:2016137" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:04.167228+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016137", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2281": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2281, + "name": "Heterogeneous Purkinje Cell model (Cirtala et al., accepted)", + "repository_type": "github", + "summary": "We propose the first computational model of cerebellar Purkinje cell with dendritic heterogeneity. Each branch in our model is an individual unit and is characterized by its own set ion channel conductance density. We model clustered parallel fiber input and we measure the peak amplitude of a response. We observe how changes in P-type calcium conductance density changes the peak amplitude response from linear to bimodal step-plateau and vice-versa for each branch of the dendritic tree. We show how the dendritic calcium spikes propagate and how Kv4 channels block spreading depolarization to nearby branches. ", + "tags": [ + { + "id": 766, + "tag": "Dendritic Action Potentials" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3039, + "tag": "ModelDB:2016138" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2025-01-27 15:33:04.738418+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016138", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2282": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2282, + "name": "A novel mechanism for ramping bursts based on slow negative feedback in model respiratory neurons (John et al., accepted)", + "repository_type": "github", + "summary": "Recordings from pre-Botzinger complex neurons responsible for the inspiratory phase of the respiratory rhythm reveal a ramping burst pattern, starting around the time that the transition from expiration to inspiration begins, in which the spike rate gradually rises until a transition into a high-frequency burst occurs. The spike rate increase along the burst is accompanied by a gradual depolarization of the plateau potential that underlies the spikes. These effects may be functionally important for inducing the onset of inspiration and hence maintaining effective respiration; however, most mathematical models for inspiratory bursting do not capture this activity pattern. Here, we study how the modulation of spike height and afterhyperpolarization via the slow inactivation of an inward current can support various activity patterns including ramping bursts. We use dynamical systems methods designed for multiple timescale systems, such as bifurcation analysis based on timescale decomposition and averaging over fast oscillations, to generate an understanding of and predictions about the specific dynamic effects that lead to ramping bursts. We also analyze how transitions between ramping and other activity patterns may occur with parameter changes, which could be associated with experimental manipulations, environmental conditions and/or development.", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 2237, + "tag": "I Na, slow inactivation" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3040, + "tag": "ModelDB:2016216" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2025-01-27 15:33:05.245628+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016216", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2283": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2283, + "name": "A model of rodent mechanoreceptors (LTMRs) (Medlock et al., 2024)", + "repository_type": "github", + "summary": "\"... unlike the dichotomous frequency preference characteristic of RA1 and RA2/Pacinian afferents in other species, rodent RAs fell along a continuum. Fitting generalized linear models (GLMs) to experimental data reproduced the reliability and precision of rodent RAs. The resulting model parameters highlight key mechanistic differences across the RA spectrum; specifically, the integration window of different RAs transitions from wide to narrow as tuning preferences across the population move from low to high frequencies...\"", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3041, + "tag": "ModelDB:2016218" + }, + { + "id": 851, + "tag": "Touch" + } + ], + "timestamp_created": "2025-01-27 15:33:05.778720+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016218", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2284": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2284, + "name": "Thalamocortical sleep model (Fink et. al., 2024, and Krishnan et al., 2016)", + "repository_type": "github", + "summary": "While many theoretical models of neural dynamics during sleep exist, few include the effects of neuromodulators on sleep oscillations and describe transitions between different states of vigilance. Here, we ported an established thalamocortical network model (https://doi.org/10.7554/eLife.18607) from C++ to NEURON. This model, which includes a biophysically realistic description of intrinsic and synaptic channels, allows for testing the effects of different neuromodulators, intrinsic cell properties, and synaptic connectivity on neural dynamics during sleep. ", + "tags": [ + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3042, + "tag": "ModelDB:2016601" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:06.295652+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016601", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2285": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2285, + "name": "Persistent Interruption in Parvalbumin-Positive Inhibitory Interneurons: Biophysical and Mathematical Mechanisms (Upchurch et al., 2024)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3043, + "tag": "ModelDB:2016658" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:33:06.968679+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016658", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2286": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2286, + "name": "Synaptic plasticity for hippocampal place field formation and dynamics (Savelli 2024)", + "repository_type": "github", + "summary": "The model illustrates how the plastic integration of spatially stable grid-cell inputs could contribute to hippocampal place fields' generation and dynamic character. Theoretically, the grid-to-place transformation is possible if a place cell can respond selectively to a combination of suitably aligned grids. A synaptic plasticity rule whereby postsynaptic activation gates synaptic change while presynaptic activation determines its direction can accomplish this task during rat foraging behavior. The synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time when the model is run in larger environments and the topographical/modular organization of grid inputs is considered. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization - ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models.", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 1539, + "tag": "Grid cell" + }, + { + "id": 891, + "tag": "Hebbian plasticity" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3044, + "tag": "ModelDB:2016659" + }, + { + "id": 882, + "tag": "Place cell/field" + }, + { + "id": 883, + "tag": "Spatial Navigation" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2025-01-27 15:33:07.489887+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016659", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2287": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2287, + "name": "Equivalent excitability achieved via different Nav subtypes (Xie et al., 2024)", + "repository_type": "github", + "summary": "Abstract: Nociceptive sensory neurons convey pain signals to the CNS using action potentials. Loss-of-function mutations in the voltage-gated sodium channel NaV1.7 cause insensitivity to pain (presumably by reducing nociceptor excitability) but efforts to treat pain by inhibiting NaV1.7 pharmacologically have largely failed. This may reflect the variable contribution of NaV1.7 to nociceptor excitability. Contrary to claims that NaV1.7 is necessary for nociceptors to initiate action potentials, we show that nociceptors can achieve equivalent excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8. Selectively blocking one of those NaV subtypes reduces nociceptor excitability only if the other two subtypes are weakly expressed. For example, excitability relies on NaV1.8 in acutely dissociated nociceptors but responsibility shifts to NaV1.7 and NaV1.3 by the fourth day in culture. A similar shift in NaV dependence occurs in vivo after inflammation, impacting ability of the NaV1.7-selective inhibitor PF-05089771 to reduce pain in behavioral tests. Flexible use of different NaV subtypes \u2013 an example of degeneracy \u2013 compromises the reliable modulation of nociceptor excitability by subtype-selective inhibitors. Identifying the dominant NaV subtype to predict drug efficacy is not trivial. Degeneracy at the cellular level must be considered when choosing drug targets at the molecular level.", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3045, + "tag": "ModelDB:2016663" + } + ], + "timestamp_created": "2025-01-27 15:33:07.990498+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016663", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2288": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2288, + "name": "Electrical properties of dendritic spines (Popovic et al. 2015)", + "repository_type": "github", + "summary": "An electrochromic dye was used to make fast, high signal/noise ratio, linear scale optical measurements of membrane potential from spines and basal dendrites of mouse somatosensory cortex layer 5 pyramidal cells. These experiments revealed close electrical coupling, with little signal loss, for membrane potential spreading from the spine head to the adjacent dendritic shaft. Modeling was used to explore the relationship between cellular properties (anatomy and biophysics) and the degree of electrical coupling between spines and dendrites. For usage instructions, see readme.pdf", + "tags": [ + { + "id": 871, + "tag": "Electrotonus" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3046, + "tag": "ModelDB:2016666" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 795, + "tag": "Synaptic Integration" + }, + { + "id": 2831, + "tag": "Two-port analysis of electrotonus" + }, + { + "id": 2832, + "tag": "Voltage transfer ratio" + } + ], + "timestamp_created": "2025-01-27 15:33:08.479414+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016666", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2289": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2289, + "name": "Cholinergic modulation of resting state networks (Sanda et al., accepted)", + "repository_type": "github", + "summary": "Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions\r\nbetween the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.\r\n", + "tags": [ + { + "id": 841, + "tag": "C or Cplusplus program" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 2829, + "tag": "Excitability" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 780, + "tag": "I K,leak" + }, + { + "id": 815, + "tag": "I Na,p" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 940, + "tag": "I_K,Na" + }, + { + "id": 1761, + "tag": "KCC2" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3047, + "tag": "ModelDB:2016670" + }, + { + "id": 816, + "tag": "Na/K pump" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 789, + "tag": "Synchronization" + } + ], + "timestamp_created": "2025-01-27 15:33:09.072750+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016670", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2290": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2290, + "name": "Markovian model for HCN-encoded current regulated by capsazepine (Wong et al., 2024)", + "repository_type": "github", + "summary": "Capsazepine (CPZ) was recognized as a synthetic inhibitor of capsaicin activation of TRPV1 channel. TRPV1 has been demonstrated to be widely distributed in endocrine or neuroendocrine cells, and different types of central neurons. However, whether and how this compound might produce any perturbations on varying types of ionic currents, other than block of capsaicin-induced TRPV1 or activation of epithelial Na+ current, remain largely unclear. In this study, we aimed to clarify the effect of CPZ on hyperpolarization-activated cationic current (Ih, or HCN-encoded current) and voltage-gated Na+ current (INa) in pituitary GH3 cells. By use of whole-cell patch-clamp recordings, the CPZ application caused a concentration-dependent inhibition of Ih amplitude or slowing in activation time course of the current with the measured IC50 or KD value of 3.1 or 3.16 \u03bcM, respectively. The steady-state activation curve of Ih during the exposure to 3 \u03bcM CPZ was shifted toward more hyperpolarized potential by approximately 20 mV; however, no change in the gating charge of the current was noticed. In this work, a modified Markovian model designed for Ih was implemented to evaluate the plausible modifications of CPZ on the hysteresis of the current; and the model was well suited to predict CPZ-mediated decrease in hysteretic strength of Ih. The INa identified in GH3 cells was also suppressed by CPZ, despite the activation or inactivation time course of the current was changed. Moreover, under cell-attached current measurements, cell exposure to CPZ resulted in a reduction of spontaneous firing. Collectively, finding the current observations suggest that CPZ-perturbed inhibition of Ih or INa appears to be direct and independent of its action on vanilloid receptor(s); hence, such actions would be a yet unidentified but important ionic mechanism underlying perturbed intrinsic membrane excitability in the in-vivo endocrine or neuroendocrine cells, or neurons.", + "tags": [ + { + "id": 794, + "tag": "I h" + }, + { + "id": 772, + "tag": "Ion Channel Kinetics" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3048, + "tag": "ModelDB:2016995" + }, + { + "id": 846, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2025-01-27 15:33:09.590015+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016995", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2291": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2291, + "name": "Subthreshold conductances regulate theta-frequency LFPs and spike phase (Sinha and Narayanan, 2015)", + "repository_type": "github", + "summary": "What are the implications for the existence of subthreshold ion channels, their localization profiles, and plasticity on local field potentials (LFPs)? Here, we assessed the role of hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels in altering hippocampal theta-frequency LFPs and the associated spike phase. We presented spatiotemporally randomized, balanced theta-modulated excitatory and inhibitory inputs to somatically aligned, morphologically realistic pyramidal neuron models spread across a cylindrical neuropil. We computed LFPs from seven electrode sites and found that the insertion of an experimentally constrained HCN-conductance gradient into these neurons introduced a location-dependent lead in the LFP phase without significantly altering its amplitude. Further, neurons fired action potentials at a specific theta phase of the LFP, and the insertion of HCN channels introduced large lags in this spike phase and a striking enhancement in neuronal spike-phase coherence. Importantly, graded changes in either HCN conductance or its half-maximal activation voltage resulted in graded changes in LFP and spike phases. Our conclusions on the impact of HCN channels on LFPs and spike phase were invariant to changes in neuropil size, to morphological heterogeneity, to excitatory or inhibitory synaptic scaling, and to shifts in the onset phase of inhibitory inputs. Finally, we selectively abolished the inductive lead in the impedance phase introduced by HCN channels without altering neuronal excitability and found that this inductive phase lead contributed significantly to changes in LFP and spike phase. Our results uncover specific roles for HCN channels and their plasticity in phase-coding schemas and in the formation and dynamic reconfiguration of neuronal cell assemblies. ", + "tags": [ + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 850, + "tag": "Intrinsic plasticity" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3049, + "tag": "ModelDB:2016996" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 2825, + "tag": "Phenomenological inductance" + }, + { + "id": 275, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2025-01-27 15:33:10.087735+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016996", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2292": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2292, + "name": "Mouse colorectal afferent ending (Feng et al 2015)", + "repository_type": "github", + "summary": "This model simulates the afferent neural encoding in response to mechanical colorectal stretching. A custom-built mechano-sensitive ion channel, gated by membrane tension induced by circumferential colorectal stretch, is incorporated. A lumped parametric model has been developed to calculate membrane tension from the overall colorectal stretch.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3050, + "tag": "ModelDB:2017006" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:10.572017+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017006", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2293": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2293, + "name": "Glutamate spillover and plateau potentials (Trpevski et al., 2023)", + "repository_type": "github", + "summary": "Plateau potentials in models of striatal projection neurons were studied. Plateau potentials are supralinear dendritic voltage elevations which have: i) high somatic voltage amplitude and ii) are threshold, or all-or-none, events (like somatic action potentials). While the high somatic amplitude can sometimes be captured in models of striatal projection neurons, their all-or-none property is not usually not. In this study, we found that including glutamate spillover consistently and robustly provides all-or-none plateaus in addition to a high somatic amplitude. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to parameters in the model for the Mg2+ block of the NMDA receptors, which is the mechanism for generating plateaus. These results suggest a potentially significant role of glutamate spillover in plateau potential generation. See the related article for more details.", + "tags": [ + { + "id": 784, + "tag": "Active Dendrites" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 2927, + "tag": "Kir, inactivating" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3051, + "tag": "ModelDB:2017143" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:11.200774+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017143", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2294": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2294, + "name": "C.elegans motor and interneurons (Nicoletti at al. 2024)", + "repository_type": "github", + "summary": "Biophysically accurate models of six classes of C. elegans neurons: AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. The implemented codes reproduce current-clamp and voltage-clamp experiments reported in literature and simulate knockout neurons, with the aim to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. These models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system.", + "tags": [ + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 782, + "tag": "I Potassium" + }, + { + "id": 2473, + "tag": "IK Bkca" + }, + { + "id": 2474, + "tag": "IK Skca" + }, + { + "id": 2006, + "tag": "Kir" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3052, + "tag": "ModelDB:2017403" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:33:11.758161+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017403", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2295": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2295, + "name": "Parkinsonian Motor Network Model during Multivariable Closed-loop DBS (Fleming et al 2023)", + "repository_type": "github", + "summary": "We developed a computational model of the parkinsonian motor network to investigate multivariable closed-loop control strategies of deep brain stimulation (DBS) for Parkinson\u2019s disease (PD). The motor network model includes a model of the cortical basal ganglia coupled to a model of the motoneuron pool. The cortical basal ganglia model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode, while (iv) the motoneuron pool model includes a model of electromyography and (v) force generated due to the activation of motoneurons in the pool. The model simulates periods of elevated beta- and tremor-band activity to facilitate investigation of tremor- and beta-based closed-loop DBS control strategies, modulating DBS amplitude, pulse duration or frequency, using clinically accessible measures of tremor- (based on the measured force signal) and beta-band activity (based on the local field potential).", + "tags": [ + { + "id": 785, + "tag": "Activity Patterns" + }, + { + "id": 834, + "tag": "Deep brain stimulation" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 783, + "tag": "I Sodium" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 1511, + "tag": "I_AHP" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3053, + "tag": "ModelDB:2017405" + }, + { + "id": 656, + "tag": "NEURON" + }, + { + "id": 787, + "tag": "Oscillations" + }, + { + "id": 884, + "tag": "Parkinson's" + }, + { + "id": 760, + "tag": "PyNN" + }, + { + "id": 89, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:33:12.287559+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017405", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2296": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2296, + "name": "Synchronization in a realistic model of CA1 pyramidal neurons (Fiasconaro and Migliore 2024)", + "repository_type": "github", + "summary": "We study the synchronisation of neurons in a realistic model under the Hodgkin-Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. Synchronisation is studied using phase spiking correlation as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay.\r\nWe found that the neurons' spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibitory weight, whereas the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay.", + "tags": [ + { + "id": 812, + "tag": "Action Potentials" + }, + { + "id": 1760, + "tag": "Ca pump" + }, + { + "id": 767, + "tag": "Detailed Neuronal Models" + }, + { + "id": 777, + "tag": "I A" + }, + { + "id": 807, + "tag": "I CAN" + }, + { + "id": 778, + "tag": "I Calcium" + }, + { + "id": 768, + "tag": "I K" + }, + { + "id": 779, + "tag": "I K,Ca" + }, + { + "id": 781, + "tag": "I L high threshold" + }, + { + "id": 786, + "tag": "I M" + }, + { + "id": 769, + "tag": "I Na,t" + }, + { + "id": 770, + "tag": "I T low threshold" + }, + { + "id": 794, + "tag": "I h" + }, + { + "id": 823, + "tag": "I_KD" + }, + { + "id": 842, + "tag": "Methods" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3054, + "tag": "ModelDB:2018009" + }, + { + "id": 656, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:33:12.876994+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2018009", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2297": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2297, + "name": "Disinhibitory circuit for arousal effects in perceptual decision making tasks (Beerendonk et al., 2024)", + "repository_type": "github", + "summary": "This code reproduces the computational model of cortical circuits presented in Beerendonk, Mejias et al., PNAS 2024. The model describes a simplified population-level cortical circuit with pyramidal neurons and multiple interneuron types (PV, SST, VIP) which performs a simple perceptual decision making task. Notably, the model incorporates the effect of arousal signals on the circuit to reproduce the inverted-U relationship between task performance and arousal level found experimentally (and the corresponding U-shape between reaction times and arousal levels).", + "tags": [ + { + "id": 273, + "tag": "MATLAB" + }, + { + "id": 773, + "tag": "ModelDB" + }, + { + "id": 3055, + "tag": "ModelDB:2018021" + } + ], + "timestamp_created": "2025-01-27 15:33:13.420614+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2018021", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2298": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2298, + "name": "Edelstein1996 - EPSP ACh event", + "repository_type": "biomodels", + "summary": "
Edelstein1996 - EPSP ACh event

Model of a nicotinic Excitatory Post-Synaptic Potential in a Torpedo electric organ. Acetylcholine is not represented explicitely, but by an event that changes the constants of transition from unliganded to liganded.\u00a0

This model has initially been encoded using StochSim.

This model is described in the article:

Edelstein SJ, Schaad O, Henry E, Bertrand D, Changeux JP.
Biol Cybern 1996 Nov; 75(5): 361-379

Abstract:

Nicotinic acetylcholine receptors are transmembrane oligomeric proteins that mediate interconversions between open and closed channel states under the control of neurotransmitters. Fast in vitro chemical kinetics and in vivo electrophysiological recordings are consistent with the following multi-step scheme. Upon binding of agonists, receptor molecules in the closed but activatable resting state (the Basal state, B) undergo rapid transitions to states of higher affinities with either open channels (the Active state, A) or closed channels (the initial Inactivatable and fully Desensitized states, I and D). In order to represent the functional properties of such receptors, we have developed a kinetic model that links conformational interconversion rates to agonist binding and extends the general principles of the Monod-Wyman-Changeux model of allosteric transitions. The crucial assumption is that the linkage is controlled by the position of the interconversion transition states on a hypothetical linear reaction coordinate. Application of the model to the peripheral nicotine acetylcholine receptor (nAChR) accounts for the main properties of ligand-gating, including single-channel events, and several new relationships are predicted. Kinetic simulations reveal errors inherent in using the dose-response analysis, but justify its application under defined conditions. The model predicts that (in order to overcome the intrinsic stability of the B state and to produce the appropriate cooperativity) channel activation is driven by an A state with a Kd in the 50 nM range, hence some 140-fold stronger than the apparent affinity of the open state deduced previously. According to the model, recovery from the desensitized states may occur via rapid transit through the A state with minimal channel opening, thus without necessarily undergoing a distinct recovery pathway, as assumed in the standard 'cycle' model. Transitions to the desensitized states by low concentration 'pre-pulses' are predicted to occur without significant channel opening, but equilibrium values of IC50 can be obtained only with long pre-pulse times. Predictions are also made concerning allosteric effectors and their possible role in coincidence detection. In terms of future developments, the analysis presented here provides a physical basis for constructing more biologically realistic models of synaptic modulation that may be applied to artificial neural networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3057, + "tag": "BioModels:BIOMD0000000001" + }, + { + "id": 3058, + "tag": "Cell surface receptor signaling pathway" + }, + { + "id": 3059, + "tag": "Neuromuscular synaptic transmission" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3060, + "tag": "Tetronarce californica" + }, + { + "id": 3061, + "tag": "Transmission of nerve impulse" + } + ], + "timestamp_created": "2025-02-03 15:02:44.047196+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000001", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2299": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2299, + "name": "Edelstein1996 - EPSP ACh species", + "repository_type": "biomodels", + "summary": "
Edelstein1996 - EPSP ACh species

Model of a nicotinic Excitatory Post-Synaptic Potential in a Torpedo electric organ. Acetylcholine is represented explicitely as a molecular species.

This model has initially been encoded using StochSim.

This model is described in the article:

Edelstein SJ, Schaad O, Henry E, Bertrand D, Changeux JP.
Biol. Cybern. 1996 Nov; 75(5):361-79

Abstract:

Nicotinic acetylcholine receptors are transmembrane oligomeric proteins that mediate interconversions between open and closed channel states under the control of neurotransmitters. Fast in vitro chemical kinetics and in vivo electrophysiological recordings are consistent with the following multi-step scheme. Upon binding of agonists, receptor molecules in the closed but activatable resting state (the Basal state, B) undergo rapid transitions to states of higher affinities with either open channels (the Active state, A) or closed channels (the initial Inactivatable and fully Desensitized states, I and D). In order to represent the functional properties of such receptors, we have developed a kinetic model that links conformational interconversion rates to agonist binding and extends the general principles of the Monod-Wyman-Changeux model of allosteric transitions. The crucial assumption is that the linkage is controlled by the position of the interconversion transition states on a hypothetical linear reaction coordinate. Application of the model to the peripheral nicotine acetylcholine receptor (nAChR) accounts for the main properties of ligand-gating, including single-channel events, and several new relationships are predicted. Kinetic simulations reveal errors inherent in using the dose-response analysis, but justify its application under defined conditions. The model predicts that (in order to overcome the intrinsic stability of the B state and to produce the appropriate cooperativity) channel activation is driven by an A state with a Kd in the 50 nM range, hence some 140-fold stronger than the apparent affinity of the open state deduced previously. According to the model, recovery from the desensitized states may occur via rapid transit through the A state with minimal channel opening, thus without necessarily undergoing a distinct recovery pathway, as assumed in the standard 'cycle' model. Transitions to the desensitized states by low concentration 'pre-pulses' are predicted to occur without significant channel opening, but equilibrium values of IC50 can be obtained only with long pre-pulse times. Predictions are also made concerning allosteric effectors and their possible role in coincidence detection. In terms of future developments, the analysis presented here provides a physical basis for constructing more biologically realistic models of synaptic modulation that may be applied to artificial neural networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000002 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3062, + "tag": "BioModels:BIOMD0000000002" + }, + { + "id": 3058, + "tag": "Cell surface receptor signaling pathway" + }, + { + "id": 3059, + "tag": "Neuromuscular synaptic transmission" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3060, + "tag": "Tetronarce californica" + }, + { + "id": 3061, + "tag": "Transmission of nerve impulse" + } + ], + "timestamp_created": "2025-02-03 15:15:38.284721+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000002", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2300": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2300, + "name": "Goldbeter1991 - Min Mit Oscil", + "repository_type": "biomodels", + "summary": "
Goldbeter1991 - Min Mit Oscil

Minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase.

This model has been generated by MathSBML 2.4.6 (14-January-2005) 14-January-2005 18:33:39.806932.

This model is described in the article:

Goldbeter A.
Proc. Natl. Acad. Sci. U.S.A. 1991; 88(20):9107-11

Abstract:

A minimal model for the mitotic oscillator is presented. The model, built on recent experimental advances, is based on the cascade of post-translational modification that modulates the activity of cdc2 kinase during the cell cycle. The model pertains to the situation encountered in early amphibian embryos, where the accumulation of cyclin suffices to trigger the onset of mitosis. In the first cycle of the bicyclic cascade model, cyclin promotes the activation of cdc2 kinase through reversible dephosphorylation, and in the second cycle, cdc2 kinase activates a cyclin protease by reversible phosphorylation. That cyclin activates cdc2 kinase while the kinase triggers the degradation of cyclin has suggested that oscillations may originate from such a negative feedback loop [F\u00e9lix, M. A., Labb\u00e9, J. C., Dor\u00e9e, M., Hunt, T. & Karsenti, E. (1990) Nature (London) 346, 379-382]. This conjecture is corroborated by the model, which indicates that sustained oscillations of the limit cycle type can arise in the cascade, provided that a threshold exists in the activation of cdc2 kinase by cyclin and in the activation of cyclin proteolysis by cdc2 kinase. The analysis shows how miototic oscillations may readily arise from time lags associated with these thresholds and from the delayed negative feedback provided by cdc2-induced cyclin degradation. A mechanism for the origin of the thresholds is proposed in terms of the phenomenon of zero-order ultrasensitivity previously described for biochemical systems regulated by covalent modification.

This model is hosted on BioModels Database and identified by: BIOMD0000000003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3063, + "tag": "Amphibia" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3064, + "tag": "BioModels:BIOMD0000000003" + }, + { + "id": 3065, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:15:39.048994+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000003", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2301": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2301, + "name": "Goldbeter1991 - Min Mit Oscil, Expl Inact", + "repository_type": "biomodels", + "summary": "
Goldbeter1991 - Min Mit Oscil, Expl Inact

This model represents the inactive forms of CDC-2 Kinase and Cyclin Protease as separate species, unlike the ODEs in the published paper, in which the equations for the inactive forms are substituted into the equations for the active forms using a mass conservation rule M+MI=1,X+XI=1. Mass is still conserved in this model through the explicit reactions M<->MI and X<->XI. The terms in the kinetic laws are identical to the corresponding terms in the kinetic laws in the published paper.

This model has been generated by MathSBML 2.4.6 (14-January-2005) 14-January-2005 18:37:35.503857.

This model is described in the article:

Goldbeter A.
Proc. Natl. Acad. Sci. USA 1991 Oct; 88(20):9107-11

Abstract:

A minimal model for the mitotic oscillator is presented. The model, built on recent experimental advances, is based on the cascade of post-translational modification that modulates the activity of cdc2 kinase during the cell cycle. The model pertains to the situation encountered in early amphibian embryos, where the accumulation of cyclin suffices to trigger the onset of mitosis. In the first cycle ofthe bicyclic cascade model, cyclin promotes the activation of cdc2 kinase through reversible dephosphorylation, and in the second cycle, cdc2 kinase activates a cyclin protease by reversible phosphorylation. That cyclin activates cdc2 kinase while the kinase triggers the degradation of cyclin has suggested that oscillations may originate from such a negative feedback loop [F\u00e9lix, M. A., Labb\u00e9, J. C., Dor\u00e9e, M., Hunt, T. & Karsenti, E. (1990) Nature (London) 346, 379-382]. Thisconjecture is corroborated by the model, which indicates that sustained oscillations of the limit cycle type can arise in the cascade, provided that a threshold exists in the activation of cdc2 kinase by cyclin and in the activation of cyclinproteolysis by cdc2 kinase. The analysis shows how miototic oscillations may readily arise from time lags associated with these thresholds and from the delayed negative feedback provided by cdc2-induced cyclin degradation. A mechanism for theorigin of the thresholds is proposed in terms of the phenomenon of zero-order ultrasensitivity previously described for biochemical systems regulated by covalent modification.

This model is hosted on BioModels Database and identified by: BIOMD0000000004 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3063, + "tag": "Amphibia" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3067, + "tag": "BioModels:BIOMD0000000004" + }, + { + "id": 3065, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:15:40.307469+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000004", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2302": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2302, + "name": "Tyson1991 - Cell Cycle 6 var", + "repository_type": "biomodels", + "summary": "
Tyson1991 - Cell Cycle 6 var

Mathematical model of the interactions of cdc2 and cyclin.

This model is described in the article:

Tyson JJ.
Proc. Natl. Acad. Sci. U.S.A. 1991; 88(16); 7328-32

Abstract:

The proteins cdc2 and cyclin form a heterodimer (maturation promoting factor) that controls the major events of the cell cycle. A mathematical model for the interactions of cdc2 and cyclin is constructed. Simulation and analysis of the model show that the control system can operate in three modes: as a steady state with high maturation promoting factor activity, as a spontaneous oscillator, or as an excitable switch. We associate the steady state with metaphase arrest in unfertilized eggs, the spontaneous oscillations with rapid division cycles in early embryos, and the excitable switch with growth-controlled division cycles typical of nonembryonic cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000005 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3068, + "tag": "BioModels:BIOMD0000000005" + }, + { + "id": 3069, + "tag": "Cell cycle - yeast - Saccharomyces cerevisiae (budding yeast)" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 3070, + "tag": "Opisthokonta" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:15:40.935557+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000005", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2303": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2303, + "name": "Tyson1991 - Cell Cycle 2 var", + "repository_type": "biomodels", + "summary": "
Tyson1991 - Cell Cycle 2 var

Mathematical model of the interactions of cdc2 and cyclin.

Description taken from the original Cellerator version of the model ( Tyson (1991, 2 variables) at http://www.cellerator.org ).

This model is described in the article:

Tyson JJ.
Proc. Natl. Acad. Sci. U.S.A. 1991; 88(16); 7328-32

Abstract:

The proteins cdc2 and cyclin form a heterodimer (maturation promoting factor) that controls the major events of the cell cycle. A mathematical model for the interactions of cdc2 and cyclin is constructed. Simulation and analysis of the model show that the control system can operate in three modes: as a steady state with high maturation promoting factor activity, as a spontaneous oscillator, or as an excitable switch. We associate the steady state with metaphase arrest in unfertilized eggs, the spontaneous oscillations with rapid division cycles in early embryos, and the excitable switch with growth-controlled division cycles typical of nonembryonic cells.

This is a two variable reduction of the larger 6-variable model published in the same paper. The equations are:

u'= k4(v-u)(alpha+u^2)-k6*u
v'=kappa-k6*u
z= v-u
with kappa = k1[aa]/[CT]

In the present implementation, an additional variable z is introduced with z = v-u is made, so that the different variables be interpreted as follows:

u=[activeMPF]/[CT]
v=([cyclin]+[preMPF]+[activeMPF])/[CT]
z=([ cyclin]+[preMPF])/[CT]
with [CT]=[CDC2]+{CDC2P]+[preMPF]+[aMPF].

The reactions included are only to show the flows between z and u, and do not influence the species, as they all are set to boundaryCondition=True , meaning, that they are only determined by the rate rules (explicit differential equations) and assignment rules.

If you set boundaryCondition=False and remove the rate rules for v, u and the the assignment rule for z, you get the more symmetrical, but equivalent, version from the Cellerator repository:

u'= k4*z*(alpha+u^2)-k6*u
z'=kappa-z*(alpha+u^2)

This model is hosted on BioModels Database and identified by: BIOMD0000000006 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3071, + "tag": "BioModels:BIOMD0000000006" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 3070, + "tag": "Opisthokonta" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:15:41.460208+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000006", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2304": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2304, + "name": "Novak1997 - Cell Cycle", + "repository_type": "biomodels", + "summary": "
Novak1997 - Cell Cycle

Modeling the control of DNA replication in fission yeast.

This model is described in the article:

Novak B., Tyson JJ.
Proc. Natl. Acad. Sci. U.S.A. 1997:94(17):9147-52

Abstract:

A central event in the eukaryotic cell cycle is the decision to commence DNA replication (S phase). Strict controls normally operate to prevent repeated rounds of DNA replication without intervening mitoses (\"endoreplication\") or initiation of mitosis before DNA is fully replicated (\"mitotic catastrophe\"). Some of the genetic interactions involved in these controls have recently been identified in yeast. From this evidence we propose a molecular mechanism of \"Start\" control in Schizosaccharomyces pombe. Using established principles of biochemical kinetics, we compare the properties of this model in detail with the observed behavior of various mutant strains of fission yeast: wee1(-) (size control at Start), cdc13Delta and rum1(OP) (endoreplication), and wee1(-) rum1Delta (rapid division cycles of diminishing cell size). We discuss essential features of the mechanism that are responsible for characteristic properties of Start control in fission yeast, to expose our proposal to crucial experimental tests.

This model is hosted on BioModels Database and identified by: BIOMD0000000007 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3072, + "tag": "BioModels:BIOMD0000000007" + }, + { + "id": 3073, + "tag": "Cell cycle" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3074, + "tag": "Schizosaccharomyces pombe" + } + ], + "timestamp_created": "2025-02-03 15:15:41.957466+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000007", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2305": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2305, + "name": "Gardner1998 - Cell Cycle Goldbeter", + "repository_type": "biomodels", + "summary": "
Gardner1998 - Cell Cycle Goldbeter

Mathematical modeling of cell division cycle (CDC) dynamics.

The SBML file has been generated by MathSBML 2.6.0.p960929 (Prerelease Version of 29-Sept-2006) 1-October-2006 15:36:36.076517.

This model is described in the article:

Gardner TS, Dolnik M, Collins JJ.
Proc. Natl. Acad. Sci. U.S.A. 1998 Nov; 95(24): 14190-14195

Abstract:

We demonstrate, by using mathematical modeling of cell division cycle (CDC) dynamics, a potential mechanism for precisely controlling the frequency of cell division and regulating the size of a dividing cell. Control of the cell cycle is achieved by artificially expressing a protein that reversibly binds and inactivates any one of the CDC proteins. In the simplest case, such as the checkpoint-free situation encountered in early amphibian embryos, the frequency of CDC oscillations can be increased or decreased by regulating the rate of synthesis, the binding rate, or the equilibrium constant of the binding protein. In a more complex model of cell division, where size-control checkpoints are included, we show that the same reversible binding reaction can alter the mean cell mass in a continuously dividing cell. Because this control scheme is general and requires only the expression of a single protein, it provides a practical means for tuning the characteristics of the cell cycle in vivo.

This model is hosted on BioModels Database and identified by: BIOMD0000000008.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3063, + "tag": "Amphibia" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3075, + "tag": "BioModels:BIOMD0000000008" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:15:42.442905+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000008", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2306": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2306, + "name": "Huang1996 - Ultrasensitivity in MAPK cascade", + "repository_type": "biomodels", + "summary": "
Huang1996 - Ultrasensitivity in MAPK cascade

The temporal sequence of kinase activation, from MAPKKK (activated RAF) to the final effector MAPK (activated ERK), is described here. It is observed from the model that there is an increase in sensitivity along the levels of the cascade, where the activity of MAPK reaches its maximal before MAPKKK.

This model is described in the article:

Huang CY, Ferrell JE Jr
Proc. Natl. Acad. Sci. U.S.A. 1996:93(19):10078-83

Abstract:

The mitogen-activated protein kinase (MAPK) cascade is a highly conserved series of three protein kinases implicated in diverse biological processes. Here we demonstrate that the cascade arrangement has unexpected consequences for the dynamics of MAPK signaling. We solved the rate equations for the cascade numerically and found that MAPK is predicted to behave like a highly cooperative enzyme, even though it was not assumed that any of the enzymes in the cascade were regulated cooperatively. Measurements of MAPK activation in Xenopus oocyte extracts confirmed this prediction. The stimulus/response curve of the MAPK was found to be as steep as that of a cooperative enzyme with a Hill coefficient of 4-5, well in excess of that of the classical allosteric protein hemoglobin. The shape of the MAPK stimulus/ response curve may make the cascade particularly appropriate for mediating processes like mitogenesis, cell fate induction, and oocyte maturation, where a cell switches from one discrete state to another.

The species K_PP_norm, KKK_P_norm and KK_PP_norm are the relative concentrations of the active MAPK, MAPKK and MAPKKK, that is the double, or single resp. phophorylated forms divided by the total concentrations of each kinase. For MAPK additionally the also active MAPK divided by the maximal concentration of active MAPK is given by rel_K_PP_max. The parameter K_PP_norm_max, the maximal ratio of active MapK, has to be calculated for each change of parameters.

This model is hosted on BioModels Database and identified by: BIOMD0000000009 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3076, + "tag": "BioModels:BIOMD0000000009" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:15:42.968296+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000009", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2307": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2307, + "name": "Kholodenko2000 - Ultrasensitivity and negative feedback bring oscillations in MAPK cascade", + "repository_type": "biomodels", + "summary": "
Kholodenko2000 - Ultrasensitivity and negative feedback bring oscillations in MAPK cascade

The combination of ultrasensitivity and negative feedback bring sustained oscillations in the mitogen-activated protein kinase cascades.

This model is described in the article:

Kholodenko BN
Eur. J. Biochem. 2000; 267(6):1583-8

Abstract:

Functional organization of signal transduction into protein phosphorylation cascades, such as the mitogen-activated protein kinase (MAPK) cascades, greatly enhances the sensitivity of cellular targets to external stimuli. The sensitivity increases multiplicatively with the number of cascade levels, so that a tiny change in a stimulus results in a large change in the response, the phenomenon referred to as ultrasensitivity. In a variety of cell types, the MAPK cascades are imbedded in long feedback loops, positive or negative, depending on whether the terminal kinase stimulates or inhibits the activation of the initial level. Here we demonstrate that a negative feedback loop combined with intrinsic ultrasensitivity of the MAPK cascade can bring about sustained oscillations in MAPK phosphorylation. Based on recent kinetic data on the MAPK cascades, we predict that the period of oscillations can range from minutes to hours. The phosphorylation level can vary between the base level and almost 100% of the total protein. The oscillations of the phosphorylation cascades and slow protein diffusion in the cytoplasm can lead to intracellular waves of phospho-proteins.

This model is hosted on BioModels Database and identified by: BIOMD0000000010 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3079, + "tag": "BioModels:BIOMD0000000010" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:15:43.471669+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000010", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2308": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2308, + "name": "Levchenko2000_MAPK_noScaffold", + "repository_type": "biomodels", + "summary": "

MAPK cascade in solution (no scaffold)

Description
This model describes a basic 3-\t\t\t\t\t\t\tstage Mitogen Activated Protein Kinase (MAPK)\t\t\t\t\t\t\t cascade in solution. This cascade is typically expressed as RAF=\t\t\t\t\t\t\t=>MEK==>MAPK (alternative forms are K3==>K2==>\t\t\t\t\t\t\tK1 and KKK==>KK==>K)\t\t\t\t\t\t\t. The input signal is RAFK (RAF Kinase)\t\t\t\t\t\t\t and the output signal is MAPKpp (\t\t\t\t\t\t\tdoubly phosphorylated form of MAPK)\t\t\t\t\t\t\t. RAFK phosphorylates RAF once to RAFp. RAFp,\t\t\t\t\t\t\t the phosphorylated form of RAF induces two phoshporylations of MEK,\t\t\t\t\t\t\tto MEKp and MEKpp. MEKpp,\t\t\t\t\t\t\t the doubly phosphorylated form of MEK,\t\t\t\t\t\t\t induces two phosphorylations of MAPK to MAPKp and MAPKpp.
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Reaction
a10\u00a0=\u00a05. MAPKPH\u00a0+\u00a0MAPKpp\u00a0->\u00a0MAPKppMAPKPH
a1\u00a0=\u00a01. RAF\u00a0+\u00a0RAFK\u00a0->\u00a0RAFRAFK
a2\u00a0=\u00a00.5 RAFp\u00a0+\u00a0RAFPH\u00a0->\u00a0RAFpRAFPH
a3\u00a0=\u00a03.3 MEK\u00a0+\u00a0RAFp\u00a0->\u00a0MEKRAFp
a4\u00a0=\u00a010. MEKp\u00a0+\u00a0MEKPH\u00a0->\u00a0MEKpMEKPH
a5\u00a0=\u00a03.3 MEKp\u00a0+\u00a0RAFp\u00a0->\u00a0MEKpRAFp
a6\u00a0=\u00a010. MEKPH\u00a0+\u00a0MEKpp\u00a0->\u00a0MEKppMEKPH
a7\u00a0=\u00a020. MAPK\u00a0+\u00a0MEKpp\u00a0->\u00a0MAPKMEKpp
a8\u00a0=\u00a05. MAPKp\u00a0+\u00a0MAPKPH\u00a0->\u00a0MAPKpMAPKPH
a9\u00a0=\u00a020. MAPKp\u00a0+\u00a0MEKpp\u00a0->\u00a0MAPKpMEKpp
d10\u00a0=\u00a00.4 MAPKppMAPKPH\u00a0->\u00a0MAPKPH\u00a0+\u00a0MAPKpp
d1\u00a0=\u00a00.4 RAFRAFK\u00a0->\u00a0RAF\u00a0+\u00a0RAFK
d2\u00a0=\u00a00.5 RAFpRAFPH\u00a0->\u00a0RAFp\u00a0+\u00a0RAFPH
d3\u00a0=\u00a00.42 MEKRAFp\u00a0->\u00a0MEK\u00a0+\u00a0RAFp
d4\u00a0=\u00a00.8 MEKpMEKPH\u00a0->\u00a0MEKp\u00a0+\u00a0MEKPH
d5\u00a0=\u00a00.4 MEKpRAFp\u00a0->\u00a0MEKp\u00a0+\u00a0RAFp
d6\u00a0=\u00a00.8 MEKppMEKPH\u00a0->\u00a0MEKPH\u00a0+\u00a0MEKpp
d7\u00a0=\u00a00.6 MAPKMEKpp\u00a0->\u00a0MAPK\u00a0+\u00a0MEKpp
d8\u00a0=\u00a00.4 MAPKpMAPKPH\u00a0->\u00a0MAPKp\u00a0+\u00a0MAPKPH
d9\u00a0=\u00a00.6 MAPKpMEKpp\u00a0->\u00a0MAPKp\u00a0+\u00a0MEKpp
k10\u00a0=\u00a00.1 MAPKppMAPKPH\u00a0->\u00a0MAPKp\u00a0+\u00a0MAPKPH
k1\u00a0=\u00a00.1 RAFRAFK\u00a0->\u00a0RAFK\u00a0+\u00a0RAFp
k2\u00a0=\u00a00.1 RAFpRAFPH\u00a0->\u00a0RAF\u00a0+\u00a0RAFPH
k3\u00a0=\u00a00.1 MEKRAFp\u00a0->\u00a0MEKp\u00a0+\u00a0RAFp
k4\u00a0=\u00a00.1 MEKpMEKPH\u00a0->\u00a0MEK\u00a0+\u00a0MEKPH
k5\u00a0=\u00a00.1 MEKpRAFp\u00a0->\u00a0MEKpp\u00a0+\u00a0RAFp
k6\u00a0=\u00a00.1 MEKppMEKPH\u00a0->\u00a0MEKp\u00a0+\u00a0MEKPH
k7\u00a0=\u00a00.1 MAPKMEKpp\u00a0->\u00a0MAPKp\u00a0+\u00a0MEKpp
k8\u00a0=\u00a00.1 MAPKpMAPKPH\u00a0->\u00a0MAPK\u00a0+\u00a0MAPKPH
k9\u00a0=\u00a00.1 MAPKpMEKpp\u00a0->\u00a0MAPKpp\u00a0+\u00a0MEKpp
Variable IC\u00a0\u00a0 ODE
MAPK 0.3 MAPK'[t]\u00a0==\u00a0d7*MAPKMEKpp[t]\u00a0+\u00a0k8*MAPKpMAPKPH[t]\u00a0-\t\t\t\t\t\t\t\u00a0a7*MAPK[t]*MEKpp[t]
MAPKMEKpp 0 MAPKMEKpp'[t]\u00a0==\u00a0-(d7*MAPKMEKpp[t])\u00a0-\u00a0k7*MAPKMEKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0a7*MAPK[t]*MEKpp[t]
MAPKp 0 MAPKp'[t]\u00a0==\u00a0k7*MAPKMEKpp[t]\u00a0-\u00a0a8*MAPKp[t]*MAPKPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0d8*MAPKpMAPKPH[t]\u00a0+\u00a0d9*MAPKpMEKpp[t]\u00a0+\u00a0k10*\t\t\t\t\t\t\tMAPKppMAPKPH[t]\u00a0-\u00a0a9*MAPKp[t]*MEKpp[t]
MAPKPH 0.3 MAPKPH'[t]\u00a0==\u00a0-(a8*MAPKp[t]*MAPKPH[t])\u00a0+\u00a0d8*MAPKpMAPKPH[\t\t\t\t\t\t\tt]\u00a0+\u00a0k8*MAPKpMAPKPH[t]\u00a0-\u00a0a10*MAPKPH[t]*MAPKpp[t]\u00a0+\t\t\t\t\t\t\t\u00a0d10*MAPKppMAPKPH[t]\u00a0+\u00a0k10*MAPKppMAPKPH[t]
MAPKpMAPKPH 0 MAPKpMAPKPH'[t]\u00a0==\u00a0a8*MAPKp[t]*MAPKPH[t]\u00a0-\u00a0d8*\t\t\t\t\t\t\tMAPKpMAPKPH[t]\u00a0-\u00a0k8*MAPKpMAPKPH[t]
MAPKpMEKpp 0 MAPKpMEKpp'[t]\u00a0==\u00a0-(d9*MAPKpMEKpp[t])\u00a0-\u00a0k9*MAPKpMEKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0a9*MAPKp[t]*MEKpp[t]
MAPKpp 0 MAPKpp'[t]\u00a0==\u00a0k9*MAPKpMEKpp[t]\u00a0-\u00a0a10*MAPKPH[t]*MAPKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0d10*MAPKppMAPKPH[t]
MAPKppMAPKPH 0 MAPKppMAPKPH'[t]\u00a0==\u00a0a10*MAPKPH[t]*MAPKpp[t]\u00a0-\u00a0d10*\t\t\t\t\t\t\tMAPKppMAPKPH[t]\u00a0-\u00a0k10*MAPKppMAPKPH[t]
MEK 0.2 MEK'[t]\u00a0==\u00a0k4*MEKpMEKPH[t]\u00a0+\u00a0d3*MEKRAFp[t]\u00a0-\t\t\t\t\t\t\t\u00a0a3*MEK[t]*RAFp[t]
MEKp 0 MEKp'[t]\u00a0==\u00a0-(a4*MEKp[t]*MEKPH[t])\u00a0+\u00a0d4*MEKpMEKPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0k6*MEKppMEKPH[t]\u00a0+\u00a0d5*MEKpRAFp[t]\u00a0+\u00a0k3*MEKRAFp[\t\t\t\t\t\t\tt]\u00a0-\u00a0a5*MEKp[t]*RAFp[t]
MEKPH 0.2 MEKPH'[t]\u00a0==\u00a0-(a4*MEKp[t]*MEKPH[t])\u00a0+\u00a0d4*MEKpMEKPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0k4*MEKpMEKPH[t]\u00a0-\u00a0a6*MEKPH[t]*MEKpp[t]\u00a0+\u00a0d6*\t\t\t\t\t\t\tMEKppMEKPH[t]\u00a0+\u00a0k6*MEKppMEKPH[t]
MEKpMEKPH 0 MEKpMEKPH'[t]\u00a0==\u00a0a4*MEKp[t]*MEKPH[t]\u00a0-\u00a0d4*MEKpMEKPH[t]\t\t\t\t\t\t\t\u00a0-\u00a0k4*MEKpMEKPH[t]
MEKpp 0 MEKpp'[t]\u00a0==\u00a0d7*MAPKMEKpp[t]\u00a0+\u00a0k7*MAPKMEKpp[t]\u00a0+\t\t\t\t\t\t\t\u00a0d9*MAPKpMEKpp[t]\u00a0+\u00a0k9*MAPKpMEKpp[t]\u00a0-\u00a0a7*MAPK[t]*\t\t\t\t\t\t\tMEKpp[t]\u00a0-\u00a0a9*MAPKp[t]*MEKpp[t]\u00a0-\u00a0a6*MEKPH[t]*MEKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0d6*MEKppMEKPH[t]\u00a0+\u00a0k5*MEKpRAFp[t]
MEKppMEKPH 0 MEKppMEKPH'[t]\u00a0==\u00a0a6*MEKPH[t]*MEKpp[t]\u00a0-\u00a0d6*MEKppMEKPH[\t\t\t\t\t\t\tt]\u00a0-\u00a0k6*MEKppMEKPH[t]
MEKpRAFp 0 MEKpRAFp'[t]\u00a0==\u00a0-(d5*MEKpRAFp[t])\u00a0-\u00a0k5*MEKpRAFp[t]\t\t\t\t\t\t\t\u00a0+\u00a0a5*MEKp[t]*RAFp[t]
MEKRAFp 0 MEKRAFp'[t]\u00a0==\u00a0-(d3*MEKRAFp[t])\u00a0-\u00a0k3*MEKRAFp[t]\u00a0+\t\t\t\t\t\t\t\u00a0a3*MEK[t]*RAFp[t]
RAF 0.4 RAF'[t]\u00a0==\u00a0-(a1*RAF[t]*RAFK[t])\u00a0+\u00a0k2*RAFpRAFPH[t]\u00a0+\t\t\t\t\t\t\t\u00a0d1*RAFRAFK[t]
RAFK 0.1 RAFK'[t]\u00a0==\u00a0-(a1*RAF[t]*RAFK[t])\u00a0+\u00a0d1*RAFRAFK[t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*RAFRAFK[t]
RAFp 0 RAFp'[t]\u00a0==\u00a0d5*MEKpRAFp[t]\u00a0+\u00a0k5*MEKpRAFp[t]\u00a0+\t\t\t\t\t\t\t\u00a0d3*MEKRAFp[t]\u00a0+\u00a0k3*MEKRAFp[t]\u00a0-\u00a0a3*MEK[t]*RAFp[t]\t\t\t\t\t\t\t\u00a0-\u00a0a5*MEKp[t]*RAFp[t]\u00a0-\u00a0a2*RAFp[t]*RAFPH[t]\u00a0+\u00a0d2*\t\t\t\t\t\t\tRAFpRAFPH[t]\u00a0+\u00a0k1*RAFRAFK[t]
RAFPH 0.3 RAFPH'[t]\u00a0==\u00a0-(a2*RAFp[t]*RAFPH[t])\u00a0+\u00a0d2*RAFpRAFPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0k2*RAFpRAFPH[t]
RAFpRAFPH 0 RAFpRAFPH'[t]\u00a0==\u00a0a2*RAFp[t]*RAFPH[t]\u00a0-\u00a0d2*RAFpRAFPH[t]\t\t\t\t\t\t\t\u00a0-\u00a0k2*RAFpRAFPH[t]
RAFRAFK 0 RAFRAFK'[t]\u00a0==\u00a0a1*RAF[t]*RAFK[t]\u00a0-\u00a0d1*RAFRAFK[t]\u00a0-\t\t\t\t\t\t\t\u00a0k1*RAFRAFK[t]

Generated by Cellerator Version 1.4.3 (6-March-2004) using Mathematica 5.0 \t\t\t\tfor Mac OS X (November 19, 2003), March 6, 2004 12:18:07, using (PowerMac,\t\t\t\tPowerPC,Mac OS X,MacOSX,Darwin)

author=B.E.Shapiro

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Elowitz2000 - Repressilator

This model describes the deterministic version of the repressilator system.

The authors of this model (see reference) use three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network that they called the repressilator. The model system was induced in Escherichia coli.

In this system, LacI (variable X is the mRNA, variable PX is the protein) inhibits the tetracycline-resistance transposon tetR (Y, PY describe mRNA and protein). Protein tetR inhibits the gene Cl from phage Lambda (Z, PZ: mRNA, protein),and protein Cl inhibits lacI expression. With the appropriate parameter values this system oscillates.

This model is described in the article:

Elowitz MB, Leibler S.
Nature. 2000 Jan; 403(6767):335-338

Abstract:

Networks of interacting biomolecules carry out many essential functions in living cells, but the 'design principles' underlying the functioning of such intracellular networks remain poorly understood, despite intensive efforts including quantitative analysis of relatively simple systems. Here we present a complementary approach to this problem: the design and construction of a synthetic network to implement a particular function. We used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli. The network periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells. The resulting oscillations, with typical periods of hours, are slower than the cell-division cycle, so the state of the oscillator has to be transmitted from generation to generation. This artificial clock displays noisy behaviour, possibly because of stochastic fluctuations of its components. Such 'rational network design may lead both to the engineering of new cellular behaviours and to an improved understanding of naturally occurring networks.

The model is based upon the equations in Box 1 of the paper; however, these equations as printed are dimensionless, and the correct dimensions have been returned to the equations, and the parameters set to reproduce Figure 1C (left).

The original model was generated by B.E. Shapiro using Cellerator version 1.0 update 2.1127 using Mathematica 4.2 for Mac OS X (June 4, 2002), November 27, 2002 12:15:32, using (PowerMac,PowerPC, Mac OS X,MacOSX,Darwin).

Nicolas Le Novere provided a corrected version generated by SBMLeditor on Sun Aug 20 00:44:05 BST 2006. This removed the EmptySet species. Ran fine on COPASI 4.0 build 18.

Bruce Shapiro revised the model with SBMLeditor on 23 October 2006 20:39 PST. This defines default units and correct reactions. The original Cellerator reactions while being mathematically correct did not accurately reflect the intent of the authors. The original notes were mostly removed because they were mostly incorrect in the revised version. Tested with MathSBML 2.6.0.

Nicolas Le Novere changed the volume to 1 cubic micrometre, to allow for stochastic simulation.

Changed by Lukas Endler to use the average livetime of mRNA instead of its halflife and a corrected value of alpha and alpha0.

Moreover, the equations used in this model were clarified, cf. below.

The equations given in box 1 of the original publication are rescaled in three respects (lowercase letters denote the rescaled, uppercase letters the unscaled number of molecules per cell):

\u03b1 in the equations should be in units of rescaled proteins per promotor and cell, and \u03b2 is the ratio of the protein to the mRNA decay rates or the ratio of the mRNA to the protein halflife.

In this version of the model \u03b1 and \u03b2 are calculated correspondingly to the article, while p and m where just replaced by P/Km resp. M/eff and all equations multiplied by 1/t_ave . Also, to make the equations easier to read, commonly used variables derived from the parameters given in the article by simple rules were introduced.

The parameters given in the article were:

promotor strength (repressed) ( tps_repr ): 5*10 -4 transcripts/(promotor*s)
promotor strength (full) ( tps_active ): 0.5 transcripts/(promotor*s)
mRNA half life, \u03c4 1/2,mRNA : 2 min
protein half life, \u03c4 1/2,prot : 10 min
K M : 40 monomers/cell
Hill coefficient n: 2

From these the following constants can be derived:

average mRNA lifetime ( t_ave ): \u03c4 1/2,mRNA /ln(2) = 2.89 min
mRNA decay rate ( kd_mRNA ): ln(2)/ \u03c4 1/2,mRNA = 0.347 min -1
protein decay rate ( kd_prot ): ln(2)/ \u03c4 1/2,prot
transcription rate ( a_tr ): tps_active*60 = 29.97 transcripts/min
transcription rate (repressed) ( a0_tr ): tps_repr*60 = 0.03 transcripts/min
translation rate ( k_tl ): eff*kd_mRNA = 6.93 proteins/(mRNA*min)
\u03b1 : a_tr*eff*\u03c4 1/2,prot /(ln(2)*K M ) = 216.4 proteins/(promotor*cell*Km)
\u03b1 0 : a0_tr*eff*\u03c4 1/2,prot /(ln(2)*K M ) = 0.2164 proteins/(promotor*cell*Km)
\u03b2 : k_dp/k_dm = 0.2

Annotation by the Kinetic Simulation Algorithm Ontology (KiSAO):

To reproduce the simulations run published by the authors, the model has to be simulated with any of two different approaches. First, one could use a deterministic method ( KISAO_0000035 ) with continuous variables ( KISAO_0000018 ). One sample algorithm to use is the CVODE solver ( KISAO_0000019 ). Second, one could simulate the system using Gillespie's direct method ( KISAO_0000029 ), which is a stochastic method ( KISAO_0000036 ) supporting adaptive timesteps ( KISAO_0000041 ) and using discrete variables ( KISAO_0000016 ).

This model is hosted on BioModels Database and identified by: BIOMD0000000012 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Applications of metabolic modelling to plant metabolism.
\t Poolman MG ,Assmus HE, Fell DA J. Exp. Bot.[2004 May; Volume: 55 (Issue: 400 )]: 1177-86 15073223,
Abstract:
In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes.



This model describes the non oxidative Calvin cycle as depicted in Poolman et al; J Exp Bot (2004) 55:1177-1186, fig 2. Reaction E20: E4P + F6P \u2194 S7P + GAP, is depicted in the figure, but not included in the model. The light reaction: ADP + P i \u2192 ATP, is included in the model, but only mentioned in the figure caption. The parameters and initial concentrations are the same as in Poolman, 1999, Computer Modelling Applied to the Calvin Cycle, PhD Thesis, Oxford Brookes University, Appendix A (available at at http://mudshark.brookes.ac.uk/index.php/Publications/Theses/Mark)

\u00a9 Mark Poolman (mgpoolman@brookes.ac.uk) 1995-2002
Based on a description by Pettersson 1988, Eur. J. Biochem. 175, 661-672
Differences are:
1 - Reactions assumed by Pettersson to be in equilibrium have fast mass action kinetics.
2 - Introduction of the parameter PGAxpMult to modulate PGA export through TPT.
3 - Introduction of Starch phosphorylase reaction.
This file may be freely copied or translated into other formats provided:
1 - This notice is reproduced in its entirety
2 - Published material making use of (information gained from) this model cites at least:
(a) Poolman, 1999, Computer Modelling Applied to the Calvin Cycle, PhD Thesis, Oxford Brookes University
(b) Poolman, Fell, and Thomas. 2000, Modelling Photosynthesis and its control, J. Exp. Bot. 51, 319-328
or
(c) Poolman et al. 2001, Computer modelling and experimental evidence for two steady states in the photosynthetic Calvin cycle. Eur. J. Biochem. 268, 2810-2816
Further related information may be found at http://mudshark.brookes.ac.uk.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3084, + "tag": "BioModels:BIOMD0000000013" + }, + { + "id": 3085, + "tag": "Nicotiana tabacum" + }, + { + "id": 3086, + "tag": "Reductive pentose-phosphate cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:16:56.007982+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000013", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2311": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2311, + "name": "Levchenko2000_MAPK_Scaffold", + "repository_type": "biomodels", + "summary": " \t\t\t

MAPK cascade on a scaffold

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Citation
Levchenko, A., Bruck, J., Sternberg, P.W. (2000)\t\t\t\t\t\t\t.Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties. Proc. Natl. Acad. Sci. USA 97(11):5818-5823.\t\t\t\t\t\t\t\t\t\t\t\t\thttp://www.pnas.org/cgi/content/abstract/97/11/5818\t\t\t\t\t\t
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Description
This model describes a basic 3-stage Mitogen Activated Protein Kinase (MAPK). Kinases in solution are written as K[3,J], K[2,J], K[1,J] for MAPKKK, MAPKK, and MAPK, respectively, J indicates the phosphorylation level, J=0,1 for K3 and J=0,1,2 for K2 and K1. Scaffolds have three slots, for MAPK, MAPKK, and MAPKKK, respectively. Bound and free scaffold are denoted as S[i,j,k], where i, j, and k indicate the binding of K[1,i], K[2,j] and K[3,k] in their respective slots. Here i,j=-1,0,1,or,2 and k=-1,0,or,1. A value of -1 means the slot is empty, 0 means the unphorphorylated kinase is bound, 1 means the singly phosphorylated kinase is bound, and 2 means the doubly phosphorylated kinase is bound. Thus S[1,-1,2] is a scaffold with K[3,1] bound in the first slot and K[1,2] in the third slot, while the second slot is empty.Note: Indices X[I,J,K] are translated into the unindexed variable X_I_J_K and so forth in the SBML. Negative indices are translated as mI, etc, thus S[1,-1,2] becomes S_1_m1_2.
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\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Reaction
a10\u00a0=\u00a05.MAPKP\u00a0+\u00a0K[1,\u00a02]\u00a0->\u00a0K_MAPKP[1,\u00a02]
a1\u00a0=\u00a01.RAFK\u00a0+\u00a0K[3,\u00a00]\u00a0->\u00a0K_RAFK[3,\u00a00]
a2\u00a0=\u00a00.5RAFP\u00a0+\u00a0K[3,\u00a01]\u00a0->\u00a0K_RAFP[3,\u00a01]
a3\u00a0=\u00a03.3K[2,\u00a00]\u00a0+\u00a0K[3,\u00a01]\u00a0->\u00a0K_K[2,\u00a00,\u00a03,\t\t\t\t\t\t\t\u00a01]
a4\u00a0=\u00a010.MEKP\u00a0+\u00a0K[2,\u00a01]\u00a0->\u00a0K_MEKP[2,\u00a01]
a5\u00a0=\u00a03.3K[2,\u00a01]\u00a0+\u00a0K[3,\u00a01]\u00a0->\u00a0K_K[2,\u00a01,\u00a03,\t\t\t\t\t\t\t\u00a01]
a6\u00a0=\u00a010.MEKP\u00a0+\u00a0K[2,\u00a02]\u00a0->\u00a0K_MEKP[2,\u00a02]
a7\u00a0=\u00a020.K[1,\u00a00]\u00a0+\u00a0K[2,\u00a02]\u00a0->\u00a0K_K[1,\u00a00,\u00a02,\t\t\t\t\t\t\t\u00a02]
a8\u00a0=\u00a05.MAPKP\u00a0+\u00a0K[1,\u00a01]\u00a0->\u00a0K_MAPKP[1,\u00a01]
a9\u00a0=\u00a020.K[1,\u00a01]\u00a0+\u00a0K[2,\u00a02]\u00a0->\u00a0K_K[1,\u00a01,\u00a02,\t\t\t\t\t\t\t\u00a02]
d10\u00a0=\u00a00.4K_MAPKP[1,\u00a02]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a02]
d1\u00a0=\u00a00.4K_RAFK[3,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0K[3,\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a00,\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a01,\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a02,\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a00,\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a01,\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a02,\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a00,\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a01,\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a02,\u00a00]
d2\u00a0=\u00a00.5K_RAFP[3,\u00a01]\u00a0->\u00a0RAFP\u00a0+\u00a0K[3,\u00a01]
d3\u00a0=\u00a00.42K_K[2,\u00a00,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
d4\u00a0=\u00a00.8K_MEKP[2,\u00a01]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a01]
d5\u00a0=\u00a00.4K_K[2,\u00a01,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
d6\u00a0=\u00a00.8K_MEKP[2,\u00a02]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a02]
d7\u00a0=\u00a00.6K_K[1,\u00a00,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
d8\u00a0=\u00a00.4K_MAPKP[1,\u00a01]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a01]
d9\u00a0=\u00a00.6K_K[1,\u00a01,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
k10\u00a0=\u00a00.1K_MAPKP[1,\u00a02]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a01]
k1\u00a0=\u00a00.1K_RAFK[3,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0K[3,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a00,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a01,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a02,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a00,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a01,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a02,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a00,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a01,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a02,\u00a01]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a00,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a01,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a02,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a00,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a01,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a02,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a00,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a01,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a02,\u00a00]
k2\u00a0=\u00a00.1K_RAFP[3,\u00a01]\u00a0->\u00a0RAFP\u00a0+\u00a0K[3,\u00a00]
k3\u00a0=\u00a00.1K_K[2,\u00a00,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
k3\u00a0=\u00a00.1S[0,\u00a00,\u00a01]\u00a0->\u00a0S[0,\u00a01,\u00a01]
k3\u00a0=\u00a00.1S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[-1,\u00a01,\u00a01]
k3\u00a0=\u00a00.1S[1,\u00a00,\u00a01]\u00a0->\u00a0S[1,\u00a01,\u00a01]
k3\u00a0=\u00a00.1S[2,\u00a00,\u00a01]\u00a0->\u00a0S[2,\u00a01,\u00a01]
k4\u00a0=\u00a00.1K_MEKP[2,\u00a01]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a00]
k5\u00a0=\u00a00.1K_K[2,\u00a01,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
k5a\u00a0=\u00a00.1S[0,\u00a01,\u00a01]\u00a0->\u00a0S[0,\u00a02,\u00a01]
k5a\u00a0=\u00a00.1S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[-1,\u00a02,\u00a01]
k5a\u00a0=\u00a00.1S[1,\u00a01,\u00a01]\u00a0->\u00a0S[1,\u00a02,\u00a01]
k5a\u00a0=\u00a00.1S[2,\u00a01,\u00a01]\u00a0->\u00a0S[2,\u00a02,\u00a01]
k6\u00a0=\u00a00.1K_MEKP[2,\u00a02]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a01]
k7\u00a0=\u00a00.1K_K[1,\u00a00,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
k7\u00a0=\u00a00.1S[0,\u00a02,\u00a00]\u00a0->\u00a0S[1,\u00a02,\u00a00]
k7\u00a0=\u00a00.1S[0,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\u00a0-1]
k7\u00a0=\u00a00.1S[0,\u00a02,\u00a01]\u00a0->\u00a0S[1,\u00a02,\u00a01]
k8\u00a0=\u00a00.1K_MAPKP[1,\u00a01]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a00]
k9\u00a0=\u00a00.1K_K[1,\u00a01,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
k9a\u00a0=\u00a00.1S[1,\u00a02,\u00a00]\u00a0->\u00a0S[2,\u00a02,\u00a00]
k9a\u00a0=\u00a00.1S[1,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\u00a0-1]
k9a\u00a0=\u00a00.1S[1,\u00a02,\u00a01]\u00a0->\u00a0S[2,\u00a02,\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[2,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a01]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a00,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a01,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a02,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a00,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a01,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a00,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a01,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[0,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[0,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a01]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\u00a01]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a00,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a01,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a02,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a00,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a01,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a00,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a01,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a01]
\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
VariableIC\u00a0\u00a0ODE
MAPKP0.3MAPKP'[t]\u00a0==\u00a0-(a8*MAPKP[t]*K[1,\u00a01][t])\u00a0-\u00a0a10*MAPKP[\t\t\t\t\t\t\tt]*K[1,\u00a02][t]\u00a0+\u00a0d8*K_MAPKP[1,\u00a01][t]\u00a0+\u00a0k8*K_MAPKP[\t\t\t\t\t\t\t1,\u00a01][t]\u00a0+\u00a0d10*K_MAPKP[1,\u00a02][t]\u00a0+\u00a0k10*K_MAPKP[1,\t\t\t\t\t\t\t\u00a02][t]
MEKP0.2MEKP'[t]\u00a0==\u00a0-(a4*MEKP[t]*K[2,\u00a01][t])\u00a0-\u00a0a6*MEKP[t]*\t\t\t\t\t\t\tK[2,\u00a02][t]\u00a0+\u00a0d4*K_MEKP[2,\u00a01][t]\u00a0+\u00a0k4*K_MEKP[2,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0d6*K_MEKP[2,\u00a02][t]\u00a0+\u00a0k6*K_MEKP[2,\u00a02]\t\t\t\t\t\t\t[t]
RAFK0.1RAFK'[t]\u00a0==\u00a0-(a1*RAFK[t]*K[3,\u00a00][t])\u00a0+\u00a0d1*K_RAFK[3,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k1*K_RAFK[3,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a00,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0k1a*RAFK[t]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[0,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[1,\u00a00,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0k1a*RAFK[t]*S[1,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[2,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[-1,\u00a00,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a01,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[-1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a0-1,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[\t\t\t\t\t\t\t0,\u00a00,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[0,\u00a01,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a01,\u00a00][\t\t\t\t\t\t\tt]\u00a0+\u00a0d1a*S_RAFK[0,\u00a02,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a0-1,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a0-1,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[1,\u00a00,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a01,\u00a00][\t\t\t\t\t\t\tt]\u00a0+\u00a0k1*S_RAFK[1,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[\t\t\t\t\t\t\t2,\u00a0-1,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[2,\u00a00,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a00,\u00a00][\t\t\t\t\t\t\tt]\u00a0+\u00a0d1a*S_RAFK[2,\u00a01,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a02,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a02,\u00a00][t]
RAFP0.3RAFP'[t]\u00a0==\u00a0-(a2*RAFP[t]*K[3,\u00a01][t])\u00a0+\u00a0d2*K_RAFP[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k2*K_RAFP[3,\u00a01][t]
K[1,\u00a00]0.4(K[1,\u00a00])'[t]\u00a0==\u00a0-(a7*K[1,\u00a00][t]*K[2,\u00a02][t])\u00a0+\t\t\t\t\t\t\t\u00a0d7*K_K[1,\u00a00,\u00a02,\u00a02][t]\u00a0+\u00a0k8*K_MAPKP[1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\u00a0+\u00a0koff*\t\t\t\t\t\t\tS[0,\u00a00,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a0-1][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a00]\t\t\t\t\t\t\t[t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a01][t]
K[1,\u00a01]0(K[1,\u00a01])'[t]\u00a0==\u00a0-(a8*MAPKP[t]*K[1,\u00a01][t])\u00a0-\t\t\t\t\t\t\t\u00a0a9*K[1,\u00a01][t]*K[2,\u00a02][t]\u00a0+\u00a0k7*K_K[1,\u00a00,\u00a02,\t\t\t\t\t\t\t\u00a02][t]\u00a0+\u00a0d9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0+\u00a0d8*\t\t\t\t\t\t\tK_MAPKP[1,\u00a01][t]\u00a0+\u00a0k10*K_MAPKP[1,\u00a02][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a01][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\u00a01][t]\u00a0+\u00a0kpoff*S[\t\t\t\t\t\t\t1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\t\t\t\t\t\t\t\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[1,\u00a02,\u00a01][t]
K[1,\u00a02]0(K[1,\u00a02])'[t]\u00a0==\u00a0-(a10*MAPKP[t]*K[1,\u00a02][t])\u00a0+\t\t\t\t\t\t\t\u00a0k9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0+\u00a0d10*K_MAPKP[1,\u00a02][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a02][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[2,\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
K[2,\u00a00]0.2(K[2,\u00a00])'[t]\u00a0==\u00a0-(a3*K[2,\u00a00][t]*K[3,\u00a01][t])\u00a0+\t\t\t\t\t\t\t\u00a0d3*K_K[2,\u00a00,\u00a03,\u00a01][t]\u00a0+\u00a0k4*K_MEKP[2,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a0-1][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[-1,\u00a00,\u00a00][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-\t\t\t\t\t\t\t1,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t2,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a00]\t\t\t\t\t\t\t[t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[\t\t\t\t\t\t\t2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a00,\u00a00][t]\u00a0+\u00a0koff*\t\t\t\t\t\t\tS[2,\u00a00,\u00a01][t]
K[2,\u00a01]0(K[2,\u00a01])'[t]\u00a0==\u00a0-(a4*MEKP[t]*K[2,\u00a01][t])\u00a0-\t\t\t\t\t\t\t\u00a0a5*K[2,\u00a01][t]*K[3,\u00a01][t]\u00a0+\u00a0k3*K_K[2,\u00a00,\u00a03,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0d5*K_K[2,\u00a01,\u00a03,\u00a01][t]\u00a0+\u00a0d4*\t\t\t\t\t\t\tK_MEKP[2,\u00a01][t]\u00a0+\u00a0k6*K_MEKP[2,\u00a02][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a01][t]
K[2,\u00a02]0(K[2,\u00a02])'[t]\u00a0==\u00a0-(a6*MEKP[t]*K[2,\u00a02][t])\u00a0-\t\t\t\t\t\t\t\u00a0a7*K[1,\u00a00][t]*K[2,\u00a02][t]\u00a0-\u00a0a9*K[1,\u00a01][t]*K[2,\t\t\t\t\t\t\t\u00a02][t]\u00a0+\u00a0d7*K_K[1,\u00a00,\u00a02,\u00a02][t]\u00a0+\u00a0k7*K_K[\t\t\t\t\t\t\t1,\u00a00,\u00a02,\u00a02][t]\u00a0+\u00a0d9*K_K[1,\u00a01,\u00a02,\u00a02][t]\t\t\t\t\t\t\t\u00a0+\u00a0k9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0+\u00a0k5*K_K[2,\u00a01,\t\t\t\t\t\t\t\u00a03,\u00a01][t]\u00a0+\u00a0d6*K_MEKP[2,\u00a02][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
K[3,\u00a00]0.3(K[3,\u00a00])'[t]\u00a0==\u00a0-(a1*RAFK[t]*K[3,\u00a00][t])\u00a0+\t\t\t\t\t\t\t\u00a0d1*K_RAFK[3,\u00a00][t]\u00a0+\u00a0k2*K_RAFP[3,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kon*\t\t\t\t\t\t\tK[3,\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a02,\u00a00]\t\t\t\t\t\t\t[t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*\t\t\t\t\t\t\tS[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a00,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t3,\u00a00][t]*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t3,\u00a00][t]*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[2,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t3,\u00a00][t]*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a02,\u00a00][t]
K[3,\u00a01]0(K[3,\u00a01])'[t]\u00a0==\u00a0-(a2*RAFP[t]*K[3,\u00a01][t])\u00a0-\t\t\t\t\t\t\t\u00a0a3*K[2,\u00a00][t]*K[3,\u00a01][t]\u00a0-\u00a0a5*K[2,\u00a01][t]*K[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0d3*K_K[2,\u00a00,\u00a03,\u00a01][t]\u00a0+\u00a0k3*K_K[\t\t\t\t\t\t\t2,\u00a00,\u00a03,\u00a01][t]\u00a0+\u00a0d5*K_K[2,\u00a01,\u00a03,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0k5*K_K[2,\u00a01,\u00a03,\u00a01][t]\u00a0+\u00a0k1*K_RAFK[3,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d2*K_RAFP[3,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]\t\t\t\t\t\t\t*S[-1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a0-1,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[-\t\t\t\t\t\t\t1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*\t\t\t\t\t\t\tK[3,\u00a01][t]*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01][\t\t\t\t\t\t\tt]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a00,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[\t\t\t\t\t\t\t1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\t\t\t\t\t\t\t\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[\t\t\t\t\t\t\t2,\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
K_K[1,\u00a00,\u00a02,\u00a02]0(K_K[1,\u00a00,\u00a02,\u00a02])'[t]\u00a0==\u00a0a7*K[1,\u00a00][t]*K[2,\t\t\t\t\t\t\t\u00a02][t]\u00a0-\u00a0d7*K_K[1,\u00a00,\u00a02,\u00a02][t]\u00a0-\u00a0k7*K_K[\t\t\t\t\t\t\t1,\u00a00,\u00a02,\u00a02][t]
K_K[1,\u00a01,\u00a02,\u00a02]0(K_K[1,\u00a01,\u00a02,\u00a02])'[t]\u00a0==\u00a0a9*K[1,\u00a01][t]*K[2,\t\t\t\t\t\t\t\u00a02][t]\u00a0-\u00a0d9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0-\u00a0k9*K_K[\t\t\t\t\t\t\t1,\u00a01,\u00a02,\u00a02][t]
K_K[2,\u00a00,\u00a03,\u00a01]0(K_K[2,\u00a00,\u00a03,\u00a01])'[t]\u00a0==\u00a0a3*K[2,\u00a00][t]*K[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0d3*K_K[2,\u00a00,\u00a03,\u00a01][t]\u00a0-\u00a0k3*K_K[\t\t\t\t\t\t\t2,\u00a00,\u00a03,\u00a01][t]
K_K[2,\u00a01,\u00a03,\u00a01]0(K_K[2,\u00a01,\u00a03,\u00a01])'[t]\u00a0==\u00a0a5*K[2,\u00a01][t]*K[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0d5*K_K[2,\u00a01,\u00a03,\u00a01][t]\u00a0-\u00a0k5*K_K[\t\t\t\t\t\t\t2,\u00a01,\u00a03,\u00a01][t]
K_MAPKP[1,\u00a01]0(K_MAPKP[1,\u00a01])'[t]\u00a0==\u00a0a8*MAPKP[t]*K[1,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0d8*K_MAPKP[1,\u00a01][t]\u00a0-\u00a0k8*K_MAPKP[1,\u00a01][t]
K_MAPKP[1,\u00a02]0(K_MAPKP[1,\u00a02])'[t]\u00a0==\u00a0a10*MAPKP[t]*K[1,\u00a02][t]\u00a0-\t\t\t\t\t\t\t\u00a0d10*K_MAPKP[1,\u00a02][t]\u00a0-\u00a0k10*K_MAPKP[1,\u00a02][t]
K_MEKP[2,\u00a01]0(K_MEKP[2,\u00a01])'[t]\u00a0==\u00a0a4*MEKP[t]*K[2,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0d4*K_MEKP[2,\u00a01][t]\u00a0-\u00a0k4*K_MEKP[2,\u00a01][t]
K_MEKP[2,\u00a02]0(K_MEKP[2,\u00a02])'[t]\u00a0==\u00a0a6*MEKP[t]*K[2,\u00a02][t]\u00a0-\t\t\t\t\t\t\t\u00a0d6*K_MEKP[2,\u00a02][t]\u00a0-\u00a0k6*K_MEKP[2,\u00a02][t]
K_RAFK[3,\u00a00]0(K_RAFK[3,\u00a00])'[t]\u00a0==\u00a0a1*RAFK[t]*K[3,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0d1*K_RAFK[3,\u00a00][t]\u00a0-\u00a0k1*K_RAFK[3,\u00a00][t]
K_RAFP[3,\u00a01]0(K_RAFP[3,\u00a01])'[t]\u00a0==\u00a0a2*RAFP[t]*K[3,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0d2*K_RAFP[3,\u00a01][t]\u00a0-\u00a0k2*K_RAFP[3,\u00a01][t]
S[-1,\u00a0-1,\u00a0-1]0.1(S[-1,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0-(kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t])\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*\t\t\t\t\t\t\tK[2,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-\t\t\t\t\t\t\t1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\u00a0-1]\t\t\t\t\t\t\t[t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*\t\t\t\t\t\t\tK[3,\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a0-1][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a0-1,\u00a00]0(S[-1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0koff*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\u00a00]\t\t\t\t\t\t\t[t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*\t\t\t\t\t\t\tK[1,\u00a02][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0+\u00a0koff*S[-\t\t\t\t\t\t\t1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[-1,\u00a02,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a0-1,\u00a00][t]
S[-1,\u00a0-1,\u00a01]0(S[-1,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]
S[-1,\u00a00,\u00a0-1]0(S[-1,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0koff*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a00,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a00,\u00a00]0(S[-1,\u00a00,\u00a00])'[t]\u00a0==\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*koff*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a00,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a00,\u00a00][t]
S[-1,\u00a00,\u00a01]0(S[-1,\u00a00,\u00a01])'[t]\u00a0==\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0k3*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0koff*S[-1,\u00a00,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpoff*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-\t\t\t\t\t\t\t1,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a01][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[2,\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a00,\u00a00][t]
S[-1,\u00a01,\u00a0-1]0(S[-1,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a01,\u00a00]0(S[-1,\u00a01,\u00a00])'[t]\u00a0==\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kpoff*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][\t\t\t\t\t\t\tt]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]
S[-1,\u00a01,\u00a01]0(S[-1,\u00a01,\u00a01])'[t]\u00a0==\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k3*S[-1,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0k5a*S[-1,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a02*kpoff*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[\t\t\t\t\t\t\t-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[2,\u00a01,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a01,\u00a00][t]
S[-1,\u00a02,\u00a0-1]0(S[-1,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a02,\u00a00]0(S[-1,\u00a02,\u00a00])'[t]\u00a0==\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[-1,\u00a02,\u00a00][t]\u00a0-\u00a0kpoff*S[-1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a02,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][\t\t\t\t\t\t\tt]*S[-1,\u00a02,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]
S[-1,\u00a02,\u00a01]0(S[-1,\u00a02,\u00a01])'[t]\u00a0==\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k5a*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a02*kpoff*S[-1,\u00a02,\u00a01][\t\t\t\t\t\t\tt]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a01][t]*S[-1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a02,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[-1,\u00a02,\u00a00][t]
S[0,\u00a0-1,\u00a0-1]0(S[0,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0koff*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[0,\u00a0-1,\u00a00]0(S[0,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*koff*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[0,\u00a0-1,\u00a00][t]
S[0,\u00a0-1,\u00a01]0(S[0,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpoff*S[0,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[0,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[0,\u00a0-1,\u00a00][t]
S[0,\u00a00,\u00a0-1]0(S[0,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*koff*S[0,\u00a00,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a00,\u00a01]\t\t\t\t\t\t\t[t]
S[0,\u00a00,\u00a00]0(S[0,\u00a00,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[0,\u00a00,\u00a0-1][t]\u00a0-\u00a03*koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[0,\u00a00,\u00a00][t]
S[0,\u00a00,\u00a01]0(S[0,\u00a00,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a00,\u00a0-1][t]\u00a0-\u00a0k3*S[0,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a02*koff*S[0,\u00a00,\u00a01][t]\u00a0-\u00a0kpoff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a00,\u00a00][t]
S[0,\u00a01,\u00a0-1]0(S[0,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpoff*S[0,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a01][t]
S[0,\u00a01,\u00a00]0(S[0,\u00a01,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[0,\u00a01,\u00a0-1][t]\u00a0-\u00a02*koff*S[0,\t\t\t\t\t\t\t\u00a01,\u00a00][t]\u00a0-\u00a0kpoff*S[0,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[0,\u00a01,\u00a00][t]
S[0,\u00a01,\u00a01]0(S[0,\u00a01,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k3*S[0,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k5a*S[0,\u00a01,\u00a01][t]\u00a0-\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0-\u00a02*kpoff*S[0,\u00a01,\u00a01][t]\u00a0+\u00a0k1*\t\t\t\t\t\t\tS_RAFK[0,\u00a01,\u00a00][t]
S[0,\u00a02,\u00a0-1]0(S[0,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0k7*S[0,\u00a02,\u00a0-1][t]\u00a0-\u00a0koff*S[0,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpoff*S[0,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01]\t\t\t\t\t\t\t[t]
S[0,\u00a02,\u00a00]0(S[0,\u00a02,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[0,\u00a02,\u00a0-1][t]\u00a0-\u00a0k7*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*koff*S[0,\u00a02,\u00a00][t]\u00a0-\u00a0kpoff*S[0,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[0,\u00a02,\u00a00][t]
S[0,\u00a02,\u00a01]0(S[0,\u00a02,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k5a*S[0,\u00a01,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k7*S[0,\u00a02,\u00a01][t]\u00a0-\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a02,\u00a01][t]\u00a0-\u00a02*kpoff*S[0,\u00a02,\u00a01][t]\u00a0+\u00a0k1*\t\t\t\t\t\t\tS_RAFK[0,\u00a02,\u00a00][t]
S[1,\u00a0-1,\u00a0-1]0(S[1,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[1,\u00a0-1,\u00a00]0(S[1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpoff*S[1,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][\t\t\t\t\t\t\tt]*S[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]
S[1,\u00a0-1,\u00a01]0(S[1,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a0-1,\u00a00][t]
S[1,\u00a00,\u00a0-1]0(S[1,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpoff*S[1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[1,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a01][t]
S[1,\u00a00,\u00a00]0(S[1,\u00a00,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a02*koff*S[1,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0-\u00a0kpoff*S[1,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a00,\u00a00][t]
S[1,\u00a00,\u00a01]0(S[1,\u00a00,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a0k3*S[1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0koff*S[1,\u00a00,\u00a01][t]\u00a0-\u00a02*kpoff*S[1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a00,\u00a00][t]
S[1,\u00a01,\u00a0-1]0(S[1,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01]\t\t\t\t\t\t\t[t]
S[1,\u00a01,\u00a00]0(S[1,\u00a01,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[1,\u00a01,\u00a0-1][t]\u00a0-\u00a0koff*S[1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*kpoff*S[1,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[1,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a01,\u00a00][t]
S[1,\u00a01,\u00a01]0(S[1,\u00a01,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k3*S[1,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k5a*S[1,\u00a01,\u00a01][t]\u00a0-\u00a03*kpoff*S[1,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a01,\u00a00][t]
S[1,\u00a02,\u00a0-1]0(S[1,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0k7*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0k9a*S[1,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a02*kpoff*S[1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[\t\t\t\t\t\t\t1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a01]\t\t\t\t\t\t\t[t]
S[1,\u00a02,\u00a00]0(S[1,\u00a02,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k7*S[0,\u00a02,\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a02]\t\t\t\t\t\t\t[t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[1,\u00a02,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0-\u00a0k9a*S[1,\u00a02,\u00a00][t]\u00a0-\u00a0koff*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*kpoff*S[1,\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a02,\u00a00][t]
S[1,\u00a02,\u00a01]0(S[1,\u00a02,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k7*S[0,\u00a02,\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a02]\t\t\t\t\t\t\t[t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0k5a*S[1,\u00a01,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a02,\u00a0-1][t]\u00a0-\u00a0k9a*S[1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a03*kpoff*S[1,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\t\t\t\t\t\t\t\u00a02,\u00a00][t]
S[2,\u00a0-1,\u00a0-1]0(S[2,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[2,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[2,\u00a0-1,\u00a00]0(S[2,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpoff*S[2,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][\t\t\t\t\t\t\tt]*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[2,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]
S[2,\u00a0-1,\u00a01]0(S[2,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[2,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a0-1,\u00a00][t]
S[2,\u00a00,\u00a0-1]0(S[2,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpoff*S[2,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[2,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a00,\u00a01][t]
S[2,\u00a00,\u00a00]0(S[2,\u00a00,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a02*koff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0-\u00a0kpoff*S[2,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[2,\u00a00,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a00,\u00a00][t]
S[2,\u00a00,\u00a01]0(S[2,\u00a00,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a0k3*S[2,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0koff*S[2,\u00a00,\u00a01][t]\u00a0-\u00a02*kpoff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a00,\u00a00][t]
S[2,\u00a01,\u00a0-1]0(S[2,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[2,\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[2,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a01]\t\t\t\t\t\t\t[t]
S[2,\u00a01,\u00a00]0(S[2,\u00a01,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[2,\u00a01,\u00a0-1][t]\u00a0-\u00a0koff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*kpoff*S[2,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[2,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a01,\u00a00][t]
S[2,\u00a01,\u00a01]0(S[2,\u00a01,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k3*S[2,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k5a*S[2,\u00a01,\u00a01][t]\u00a0-\u00a03*kpoff*S[2,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a01,\u00a00][t]
S[2,\u00a02,\u00a0-1]0(S[2,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0k9a*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a02*kpoff*S[2,\u00a02,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t3,\u00a01][t]*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
S[2,\u00a02,\u00a00]0(S[2,\u00a02,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k9a*S[1,\u00a02,\u00a00][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0koff*S[2,\u00a02,\u00a00][t]\u00a0-\u00a02*\t\t\t\t\t\t\tkpoff*S[2,\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0d1a*S_RAFK[2,\u00a02,\u00a00][t]
S[2,\u00a02,\u00a01]0(S[2,\u00a02,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k9a*S[1,\u00a02,\u00a01][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0k5a*S[2,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a02,\u00a0-1][t]\u00a0-\u00a03*kpoff*\t\t\t\t\t\t\tS[2,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a02,\u00a00][t]
S_RAFK[-1,\u00a0-1,\u00a00]0(S_RAFK[-1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*\t\t\t\t\t\t\tS_RAFK[-1,\u00a0-1,\u00a00][t]
S_RAFK[-1,\u00a00,\u00a00]0(S_RAFK[-1,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a00,\u00a00][t]
S_RAFK[-1,\u00a01,\u00a00]0(S_RAFK[-1,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a01,\u00a00][t]
S_RAFK[-1,\u00a02,\u00a00]0(S_RAFK[-1,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a02,\u00a00][t]
S_RAFK[0,\u00a0-1,\u00a00]0(S_RAFK[0,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a0-1,\u00a00][t]
S_RAFK[0,\u00a00,\u00a00]0(S_RAFK[0,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a00,\u00a00][t]
S_RAFK[0,\u00a01,\u00a00]0(S_RAFK[0,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a01,\u00a00][t]
S_RAFK[0,\u00a02,\u00a00]0(S_RAFK[0,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a02,\u00a00][t]
S_RAFK[1,\u00a0-1,\u00a00]0(S_RAFK[1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a0-1,\u00a00][t]
S_RAFK[1,\u00a00,\u00a00]0(S_RAFK[1,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a00,\u00a00][t]
S_RAFK[1,\u00a01,\u00a00]0(S_RAFK[1,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a01,\u00a00][t]
S_RAFK[1,\u00a02,\u00a00]0(S_RAFK[1,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a02,\u00a00][t]
S_RAFK[2,\u00a0-1,\u00a00]0(S_RAFK[2,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a0-1,\u00a00][t]
S_RAFK[2,\u00a00,\u00a00]0(S_RAFK[2,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a00,\u00a00][t]
S_RAFK[2,\u00a01,\u00a00]0(S_RAFK[2,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a01,\u00a00][t]
S_RAFK[2,\u00a02,\u00a00]0(S_RAFK[2,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a02,\u00a00][t]
\t\t\t

Generated by Cellerator Version 1.0 update 2.1203 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), December 4, 2002 15:06:10, using (PowerMac,PowerPC,Mac \t\t\t\tOS X,MacOSX,Darwin)

\t\t\t

author=B.E.Shapiro

\t\t
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3087, + "tag": "BioModels:BIOMD0000000014" + }, + { + "id": 3088, + "tag": "MAP-kinase scaffold activity" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:16:56.534859+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000014", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2312": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2312, + "name": "Curto1998 - purine metabolism", + "repository_type": "biomodels", + "summary": "
Curto1998 - purine metabolism

This is a purine metabolism model that is geared toward studies of gout.

The model uses Generalized Mass Action (GMA; i.e. power law) descriptions of reaction rate laws.

Such descriptions are local approximations that assume independent substrate binding.

This model is described in the article:

Curto R, Voit EO, Sorribas A, Cascante M.
Math Biosci 1998 Jul; 151(1): 1-49

Abstract:

Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy.

This model is hosted on BioModels Database and identified by: BIOMD0000000015.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3089, + "tag": "BioModels:BIOMD0000000015" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3091, + "tag": "Purine nucleobase metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:16:57.884061+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000015", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2313": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2313, + "name": "Goldbeter1995_CircClock", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3092, + "tag": "BioModels:BIOMD0000000016" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:16:58.375455+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000016", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2314": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2314, + "name": "Hoefnagel2002_PyruvateBranches", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Metabolic engineering of lactic acid bacteria, the combined approach: kinetic modelling, metabolic control and experimental analysis.
Hoefnagel MH, Starrenburg MJ, Martens DE, Hugenholtz J, Kleerebezem M, Van Swam II, Bongers R, Westerhoff HV, Snoep JL Microbiology2002 Apr; 148(4):1003-13 11932446,
Abstract:
Everyone who has ever tried to radically change metabolic fluxes knows that it is often harder to determine which enzymes have to be modified than it is to actually implement these changes. In the more traditional genetic engineering approaches \u2019bottle-necks\u2019 are pinpointed using qualitative, intuitive approaches, but the alleviation of suspected \u2019rate-limiting\u2019 steps has not often been successful. Here the authors demonstrate that a model of pyruvate distribution in Lactococcus lactis based on enzyme kinetics in combination with metabolic control analysis clearly indicates the key control points in the flux to acetoin and diacetyl, important flavour compounds. The model presented here (available at http://jjj.biochem.sun.ac.za/wcfs.html) showed that the enzymes with the greatest effect on this flux resided outside the acetolactate synthase branch itself. Experiments confirmed the predictions of the model, i.e. knocking out lactate dehydrogenase and overexpressing NADH oxidase increased the flux through the acetolactate synthase branch from 0 to 75% of measured product formation rates.

The paper does not have any figure to be put as a curation figure in the BioModels database. The model does reproduce the fluxes and control-coefficients given in Figure 2 and Table 4. To reproduce the results, the model was changed from the description in the article according to the model on JWS: the parameter Kmpyr was changed to 2.5 from 25. The equillibrium constant for PTA reaction (R4) was changed from 0.0281 to 0.0065. The Km for oxygen in the NOX reaction (R13) was changed from 0.01 to 0.2. Slight deviations between the values in the article and the model results may stem from different algorithms used for finding the steady state.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3095, + "tag": "BioModels:BIOMD0000000017" + }, + { + "id": 3096, + "tag": "Lactate metabolic process" + }, + { + "id": 3097, + "tag": "Lactococcus lactis" + }, + { + "id": 3098, + "tag": "Pyruvate metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:16:58.941849+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000017", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2315": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2315, + "name": "Morrison1989 - Folate Cycle", + "repository_type": "biomodels", + "summary": "
Morrison1989 - Folate Cycle
The model describes the folate cyclekinetics in breast cancer cells.

This model is described in the article:

Morrison PF, Allegra CJ.
J. Biol. Chem. 1989 Jun; 264(18): 10552-10566

Abstract:

A mathematical description of polyglutamated folate kinetics for human breast carcinoma cells (MCF-7) has been formulated based upon experimental folate, methotrexate (MTX), purine, and pyrimidine pool sizes as well as reaction rate parameters obtained from intact MCF-7 cells and their enzyme isolates. The schema accounts for the interconversion of highly polyglutamated tetrahydrofolate, 5-methyl-FH4, 5-10-CH2FH4, dihydrofolate (FH2), 10-formyl-FH4 (FFH4), and 10-formyl-FH2 (FFH2), as well as formation and transport of the MTX polyglutamates. Inhibition mechanisms have been chosen to reproduce all observed non-, un-, and pure competition inhibition patterns. Steady state folate concentrations and thymidylate and purine synthesis rates in drug-free intact cells were used to determine normal folate Vmax values. The resulting average-cell folate model, examined for its ability to predict folate pool behavior following exposure to 1 microM MTX over 21 h, agreed well with the experiment, including a relative preservation of the FFH4 and CH2FH4 pools. The results depend strongly on thymidylate synthase (TS) reaction mechanism, especially the assumption that MTX di- and triglutamates inhibit TS synthesis as greatly in the intact cell as they do with purified enzyme. The effects of cell cycle dependence of TS and dihydrofolate reductase activities were also examined by introducing G- to S-phase activity ratios of these enzymes into the model. For activity ratios down to at least 5%, cell population averaged folate pools were only slightly affected, while CH2FH4 pools in S-phase cells were reduced to as little as 10% of control values. Significantly, these folate pool dynamics were indicated to arise from both direct inhibition by MTX polyglutamates as well as inhibition by elevated levels of polyglutamated FH2 and FFH2.

Note: two flow BCs were converted into two downstream concentration BCs, thus removing the GAR and dUMP state variables. This dropped the number of ODEs from 21 to 19.

This model is hosted on BioModels Database and identified by: BIOMD0000000018.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3099, + "tag": "BioModels:BIOMD0000000018" + }, + { + "id": 3100, + "tag": "Folic acid metabolic process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3245, + "tag": "Breast cancer" + } + ], + "timestamp_created": "2025-02-03 15:16:59.413052+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000018", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2316": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2316, + "name": "Schoeberl2002 - EGF MAPK", + "repository_type": "biomodels", + "summary": " \t
\t Schoeberl2002 - EGF MAPK\t
\t
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Computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways.

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The initial model was constructed by Ken Lau from the MATLAB source code.

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This model is described in the article:

\t \t
Schoeberl B, Eichler-Jonsson C, Gilles ED, M\u00fcller G
\t
Nat. Biotechnol. 2002 Apr; 20(4): 370-375
\t

Abstract:

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We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos.

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This model does not exactly reproduce the results given in the original publication. It has, though, the same reaction graph and gives very similar time courses for the conditions depicted in the article.

\t
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Several corrections were applied to the parameters described in the paper's supplementary materials. Some parameter names were replaced by the corresponding identical ones: k(r)26 by k(r)18, k(r)27 by k(r)19, k(r)30 by k(r)20, k(r)38 by k(r)24, k(r)39 by k(r)37, k(r)46 by k(r)44, k51 by k49, k(r)54 by k(r)52 and k62 by k62. In particular the parameter values described in the column \"remark\" of supplementary table 1 override the values explicitely written in the numerical columns:

name in suppl. value used in model value used remarks
kr16 0.055 0.275
k30 7.9e6 2.1e6 as k20
kr30 0.3 0.4 as kr24
k38 3e7 1e7 as k20
kr38 0.055 0.55 as kr24
k52 1.1e5 5.34e7

k5 was used for v116, v119, v122 and v125 in addition of v107, v110 and v113 as listed in the legend of supplementary figure 2. k5 is calculated using th eformula from the matlab file not given in the supplements.

All rate constants were rescaled to minutes (k[min] = 60*k[sec]) and all second order rate constants additionally to molecules/cell with a cell volume of 1 picolitre (k[molecs/cell] = k[M]/(Vc*Na), with Vc=1e-12 l and Na = 6e23).

The association constant of internalized EGF was rescaled to molecules/endosome using an endosomal volume of 4.3 al (= 4.3*10 -18 litre).

The extracellular EGF concentration was converted to molecules per picolitre with a MW of 6045 Da.

[ng/ml] [numb/pl]
50 4962
0.5 49.6
0.125 12.4

With the initial conditions given in the paper, the results could not be reproduced at all. Therefore the initial conditions used in the MATLAB file were adopted for SHC (1.01 * 10 5 instead of 1.01 * 10 6 ) and Ras_GDP. (7.2 * 10 4 instead of 1.14 * 10 7 )

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This model is hosted on BioModels Database and identified by: BIOMD0000000019.

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To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3101, + "tag": "BioModels:BIOMD0000000019" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3103, + "tag": "Signaling by EGFR" + } + ], + "timestamp_created": "2025-02-03 15:16:59.963925+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000019", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2317": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2317, + "name": "hodgkin-huxley squid-axon 1952", + "repository_type": "biomodels", + "summary": "

This is an implementation of the Hodgkin-Huxley model of the electrical behavior of the squid axon membrane from:
A quantitative description of membrane current and its application to conduction and excitation in nerve.
A. L. Hodgkin and A. F. Huxley. (1952 ) Journal of Physiology 119(4): pp 500-544; pmID: 12991237 .

Abstract:
This article concludes a series of papers concerned with the flow of electric current through the surface membrane of a giant nerve fibre (Hodgkin,Huxley & Katz, 1952; Hodgkin & Huxley, 1952 a-c). Its general object is to discuss the results of the preceding papers (Part I), to put them into mathematical form (Part II) and to show that they will account for conduction and excitation in quantitative terms (Part III).

This SBML model uses the same formalism as the one described in the paper, contrary to modern versions:
* V describes the the membrane depolarisation relative to the resting potential of the membrane
* opposing to modern practice, depolarization is negative , not positive , so the sign of V is different
* inward transmembrane currents are considered positive (inward current positive), contrary to modern use
The changeable parameters are the equilibrium potentials( E_R, E_K, E_L, E_Na ), the membrane depolarization ( V ) and the initial sodium and potassium channel activation and inactivation coefficients ( m,h,n ). The initial values of m,h,n for the model were calculated for V = 0 using the equations from the article: n t=0 = \u03b1_n V=0 /(\u03b1_n V=0 + \u03b2_n V=0 ) and equivalent expressions for h and m .
For single excitations apply a negative membrane depolarization (V < 0). To achieve oscillatory behavior either change the resting potential to a more positive value or apply a constant negative ionic current (I < 0).
Two assignments for parameters in the model, alpha_n and alpha_m, are not defined at V=-10 resp. -25 mV. We did not change this to keep the formulas similar to the original publication and as most integrators seem not to have any problem with it. The limits at V=-10 and -25 mV are 0.1 for alpha_n resp. 1 for alpha_m.
We thank Mark W. Johnson for finding a bug in the model and his helpful comments.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3104, + "tag": "BioModels:BIOMD0000000020" + }, + { + "id": 3105, + "tag": "Loligo forbesii" + }, + { + "id": 3106, + "tag": "Neuronal action potential propagation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3107, + "tag": "Voltage-gated potassium channel activity" + }, + { + "id": 3108, + "tag": "Voltage-gated sodium channel activity" + }, + { + "id": 3109, + "tag": "giant axon" + } + ], + "timestamp_created": "2025-02-03 15:17:00.476886+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000020", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2318": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2318, + "name": "Leloup1999_CircClock", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3110, + "tag": "BioModels:BIOMD0000000021" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:01.006843+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000021", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2319": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2319, + "name": "Ueda2001_CircClock", + "repository_type": "biomodels", + "summary": "

Bruce Shapiro: Generated by Cellerator Version 1.0 update 3.0303 using Mathematica 4.1 for Microsoft Windows (June 13, 2001), April 2, 2003 16:49:13, using (PC,x86, Microsoft Windows,WindowsNT,Windows)

Bruce Shapiro: Corrected 29 March 2005

Nicolas Le Nov\u00e8re: Added Dbt and Cyc species, and the corresponding reactions. 23 April 2005


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3111, + "tag": "BioModels:BIOMD0000000022" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:01.476399+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2320": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2320, + "name": "Rohwer2001_Sucrose", + "repository_type": "biomodels", + "summary": "

SBML Level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS .

Run this model online at http://jjj.biochem.sun.ac.za .

To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3112, + "tag": "BioModels:BIOMD0000000023" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3113, + "tag": "Saccharum officinarum" + }, + { + "id": 3114, + "tag": "Sucrose biosynthetic process" + } + ], + "timestamp_created": "2025-02-03 15:17:01.959185+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000023", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2321": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2321, + "name": "Scheper1999_CircClock", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3115, + "tag": "BioModels:BIOMD0000000024" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3116, + "tag": "Mesocricetus auratus" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:02.444461+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000024", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2322": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2322, + "name": "Smolen2002_CircClock", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3117, + "tag": "BioModels:BIOMD0000000025" + }, + { + "id": 3118, + "tag": "Circadian regulation of gene expression" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:02.991124+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000025", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2323": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2323, + "name": "Markevich2004_MAPK_orderedElementary", + "repository_type": "biomodels", + "summary": "

The model corresponds to the schemas 1 and 2 of Markevich et al 2004, as described in the figure 1 and the supplementary table S1. Phosphorylations and dephosphorylations follow distributive ordered kinetics. The phosphorylations are modeled with three elementary reactions:
E+S<=>ES->E+P
The dephosphorylations are modeled with five elementary reactions:
E+S<=>ES->EP<=>E+P

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3119, + "tag": "BioModels:BIOMD0000000026" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:17:03.487334+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000026", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2324": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2324, + "name": "Markevich2004 - MAPK double phosphorylation, ordered Michaelis-Menton", + "repository_type": "biomodels", + "summary": "
Markevich2004 - MAPK double phosphorylation,ordered Michaelis-Menton
The model corresponds to the schemas 1and 2 of Markevich et al 2004, as described in the figure 1 andmodelled using Michaelis-Menten like kinetics. Phosphorylations anddephosphorylations follow distributive ordered kinetics. Itreproduces figure 3 of the main article.

This model is described in the article:

Markevich NI, Hoek JB, Kholodenko BN.
J. Cell Biol. 2004 Feb; 164(3): 353-359

Abstract:

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model is hosted on BioModels Database and identified by: BIOMD0000000027.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3120, + "tag": "BioModels:BIOMD0000000027" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:17:03.946376+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000027", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2325": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2325, + "name": "Markevich2004_MAPK_phosphoRandomElementary", + "repository_type": "biomodels", + "summary": "

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table S2. Phosphorylations follow distributive random kinetics, while dephosphorylations follow an ordered mechanism. The phosphorylations are modeled with three elementary reactions:
E+S<=>ES->E+P
The dephosphorylations are modeled with five elementary reactions:
E+S<=>ES->EP<=>E+P
The model reproduces figure 5 in the main article.

The model is further described in:
Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. Markevich NI, Hoek JB, Kholodenko BN. J Cell Biol. 2004 Feb 2;164(3):353-9.
PMID: 14744999 ; DOI: 10.1083/jcb.200308060
Abstract:
Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3121, + "tag": "BioModels:BIOMD0000000028" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:17:04.416866+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000028", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2326": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2326, + "name": "Markevich2004_MAPK_phosphoRandomMM", + "repository_type": "biomodels", + "summary": "

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table S3, and modelled using Michaelis-Menten like kinetics. Phosphorylations follow distributive random kinetics, while dephosphorylations follow an ordered mechanism.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2007 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3122, + "tag": "BioModels:BIOMD0000000029" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:17:05.011767+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000029", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2327": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2327, + "name": "Markevich2004_MAPK_AllRandomElementary", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3123, + "tag": "BioModels:BIOMD0000000030" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:17:05.487736+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000030", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2328": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2328, + "name": "Markevich2004_MAPK_orderedMM2kinases", + "repository_type": "biomodels", + "summary": "

The model describes the double phosphorylation of MAP kinase by an ordered mechanism using the Michaelis-Menten formalism. Two different enzymes, MAPKK1 and MAPKK2, successively phosphorylate the MAP kinase, but one and the same phosphatase dephosphorylates both sites.
The model reproduces figure S9 in the supplemental material of the article.

The model is further described in:
Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. Markevich NI, Hoek JB, Kholodenko BN. J Cell Biol. 2004 Feb 2;164(3):353-9.
PMID: 14744999 ; DOI: 10.1083/jcb.200308060
Abstract:
Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3124, + "tag": "BioModels:BIOMD0000000031" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 15:17:06.073541+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000031", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2329": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2329, + "name": "Kofahl2004_PheromonePathway", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modelling the dynamics of the yeast pheromone pathway.
Kofahl B, Klipp E Yeast[2004 Jul; Volume: 21 (Issue: 10 )] Page info: 831-50 15300679,
Abstract:
We present a mathematical model of the dynamics of the pheromone pathways in haploid yeast cells of mating type MATa after stimulation with pheromone alpha-factor. The model consists of a set of differential equations and describes the dynamics of signal transduction from the receptor via several steps, including a G protein and a scaffold MAP kinase cascade, up to changes in the gene expression after pheromone stimulation in terms of biochemical changes (complex formations, phosphorylations, etc.). The parameters entering the models have been taken from the literature or adapted to observed time courses or behaviour. Using this model we can follow the time course of the various complex formation processes and of the phosphorylation states of the proteins involved. Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone.


The model was updated on 21st October 2010, by Vijayalakshmi Chelliah.
The following changes were made: 1) The model has been converted to SBML l2v4.2) The model has been recurated and the curation figure was updated (units are in nanoMolar; but the publication has units in microMolar). Simulations were done using Copasi v4.6 (Build 32).3) Notes have been added.4) Annotation for one of the species has been corrected (Complex M).



SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

The following are the four major differences between the original publication by Kofahl et al and the model that actually is able to replicate the results as depicted in the publication (those corrections have been made in agreement with the authors):
1. Bar1 is the inactive protease present inside the cell but the publication wrongly mentions that Bar1 is also the protease that is present on the extracellular surface.
The model correctly names the protease in it's different forms by calling inactive Bar1 within the cell as Bar1, active Bar1 within the cell as Bar1a and extracellular Bar1 as Bar1aex
2. The initial amount of Alpha-factor is given as 1000nM but the model uses a value of 100nM.
3. The value of the paramenter k8 is given as 0.33 but the model uses a value of 0.033.
4. The value of the paramenter k41 is given as 0.002 but the model uses a value of 0.02.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/).(http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3125, + "tag": "BioModels:BIOMD0000000032" + }, + { + "id": 3126, + "tag": "Response to pheromone" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:06.543389+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000032", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2330": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2330, + "name": "Brown2004 - NGF and EGF signaling", + "repository_type": "biomodels", + "summary": "
Brown2004 - NGF and EGF signaling

This model is described in the article:

Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA.
Phys Biol 2004 Dec; 1(3-4): 184-195

Abstract:

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'


The figures in the paper show results from computationsperformed over an ensemble of all parameter sets that fit theavailable data. This file contains only the best fit parameters.The full ensemble of parameters is available athttp://www.lassp.cornell.edu/sethna/GeneDynamics/PC12DataFiles/(Also, the best-fit parameter set produces a curve for DN Rap1 thatis less \"peakish\" than the ensemble average.)

The conversion factors for EGF and NGF concentrations accountfor their molecular weights and the density of cells in the culturedish. These concentrations are saturating, so the exact values arenot critical.

Because the Erk data fit to measure only fold changes inactivity, there is no absolute scale for the y-axes. Thus thecurves from this file have different magnitudes than thosepublished.

To reproduce the figures from the paper:
2a) For EGF stimulation, set the initial concentration of EGFto 100 ng/ml * 100020 (molecule/cell)/(ng/ml) = 10002000.
For NGF stimulation, set the initial concentration of NGF to50 ng/ml * 4560 (molecule/cell)/(ng/ml) = 456000
5a) To simulate LY294002 addition, set kPI3KRas and kPI3K to0.
5b) To simulate a dominant negative Rap1, set kRap1ToBRaf to0.
To simulate a dominant negative Ras, set kRasToRaf1 andkPI3KRas to 0.

Almost all the data fit with this model by the authors arefrom Western blots. Given the uncertainties in antibodyeffectiveness and other factors, one can't a priori derive aconversion between the arbitrary units for a given set of data andmolecules per cell. So the authors used an adjustable \"scalefactor\" that converts between molecules per cell and Western blotunits.

For the EGF stimulation data in figure 2a) the scale factorconversion is 1.414e-05 (U/mg)/(molecule/cell). For the NGFstimulation data in figure 2a) it is 7.135e-06(U/mg)/(molecule/cell).

This model is hosted on BioModels Database and identified by: BIOMD0000000033.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3128, + "tag": "BioModels:BIOMD0000000033" + }, + { + "id": 3129, + "tag": "ERK1 and ERK2 cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:07.012037+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000033", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2331": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2331, + "name": "Smolen2004_CircClock", + "repository_type": "biomodels", + "summary": "

No inititial conditions are specified in the paper. Because there is a basal rate of transcription for each gene, it doesn't matter much. With the agreement of Paul Smolen, I put all the initial concentration at 0.001 nanomoles. N Le Nov\u00e8re.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3131, + "tag": "BioModels:BIOMD0000000034" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:07.471915+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000034", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2332": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2332, + "name": "Vilar2002_Oscillator", + "repository_type": "biomodels", + "summary": "

Minimal Model for Circadian Oscillations

Citation
Vilar JMG, Kueh HY, Barkai N, Leibler S,\t\t\t\t\t\t\t (2002)\t\t\t\t\t\t\t. Mechanisms of noise resistance in genetic oscillators,\t\t\t\t\t\t\t PNAS, 99(9):5988-5992. http://www.pnas.org/cgi/content/abstract/\t\t\t\t\t\t\t99/9/5988
Description
A minimal model of genomically based oscillation,\t\t\t\t\t\t\t based on two mutually interacting genes,\t\t\t\t\t\t\t an activator and a repressor. Postive feedback is provided by the activator protein,\t\t\t\t\t\t\t which binds to the promotors of both the activator and the repressor genes. Negative feedback is provided by the repressor protein which binds to the activator protein.
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Reaction
alphaA\u00a0=\u00a050 DA\u00a0->\u00a0DA\u00a0+\u00a0MA
alphaAp\u00a0=\u00a0500 DAp\u00a0->\u00a0DAp\u00a0+\u00a0MA
alphaR\u00a0=\u00a00.01 DR\u00a0->\u00a0DR\u00a0+\u00a0MR
alphaRp\u00a0=\u00a050 DRp\u00a0->\u00a0DRp\u00a0+\u00a0MR
betaA\u00a0=\u00a050 MA\u00a0->\u00a0A\u00a0+\u00a0MA
betaR\u00a0=\u00a05 MR\u00a0->\u00a0MR\u00a0+\u00a0R
gammaA\u00a0=\u00a01 A\u00a0+\u00a0DA\u00a0->\u00a0DAp
gammaC\u00a0=\u00a02 A\u00a0+\u00a0R\u00a0->\u00a0C
gammaR\u00a0=\u00a01 A\u00a0+\u00a0DR\u00a0->\u00a0DRp
deltaA\u00a0=\u00a01 A\u00a0->\u00a0EmptySet
deltaA\u00a0=\u00a01 C\u00a0->\u00a0R
deltaMA\u00a0=\u00a010 MA\u00a0->\u00a0EmptySet
deltaMR\u00a0=\u00a00.5 MR\u00a0->\u00a0EmptySet
deltaR\u00a0=\u00a00.2 R\u00a0->\u00a0EmptySet
thetaA\u00a0=\u00a050 DAp\u00a0->\u00a0A\u00a0+\u00a0DA
thetaR\u00a0=\u00a0100 DRp\u00a0->\u00a0A\u00a0+\u00a0DR
Variable IC\u00a0\u00a0 ODE
A 0 A'[t]\u00a0==\u00a0-(deltaA*A[t])\u00a0-\u00a0gammaA*A[t]*DA[t]\u00a0+\u00a0thetaA*DAp[\t\t\t\t\t\t\tt]\u00a0-\u00a0gammaR*A[t]*DR[t]\u00a0+\u00a0thetaR*DRp[t]\u00a0+\u00a0betaA*MA[t]\u00a0-\t\t\t\t\t\t\t\u00a0gammaC*A[t]*R[t]
C 0 C'[t]\u00a0==\u00a0-(deltaA*C[t])\u00a0+\u00a0gammaC*A[t]*R[t]
DA 1 DA'[t]\u00a0==\u00a0-(gammaA*A[t]*DA[t])\u00a0+\u00a0thetaA*DAp[t]
DAp 0 DAp'[t]\u00a0==\u00a0gammaA*A[t]*DA[t]\u00a0-\u00a0thetaA*DAp[t]
DR 1 DR'[t]\u00a0==\u00a0-(gammaR*A[t]*DR[t])\u00a0+\u00a0thetaR*DRp[t]
DRp 0 DRp'[t]\u00a0==\u00a0gammaR*A[t]*DR[t]\u00a0-\u00a0thetaR*DRp[t]
MA 0 MA'[t]\u00a0==\u00a0alphaA*DA[t]\u00a0+\u00a0alphaAp*DAp[t]\u00a0-\u00a0deltaMA*MA[t]
MR 0 MR'[t]\u00a0==\u00a0alphaR*DR[t]\u00a0+\u00a0alphaRp*DRp[t]\u00a0-\u00a0deltaMR*MR[t]
R 0 R'[t]\u00a0==\u00a0deltaA*C[t]\u00a0+\u00a0betaR*MR[t]\u00a0-\u00a0deltaR*R[t]\u00a0-\t\t\t\t\t\t\t\u00a0gammaC*A[t]*R[t]

Generated by Cellerator Version 1.0 update 2.1127 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), November 27, 2002 12:17:46, using (PowerMac,PowerPC,\t\t\t\tMac OS X,MacOSX,Darwin)

author=B.E.Shapiro

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3132, + "tag": "BioModels:BIOMD0000000035" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:07.970910+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000035", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2333": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2333, + "name": "Tyson1999_CircClock", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3133, + "tag": "BioModels:BIOMD0000000036" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:08.436911+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000036", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2334": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2334, + "name": "Marwan2003 - Genetics, regulatory hierarchy between genes", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3134, + "tag": "BioModels:BIOMD0000000037" + }, + { + "id": 3135, + "tag": "Far-red light signaling pathway" + }, + { + "id": 3136, + "tag": "Physarum polycephalum" + }, + { + "id": 3137, + "tag": "Regulation of sporulation resulting in formation of a cellular spore" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:08.936635+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000037", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2335": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2335, + "name": "Rohwer2000_Phosphotransferase_System", + "repository_type": "biomodels", + "summary": "

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3138, + "tag": "BioModels:BIOMD0000000038" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 3139, + "tag": "Phosphoenolpyruvate-dependent sugar phosphotransferase system" + }, + { + "id": 3140, + "tag": "Regulation of glucose transport" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:09.519226+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000038", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2336": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2336, + "name": "Marhl2000_CaOscillations", + "repository_type": "biomodels", + "summary": "

In order to reproduce the model, the volume of all compartment is set to 1, and the stoichiometry of CaER and CaM has been set to 0.25, corresponding to betaER/rhoER and betaM/rhoM described in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Field-Noyes Model of BZ Reaction

Citation
R.J.Field and R.M.Noyes,J.Chem.Phys.60,1877 (1974)
Description
Field Noyes Version of Belousov-Zhabotinsky Reaction. BrO3 is held constant; HOBr is typically ignored,\t\t\t\t\t\t\t and can be replaced by an empty-set. The stoichiometry f is typically taken as 1/2 or 1.\t\t\t\t\t\t\t.

Initially Generated by Cellerator Version 1.0 update 2.1220 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), December 26, 2002 10:43:53, using (PowerMac,PowerPC,\t\t\t\tMac OS X,MacOSX,Darwin). author=B.E.Shapiro

Modified with SBMLeditor by Nicolas Le Nov\u00e8re, to fit the original article.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3142, + "tag": "BioModels:BIOMD0000000040" + }, + { + "id": 3143, + "tag": "Rhythmic behavior" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:10.468307+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000040", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2338": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2338, + "name": "Kongas2007 - Creatine Kinase in energy metabolic signaling in muscle", + "repository_type": "biomodels", + "summary": "
Kongas2007 - Creatine Kinase in energy metabolic signaling in muscle

This model is described in the article:

Olav Kongas and Johannes H. G. M. van Beek
Available from Nature Precedings

Abstract:

There has been much debate on the mechanism of regulation of mitochondrial ATP synthesis to balance ATP consumption during changing cardiac workloads. A key role of creatine kinase (CK) isoenzymes in this regulation of oxidative phosphorylation and in intracellular energy transport had been proposed, but has in the mean time been disputed for many years. It was hypothesized that high-energy phosphorylgroups are obligatorily transferred via CK; this is termed the phosphocreatine shuttle. The other important role ascribed to the CK system is its ability to buffer ADP concentration in cytosol near sites of ATP hydrolysis.

Almost all of the experiments to determine the role of CK had been done in the steady state, but recently the dynamic response of oxidative phosphorylation to quick changes in cytosolic ATP hydrolysis has been assessed at various levels of inhibition of CK. Steady state models of CK function in energy transfer existed but were unable to explain the dynamic response with CK inhibited.

The aim of this study was to explain the mode of functioning of the CK system in heart, and in particular the role of different CK isoenzymes in the dynamic response to workload steps. For this purpose we used a mathematical model of cardiac muscle cell energy metabolism containing the kinetics of the key processes of energy production, consumption and transfer pathways. The model underscores that CK plays indeed a dual role in the cardiac cells. The buffering role of CK system is due to the activity of myofibrillar CK (MMCK) while the energy transfer role depends on the activity of mitochondrial CK (MiCK). We propose that this may lead to the differences in regulation mechanisms and energy transfer modes in species with relatively low MiCK activity such as rabbit in comparison with species with high MiCK activity such as rat.

The model needed modification to explain the new type of experimental data on the dynamic response of the mitochondria. We submit that building a Virtual Muscle Cell is not possible without continuous experimental tests to improve the model. In close interaction with experiments we are developing a model for muscle energy metabolism and transport mediated by the creatine kinase isoforms which now already can explain many different types of experiments.

The model has been designed according to the spirit of the paper. The list of rate in the appendix has been corrected as follow:

  1. d[ATP]/dt = (-Vhyd -Vmmck +Jatp) / Vcyt
  2. d[ADP]/dt = ( Vhyd +Vmmck +Jadp) / Vcyt
  3. d[PCr]/dt = ( Vmmck +Jpcr ) / Vcyt
  4. d[Cr]/dt = (-Vmmck +Jpcr ) / Vcyt
  5. d[Pi]/dt = ( Vhyd + Jpi ) / Vcyt
  6. d[ATPi]/dt = (+Vsyn -Vmick -Jatp) / Vims
  7. d[ADPi]/dt = (-Vsyn +Vmick -Jadp) / Vims
  8. d[PCri]/dt = ( Vmick -Jpcr ) / Vims
  9. d[Cri]/dt = (-Vmick -Jpcr ) / Vims
  10. d[Pii]/dt = (-Vsyn -Jpi ) / Vims

This model is hosted on BioModels Database and identified by: BIOMD0000000041 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3144, + "tag": "ATP metabolic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3145, + "tag": "BioModels:BIOMD0000000041" + }, + { + "id": 3146, + "tag": "Creatine metabolic process" + }, + { + "id": 3147, + "tag": "Oryctolagus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:10.934443+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000041", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2339": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2339, + "name": "Nielsen1998_Glycolysis", + "repository_type": "biomodels", + "summary": "

This model was automatically converted from model BIOMD0000000042 by using libSBML .

According to the BioModels Database terms of use , this generated model is not related with model BIOMD0000000042 any more.

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Borghans1997 - Calcium Oscillation - Model 1

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis (see below). This model corresponds to the first hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6622689184 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3150, + "tag": "BioModels:BIOMD0000000043" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3152, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:12.105705+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000043", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2341": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2341, + "name": "Borghans1997 - Calcium Oscillation - Model 2", + "repository_type": "biomodels", + "summary": "
Borghans1997 - Calcium Oscillation - Model 2

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis (see below). This model corresponds to the second hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6622948601 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3154, + "tag": "BioModels:BIOMD0000000044" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3152, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:12.623891+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000044", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2342": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2342, + "name": "Borghans1997 - Calcium Oscillation - Model 3", + "repository_type": "biomodels", + "summary": "
Borghans1997 - Calcium Oscillation - Model 3

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis. This model corresponds to the third hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6623009547 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3155, + "tag": "BioModels:BIOMD0000000045" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3152, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:13.165331+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000045", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2343": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2343, + "name": "Olsen2003_peroxidase", + "repository_type": "biomodels", + "summary": "

Notes of the BioModels curators:

The current model reproduce the figure 7, panel B of the paper. Note that there is a typo in the figure. The ordinates represent the concentration of peroxyde, as stated in the legend, and not of oxygen. The model has been tested in COPASI (http://www.copasi.org/, build 13).

Notes of the original version of the SBML file:

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3156, + "tag": "Armoracia rusticana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3157, + "tag": "BioModels:BIOMD0000000046" + }, + { + "id": 3158, + "tag": "NADH peroxidase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:13.664425+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000046", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2344": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2344, + "name": "Oxhamre2005_Ca_oscillation", + "repository_type": "biomodels", + "summary": "

The model should reproduce the figure 1C of the article (successfully reproduced in MathSBML). If your software does not support the variable \"time\", you can replace the assignmentRule:
n = n0 * [ exp(-kbN*time) + kappa * (1 - exp(-kbN*time))]
by
n = n0 * kappa


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Kholodenko1999 - EGFR signaling

This model has been generated by the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based \t modelling using JWS Online , Bioinformatics, 20:2143-2144

This model is described in the article:

Kholodenko BN, Demin OV, Moehren G, Hoek JB
J. Biol. Chem. 1999 Oct; 274(42): 30169-30181

Abstract:

During the past decade, our knowledge of molecular mechanisms involved in growth factor signaling has proliferated almost explosively. However, the kinetics and control of information transfer through signaling networks remain poorly understood. This paper combines experimental kinetic analysis and computational modeling of the short term pattern of cellular responses to epidermal growth factor (EGF) in isolated hepatocytes. The experimental data show transient tyrosine phosphorylation of the EGF receptor (EGFR) and transient or sustained response patterns in multiple signaling proteins targeted by EGFR. Transient responses exhibit pronounced maxima, reached within 15-30 s of EGF stimulation and followed by a decline to relatively low (quasi-steady-state) levels. In contrast to earlier suggestions, we demonstrate that the experimentally observed transients can be accounted for without requiring receptor-mediated activation of specific tyrosine phosphatases, following EGF stimulation. The kinetic model predicts how the cellular response is controlled by the relative levels and activity states of signaling proteins and under what conditions activation patterns are transient or sustained. EGFR signaling patterns appear to be robust with respect to variations in many elemental rate constants within the range of experimentally measured values. On the other hand, we specify which changes in the kinetic scheme, rate constants, and total amounts of molecular factors involved are incompatible with the experimentally observed kinetics of signal transfer. Quantitation of signaling network responses to growth factors allows us to assess how cells process information controlling their growth and differentiation.

The model correctly reproduces all the figures from the paper. The curation has been done using SBMLodeSolver.

This model is hosted on BioModels Database and identified by: BIOMD0000000048 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3160, + "tag": "BioModels:BIOMD0000000048" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:14.706042+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000048", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2346": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2346, + "name": "Sasagawa2005_MAPK", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Prediction and validation of the distinct dynamics of transient and sustained ERK activation.
Sasagawa S, Ozaki Y, Fujita K, Kuroda S Nat. Cell Biol.[2005 Apr; Volume: 7 (Issue: 4 )]: 365-73 15793571,
Abstract:
To elucidate the hidden dynamics of extracellular-signal-regulated kinase (ERK) signalling networks, we developed a simulation model of ERK signalling networks by constraining in silico dynamics based on in vivo dynamics in PC12 cells. We predicted and validated that transient ERK activation depends on rapid increases of epidermal growth factor and nerve growth factor (NGF) but not on their final concentrations, whereas sustained ERK activation depends on the final concentration of NGF but not on the temporal rate of increase. These ERK dynamics depend on Ras and Rap1 dynamics, the inactivation processes of which are growth-factor-dependent and -independent, respectively. Therefore, the Ras and Rap1 systems capture the temporal rate and concentration of growth factors, and encode these distinct physical properties into transient and sustained ERK activation, respectively.

Dynamics of active Ras, active Rap1 and phosphorylated ERK were correctly reproduced with CellDesigner 3.0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3162, + "tag": "BioModels:BIOMD0000000049" + }, + { + "id": 3129, + "tag": "ERK1 and ERK2 cascade" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:15.230373+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000049", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2347": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2347, + "name": "Martins2003_AmadoriDegradation", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Kinetic modelling of Amadori N-(1-deoxy-D-fructos-1-yl)-glycine degradation pathways. Part II--kinetic analysis.
Martins SI, Van Boekel MA. Carbohydr Res2003 Jul;338(16):1665-78. 12873422,
Abstract:
A kinetic model for N-(1-deoxy-Image -fructos-1-yl)-glycine (DFG) thermal decomposition was proposed. Two temperatures (100 and 120 \u00b0C) and two pHs (5.5 and 6.8) were studied. The measured responses were DFG, 3-deoxyosone, 1-deoxyosone, methylglyoxal, acetic acid, formic acid, glucose, fructose, mannose and melanoidins. For each system the model parameters, the rate constants, were estimated by non-linear regression, via multiresponse modelling. The determinant criterion was used as the statistical fit criterion. Model discrimination was performed by both chemical insight and statistical tests (Posterior Probability and Akaike criterion). Kinetic analysis showed that at lower pH DFG 1,2-enolization is favoured whereas with increasing pH 2,3-enolization becomes a more relevant degradation pathway. The lower amount observed of 1-DG is related with its high reactivity. It was shown that acetic acid, a main degradation product from DFG, was mainly formed through 1-DG degradation. Also from the estimated parameters 3-DG was found to be the main precursor in carbohydrate fragments formation, responsible for colour formation. Some indication was given that as the reaction proceeded other compounds besides DFG become reactants themselves with the formation among others of methylglyoxal. The multiresponse kinetic analysis was shown to be both helpful in deriving relevant kinetic parameters as well as in obtaining insight into the reaction mechanism.

Model was intially tested in Jarnac.

The model was recently updated on 9th July 2010. The reference publication has reported two models M1 and M2, where the parameter values are given for conditions A) 100oC, pH5.5, B) 120oC, pH5.5, C) 100oC, pH6.8 and D) 120oC, pH6.8.

This model corresponds to the model M2 with condition 100oC, pH6.8

The model reproduces Figure 6 of the reference publication. The curation figure was recently added

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3163, + "tag": "BioModels:BIOMD0000000050" + }, + { + "id": 3164, + "tag": "Organic substance catabolic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:15.781637+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000050", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2348": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2348, + "name": "Chassagnole2002_Carbon_Metabolism", + "repository_type": "biomodels", + "summary": "

The model reproduces Figures 4,5 and 6 of the publication. The analytical functions for cometabolites Catp, Camp, Cnadph, and Cnadp slightly differ from the equations given in the paper. These changes were made in consultation with Dr. Christophe Chassagnole and are essential for reproducing the figures. The dependency of the rate of change of extracellular glucose concentration on the ratio of biomass concentration to specific weight of biomass (Cx*rPTS/Rhox) is taken into account by appropriately adjusting the stoichiometries of the species involved in the phosphotransferase system (rPTS). The rmax values for the various reactions are obtained from experiments and are not provided in the paper. However, these were personally communicated to the JWS repository. The model has been successfully tested on MathSBML.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3165, + "tag": "BioModels:BIOMD0000000051" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:16.499308+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000051", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2349": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2349, + "name": "Brands2002 - Monosaccharide-casein systems", + "repository_type": "biomodels", + "summary": "
Brands2002 - Monosaccharide-casein systems

A kinetic model of the Maillard reaction occurring in heated monosaccharide-casein system.

This model is described in the article:

Brands CM, van Boekel MA
Journal of Agricultural and Food Chemistry. 2002, 50(23):6725-6739

Abstract:

In the present study, a kinetic model of the Maillard reaction occurring in heated monosaccharide-casein systems was proposed. Its parameters, the reaction rate constants, were estimated via multiresponse modeling. The determinant criterion was used as the statistical fit criterion instead of the familiar least squares to avoid statistical problems. The kinetic model was extensively tested by varying the reaction conditions. Different sugars (glucose, fructose, galactose, and tagatose) were studied regarding their effect on the reaction kinetics. This study has shown the power of multiresponse modeling for the unraveling of complicated reaction routes as occur in the Maillard reaction. The iterative process of proposing a model, confronting it with experiments, and criticizing the model was passed through four times to arrive at a model that was largely consistent with all results obtained. A striking difference was found between aldose and ketose sugars as suggested by the modeling results: not the ketoses themselves but only their reaction products were found to be reactive in the Maillard reaction.

This model is hosted on BioModels Database and identifiedby: MODEL8177704759 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3166, + "tag": "BioModels:BIOMD0000000052" + }, + { + "id": 3167, + "tag": "Monosaccharide binding" + }, + { + "id": 3168, + "tag": "Monosaccharide biosynthetic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:16.980989+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000052", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2350": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2350, + "name": "Ferreira2003_CML_generation2", + "repository_type": "biomodels", + "summary": "

The model should reproduce the figure 2F of the article.

The equation 7 has been split into equations 7a-7c, in order to take into account the different flux rates of Lysine and CML formation from Schiff.

The model was tested in Jarnac (SBML L2 V1) and Copasi (SBML L2 V3).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3169, + "tag": "BioModels:BIOMD0000000053" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + }, + { + "id": 3246, + "tag": "Amyloidosis" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-02-03 15:17:17.457527+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000053", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2351": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2351, + "name": "Ataullahkhanov1996_Adenylate", + "repository_type": "biomodels", + "summary": "

The model reproduces ion and adenylate pool concentration corresponding to line 2 of Fig 3 of the publication. This model was tested successfully on Jarnac


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3170, + "tag": "BioModels:BIOMD0000000054" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:17.938908+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000054", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2352": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2352, + "name": "Locke2005 - Circadian Clock", + "repository_type": "biomodels", + "summary": "
Locke2005 - Circadian Clock

SBML model of the interlocked feedback loop network

The model describes the circuit depicted in Fig. 4 and reproduces the simulations in Figure 5A and 5B. It provides initial conditions, parameter values and rules for the production rates of the following species: LHY mRNA (cLm), cytoplasmic LHY (cLc), nuclear LHY (cLn), TOC1 mRNA (cTm), cytoplasmic TOC1 (cTc), nuclear TOC1 (cTn),X mRNA (cXm), cytoplasmic X (cXc), nuclear X (cXn), Y mRNA (cYm), cytoplasmic Y (cYc), nuclear Y (cYn), nuclear P (cPn). This model was successfully tested on MathSBML and SBML ODE Solver.

Fig 5B is not in the right phase. However, the data is correct relative to the light/dark bars at the top of the figure.

This model is described in the article:

Locke JC, Southern MM, Kozma-Bogn\u00e1r L, Hibberd V, Brown PE, Turner MS, Millar AJ
Molecular Systems Biology [2005; 1: 2005.0013]

Abstract:

Circadian clocks involve feedback loops that generate rhythmic expression of key genes. Molecular genetic studies in the higher plant Arabidopsis thaliana have revealed a complex clock network. The first part of the network to be identified, a transcriptional feedback loop comprising TIMING OF CAB EXPRESSION 1 (TOC1), LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), fails to account for significant experimental data. We develop an extended model that is based upon a wider range of data and accurately predicts additional experimental results. The model comprises interlocking feedback loops comparable to those identified experimentally in other circadian systems. We propose that each loop receives input signals from light, and that each loop includes a hypothetical component that had not been explicitly identified. Analysis of the model predicted the properties of these components, including an acute light induction at dawn that is rapidly repressed by LHY and CCA1. We found this unexpected regulation in RNA levels of the evening-expressed gene GIGANTEA (GI), supporting our proposed network and making GI a strong candidate for this component.

This model is hosted on BioModels Database and identified by: BIOMD0000000055 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3172, + "tag": "BioModels:BIOMD0000000055" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:18.412076+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000055", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2353": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2353, + "name": "Chen2004 - Cell Cycle Regulation", + "repository_type": "biomodels", + "summary": "
Chen2004 - Cell Cycle Regulation

This is a hypothetical model of cell cycle that describes the molecular mechanism for regulating DNA synthesis, bud emergence, mitosis, and cell division in budding yeast.

This model is described in the article:

Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ
Mol. Biol. Cell. [2004 Aug; Volume: 15 (Issue: 8 )] Page info: 3841-62

Abstract:

The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.

The model reproduces the time profiles of the different species in Figure 2 of the paper. The figure depicts the cycle of a daughter cell. Since the Mass Doubling Time (MDT) is 90 minutes, time t=90 from the model simulation will correspond to time t=0 in the paper. The model was successfully tested using MathSBML and SBML odeSolver.

To create a valid SBML file, a local parameter k=1 was added in the reaction 'Inactivation_274_CDC20'. Also, in order to annotate the protein and to have the interaction in the reaction graph to match figure 1 of the article, the reaction rate constants k_{mad2}, k_{bub2} and k_{lte1} are considered as species and renamed as MAD2, BUB2 and LTE1 in the model.

This model is hosted on BioModels Database and identified by: BIOMD0000000056 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3173, + "tag": "BioModels:BIOMD0000000056" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:18.883375+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000056", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2354": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2354, + "name": "Sneyd2002_IP3_Receptor", + "repository_type": "biomodels", + "summary": "

This model was successfully tested on Jarnac and MathSBML. The model reproduces the time profile of \"Open Probability\" of the receptor as shown in Figure 4 of the publication. The value of calcium ion concentration \"c\" in this model is 10 microM.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3174, + "tag": "BioModels:BIOMD0000000057" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3152, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3175, + "tag": "Rattus rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:19.373832+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000057", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2355": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2355, + "name": "Bindschadler2001_coupled_Ca_oscillators", + "repository_type": "biomodels", + "summary": "

The model reproduces the same amplitude antiphase calcium oscillations of coupled cells depicted in Figure 5B of the publication. This model was successfully tested on Jarnac and MathSBML. The values of \"h1\" and \"h2\" are not given in the publication, but the antiphase oscillations are reproduced over a narrow range of values of h1, h2,c1,c2,D and p. The values of D and p are given, while the other values were plugged in, in order to simulate the time profiles shown in the Figure. The time t=0 in the figure may have been fixed after the system was allowed to settle, and hence does not correspond to the t=0 of the simulation.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3176, + "tag": "BioModels:BIOMD0000000058" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:20.122107+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000058", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2356": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2356, + "name": "Fridlyand2003_Calcium_flux", + "repository_type": "biomodels", + "summary": "

The model reproduces block A of Fig 5 and also Fig 3 (without the inclusion of Tg action). The model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3177, + "tag": "BioModels:BIOMD0000000059" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:20.651745+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000059", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2357": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2357, + "name": "Keizer1996_Ryanodine_receptor_adaptation", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of Open probability of the ryanodine receptor as shown in Fig 2A and 2B of the paper. The model was successfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3179, + "tag": "BioModels:BIOMD0000000060" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:21.199235+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000060", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2358": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2358, + "name": "Hynne2001_Glycolysis", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 6 of the paper. The stoichiometry and rate of reactions involving uptake of metabolites from extracellular medium have been changed corresponding to Yvol (ratio of extracellular volume to cytosolic volume) mentioned in the publication. The extracellular and cytosolic compartments have been set to 1. Concentration of extracellular glucose, GlcX, is set to 6.7 according to the equation for cellular glucose uptake rate in Table 7 of the paper. The model was successfully tested on MathSBML and Jarnac

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3180, + "tag": "BioModels:BIOMD0000000061" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:21.676310+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000061", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2359": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2359, + "name": "Bhartiya2003_Tryptophan_operon", + "repository_type": "biomodels", + "summary": "

SBML level 2 code originaly generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

BioModels Curation : The model reproduces Fig 3 of the publication. By substituting a value of 1.4 for Tex it is possible to reproduce Fig 3C and 3D(iii), Fig 3A and 3D(i), are obtained by setting Tex=0. Also, note that the tryptophan concentrations have been normalized by 82 micromolar in the figures; the normalized concetrations can be obtained via the parameters To/s/t_norm. The model was successfully tested on MathSBML and Copasi.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3181, + "tag": "BioModels:BIOMD0000000062" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3182, + "tag": "Tryptophan biosynthetic process" + } + ], + "timestamp_created": "2025-02-03 15:17:22.167415+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000062", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2360": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2360, + "name": "Galazzo1990_FermentationPathwayKinetics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Fermentation pathway kinetics and metabolic flux control in suspended and immobilized Saccharomyces cerevisiae
Jorge L. Galazzo and James E. Bailey Enzyme and Microbial TechnologyVolume 12, Issue 3, 1990, Pages 162-172.
DOI:10.1016/0141-0229(90)90033-M
Abstract:
Measurements of rates of glucose uptake and of glycerol and ethanol formation combined with knowledge of the metabolic pathways involved in S. cerevisiae were employed to obtain in vivo rates of reaction catalysed by pathway enzymes for suspended and alginate-entrapped cells at pH 4.5 and 5.5. Intracellular concentrations of substrates and effectors for most key pathway enzymes were estimated from in vivo phosphorus-31 nuclear magnetic resonance measurements. These data show the validity in vivo of kinetic models previously proposed for phosphofructokinase and pyruvate kinase based on in vitro studies. Kinetic representations of hexokinase, glycogen synthetase, and glyceraldehyde 3-phosphate dehydrogenase, which incorporate major regulatory properties of these enzymes, are all consistent with the in vivo data. This detailed model of pathway kinetics and these data on intracellular metabolite concentrations allow evaluation of flux-control coefficients for all key enzymes involved in glucose catabolism under the four different cell environments examined. This analysis indicates that alginate entrapment increases the glucose uptake rate and shifts the step most influencing ethanol production from glucose uptake to phosphofructokinase. The rate of ATP utilization in these nongrowing cells strongly limits ethanol production at pH 5.5 but is relatively insignificant at pH 4.5.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

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Biomodels Curation: The model reproduces Fig 2 of the paper. However, it appears that the figures are swapped, hence the plot for V/Vmax vs Glucose actually represnts V/Vmax vs ATP and the vice versa is true for the other figure. The rate of hexokinase reaction that is obtained upon simulation of the model is 17.24 mM/min, therefore V/Vmax has a value of 17.24/68.5=0.25. For steady state values of Glucose and ATP (0.038 and 1.213 mM respectively), the V/Vmax values correctly correspond to 0.25, if we were to assume that the figures are swapped.

BioModels Curation updated on 25th November 2010: Figure 3 of the reference publication has been reproduced and added as a curation figure for the model.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3183, + "tag": "BioModels:BIOMD0000000063" + }, + { + "id": 3184, + "tag": "Glycolytic fermentation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:22.686925+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000063", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2361": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2361, + "name": "Teusink2000_Glycolysis", + "repository_type": "biomodels", + "summary": "

Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry.
Teusink,B et al.: Eur J Biochem 2000 Sep;267(17):5313-29.
The model reproduces the steady-state fluxes and metabolite concentrations of the branched model as given in Table 4 of the paper. It is derived from the model on JWS online, but has the ATP consumption in the succinate branch with the same stoichiometrie as in the publication. The model was successfully tested on copasi v.4.4(build 26).
For Vmax values, please note that there is a conversion factor of approx. 270 to convert from U/mg-protein as shown in Table 1 of the paper to mmol/(min*L_cytosol). The equilibrium constant for the ADH reaction in the paper is given for the reverse reaction (Keq = 1.45*10 4 ). The value used in this model is for the forward reaction: 1/Keq = 6.9*10 -5 .
Vmax parameters values used (in [mM/min] except VmGLT):
VmGLT 97.264 mmol/min
VmGLK 226.45
VmPGI 339.667
VmPFK 182.903
VmALD 322.258
VmGAPDH_f 1184.52
VmGAPDH_r 6549.68
VmPGK 1306.45
VmPGM 2525.81
VmENO 365.806
VmPYK 1088.71
VmPDC 174.194
VmG3PDH 70.15
The result of the G6P steady state concentration (marked in red) differs slightly from the one given in table 4. of the publication
Results for steady state:
orig. article this model
Fluxes[mM/min] \u00a0
Glucose\u00a0 88\u00a0 88\u00a0
Ethanol\u00a0 129\u00a0 129\u00a0
Glycogen\u00a0 6\u00a0 6\u00a0
Trehalose\u00a0 4.8\u00a0 4.8\u00a0 (G6P flux through trehalose branch)
Glycerol\u00a0 18.2\u00a0 18.2\u00a0
Succinate\u00a0 3.6\u00a0 3.6\u00a0
Conc.[mM] \u00a0
G6P\u00a0 1.07\u00a0 1.03\u00a0
F6P\u00a0 0.11\u00a0 0.11\u00a0
F1,6P\u00a0 0.6\u00a0 0.6\u00a0
DHAP\u00a0 0.74\u00a0 0.74\u00a0
3PGA\u00a0 0.36\u00a0 0.36\u00a0
2PGA\u00a0 0.04\u00a0 0.04\u00a0
PEP\u00a0 0.07\u00a0 0.07\u00a0
PYR\u00a0 8.52\u00a0 8.52\u00a0
AcAld\u00a0 0.17\u00a0 0.17\u00a0
ATP\u00a0 2.51\u00a0 2.51\u00a0
ADP\u00a0 1.29\u00a0 1.29\u00a0
AMP\u00a0 0.3\u00a0 0.3\u00a0
NAD\u00a0 1.55\u00a0 1.55\u00a0
NADH\u00a0 0.04\u00a0 0.04\u00a0
Authors of the publication also mentioned a few misprints in the original article:
in the kinetic law for ADH :

  1. the species a should denote NAD and b Ethanol
  2. the last term in the equation should read bpq /( K ib K iq K p )
in the kinetic law for PFK :
  1. R = 1 + \u03bb 1 + \u03bb 2 + g r \u03bb 1 \u03bb 2
  2. equation L should read: L = L0*(..) 2 *(..) 2 *(..) 2 not L = L0*(..) 2 *(..) 2 *(..)
To make the model easier to curate, the species ATP , ADP and AMP were added. These are calculated via assignment rules from the active phosphate species, P , and the sum of all AXP , SUM_P .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3185, + "tag": "BioModels:BIOMD0000000064" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:23.227157+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000064", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2362": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2362, + "name": "Yildirim2003_Lac_Operon", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Feedback regulation in the lactose operon: a mathematical modeling study and comparison with experimental data.
Yildirim N, Mackey MC Biophys. J. 2003 12719218 ,
Abstract:
A mathematical model for the regulation of induction in the lac operon in Escherichia coli is presented. This model takes into account the dynamics of the permease facilitating the internalization of external lactose; internal lactose; beta-galactosidase, which is involved in the conversion of lactose to allolactose, glucose and galactose; the allolactose interactions with the lac repressor; and mRNA. The final model consists of five nonlinear differential delay equations with delays due to the transcription and translation process. We have paid particular attention to the estimation of the parameters in the model. We have tested our model against two sets of beta-galactosidase activity versus time data, as well as a set of data on beta-galactosidase activity during periodic phosphate feeding. In all three cases we find excellent agreement between the data and the model predictions. Analytical and numerical studies also indicate that for physiologically realistic values of the external lactose and the bacterial growth rate, a regime exists where there may be bistable steady-state behavior, and that this corresponds to a cusp bifurcation in the model dynamics.

The model reproduces the time profile of beta-galactosidase activity as shown in Fig 3 of the paper. The delay functions for transcription (M) and translation (B and P) have been implemented by introducing intermediates ( I1, I2 and I3) in the reaction scheme which then give their respective products (I1-> M, I2 ->B and I3 ->P) after an appropriate length of time. The steady state values, attained upon simulation of model equations, for Allolactose (A), mRNA (M), beta-galactosidase (B), Lactose (L), and Permease (P) match with those predicted by the paper. The model was successfully tested on Jarnac, MathSBML and COPASI

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3186, + "tag": "BioModels:BIOMD0000000065" + }, + { + "id": 3187, + "tag": "Carbon catabolite regulation of transcription" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:23.806490+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000065", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2363": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2363, + "name": "Chassagnole2001_Threonine Synthesis", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation: The model reproduces Fig 2f of the paper. The Vmax values for different reactions are obtained by multiplying the specific activites given in Table 3 of the paper with the protein concentration and an assay correction factor that was provided by the authors. The protein concentration is 202 mg/litre. The specific activities that need to be taken into consideration are those given for \"variable threonine\" in Table 3. The following are the assay correction factors provided by the authors: vak1=1.49; vak3=1.12; vasd=1.14; vhsd=1.42; vts=1.15; vhk=1.13. The model was successfully tested on MathSBML and Jarnac

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3188, + "tag": "BioModels:BIOMD0000000066" + }, + { + "id": 3189, + "tag": "Escherichia coli (strain K12)" + }, + { + "id": 3190, + "tag": "Glycine, serine and threonine metabolism" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:24.307420+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000066", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2364": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2364, + "name": "Fung2005_Metabolic_Oscillator", + "repository_type": "biomodels", + "summary": "

A Synthetic Gene-Metabolic Oscillator

Reference: Fung et al; Nature (2005) 435:118-122
Name of kinetic law Reaction
Glycolytic flux, V_gly: nil -> AcCoA;
Flux to TCA cycle/ETOH, V_TCA: AcCoA -> TCA/EtOH;
HOAc ex/import,reversible, V_out: HOAc -> HOAc_E
V_Pta: AcCoA + Pi -> AcP + CoA
reversible, V_Ack: AcP + ADP -> OAc + ATP
V_Acs: OAC + ATP -> AcCoA +PPi
Acetic acid-base equillibrium, reversible, V_Ace: OAC + H -> HOAc
Expression of LacI, R_LacI: nil -> LacI
Expression of Acs, R_Acs: nil -> Acs
Expression of Pta, R_Pta: nil -> Pta
LacI degradation, R_dLacI: LacI -> nil
Acs degradation, R_dAcs: Acs -> nil
Pta degradation, R_dPta: Pta -> nil

For this model the differential equation for V_Ace was changed from:
C*(AcP*H-K_eq*OAC) with C = 100 in the supplemental material
to C*(OAc*H-K_eq*HOAc) with C = 100, as in Bulter et. al; PNAS(2004),101,2299-2304 , and a value for K_eq of 5*10^-4 after communication with the authors.

translated to SBML by:
Lukas Endler(luen at tbi.univie.ac.at), Christoph Flamm (xtof at tbi.univie.ac.at)

Biomodels Curation The model reproduces 3a of the paper for glycolytic flux Vgly = 0.5. The authors have agreed that the values on Y-axis are marked wrong and hence there is a discrepancy between model simulation results and the figure. Also, note that the values of concentration and time are in dimensionless units. The model was successfully tested on MathSBML and Jarnac.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3191, + "tag": "BioModels:BIOMD0000000067" + }, + { + "id": 3187, + "tag": "Carbon catabolite regulation of transcription" + }, + { + "id": 3189, + "tag": "Escherichia coli (strain K12)" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:24.808943+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000067", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2365": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2365, + "name": "Curien2003_MetThr_synthesis", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A kinetic model of the branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana.
Curien G, Ravanel S, Dumas R Eur. J. Biochem. 2003 Dec; Volume: 270 (Issue: 23 )]:4615-27 14622248 ,
Abstract:
This work proposes a model of the metabolic branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana which involves kinetic competition for phosphohomoserine between the allosteric enzyme threonine synthase and the two-substrate enzyme cystathionine gamma-synthase. Threonine synthase is activated by S-adenosylmethionine and inhibited by AMP. Cystathionine gamma-synthase condenses phosphohomoserine to cysteine via a ping-pong mechanism. Reactions are irreversible and inhibited by inorganic phosphate. The modelling procedure included an examination of the kinetic links, the determination of the operating conditions in chloroplasts and the establishment of a computer model using the enzyme rate equations. To test the model, the branch-point was reconstituted with purified enzymes. The computer model showed a partial agreement with the in vitro results. The model was subsequently improved and was then found consistent with flux partition in vitro and in vivo. Under near physiological conditions, S-adenosylmethionine, but not AMP, modulates the partition of a steady-state flux of phosphohomoserine. The computer model indicates a high sensitivity of cystathionine flux to enzyme and S-adenosylmethionine concentrations. Cystathionine flux is sensitive to modulation of threonine flux whereas the reverse is not true. The cystathionine gamma-synthase kinetic mechanism favours a low sensitivity of the fluxes to cysteine. Though sensitivity to inorganic phosphate is low, its concentration conditions the dynamics of the system. Threonine synthase and cystathionine gamma-synthase display similar kinetic efficiencies in the metabolic context considered and are first-order for the phosphohomoserine substrate. Under these conditions outflows are coordinated.


SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

Biomodels Curation The model simulates the flux for TS and CGS under conditions given in Table 2 and reproduces the dotted lines given in Table 3 of the paper. There is a typo in the equation for the apparent specificity constant for Phser, Kts (equation13). This was changed after communication with the authors to be: Kts = 5.9E-4+6.2E-2*pow(AdoMet,2.9)/(pow(32,2.9)+pow(AdoMet,2.9)). The model was successfully tested on Jarnac and Copasi. Due to a suggestion from Pedro Mendez the parameter AdoMet, TS and CGS where made constant species.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3193, + "tag": "BioModels:BIOMD0000000068" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:25.297400+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000068", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2366": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2366, + "name": "Fuss2006_MitoticActivation", + "repository_type": "biomodels", + "summary": "

The model was curated with XPP. The figure 3 was successfully reproduced.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3194, + "tag": "BioModels:BIOMD0000000069" + }, + { + "id": 3195, + "tag": "Cell Cycle, Mitotic" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:25.845909+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000069", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2367": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2367, + "name": "Holzhutter2004_Erythrocyte_Metabolism", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

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Biomodels CurationThe model simulates the flux values as given for \"kinetic model\" in Table 1 of the paper. The model was successfully tested on Jarnac.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3196, + "tag": "BioModels:BIOMD0000000070" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:26.386081+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000070", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2368": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2368, + "name": "Bakker2001_Glycolysis", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation: The paper refers to the model equations present in Bakker et al's \" Glycolysis in bloodstream from Trypanosoma brucei can be understood in terms of the kinetics of glycolytic enzymes\" (Pubmed ID: 9013556), also, the authors claim that some of the modifications in these equations were made based on the experimental results from the paper \"Contribution of glucose transport in the control of glycolytic flux in Trypanosoma brucei\" (Pubmed ID: 10468568). The model reproduces the various flux values in Fig 3 for 100% TPI activity. It also matches with the values provided in Table 2 of the paper. The model was successfully tested with Copasi and SBML ODE Solver.
The volumes are set to the values containing 1 mg of total protein per microlitre total cell volume. To change the protein concentration use Vt , the total cell volume in micro litre per mg protein.
To change the TPI activity use the global parameter TPIact .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3197, + "tag": "BioModels:BIOMD0000000071" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 15:17:26.847267+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000071", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2369": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2369, + "name": "Yi2003_GproteinCycle", + "repository_type": "biomodels", + "summary": "

The paper describes both wild-type and mutant cells of G protein cycle by using different values of G protein deactivation. We chosed the wild-type, k=0.11 s-1.

The unit of the concentration for the proteins are numbers of molecules per cell.

Figure5(A) was reproduced with COPASI 4.0 (Build 18) and SBML_odeSolver. Figure5(B) was reproduced with COPASI 4.0 (Build 18).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3199, + "tag": "BioModels:BIOMD0000000072" + }, + { + "id": 3200, + "tag": "Heterotrimeric G-protein complex cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:27.317487+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000072", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2370": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2370, + "name": "Leloup2003_CircClock_DD", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Toward a detailed computational model for the mammalian circadian clock . In this model only interlocked negative and positive regulation of Per, Cry, Bmal gene are involved. Some initial values were not provided, therefore they were chosen to fit the curve from the paper.

Figure2A re-produced by Copasi 4.0.19 and roadRunner online.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3201, + "tag": "BioModels:BIOMD0000000073" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:27.787458+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000073", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2371": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2371, + "name": "Leloup2003_CircClock_DD_REV-ERBalpha", + "repository_type": "biomodels", + "summary": "

This is model in continous darkness (DD) described in the article Toward a detailed computational model for the mammalian circadian clock

This model features the full interlocked negative and positive regulation of Per,Cry,Bmal and REV-ERBalpha. The model exhibits robust oscillations quite independent of the initial conditions for teh parameters given. Each species is assigned zero as initial value, and the graph started at time=120h.

Simulation results could be reproduced using Copasi 4.0.19(development) and roadRunner online.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3203, + "tag": "BioModels:BIOMD0000000074" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:28.247399+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000074", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2372": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2372, + "name": "Xu2003 - Phosphoinositide turnover", + "repository_type": "biomodels", + "summary": "
Xu2003 - Phosphoinositide turnover

The model reproduces the percentage change of PIP_PM, PIP2_PM and IP3_Cyt as depicted in Figure 1 of the paper. The model also contains the equations for the analysis of PH-GFP experiments, however the initial value of PH_GFP has been set to zero to more accurately reproduce Figure 1. The units of cytosolic species are given in molecules/um^3. In order to convert them to uM, divide the concentration by 602. For the analysis of PH_GFP experiments, one should plug in the values of PH_GFP, IP3_PHGFP and PIP2_PHGFP from Table AI in the appendix. The model was successfully tested on MathSBML.

This model has been generated by VCell

This model is described in the article:

Xu C, Watras J, Loew LM.
J. Cell Biol. 2003 May; 161(4): 779-791

Abstract:

We studied the bradykinin-induced changes in phosphoinositide composition of N1E-115 neuroblastoma cells using a combination of biochemistry, microscope imaging, and mathematical modeling. Phosphatidylinositol-4,5-bisphosphate (PIP2) decreased over the first 30 s, and then recovered over the following 2-3 min. However, the rate and amount of inositol-1,4,5-trisphosphate (InsP3) production were much greater than the rate or amount of PIP2 decline. A mathematical model of phosphoinositide turnover based on this data predicted that PIP2 synthesis is also stimulated by bradykinin, causing an early transient increase in its concentration. This was subsequently confirmed experimentally. Then, we used single-cell microscopy to further examine phosphoinositide turnover by following the translocation of the pleckstrin homology domain of PLCdelta1 fused to green fluorescent protein (PH-GFP). The observed time course could be simulated by incorporating binding of PIP2 and InsP3 to PH-GFP into the model that had been used to analyze the biochemistry. Furthermore, this analysis could help to resolve a controversy over whether the translocation of PH-GFP from membrane to cytosol is due to a decrease in PIP2 on the membrane or an increase in InsP3 in cytosol; by computationally clamping the concentrations of each of these compounds, the model shows how both contribute to the dynamics of probe translocation.

This model is hosted on BioModels Database and identified by: BIOMD0000000075 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3204, + "tag": "BioModels:BIOMD0000000075" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3205, + "tag": "Phosphatidylinositol metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:28.724459+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000075", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2373": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2373, + "name": "Cronwright2002_Glycerol_Synthesis", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation The model reproduces the flux value of \"Gpp p\" (rate of Glycerol synthesis) as depicted in Fig 3 of the paper. The model reproduces the flux for early exponential phase , however it can be used to reproduce the values for other phases by plugging in appropriate values for maximal rates as given in Table 1 and metabolite concentrations as given in Table 2 of the paper. The model was succesfully reproduced using Jarnac.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3206, + "tag": "BioModels:BIOMD0000000076" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:29.187433+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000076", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2374": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2374, + "name": "Blum2000_LHsecretion_1", + "repository_type": "biomodels", + "summary": "

A mathematical model quantifying GnRH-induced LH secretion from gonadotropes by Blum et al (2000)

This paper includes three stages, and the model does not include the third stage. Also an event is included which remove the hormone GnRH at time=5min. Figure 1 and Figure 2 of the paper are reproduced, using SBML odeSolver. We choose to encode the model with the concentration of GnRH equal to 1.0nM.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3207, + "tag": "BioModels:BIOMD0000000077" + }, + { + "id": 3208, + "tag": "Response to gonadotropin-releasing hormone" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:29.671261+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000077", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2375": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2375, + "name": "Leloup2003_CircClock_LD", + "repository_type": "biomodels", + "summary": "

This model is described in the paper Toward a detailed computational model for the mammalian circadian clock . In this model only interlocked negative and positive regulation of Per, Cry, Bmal gene are involved. Some initial values were not provided, therefore they were chosen to fit the curves from the paper.

Figure2C is re-produced by odeSolver.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3209, + "tag": "BioModels:BIOMD0000000078" + }, + { + "id": 3210, + "tag": "Entrainment of circadian clock" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:30.198471+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000078", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2376": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2376, + "name": "Goldbeter2006_weightCycling", + "repository_type": "biomodels", + "summary": "

This model is according to the paper of A model for the dynamics of human weight cycling by A. Goldbeter 2006.The figure3 (A) and (B) have been reproduced by Copasi 4.0.19(development) and SBMLodeSolver.The writer of the paper did not specify any units for the metabolites, so the creator of the model did not define the units as well.Both Q and R are normalized to vary between 0 and 1.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3211, + "tag": "BioModels:BIOMD0000000079" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3212, + "tag": "Regulation of multicellular organism growth" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:30.774385+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000079", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2377": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2377, + "name": "Thomsen1989_AdenylateCyclase", + "repository_type": "biomodels", + "summary": "

This model reproduces figure 5 and figure 4(B)of the paper, with Kinh represented by [G-GTP]. We arbitrarily chosed to set the initial concentration of D to 31 micorMolar based on legend of figure 4. [R] was not given anywhere in the paper and was chosen to calibrate the sigmoid response to an increased [GTP]. THe figure 5 in the model was successfully simulated on COPASI 4.0 ,the figure 4(B) was sucessfully simulated on both COPASI and SBML_odeSolver.

There are two curves for Kinh in the absence and presence of NaCl in the figure obtained from simulations of the model using parameters of set C and set D.Here in the model the initial value given is from set D.The parameters in set C :k7=0.5, k10=1.0,k5=0.1,the others are the same with set D.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3213, + "tag": "BioModels:BIOMD0000000080" + }, + { + "id": 3200, + "tag": "Heterotrimeric G-protein complex cycle" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3214, + "tag": "Negative regulation of adenylate cyclase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:31.271158+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000080", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2378": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2378, + "name": "Suh2004_KCNQ_Regulation", + "repository_type": "biomodels", + "summary": "

The model reproduces FIG 11A and FIG 11B of the paper. However, please note that FIG 11B is a plot of normalised amounts versus time. The \"stoichiometry\" field has been used to convert fluxes from membrane species to volume species. The value of 0.0009967 is a product of (Surface to Volume_M*(1/Avagadro's number)*1E21. 0.6 is the surface to volume ratio of the plasma membrane, 1E21 is required for a unit surface to volume ratio and the Avagadro's number is present in the denominator to convert molecules to moles. The model was successfully tested using MathSBML and SBML ODESolver.
All the kinetic laws have the unit items per second , which requires the one reaction taking place in the cytoplasm - IP3Phosphatase - to include an explicit conversion factor both in the kinetic law and the stoichiometry of IP3_C . The kinetic law is multiplied and the stoichiometry divided by the number of molecules per micro-mole. This conversion factor is only required for correct units and can be replaced by 1, if it should lead to numerical problems.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3215, + "tag": "BioModels:BIOMD0000000081" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:31.769662+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000081", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2379": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2379, + "name": "Thomsen1988_AdenylateCyclase_Inhibition", + "repository_type": "biomodels", + "summary": "

This model was created according to the paper Inhibition of Adenylate Cyclase Is Mediated by the High Affinity Conformation of the alpha2-Adrenergic Receptor published in 1988.

The figure4 (steady state curve) in the paper has been simulated having the same plot with Copasi 4.0.19 (development) and roadRunner(online).Because the initial concentration of R and D were not given in the paper ,so we gave it 1e-9 Mol/L and 1e-8 Mol/L respectively.

Pay attention that the simulations of steady state concentration of species in arbitrary units are shown for figure4 and figure6 in the paper.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3216, + "tag": "BioModels:BIOMD0000000082" + }, + { + "id": 3200, + "tag": "Heterotrimeric G-protein complex cycle" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3214, + "tag": "Negative regulation of adenylate cyclase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:32.262953+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000082", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2380": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2380, + "name": "Leloup2003_CircClock_LD_REV-ERBalpha", + "repository_type": "biomodels", + "summary": "

This is model is according to the paperToward a detailed computational model for the mammalian circadian clock

In this model interlocked negative and positive regulation of Per,Cry,Bmal,REV-ERBalpha genes are all involved.The model is actually robust so the initial conditions are unimportant.We gave every entity zero as initial value,and start the graph at time=132h.

The simulation results in figure 8B can be reproduced by roadRunner online and Copasi. We use a ceiling function to simulate the day-light cycle.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3217, + "tag": "BioModels:BIOMD0000000083" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:32.801040+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000083", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2381": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2381, + "name": "Hornberg2005_ERKcascade", + "repository_type": "biomodels", + "summary": "

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

Biomodels Curation The model reproduces the time series depicted in Fig 2 of the paper. Also, by varying the values of Vmax for the second kinase (k5) the time series of X3P as shown in Fig3 can be reproduced. The model was successfully tested on MathSBML and Jarnac.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3218, + "tag": "BioModels:BIOMD0000000084" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:33.262223+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000084", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2382": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2382, + "name": "Maurya2005_GTPaseCycle_reducedOrder", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Reduced-order modeling of biochemical networks: application to the GTPase-cycle signalling module by Maurya et al 2006.The figure 4c is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z at various concentrations of R and GAP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3219, + "tag": "BioModels:BIOMD0000000085" + }, + { + "id": 3220, + "tag": "Regulation of G-protein coupled receptor protein signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:33.720895+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000085", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2383": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2383, + "name": "Bornheimer2004_GTPaseCycle", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction by Bornheimer et al 2004.The figure 3 is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z and v at various concentrations of R and GAP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3221, + "tag": "BioModels:BIOMD0000000086" + }, + { + "id": 3220, + "tag": "Regulation of G-protein coupled receptor protein signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:34.203899+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000086", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2384": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2384, + "name": "Proctor2006_telomere", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3222, + "tag": "BioModels:BIOMD0000000087" + }, + { + "id": 3223, + "tag": "Cell cycle checkpoint" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:17:34.667409+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000087", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2385": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2385, + "name": "Maeda2006_MyosinPhosphorylation", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2B, D, F, and 2H. The dynamics correspond to a stimulus of 1 U/ml of thrombin which is equal to 0.01 uM. Phosphorylated MLC is the sum of pMLC (s359) and ppMLC (s360). A slight discrepancy in peak values of species between the figure in the paper and simulation result might be due to different initial conditions in the two sets. The model was successfully tested on MathSBML. It is possible to simulate the model on other software that do not support \"Events\" at this time by removing the \"listOfEvents\" and substituting a value of 0.01 for thrombin (s2). This does not change the model very much. With the latter format, the model was also successfully tested on Copasi.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3224, + "tag": "BioModels:BIOMD0000000088" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:35.157296+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000088", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2386": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2386, + "name": "Locke2006_CircClock_LL", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana.
Locke JC, Kozma-Bogn\u00e1r L, Gould PD, Feh\u00e9r B, Kevei E, Nagy F, Turner MS, Hall A, Millar AJ Mol. Syst. Biol.2006;Volume:2;Page:59 17102804,
Abstract:
Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse.


The model describes a three loops model of the Arabidopsis circadian clock. It provides initial conditions, parameter values and reactions for the production rates of the following species: LHY mRNA (cLm), cytoplasmic LHY (cLc), nuclear LHY (cLn), TOC1 mRNA (cTm), cytoplasmic TOC1 (cTc), nuclear TOC1 (cTn), X mRNA (cXm), cytoplasmic X (cXc), nuclear X (cXn), Y mRNA (cYm), cytoplasmic Y (cYc), nuclear Y (cYn), nuclear P (cPn), APRR7/9 mRNA, cytoplasmic APRR7/9, and nuclear APRR7/9.

The paper describes the behaviour of the model in constant light (LL) and day-night cycle (LD). However, the current model only contains the LL cycle. Some parameter values should be changed from the wild-type (WT) ones in order to simulate the effect of mutations. These changes are listed in the notes of relevant parameters.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3225, + "tag": "BioModels:BIOMD0000000089" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:36.016672+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000089", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2387": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2387, + "name": "Wolf2001_Respiratory_Oscillations", + "repository_type": "biomodels", + "summary": "

This model by Jana Wolf et al. 2001 is the first mechanistic model of respiratory oscillations in Saccharomyces cerevisae. It is based on the assumption that feedback inhibition of cysteine on the sulfate transporters leads to oscillations in this pathway and causes oscillations in respiratory activity via inhibition of cytochrome c oxidase by hydrogen disulfide. The model is qualitative/semi-quantitative and reproduces the respiratory oscillation pattern quite well. It is based on very coarse-grained representations of the mitochondrial tricarboxylic acid cycle and the mitochondrial electron transport chain (oxidative phosphorylation). The sulfate assimilatory pathways also contains some significant simplifcations.

The model corresponds to Fig. 2B of the paper, with a slight phase shift of the oscillations. No initial conditions were given in the paper, and thus they were chosen arbitrarily in a range that lies within the basin of attraction of the limit cycle oscillations. Species IDs correspond to IDs used by the authors, while SBML names are more common abbreviations.

Caveats:
1) Equilibrated transport:
The model assumes fast equilibration between mitochondria and cytoplasm for the metabolites NADH, NAD+, H2S and Acetyl-CoA.
2) Cytosolic mass conservation ATP/ADP:
The model uses mass conservation for cytosolic adenosine nucleotides with is however not encoded in the stoichiometry, but is implied by the lumped reaction v4. This reaction combines the enzymatic reactions of phosphoadenylyl-sulfate reductase (thioredoxin) (yeast protein Met16p, EC 1.8.4.8) and sulfite reductase (NADPH) (subunits Met5p and Met10p, EC 1.8.1.2). EC 1.8.4.8 also has adenosine-3',5'-bismonophosphate (PAP, not to confuse with ID pap in this model, standing for PAPS) as a product. PAP is the substrate for enzyme 3'(2'),5'-bisphosphate nucleotidase (Met22p, EC:3.1.3.7) which would revover AMP (and Pi). Then AMP can be assumed to be equilibrated with ATP and ADP via adenylate kinase, as often used in metabolic models. This AMP production is implied in the mass conservation for cytosolic adenosine phosphates. Accounting for these reactions explicitly does not change the dynamics of the model significantly. An according version can be obtained from the SBML creator (Rainer Machne, mailto:raim@tbi.univie.ac.at).
3) Redox balance:
The enzyme sulfite reductase (NADPH) (subunits Met5p and Met10p, EC 1.8.1.2, part of reaction v4) actually uses NADPH, and the authors assume equilibration of NADH and NADPH. But actually S. cerevisiae specifically is missing the according enzyme transhydrogenase (EC 1.6.1.1 or EC 1.6.1.2). EC 1.8.4.8 also oxidizes thioredoxin and would actually require an additional NADPH for thioredoxin recovery (reduction). This would slightly affect the redox balance of the model.
4) Energy balance:
Reaction v7 lumps NAD-dependent alcohol dehydrogenase (EC 1.1.1.1), aldehyde dehydrogenase (NAD+) (EC 1.2.1.3) and acetyl-CoA synthase (EC 6.2.1.1). The latter reaction would actually consume ATP as a co-factor, producing AMP+PPi, and this is not included in the model. This would slightly bias the model's energy balance.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3226, + "tag": "BioModels:BIOMD0000000090" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3227, + "tag": "Sulfate assimilation, phosphoadenylyl sulfate reduction by phosphoadenylyl-sulfate reductase (thioredoxin)" + } + ], + "timestamp_created": "2025-02-03 15:17:36.600422+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000090", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2388": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2388, + "name": "Proctor2005 - Actions of chaperones and their role in ageing", + "repository_type": "biomodels", + "summary": "
Proctor2005 - Actions of chaperones and theirrole in ageing

This model is described in the article:

Proctor CJ, Soti C, Boys RJ, Gillespie CS, Shanley DP, Wilkinson DJ, Kirkwood TB.
Mech. Ageing Dev. 2005 Jan; 126(1): 119-131

Abstract:

Many molecular chaperones are also known as heat shock proteins because they are synthesised in increased amounts after brief exposure of cells to elevated temperatures. They have many cellular functions and are involved in the folding of nascent proteins, the re-folding of denatured proteins, the prevention of protein aggregation, and assisting the targeting of proteins for degradation by the proteasome and lysosomes. They also have a role in apoptosis and are involved in modulating signals for immune and inflammatory responses. Stress-induced transcription of heat shock proteins requires the activation of heat shock factor (HSF). Under normal conditions, HSF is bound to heat shock proteins resulting in feedback repression. During stress, cellular proteins undergo denaturation and sequester heat shock proteins bound to HSF, which is then able to become transcriptionally active. The induction of heat shock proteins is impaired with age and there is also a decline in chaperone function. Aberrant/damaged proteins accumulate with age and are implicated in several important age-related conditions (e.g. Alzheimer's disease, Parkinson's disease, and cataract). Therefore, the balance between damaged proteins and available free chaperones may be greatly disturbed during ageing. We have developed a mathematical model to describe the heat shock system. The aim of the model is two-fold: to explore the heat shock system and its implications in ageing; and to demonstrate how to build a model of a biological system using our simulation system (biology of ageing e-science integration and simulation (BASIS)).

This model is hosted on BioModels Database and identified by: BIOMD0000000091.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3228, + "tag": "BioModels:BIOMD0000000091" + }, + { + "id": 3229, + "tag": "Cell aging" + }, + { + "id": 3230, + "tag": "Chaperone mediated protein folding requiring cofactor" + }, + { + "id": 3231, + "tag": "Response to heat" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + }, + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3249, + "tag": "Cataract" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-02-03 15:17:37.076568+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000091", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2389": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2389, + "name": "Fuentes2005_ZymogenActivation", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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The model reproduces Fig 1A of the paper. The model was successfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3232, + "tag": "BioModels:BIOMD0000000092" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:37.551662+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000092", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2390": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2390, + "name": "Yamada2003_JAK_STAT_pathway", + "repository_type": "biomodels", + "summary": "

NCBS Curation Comments This model shows the control mechanism of Jak-Stat pathway, here SOCS1 (Suppressor of cytokine signaling-I) was identified as the negative regulator of Jak and STAT signal transduction pathway. Note: There are a few ambiguities in the paper like initial concentration of IFN and some reactions were missing in the paper that were employed for obtaining the results. The graphs are almost similar to the graphs as shown in the paper but still some ambiguities regarding the concentration are there. Thanks to Dr Satoshi Yamada for clarifying some of those ambiguities and providing the values used in simulations.

Biomodels Curation Comments The model reproduces Fig 2 (A,C,E,G,I,K,M) of the paper. The set of equations present in the paper are inadequate to reproduce the figures mentioned . The model appears to have been fine tuned after correspondence between the curators at NCBS and the authors. There is however a slight discrepancy between the simulation results and the plots in the paper. The model was tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3233, + "tag": "BioModels:BIOMD0000000093" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:38.032171+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000093", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2391": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2391, + "name": "Yamada2003_JAK_STAT_SOCS1_knockout", + "repository_type": "biomodels", + "summary": "

NCBS Curation Comments: This model shows the control mechanism of Jak-Stat pathway, here SOCS1 (Suppressor of cytokine signaling-I) was identified as the negative regulator of Jak and STAT signal transduction pathway. This is the knockout version of Jak-Stat pathway in this model the SOCS1 has been knocked out i.e it formation is not shown. The graphs are almost similar to the graphs as shown in the paper but STAT1n graph has some ambiguities. Thanks to Dr Satoshi Yamada for clarifying some of those ambiguities and providing the values used in simulations.

Biomodels Curation Comments: The model reproduces the figures 2 (B,D,F,H,J,L,N) corresponding to JAK/STAT activation in SOCS1 knock out cells. The model was successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3234, + "tag": "BioModels:BIOMD0000000094" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:38.498580+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000094", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2392": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2392, + "name": "Zeilinger2006_PRR7-PRR9-Y", + "repository_type": "biomodels", + "summary": "

The model reproduces the circadian charecteristics as given in Table 1 for the PRR7-PRR9-Y model. The model makes use of the event section to introduce light at 30 hours. The Zeitgeber (ZT) times for species shown in Table 1 can be reproduced by looking at the time it takes for species to reach peak values after the introduction of light. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3235, + "tag": "BioModels:BIOMD0000000095" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:39.090048+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000095", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2393": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2393, + "name": "Zeilinger2006_PRR7-PRR9light-Y", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of cYm and cTm under light-dark cycles as depicted in Fig 4 and Fig 5 respectively. 12 hour light-dark cycles are accomplished using a simple algorithm in the event section. The model was successfully tested using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3236, + "tag": "BioModels:BIOMD0000000096" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:40.023362+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000096", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2394": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2394, + "name": "Zeilinger2006_PRR7-PRR9light-Yprime", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of TOC1 and Y mRNA for a 8:16 cycle as depicted in Fig7A and 7B. A simple algorithm in the event section accomplishes the 8 hour light and 16 hour dark cycle. The model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3237, + "tag": "BioModels:BIOMD0000000097" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:40.655286+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000097", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2395": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2395, + "name": "Goldbeter1990_CalciumSpike_CICR", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of cytosolic and intracellular calcium as depicted in the upper panel of Fig 2 in the paper. The model was successfully tested on MathSBML and Jarnac.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3238, + "tag": "BioModels:BIOMD0000000098" + }, + { + "id": 3239, + "tag": "Calcium ion transport" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3240, + "tag": "Inositol 1,4,5-trisphosphate-sensitive calcium-release channel activity" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:41.219924+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000098", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2396": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2396, + "name": "Laub1998_SpontaneousOscillations", + "repository_type": "biomodels", + "summary": "

This is model according to the paper \"A Molecular Network That Produces Spontaneous Oscillations in Excitalbe Cells of Dictyostelium. Figure 3 has been reproduced by Copasi 4.0.20(development) \". However four of the parameters have been changed , see details in notes.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3241, + "tag": "BioModels:BIOMD0000000099" + }, + { + "id": 3242, + "tag": "CAMP-mediated signaling" + }, + { + "id": 3243, + "tag": "Dictyostelium" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:17:41.689522+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000099", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2397": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2397, + "name": "Rozi2003_GlycogenPhosphorylase_Activation", + "repository_type": "biomodels", + "summary": "

The model reproduces the temporal evolution of Glycogen phosphorylase for a vale of Vm5=30 as depicted in Fig 1a of the paper. The model makes use of calcium oscillations from the Borghans model to stimulate the activation of glycogen phosphorylase. Hence, this is a simple extension of the Borghans model. The model was succesfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3244, + "tag": "BioModels:BIOMD0000000100" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3152, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:17:42.261377+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000100", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2398": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2398, + "name": "Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity", + "repository_type": "biomodels", + "summary": "
Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity

CD4+ T cells can differentiate into different phenotypes depending on the cytokine milieu. Here a computational and mathematical model with sixty ordinary differential equations representing a CD4+ T cell differentiating into either Th1, Th2, Th17 or iTreg cells, has been constructed. The model includes cytokines, nuclear receptors and transcription factors that define fate and function of CD4+ T cells. Computational simulations illustrate how a proinflammatory Th17 cell can undergo reprogramming into an anti-inflammatory iTreg phenotype following PPARc activation.

This model is described in the article:

Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J
PLoS Computational Biology [2013, 9(4):e1003027]

Abstract:

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPAR\u03b3) in modulating plasticity between Th17 and iTreg cells. PPAR\u03b3 regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPAR\u03b3 activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPAR\u03b3. Deletion of PPAR\u03b3 in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPAR\u03b3 in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

Author's comment: CD4+ T cell computational model (Version 1.4)Steady state corrected. There was a problem in the internalization of IL-17 in its mathematical function.

This model is hosted on BioModels Database and identifiedby: MODEL1304230001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3250, + "tag": "Bacterial infectious disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3251, + "tag": "BioModels:BIOMD0000000451" + }, + { + "id": 3252, + "tag": "CD4 biosynthetic process" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3253, + "tag": "T cell differentiation" + } + ], + "timestamp_created": "2025-02-03 15:41:43.730859+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000451", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2399": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2399, + "name": "Bidkhori2012 - normal EGFR signalling", + "repository_type": "biomodels", + "summary": "
Bidkhori2012 - normal EGFR signalling

The paper describes and compares two models on EGFR signalling between normal and NSCLC cells. Moreover, it is shown that ERK (MAPK), STAT and Akt factor's activation pattern are different between normal and NSCLA models. This model corresponds to EGFR signalling in normal cells.

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

This model is described in the article:

Bidkhori G, Moeini A, Masoudi-Nejad A
PloS one [2012, 7(10):e48004]

Abstract:

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

This model is hosted on BioModels Database and identifiedby: MODEL1304020000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3254, + "tag": "BioModels:BIOMD0000000452" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:41:44.392367+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000452", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2400": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2400, + "name": "Bidkhori2012 - EGFR signalling in NSCLC", + "repository_type": "biomodels", + "summary": "
Bidkhori2012 - EGFR signalling in NSCLC

The paper describes and compares two models on EGFR signalling between normal and NSCLC cells. Moreover, it is shown that ERK (MAPK), STAT and Akt factor's activation pattern are different between normal and NSCLA models. This model corresponds to EGFR signalling in NSCLA cells.

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

This model is described in the article:

Bidkhori G, Moeini A, Masoudi-Nejad A
PloS one [2012, 7(10):e48004]

Abstract:

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

This model is hosted on BioModels Database and identifiedby: MODEL1304020001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3255, + "tag": "BioModels:BIOMD0000000453" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3256, + "tag": "Non-small cell lung carcinoma" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:41:44.885686+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000453", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2401": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2401, + "name": "Smallbone2013 - Metabolic Control Analysis - Example 1", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Metabolic Control Analysis - Example 1

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3257, + "tag": "BioModels:BIOMD0000000454" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:41:45.401110+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000454", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2402": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2402, + "name": "Smallbone2013 - Metabolic Control Analysis - Example 2", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Metabolic Control Analysis - Example 2

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3259, + "tag": "BioModels:BIOMD0000000455" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:41:45.956371+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000455", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2403": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2403, + "name": "Smallbone2013 - Metabolic Control Analysis - Example 3", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Metabolic Control Analysis - Example 3

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3260, + "tag": "BioModels:BIOMD0000000456" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:41:46.599712+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000456", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2404": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2404, + "name": "Firczuk2013 - Eukaryotic mRNA translation machinery", + "repository_type": "biomodels", + "summary": "
Firczuk2013 - Eukaryotic mRNA translation machinery

This is a model of Saccharomyces cerevisiae mRNA translation which includes the initiation, elongation and termination phases. The model is for 20 condon mRNAs. The building of a multi-factor complex in initiation and also the different processes in elongation and termination are modelled in detail. The model takes into account that ribosomes cover more than one codon of mRNA so that the movement of ribosomes are effectively blocked by other ribosomes several codons downstream. It is assumed that 15 codons are occupied by each ribosome. This blocking effect is considered in reaction R18 in initiation and also reaction R26, the reaction where translocation of ribosomes takes place in elongation. The kinetic functions of these two reactions are based on MacDonald et al. 1968 and Heinrich & Rapaport 1980. All other kinetic functions follow mass-action kinetics. The concentrations of transfer RNA species (Met-tRNA, aa-tRNA and tRNA in the model) are kept constant, while the other species' concentrations can change in the course of the simulation. The model describes the translation of a short mRNA with 20 codons. Therefore, all reactions in the elongation cycle (R22, R23, R25, R26, R28 and R29) and the corresponding species are replicated accordingly to model the species with ribosomes bound at different positions. In summary, the model contains 165 different species and 141 reactions.

The value of the 56 rate constant parameters were estimated by fitting the model against a series of experimental data consisting of modulation of the various translation factors (Figures 2, 3 and S3). Overall the parameter estimation was carried out over 212 different data points (steady states).

This model is described in the article:

Helena Firczuk, Shichina Kannambath, J\u00fcrgen Pahle, Amy Claydon, Robert Beynon, John Duncan, Hans Westerhoff, Pedro Mendes and John EG McCarthy
Molecular Systems Biology. 9:635

Abstract:

Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNAi to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5\u2032untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5\u2032UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.

This model is hosted on BioModels Database and identified by: BIOMD0000000457 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3261, + "tag": "BioModels:BIOMD0000000457" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3262, + "tag": "Translation" + } + ], + "timestamp_created": "2025-02-03 15:41:47.086061+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000457", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2405": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2405, + "name": "Smallbone2013 - Serine biosynthesis", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Serine biosynthesis

Kinetic modelling of metabolic pathways in application to Serine biosynthesis.

This model is described in the article:

Kieran Smallbone, Natalie J. Stanford
Methods in Molecular Biology. 2013; 985:113-121

Abstract:

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway using kinetic data from both experimental measurements and literature review. Our methodology is presented by using the example of serine biosynthesis in E. coli.

As there are no plots to be reproduced as curation figure, table 6 and 7 that corresponds to steady state concentration of metabolite and steady state fluxes of reactions has been reproduced.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000458 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3263, + "tag": "BioModels:BIOMD0000000458" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 3264, + "tag": "L-serine biosynthetic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:47.665847+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000458", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2406": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2406, + "name": "Liebal2012 - B.subtilis post-transcriptional instability model", + "repository_type": "biomodels", + "summary": "
Liebal2012 - B.subtilis post-transcription instability model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the post-transcription instability model (MODEL1302080001).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1302080001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3265, + "tag": "Bacillus subtilis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3266, + "tag": "BioModels:BIOMD0000000459" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:48.137020+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000459", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2407": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2407, + "name": "Liebal2012 - B.subtilis sigB proteolysis model", + "repository_type": "biomodels", + "summary": "
Liebal2012 - B.subtilis sigB proteolysis model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the sigma B proteolysis model (MODEL1302080000).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1302080000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3265, + "tag": "Bacillus subtilis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3267, + "tag": "BioModels:BIOMD0000000460" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:48.656573+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000460", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2408": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2408, + "name": "Liebal2012 - B.subtilis transcription inhibition model", + "repository_type": "biomodels", + "summary": "
Liebal2012 - B.subtilis transcription inhibition model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models are constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the Transcription inhibition model (MODEL1212180000).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1212180000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3265, + "tag": "Bacillus subtilis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3268, + "tag": "BioModels:BIOMD0000000461" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:49.179055+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000461", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2409": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2409, + "name": "Proctor2012 - Role of Amyloid-beta dimers in aggregation formation", + "repository_type": "biomodels", + "summary": "
Proctor2012 - Amyloid-beta aggregation

This model supports the current thinking that levels of dimers are important in initiating the aggregation process.

This model is described in the article:

Proctor CJ, Pienaar IS, Elson JL, Kirkwood TB
Molecular Neurodegeneration. 2012; 7:32

Abstract:

BACKGROUND: Alzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (A\u00df), particularly A\u00df\u2084\u2082, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting A\u00df\u2084\u2082 immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine A\u00df\u2084\u2082 turnover and its aggregation in detail and to test the effect of immunization against A\u00df dimers.

RESULTS: Our model demonstrates for the first time that even a slight decrease in the clearance rate of A\u00df\u2084\u2082 monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of A\u03b2 are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.

CONCLUSION: Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies.

This model is hosted on BioModels Database and identified by: BIOMD0000000462 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3269, + "tag": "Amyloid-beta clearance" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3270, + "tag": "BioModels:BIOMD0000000462" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:49.891106+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000462", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2410": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2410, + "name": "Heldt2012 - Influenza Virus Replication", + "repository_type": "biomodels", + "summary": "
Heldt2012 - Influenza Virus Replication

The model describes the life cycle of influenza A virus in a mammalian cell including the following steps: attachment of parental virions to the cell membrane, receptor-mediated endocytosis, fusion of the virus envelope with the endosomal membrane, nuclear import of vRNPs, viral transcription and replication, translation of the structural viral proteins, nuclear export of progeny vRNPs and budding of new virions. It also explicitly accounts for the stabilization of cRNA by viral polymerases and NP and the inhibition of vRNP activity by M1 protein binding. In short, the model focuses on the molecular mechanism that controls viral transcription and replication.

This model is described in the article:

Heldt FS, Frensing T, Reichl U.
J Virol.

Abstract:

Influenza viruses transcribe and replicate their negative-sense RNA genome inside the nucleus of host cells via three viral RNA species. In the course of an infection, these RNAs show distinct dynamics, suggesting that differential regulation takes place. To investigate this regulation in a systematic way, we developed a mathematical model of influenza virus infection at the level of a single mammalian cell. It accounts for key steps of the viral life cycle, from virus entry to progeny virion release, while focusing in particular on the molecular mechanisms that control viral transcription and replication. We therefore explicitly consider the nuclear export of viral genome copies (vRNPs) and a recent hypothesis proposing that replicative intermediates (cRNA) are stabilized by the viral polymerase complex and the nucleoprotein (NP). Together, both mechanisms allow the model to capture a variety of published data sets at an unprecedented level of detail. Our findings provide theoretical support for an early regulation of replication by cRNA stabilization. However, they also suggest that the matrix protein 1 (M1) controls viral RNA levels in the late phase of infection as part of its role during the nuclear export of viral genome copies. Moreover, simulations show an accumulation of viral proteins and RNA toward the end of infection, indicating that transport processes or budding limits virion release. Thus, our mathematical model provides an ideal platform for a systematic and quantitative evaluation of influenza virus replication and its complex regulation.

With the current parameter set, the model reproduces an infection at a multiplicity of infection (MOI) of 10. Figure 2A of the paper is reproduced here, with parameters kDegRnp and kSynP changed to zeros.

Initial conditions and parameter changes that were used to obtain specific figures in the article can be found in Table A2.

The model has the correct value for kAttLo as 4.55e-04. The value of this parameter mentioned as 4.55e-02 in Table 1 of the paper is incorrect. This is checked with the author.

This model is hosted on BioModels Database and identifiedby: MODEL1307270000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3272, + "tag": "BioModels:BIOMD0000000463" + }, + { + "id": 3273, + "tag": "Defense response, incompatible interaction" + }, + { + "id": 3274, + "tag": "Influenza" + }, + { + "id": 3275, + "tag": "Influenza A virus" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3276, + "tag": "Viral entry into host cell" + }, + { + "id": 3277, + "tag": "Viral life cycle" + } + ], + "timestamp_created": "2025-02-03 15:41:50.393691+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000463", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2411": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2411, + "name": "Koo2013 - Shear stress induced calcium influx and eNOS activation - Model 1", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3278, + "tag": "BioModels:BIOMD0000000464" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3279, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3280, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:50.932964+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000464", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2412": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2412, + "name": "Koo2013 - Shear stress induced AKT and eNOS phosphorylation - Model 2", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3281, + "tag": "BioModels:BIOMD0000000465" + }, + { + "id": 3282, + "tag": "ENOS synthesizes NO" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3279, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3280, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 3283, + "tag": "Response to fluid shear stress" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:51.424632+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000465", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2413": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2413, + "name": "Koo2013 - Shear stress induced eNOS expression - Model 3", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3284, + "tag": "BioModels:BIOMD0000000466" + }, + { + "id": 3282, + "tag": "ENOS synthesizes NO" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3279, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3280, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 3283, + "tag": "Response to fluid shear stress" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:51.953136+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000466", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2414": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2414, + "name": "Koo2013 - Shear stress induced NO production - Model 4", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3285, + "tag": "BioModels:BIOMD0000000467" + }, + { + "id": 3282, + "tag": "ENOS synthesizes NO" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3279, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3280, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:52.467211+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000467", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2415": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2415, + "name": "Koo2013 - Integrated shear stress induced NO production model", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3286, + "tag": "BioModels:BIOMD0000000468" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3279, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3280, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:53.019105+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000468", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2416": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2416, + "name": "Smallbone2013 - E.coli metabolic model with linlog rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3287, + "tag": "BioModels:BIOMD0000000469" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 3289, + "tag": "Organic substance biosynthetic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:53.500847+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000469", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2417": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2417, + "name": "Smallbone2013 - E.coli metabolic model with modular rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3290, + "tag": "BioModels:BIOMD0000000470" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:53.978725+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000470", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2418": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2418, + "name": "Smallbone2013 - Yeast metabolic model with linlog rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3291, + "tag": "BioModels:BIOMD0000000471" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3292, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-02-03 15:41:54.496865+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000471", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2419": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2419, + "name": "Smallbone2013 - Yeast metabolic model with modular rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3293, + "tag": "BioModels:BIOMD0000000472" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3292, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-02-03 15:41:55.010352+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000472", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2420": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2420, + "name": "Smallbone2013 - Yeast metabolic model with modular rate law, merged with Pritchard 2002", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3294, + "tag": "BioModels:BIOMD0000000473" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3292, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-02-03 15:41:55.477871+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000473", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2421": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2421, + "name": "Smith2013 - Regulation of Insulin Signalling by Oxidative Stress", + "repository_type": "biomodels", + "summary": "
Smith2013 - Regulation of Insulin Signalling by Oxidative Stress

The model describes insulin signalling (in rodent adipocytes), which includes in addition to the core pathway, the transcriptional feedback through the Forkhead box type O (FOXO) transcription factor and interaction with oxidative stress.

This model is described in the article:

Smith GR, Shanley DP.
BMC Syst Biol. 2013 May 24;7:41.

Abstract:

BACKGROUND: Existing models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO.

RESULTS: Insulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear.

CONCLUSION: Our model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level.A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000.Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012_insulin_signalling.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000474 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3295, + "tag": "BioModels:BIOMD0000000474" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:55.964022+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000474", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2422": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2422, + "name": "Amara2013 - PCNA ubiquitylation in the activation of PRR pathway", + "repository_type": "biomodels", + "summary": " Mechanistic model of the Post-Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA). This model has been defined according to the stochastic formulation of chemical kinetics [Gillespie DT, J Phys Chem 1977, 81(25):2340-2361], which requires to specify the set of molecular species occurring in the pathway and their respective interactions, formally described as a set of biochemical reactions. The volume considered for this system is 1.666667e-17L; this value can be used to convert the model into the deterministic formulation. ", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3296, + "tag": "BioModels:BIOMD0000000475" + }, + { + "id": 3297, + "tag": "Postreplication repair" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:41:56.453883+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000475", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2423": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2423, + "name": "Adams2012 - Locke2006 Circadian Rhythm model refined with Input Signal Light Function", + "repository_type": "biomodels", + "summary": "

As per BIO0000000089.xml but including a functional light.

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Created by The MathWorks, Inc. SimBiology tool, Version 3.3

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3299, + "tag": "BioModels:BIOMD0000000477" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3300, + "tag": "Immune response-regulating signaling pathway" + }, + { + "id": 3301, + "tag": "Leishmaniasis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:57.517764+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000477", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2425": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2425, + "name": "Besozzi2012 - Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiae", + "repository_type": "biomodels", + "summary": "
Besozzi2012 - Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiae

Mechanistic model of the Ras/cAMP/PKA in yeast S.cerevisiae. The Ras/cAMP/PKA pathway plays a major role in the regulation of metabolism, stress resistance and cell cycle progress and is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA). This model investigates the dynamics of the second messenger cAMP on Ras/cAMP/PKA pathway, to determine the effects of the feedback mechanisms on establising stable oscillatory regimes.

The model has been defined according to the stochastic formulation of chemical kinetics [Gillespie DT, 1977] , which requires to specify the set of molecular species occurring in the pathway and their respective interactions, formally described as a set of biochemical reactions.

The volume considered for this system is 30fL; this value can be used to convert the model into the deterministic formulation.

This model is described in the article:

Besozzi D, Cazzaniga P, Pescini D, Mauri G, Colombo S, Martegani E.
EURASIP J Bioinform Syst Biol. 2012 Jul 20;2012(1):10.

Abstract:

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. This multi-level regulation of the amplitude and frequency of oscillations in the Ras/cAMP/PKA pathway might act as a fine tuning mechanism for the downstream targets of PKA, as also recently evidenced by some experimental investigations on the nucleocytoplasmic shuttling of the transcription factor Msn2 in yeast cells.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000478 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3302, + "tag": "BioModels:BIOMD0000000478" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:41:57.994235+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000478", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2426": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2426, + "name": "Croft2013 - GPCR-RGS interaction that compartmentalizes RGS activity", + "repository_type": "biomodels", + "summary": "
Croft2013 - GPCR-RGS interaction that compartmentalizes RGS activity

Through modelling studies, the classic quaternary complex (ligand-GPCR-G-RGS) has been extended to include an additional layer of regulation through GPCR-RGS interactions, which facilitate the compartmentalization of RGS activity into the plasma membrane and non-plasma compartments.

This model is described in the article:

Croft W, Hill C, McCann E, Bond M, Esparza-Franco M, Bennett J, Rand D, Davey J, Ladds G.
J Biol Chem. 2013 Sep 20;288(38):27327-42.

Abstract:

G protein-coupled receptors (GPCRs) can interact with regulator of G protein signaling (RGS) proteins. However, the effects of such interactions on signal transduction and their physiological relevance have been largely undetermined. Ligand-bound GPCRs initiate by promoting exchange of GDP for GTP on the G\u03b1 subunit of heterotrimeric G proteins. Signaling is terminated by hydrolysis of GTP to GDP through intrinsic GTPase activity of the G\u03b1 subunit, a reaction catalyzed by RGS proteins. Using yeast as a tool to study GPCR signaling in isolation, we define an interaction between the cognate GPCR (Mam2) and RGS (Rgs1), mapping the interaction domains. This reaction tethers Rgs1 at the plasma membrane and is essential for physiological signaling response. In vivo quantitative data inform the development of a kinetic model of the GTPase cycle, which extends previous attempts by including GPCR-RGS interactions. In vivo and in silico data confirm that GPCR-RGS interactions can impose an additional layer of regulation through mediating RGS subcellular localization to compartmentalize RGS activity within a cell, thus highlighting their importance as potential targets to modulate GPCR signaling pathways.

Author's comment on reproducing the plots:
To reproduce dose-response plots in the publication, the model is simulated with 12 different ligand concentrations (see parameter Ligand_conc).
For each ligand concentration, a single value corresponding to total amount of output must be obtained, by calculating the area under the curve of the trajectory of species z3, from time=0 to time=30.
These total output values are then used to build a dose-response plot (authors used GraphPad Prism).
Mutant strains are simulated with alternative parameter values or initial conditions specified in the Supplementary Material.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000479 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3303, + "tag": "BioModels:BIOMD0000000479" + }, + { + "id": 3304, + "tag": "Regulation of GTPase activity" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:41:58.776014+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000479", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2427": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2427, + "name": "Carbo2013 - Mucosal Immune Response during H.pylori Infection", + "repository_type": "biomodels", + "summary": " ", + "tags": [ + { + "id": 3250, + "tag": "Bacterial infectious disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3305, + "tag": "BioModels:BIOMD0000000480" + }, + { + "id": 3306, + "tag": "Defense response to Gram-negative bacterium" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3307, + "tag": "T cell mediated immunity" + } + ], + "timestamp_created": "2025-02-03 15:41:59.268062+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000480", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2428": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2428, + "name": "St\u00f6tzel2012 - Bovine estrous cycle, synchronization with prostaglandin F2\u03b1", + "repository_type": "biomodels", + "summary": "C. St\u00f6tzel, J. Pl\u00f6ntzke, W. Heuwieser & S. R\u00f6blitz. Advances in modeling of the bovine estrous cycle: synchronization with PGF2\u03b1. Theriogenology 78, 7 (2012).

Our model of the bovine estrous cycle is a set of ordinary differential equations which generates hormone profiles of successive estrous cycles with several follicular waves per cycle. It describes the growth and decay of the follicles and the corpus luteum, as well as the change of the key reproductive hormones, enzymes and processes over time. In this work we describe recent developments of this model towards the administration of prostaglandin F2\u03b1. We validate our model by showing that the simulations agree with observations from synchronization studies and with measured progesterone data after single dose administrations of synthetic prostaglandin F2\u03b1.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3308, + "tag": "BioModels:BIOMD0000000481" + }, + { + "id": 3309, + "tag": "Bos taurus" + }, + { + "id": 3310, + "tag": "Ovarian disease" + }, + { + "id": 3311, + "tag": "Ovulation cycle" + }, + { + "id": 3312, + "tag": "Response to prostaglandin F" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:41:59.765732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000481", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2429": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2429, + "name": "Noguchi2013 - Insulin dependent glucose metabolism", + "repository_type": "biomodels", + "summary": "Rei Noguchi, Hiroyuki Kubota, Katsuyuki Yugi, Yu Toyoshima, Yasunori Komori, Tomoyoshi Soga & Shinya Kuroda. The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns. Molecular Systems Biology 9 (2013).

Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin-signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLC(ex)). Based on the experimental results, we constructed a simple computational model that characterises response of insulin-signalling-dependent glucose metabolism. The model revealed that the network motifs of glycolysis and glycogenesis pathways constitute a feedforward (FF) with substrate depletion and incoherent feedforward loop (iFFL), respectively, enabling glycolysis and glycogenesis responsive to temporal changes of insulin rather than its absolute concentration. In contrast, the network motifs of gluconeogenesis pathway constituted a FF inhibition, enabling gluconeogenesis responsive to absolute concentration of insulin regardless of its temporal patterns. GLC(ex) was regulated by gluconeogenesis and glycolysis. These results demonstrate the selective control mechanism of glucose metabolism by temporal patterns of insulin.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3313, + "tag": "BioModels:BIOMD0000000482" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 3314, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:00.322873+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000482", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2430": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2430, + "name": "Cao2008 - Network of a toggle switch", + "repository_type": "biomodels", + "summary": "Youfang Cao & Jie Liang. Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability. BMC Systems Biology 2 (2008).

Stochasticity plays important roles in many molecular networks when molecular concentrations are in the range of 0.1 muM to 10nM (about 100 to 10 copies in a cell). The chemical master equation provides a fundamental framework for studying these networks, and the time-varying landscape probability distribution over the full microstates, i.e., the combination of copy numbers of molecular species, provide a full characterization of the network dynamics. A complete characterization of the space of the microstates is a prerequisite for obtaining the full landscape probability distribution of a network. However, there are neither closed-form solutions nor algorithms fully describing all microstates for a given molecular network.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3315, + "tag": "BioModels:BIOMD0000000483" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:42:00.809369+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000483", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2431": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2431, + "name": "Cao2013 - Application of ABSIS method in birth-death process", + "repository_type": "biomodels", + "summary": "Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3317, + "tag": "BioModels:BIOMD0000000484" + }, + { + "id": 3318, + "tag": "Regulation of growth" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:42:01.272162+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000484", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2432": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2432, + "name": "Cao2013 - Application of ABSIS method in the bistable Schl\u00f6gl model", + "repository_type": "biomodels", + "summary": "F. Schl\ufffdgl. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift f\ufffdr Physik 253, 2 (1972).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3319, + "tag": "BioModels:BIOMD0000000485" + }, + { + "id": 3320, + "tag": "Catalytic activity" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:42:01.783066+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000485", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2433": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2433, + "name": "Cao2013 - Application of ABSIS method in the reversible isomerization model", + "repository_type": "biomodels", + "summary": "Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3321, + "tag": "BioModels:BIOMD0000000486" + }, + { + "id": 3322, + "tag": "Macromolecule modification" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:42:02.296071+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000486", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2434": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2434, + "name": "Cao2013 - Application of ABSIS in the the enzymatic futile cycle", + "repository_type": "biomodels", + "summary": "Michael Samoilov, Sergey Plyasunov & Adam P. Arkin. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proceedings of the National Academy of Sciences 102, 7 (2005).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3323, + "tag": "BioModels:BIOMD0000000487" + }, + { + "id": 3324, + "tag": "Heat generation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:42:02.823797+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000487", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2435": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2435, + "name": "Proctor2013 - Effect of A\u03b2 immunisation in Alzheimer's disease (deterministic version)", + "repository_type": "biomodels", + "summary": "
Proctor2013 - Effect of A\u03b2 immunisationin Alzheimer's disease (deterministic version)

Extension of a previously published stochastic model (designed to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau ( BIOMD0000000286, BIOMD0000000462)) to include the main processes involved in passive and active immunisation against A\u03b2 and then to demonstrate the effects of this intervention on soluble A\u03b2. This is the deterministic version of the model, the stochastic version is BIOMD0000000634.

This model is described in the article:

Proctor CJ, Boche D, Gray DA, Nicoll JA.
PLoS ONE 2013; 8(9): e73631

Abstract:

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000488.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3325, + "tag": "BioModels:BIOMD0000000488" + }, + { + "id": 3326, + "tag": "DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:03.295483+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000488", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2436": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2436, + "name": "Sharp2013 - Lipopolysaccharide induced NFkB activation", + "repository_type": "biomodels", + "summary": "Markus W. Covert, Thomas H. Leung, Jahlionais E. Gaston & David Baltimore. Achieving stability of lipopolysaccharide-induced NF-kappaB activation. Science 309, 5742 (2005).

Preterm birth is the single biggest cause of significant neonatal morbidity and mortality, and the incidence is rising. Development of new therapies to treat and prevent preterm labour is seriously hampered by incomplete understanding of the molecular mechanisms that initiate labour at term and preterm. Computational modelling provides a new opportunity to improve this understanding. It is a useful tool in (i) identifying gaps in knowledge and informing future research, and (ii) providing the basis for an in silico model of parturition in which novel drugs to prevent or treat preterm labour can be \"tested\". Despite their merits, computational models are rarely used to study the molecular events initiating labour. Here, we present the first attempt to generate a dynamic kinetic model that has relevance to the molecular mechanisms of preterm labour. Using published data, we model an important candidate signalling pathway in infection-induced preterm labour: that of lipopolysaccharide (LPS) -induced activation of Nuclear Factor kappa B. This is the first model of this pathway to explicitly include molecular interactions upstream of Nuclear Factor kappa B activation. We produced a formalised graphical depiction of the pathway and built a kinetic model based on ordinary differential equations. The kinetic model accurately reproduced published in vitro time course plots of Lipopolysaccharide-induced Nuclear Factor kappa B activation in mouse embryo fibroblasts. In this preliminary work we have provided proof of concept that it is possible to build computational models of signalling pathways that are relevant to the regulation of labour, and suggest that models that are validated with wet-lab experiments have the potential to greatly benefit the field.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3327, + "tag": "BioModels:BIOMD0000000489" + }, + { + "id": 3328, + "tag": "Lipopolysaccharide metabolic process" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3329, + "tag": "Release of cytoplasmic sequestered NF-kappaB" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:03.813117+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000489", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2437": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2437, + "name": "Demin2013 - PKPD behaviour - 5-Lipoxygenase inhibitors", + "repository_type": "biomodels", + "summary": "
Demin2013 - PKPD behaviour - 5-Lipoxygenaseinhibitors

This model is described in the article:

Demin O, Karelina T, Svetlichniy D, Metelkin E, Speshilov G, Demin O Jr, Fairman D, van der Graaf PH, Agoram BM.
CPT Pharmacometrics Syst Pharmacol 2013; 2: e74

Abstract:

Zileuton, a 5-lipoxygenase (5LO) inhibitor, displays complex pharmaokinetic (PK)-pharmacodynamic (PD) behavior. Available clinical data indicate a lack of dose-bronchodilatory response during initial treatment, with a dose response developing after ~1-2 weeks. We developed a quantitative systems pharmacology (QSP) model to understand the mechanism behind this phenomenon. The model described the release, maturation, and trafficking of eosinophils into the airways, leukotriene synthesis by the 5LO enzyme, leukotriene signaling and bronchodilation, and the PK of zileuton. The model provided a plausible explanation for the two-phase bronchodilatory effect of zileuton-the short-term bronchodilation was due to leukotriene inhibition and the long-term bronchodilation was due to inflammatory cell infiltration blockade. The model also indicated that the theoretical maximum bronchodilation of both 5LO inhibition and leukotriene receptor blockade is likely similar. QSP modeling provided interesting insights into the effects of leukotriene modulation.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e74; doi:10.1038/psp.2013.49; advance online publication 11 September 2013.

This model is hosted on BioModels Database and identified by: BIOMD0000000490.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3330, + "tag": "Asthma" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3331, + "tag": "BioModels:BIOMD0000000490" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3332, + "tag": "Inflammatory response" + }, + { + "id": 3333, + "tag": "Response to bronchodilator" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:04.321298+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000490", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2438": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2438, + "name": "Pathak2013 - MAPK activation in response to various abiotic stresses", + "repository_type": "biomodels", + "summary": "
Pathak2013 - MAPK activation in response to various abiotic stresses

MAPK activation mechanism in response to various abiotic stress conditions, such as cold, salt, drought, H2O2, heavy metal and ethylene, in plants

This model is described in the article:

Pathak RK, Taj G, Pandey D, Arora S, Kumar A.
Bioinformation 2013; 9(9): 443-449

Abstract:

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000491 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3334, + "tag": "BioModels:BIOMD0000000491" + }, + { + "id": 3335, + "tag": "Cellular response to abiotic stimulus" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 15:42:04.847788+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000491", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2439": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2439, + "name": "Pathak2013 - MAPK activation in response to various biotic stresses", + "repository_type": "biomodels", + "summary": "
Pathak2013 - MAPK activation in response to various biotic stresses

MAPK activation mechanism in response to various biotic (fungal and bacterial pathogens) stress conditions in plants

This model is described in the article:

Pathak RK, Taj G, Pandey D, Arora S, Kumar A.
Bioinformation 2013; 9(9): 443-449

Abstract:

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000492 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3337, + "tag": "BioModels:BIOMD0000000492" + }, + { + "id": 3338, + "tag": "Cellular response to biotic stimulus" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 15:42:05.347293+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000492", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2440": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2440, + "name": "Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes", + "repository_type": "biomodels", + "summary": "
Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes

Mathematical model describing the mechanism of differentiation of mesenchymal stem cells to bone (osteoblasts) or cartilage (chondrocytes) cells.

This model is described in the article:

Schittler D, Hasenauer J, Allg\u00f6wer F, Waldherr S.
Chaos 2010 Dec; 20(4): 045121

Abstract:

Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000493 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3339, + "tag": "BioModels:BIOMD0000000493" + }, + { + "id": 3340, + "tag": "Mesenchymal cell differentiation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:42:06.018067+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000493", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2441": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2441, + "name": "Roblitz2013 - Menstrual Cycle following GnRH analogue administration", + "repository_type": "biomodels", + "summary": "
Roblitz2013 - Menstrual Cycle following GnRH analogue administration

The model describes the menstrual cycle feedback mechanisms. GnRH, FSH, LH, E2, P4, inbibins A and B, and follicular development are modelled. The model predicts hormonal changes following GnRH analogue administration. Simulation results agree with measurements of hormone blood concentrations. The model gives insight into mechanisms underlying gonadotropin supression.

This model is described in the article:

R\u00f6blitz S, St\u00f6tzel C, Deuflhard P, Jones HM, Azulay DO, van der Graaf PH, Martin SW.
J. Theor. Biol. 2013 Mar; 321: 8-27

Abstract:

The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000494 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3341, + "tag": "BioModels:BIOMD0000000494" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3310, + "tag": "Ovarian disease" + }, + { + "id": 3311, + "tag": "Ovulation cycle" + }, + { + "id": 3208, + "tag": "Response to gonadotropin-releasing hormone" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3342, + "tag": "female" + } + ], + "timestamp_created": "2025-02-03 15:42:06.505387+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000494", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2442": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2442, + "name": "Sen2013 - Phospholipid Synthesis in P.knowlesi", + "repository_type": "biomodels", + "summary": "
Sen2013 - Phospholipid Synthesis in P.knowlesi

The model describes the multiple phospholipid synthetic pathways in Plasmodium knowlesi.

This model is described in the article:

Sen P, Vial HJ, Radulescu O.
BMC Syst Biol 2013 Nov; 7(1): 123

Abstract:

BACKGROUND: Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species. RESULTS: Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step.CONCLUSION: We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000495 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3343, + "tag": "BioModels:BIOMD0000000495" + }, + { + "id": 3344, + "tag": "Phospholipid biosynthetic process" + }, + { + "id": 3345, + "tag": "Plasmodium knowlesi" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:07.017524+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000495", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2443": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2443, + "name": "Stanford2013 - Kinetic model of yeast metabolic network (standard)", + "repository_type": "biomodels", + "summary": "
Stanford2013 - Kinetic model of yeast metabolic network (standard)

Large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants.

This model is described in the article:

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W.
PLoS ONE 2013; 8(11): e79195

Abstract:

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

[corrections made to the model compared to the paper, optional]

This model is hosted on BioModels Database and identifiedby: BIOMD0000000496 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3346, + "tag": "BioModels:BIOMD0000000496" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 3289, + "tag": "Organic substance biosynthetic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:42:07.713386+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000496", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2444": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2444, + "name": "Stanford2013 - Kinetic model of yeast metabolic network (regulation)", + "repository_type": "biomodels", + "summary": "
Stanford2013 - Kinetic model of yeast metabolic network (standard)

Large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. This model is built with regulatory information.

This model is described in the article:

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W.
PLoS ONE 2013; 8(11): e79195

Abstract:

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000497 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3347, + "tag": "BioModels:BIOMD0000000497" + }, + { + "id": 3288, + "tag": "Metabolic process" + }, + { + "id": 3289, + "tag": "Organic substance biosynthetic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:42:08.282388+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000497", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2445": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2445, + "name": "Mitchell2013 - Liver Iron Metabolism", + "repository_type": "biomodels", + "summary": "
Mitchell2013 - Liver Iron Metabolism

The model includes the core regulatory components of human liver iron metabolism.

This model is described in the article:

Mitchell S, Mendes P.
PLoS Comput. Biol. 2013 Nov; 9(11): e1003299

Abstract:

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000498 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3348, + "tag": "BioModels:BIOMD0000000498" + }, + { + "id": 3349, + "tag": "Hemochromatosis" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:08.796836+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000498", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2446": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2446, + "name": "Vizan2013 - TGF pathway long term signaling", + "repository_type": "biomodels", + "summary": "Pedro Viz\u00e1n, Daniel S. J. Miller, Ilaria Gori, Debipriya Das, Bernhard Schmierer & Caroline S. Hill. Controlling long-term signaling: receptor dynamics determine attenuation and refractory behavior of the TGF-\u03b2 pathway. Science Signaling 6, 305 (2013).

Understanding the complex dynamics of growth factor signaling requires both mechanistic and kinetic information. Although signaling dynamics have been studied for pathways downstream of receptor tyrosine kinases and G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors, they have not been investigated for the transforming growth factor-\u03b2 (TGF-\u03b2) superfamily pathways. Using an integrative experimental and mathematical modeling approach, we dissected the dynamic behavior of the TGF-\u03b2 to Smad pathway, which is mediated by type I and type II receptor serine/threonine kinases, in response to acute, chronic, and repeated ligand stimulations. TGF-\u03b2 exposure produced a transient response that attenuated over time, resulting in desensitized cells that were refractory to further acute stimulation. This loss of signaling competence depended on ligand binding, but not on receptor activity, and was restored only after the ligand had been depleted. Furthermore, TGF-\u03b2 binding triggered the rapid depletion of signaling-competent receptors from the cell surface, with the type I and type II receptors exhibiting different degradation and trafficking kinetics. A computational model of TGF-\u03b2 signal transduction from the membrane to the nucleus that incorporates our experimental findings predicts that autocrine signaling, such as that associated with tumorigenesis, severely compromises the TGF-\u03b2 response, which we confirmed experimentally. Thus, we have shown that the long-term signaling behavior of the TGF-\u03b2 pathway is determined by receptor dynamics, does not require TGF-\u03b2-induced gene expression, and influences context-dependent responses in vivo.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3350, + "tag": "BioModels:BIOMD0000000499" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3351, + "tag": "SMAD protein signal transduction" + }, + { + "id": 3352, + "tag": "Transforming growth factor beta receptor signaling pathway" + } + ], + "timestamp_created": "2025-02-03 15:42:09.294911+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000499", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2447": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2447, + "name": "Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer model", + "repository_type": "biomodels", + "summary": "
Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer model

The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely \"single GAS polymer model\" and \"double GAS polymer model\" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the \"single GAS polymer model\".

This model is described in the article:

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U
Nat Immunol. 2014 Feb;15(2):168-76.

Abstract:

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000500 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3353, + "tag": "BioModels:BIOMD0000000500" + }, + { + "id": 3354, + "tag": "Interferon-gamma-mediated signaling pathway" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:42:09.797145+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000500", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2448": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2448, + "name": "Vilar2006_TGFbeta", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 5A of the paper. The ligand concentration is increased from 3E-5 to 0.01 at time t=2500 to ensure that the system reaches steady state. Hence, the time t=0 of the paper corresponds to t=2500 in the model. The peak value of the active ligand receptor complex is off by a value of 1.25, the authors have stated that this discrepancy is due to the fact that the figure in the paper corresponds to a slightly different parameter set. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3355, + "tag": "BioModels:BIOMD0000000101" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3352, + "tag": "Transforming growth factor beta receptor signaling pathway" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:37.521388+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000101", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2449": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2449, + "name": "Legewie2006_apoptosis_WT", + "repository_type": "biomodels", + "summary": "

The model reproduces active Caspase-3 time profile corresponding to the total Apaf-1 value of 20 nM as depicted in Fig 2-A . The model was successfully tested on MathSBML.

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3357, + "tag": "BioModels:BIOMD0000000102" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:38.111774+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000102", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2450": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2450, + "name": "Legewie2006_apoptosis_NC", + "repository_type": "biomodels", + "summary": "

This model represents the non-competitive binding of XIAP to Casapase-3 and Caspase-9. In other words, XIAP mediated feedback is abolished in this model. The authors state that this leads to bistable-reversible behaviour as depicted in Fig 4C. The wild-type model displays a bistable-irreversible profile. This shows that irreversibility requires XIAP mediated feedback. The model was tested on MathSBML. However, please note that the paper does not contain any figure that corresponds to simulation of the Non-Competitive model.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3358, + "tag": "BioModels:BIOMD0000000103" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:38.680301+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000103", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2451": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2451, + "name": "Klipp2002_MetabolicOptimization_linearPathway(n=2)", + "repository_type": "biomodels", + "summary": "
Klipp2002_MetabolicOptimization_linearPathway(n=2)

The model describes time dependent gene expression as a means to enable cells to adapt metabolic activity optimally based on environmental conditions. It uses a simple unbranched pathway and a constraint of fixed total enzyme. It calculates enzyme profiles at different times which optimise a performance function, and compares them to experimental data. The initial model is cell-type agnostic, while the experimeental data is from yeast.

This model is described in the article:

Klipp E, Heinrich R, Holzh\u00fctter HG.
Eur. J. Biochem. 2002 Nov; 269(22): 5406-5413

Abstract:

A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time- dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.

This model is from the paper Prediction of temporal gene expression metabolic optimization by re-distribution of enzyme activities. The model describes optimal enzyme profiles and metabolite time courses for a simple linear metabolic pathway (n=2). Figure 1 was reproduced using roadRunner. The values of k1 and k2 were not explicitly stated in the publication, but calculations were performed for equal catalytic efficiencies of the enzymes (ki=k), hence the curator assigned k1=k2=1. Also enzyme concentrations are given in units of Etot; times are given in units of 1/(k*Etot) in the papaer, for simplicity , we use defalut units of the SBML to present the concentration and time.

This model is hosted on BioModels Database and identifiedby: MODEL4931762955 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3359, + "tag": "BioModels:BIOMD0000000104" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:39.355829+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000104", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2452": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2452, + "name": "Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system", + "repository_type": "biomodels", + "summary": "
Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system

This is a stochastic model of the ubiquitin-proteasome system for a generic pool of native proteins (NatP), which have a half-life of about 10 hours under normal conditions. It is assumed that these proteins are only degraded after they have lost their native structure due to a damage event. This is represented in the model by the misfolding reaction which depends on the level of reactive oxygen species (ROS) in the cell. Misfolded proteins (MisP) are first bound by an E3 ubiquitin ligase. Ubiquitin (Ub) is activated by E1 (ubiquitin-activating enzyme) and then passed to E2 (ubiquitin-conjugating enzyme). The E2 enzyme then passes the ubiquitin molecule to the E3/MisP complex with the net effect that the misfolded protein is monoubiquitinated and both E2 and E3 are released. Further ubiquitin molecules are added in a step-wise manner. When the chain of ubiquitin molecules is of length 4 or more, the polyubiquitinated misfolded protein may bind to the proteasome. The model also includes de-ubiquitinating enzymes (DUB) which cleave ubiquitin molecules from the chain in a step-wise manner. They work on chains attached to misfolded proteins both unbound and bound to the proteasomes. Misfolded proteins bound to the proteasome may be degraded releasing ubiquitin. Misfolded proteins including ubiquitinated proteins may also aggregate. Aggregates (AggP) may be sequestered (Seq_AggP) which takes them out of harm's way or they may bind to the proteasome (AggP_Proteasome). Proteasomes bound by aggregates are no longer available for protein degradation.

Figure 2 and Figure 3 has been simulated using Gillespie2.

This model is described in the article:

Proctor CJ, Tsirigotis M, Gray DA.
BMC Syst Biol 2007; 1: 17

Abstract:

BACKGROUND: The ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation. RESULTS: The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics. CONCLUSION: We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process.

This model is hosted on BioModels Database and identified by: BIOMD0000000105.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3360, + "tag": "BioModels:BIOMD0000000105" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 3362, + "tag": "Neurodegenerative disease" + }, + { + "id": 3363, + "tag": "Proteasome-mediated ubiquitin-dependent protein catabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:40.383061+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000105", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2453": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2453, + "name": "Yang2007_ArachidonicAcid", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Dynamic Simulation on the Arachidonic Acid Metabolic Network . Figure 2A has been reproduced by SBML ode solver on line. In the original model, all the reactions are presented as ODE directly. So curator rewrite each reaction according to the semantics of the paper. In this paper, the authors used quict complex kinetics law to describe the catalysis in the network, curators did not necessarily know all the complete meanings of the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3364, + "tag": "Arachidonic acid metabolic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3365, + "tag": "BioModels:BIOMD0000000106" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:41.833609+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000106", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2454": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2454, + "name": "Novak1993 - Cell cycle M-phase control", + "repository_type": "biomodels", + "summary": "
Novak1993 - Cell cycle M-phase control
The model reproduces Figure 9 of the paper. Please note that active MPF and cyclin concentrations in the paper are given relative to total cdc2 concentration (100nM). Active MPF (dimer_p) is the cyclin-cdc2 complex that is phosphorylated at Thr161. The earlier versions of the model was successfully tested on MathSBML and Jarnac, and the current version was checked in Copasi.

This model is described in the article:

Novak B, Tyson JJ.
J. Cell. Sci. 1993 Dec; 106 ( Pt 4): 1153-1168

Abstract:

To contribute to a deeper understanding of M-phase control in eukaryotic cells, we have constructed a model based on the biochemistry of M-phase promoting factor (MPF) in Xenopus oocyte extracts, where there is evidence for two positive feedback loops (MPF stimulates its own production by activating Cdc25 and inhibiting Wee1) and a negative feedback loop (MPF stimulates its own destruction by indirectly activating the ubiquitin pathway that degrades its cyclin subunit). To uncover the full dynamical possibilities of the control system, we translate the regulatory network into a set of differential equations and study these equations by graphical techniques and computer simulation. The positive feedback loops in the model account for thresholds and time lags in cyclin-induced and MPF-induced activation of MPF, and the model can be fitted quantitatively to these experimental observations. The negative feedback loop is consistent with observed time lags in MPF-induced cyclin degradation. Furthermore, our model indicates that there are two possible mechanisms for autonomous oscillations. One is driven by the positive feedback loops, resulting in phosphorylation and abrupt dephosphorylation of the Cdc2 subunit at an inhibitory tyrosine residue. These oscillations are typical of oocyte extracts. The other type is driven by the negative feedback loop, involving rapid cyclin turnover and negligible phosphorylation of the tyrosine residue of Cdc2. The early mitotic cycles of intact embryos exhibit such characteristics. In addition, by assuming that unreplicated DNA interferes with M-phase initiation by activating the phosphatases that oppose MPF in the positive feedback loops, we can simulate the effect of addition of sperm nuclei to oocyte extracts, and the lengthening of cycle times at the mid-blastula transition of intact embryos.

This model is hosted on BioModels Database and identified by: BIOMD0000000107.

To cite BioModels Database, please use: BioModels: ten-year anniversary .

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This model is according to the paper from Axel Kowald Alternative pathways as mechanism for the negative effects associated with overexpression of superoxide dismutase.

Reactions from 1 to 17 are listed in the paper, note that for clarity species whose concentrations are assumed to be constant (e.g.water, oxygen,protons, metal ions) are omitted from the diagram. In the paper, v16 is a fast reaction, but we do not use fast reaction in the model.

Figure2 has been reproduced by both SBMLodeSolver and Copasi4.0.20(development) . Figure 3 has been obtained with Copasi4.0.20(development) using parameter scan.

The steady-state of [LOO*] a little bit lower than showed on the paper, I guess it may be the simulation method used in the paper use fast reaction and also the reaction (5) listed on Page 831 on the paper is slightly different from equation (2) on Page 832. The rest of them are the quite the same.


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This model is according to the paper A systems biology dynamical model of mammalian G1 cell cycle progression. Supplementary Figure 2A has been reproduced by the MathSBML and CellDesigner. All the data of this model are from the set 2 of Supplementary talbe2.


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", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3371, + "tag": "BioModels:BIOMD0000000109" + }, + { + "id": 3372, + "tag": "G1 phase" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:43.524799+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000109", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2457": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2457, + "name": "Qu2003_CellCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Dynamics of the cell cycle: checkpoints, sizers, and timers.
\t Qu Z, MacLellan WR, Weiss JN Biophys. J.2003 Dec; 85(6): 3600-11 14645053,
Abstract:
We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings.


Figure6B has been reproduced by both SBMLodeSolver online and MathSBML. We do not include the synthesis of cyclins is proportional to cell size (Equation 2 in Page3604 of the paper) in this model. The author of the paper keep all the variables and parameters dimensionless. But in the model, we choose to use default units of SBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3373, + "tag": "BioModels:BIOMD0000000110" + }, + { + "id": 3374, + "tag": "Cell cycle G1/S phase transition" + }, + { + "id": 3375, + "tag": "Cell cycle G2/M phase transition" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:44.145570+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000110", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2458": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2458, + "name": "Novak2001_FissionYeast_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time evolution of several species as depicted in Fig 4 of the paper. Events have been used to reset cell mass when the value of M-phase promoting factor (MPF) decreases through 0.1. The model was successfully tested on Cell Designer.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the temporal evolution of four variables depicted in Fig 2a. The solution is generated for median parameter values as given in Table 3. Result shown was generated by MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3379, + "tag": "BioModels:BIOMD0000000112" + }, + { + "id": 3380, + "tag": "Neovison vison" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:45.284781+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000112", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2460": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2460, + "name": "Dupont1992_Ca_dpt_protein_phospho", + "repository_type": "biomodels", + "summary": "

Model reproduces Fig 4 of the paper. For fraction of phosphorylated protein, W_star, the model reproduces panel b in the same figure. Model successfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3381, + "tag": "BioModels:BIOMD0000000113" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:45.903413+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000113", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2461": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2461, + "name": "Somogyi1990_CaOscillations", + "repository_type": "biomodels", + "summary": "

This model encoded according to the paper Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simple One Pool Model. The values of parameters a and alpha are varioused inorder to simulate results in different situations. For Figure 3A, a=3.5,alpha=1.2 ; Figure 3B, a=3,alpha=5 ; Figure 3C a= 0.95, alpha=1.5; Figure3D, a=1, alpha=5. Keep in mind that the value for the xy axies are arbitrary value. Figures3 in the paper are reproduced by COPASI 4.0.20(development) , and SBMLodeSolver online.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Another model from Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simply One Pool Model. Anatomy of a single Ca2+ spike. Figure4A has been simulated by COPASI4.0.20(development). However, the simulated figure is slightly different from the paper, single spike of Ca2+ is around \"6\" time arbitrary units instead \"9\" time arbitrary units displayed in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model encoded according to the paper Cross-talk and decision making in MAP kinase pathways. Supplementary Figure 2 has been reproduced by COPASI4.0.20 (development) using parameter scan method. You probably need to uncheck \"always use initial conditions\" in copasi when you simulate for the second run in order to get the figure. S1 scale from 0 to 12. Keep in mind that the y axis is the fractions of excited X3 and Y3, meaning that X3P and Y3P are normalized by total concentration X3T and Y3T.

The results from modeling the pathway in Supplementary Figure1a, including both activation and inhibition. According to the paper, the value of ka and kd should in the orange region (ka belongs [0,1], kd belongs [1,10]) so assigned ka=0, kd=1.

The author made the simplifying assumption that the interactions between the pathways are symmetric. Thus the k12xy=k12yx=ka, k33xy=k33yx=kd.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is according to the paper Signal-induced Ca2+ oscillations: Properties of a model based on Ca2+-induced Ca2+ release. Figure4B in the paper has been reproduced by RoadRunner and MathSBML. Damped Ca2+ oscillations elicited by a transient pulse of InsP3 applied intracellularly to a resting, non-oscillatory cell.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3388, + "tag": "BioModels:BIOMD0000000117" + }, + { + "id": 3389, + "tag": "Calcium-induced calcium release activity" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:48.617911+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000117", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2465": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2465, + "name": "Golomb2006_SomaticBursting", + "repository_type": "biomodels", + "summary": "

Model is according to the paper Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cell: Combined Experimental. Figure6Da has been reproduced by MathSBML. The original model from ModelDB. http://senselab.med.yale.edu/modeldb/


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3390, + "tag": "BioModels:BIOMD0000000118" + }, + { + "id": 3106, + "tag": "Neuronal action potential propagation" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3107, + "tag": "Voltage-gated potassium channel activity" + }, + { + "id": 3108, + "tag": "Voltage-gated sodium channel activity" + } + ], + "timestamp_created": "2025-02-03 15:45:49.187624+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000118", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2466": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2466, + "name": "Golomb2006_SomaticBursting_nonzero[Ca]", + "repository_type": "biomodels", + "summary": "

Model is according to the paper Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cell: Combined Experimental. This is the second model from this paper for the non-zero [Ca2+] initial value, parameters and the kinetics quations from Table2 in the paper. Figure9Aa has been reproduced by MathSBML. The original model from ModelDB. http://senselab.med.yale.edu/modeldb/


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3391, + "tag": "BioModels:BIOMD0000000119" + }, + { + "id": 3106, + "tag": "Neuronal action potential propagation" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3107, + "tag": "Voltage-gated potassium channel activity" + }, + { + "id": 3108, + "tag": "Voltage-gated sodium channel activity" + } + ], + "timestamp_created": "2025-02-03 15:45:49.675964+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000119", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2467": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2467, + "name": "Chan2004_TCell_receptor_activation", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 3a of the paper. Please note that the authors mention that they used a value of 2 for n, n being the power in the positive feedback function for kinase autocatalysis, however the model here has n=1.95 because this results in a simulation that is identical to Fig 3a. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3250, + "tag": "Bacterial infectious disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3392, + "tag": "BioModels:BIOMD0000000120" + }, + { + "id": 3393, + "tag": "Regulation of T cell receptor signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3394, + "tag": "T cell receptor signaling pathway - Homo sapiens (human)" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:45:50.155041+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000120", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2468": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2468, + "name": "Clancy2001_Kchannel", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Cellular consequences of HEGR mutations in the long QT syndrome: precursors to sudden cardiac death. The author used Markovian model of cardiac Ikr in the paper. Figure4B in the paper has been reproduced using CellDesigner3.5.1. The cell is depolarized to the indicated test potential for 250ms (from 50ms to 300ms) from a holding potential of -40mV and then repolarized to -40mV. Change the value for vtest from -30,-20,-10,0,10,20,30,40 for each simulation in order to produce the different cureve in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3395, + "tag": "Andersen-Tawil syndrome" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3396, + "tag": "BioModels:BIOMD0000000121" + }, + { + "id": 3397, + "tag": "Cavia porcellus" + }, + { + "id": 3398, + "tag": "Delayed rectifier potassium channel activity" + }, + { + "id": 3399, + "tag": "Heart process" + }, + { + "id": 3400, + "tag": "Regulation of potassium ion transport" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:50.639277+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000121", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2469": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2469, + "name": "Fisher2006_Ca_Oscillation_dpdnt_NFAT_dynamics", + "repository_type": "biomodels", + "summary": "

The model reproduces the calcium oscillation dependent activation-deactivation kinetics of nuclear factor of activated T cells (NFAT) as depicted in Fig 4a of the paper. A simple algorithm in the events section takes care of the calcium oscillation. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3401, + "tag": "BioModels:BIOMD0000000122" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3402, + "tag": "Regulation of T cell mediated immunity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:51.233313+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000122", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2470": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2470, + "name": "Fisher2006_NFAT_Activation", + "repository_type": "biomodels", + "summary": "

The model reproduces the kinetics of the nuclear factor of activated cells (NFAT) as depicted in Figure 3a of the paper. Model was successfully tested on Jarnac and MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3403, + "tag": "BioModels:BIOMD0000000123" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3402, + "tag": "Regulation of T cell mediated immunity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:51.776486+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000123", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2471": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2471, + "name": "Wu2006_K+Channel", + "repository_type": "biomodels", + "summary": "

The model is described in the paper by Wu and Chang (2006). Diethyl pyrocarbonate, a histidine-modifying agent, directly stimulates activity of ATP-sensitive potassium channels in pituitary GH3 cells. Biochem Pharmacol. 71(5): 615-23.

The unit of time is ms, and the simulation time is 80 s, that is 8e4 ms. Therfore, you probably need to increase the maximum steps for your simulator.

The figure 7 has been reproduced by MathSBML. Application of DEPC as indicated at horizontal bar was mimicked by an increase of maximal conductance of Katp-channels from 500 to 530 ps at t=30 s.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3404, + "tag": "ATP-activated inward rectifier potassium channel activity" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3405, + "tag": "BioModels:BIOMD0000000124" + }, + { + "id": 3406, + "tag": "Histone modification" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:52.331861+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000124", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2472": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2472, + "name": "Komarova2005_TheoreticalFramework_BasicArchitecture", + "repository_type": "biomodels", + "summary": "

This model according to the paper A Theoretical Framework for Specificity in Cell Signalling The model is \"basic architecture\" of Figure2A. Figure2B, Figure2C have been reproduced by MathSBML. The reproduced figures are slightly different from the original ones in the paper, the peak of [x2] is higher than 1 and is not decreasing dramatically when [x0]=0. And I think maybe the author shift the or scale the curves.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3407, + "tag": "BioModels:BIOMD0000000125" + }, + { + "id": 3058, + "tag": "Cell surface receptor signaling pathway" + }, + { + "id": 3408, + "tag": "Cellular response to stimulus" + }, + { + "id": 3070, + "tag": "Opisthokonta" + }, + { + "id": 3409, + "tag": "Receptor signaling complex scaffold activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:52.903006+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000125", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2473": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2473, + "name": "Clancy2002_CardiacSodiumChannel_WT", + "repository_type": "biomodels", + "summary": "

The model is according to the paper Na+ Channel Mutation That Causes Both Brugada and Long-QT Syndrome Phenotypes: A Simulation Study of Mechanism Original model comes from ModelDB with accession number: 62661. This is the wide type model. All the values and reactions obtained from Data Supplement6: Appendix of the paper. Figure3 has been reproduced by MathSBML. The stimulus v=-30mV during the time from 5ms to 20 ms displayed in the event. The meaning for the keyword, C: Close states; O: Open states; IF: Fast inactivation states; IC: Closed-Inactivation states; IM: Intermediat Inactivation states.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3410, + "tag": "BioModels:BIOMD0000000126" + }, + { + "id": 3411, + "tag": "Brugada syndrome" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3399, + "tag": "Heart process" + }, + { + "id": 3413, + "tag": "Regulation of sodium ion transport" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3414, + "tag": "Sodium channel activity" + } + ], + "timestamp_created": "2025-02-03 15:45:53.419852+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000126", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2474": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2474, + "name": "Izhikevich2003_SpikingNeuron", + "repository_type": "biomodels", + "summary": "

The model is according to the paper Simple Model of Spiking Neurons In this paper, a simple spiking model is presented that is as biologically plausible as the Hodgkin-Huxley model, yet as computationally efficient as the integrate-and-fire model. Known types of neurons correspond to different values of the parameters a,b,c,d in the model. Figure2RS,IB,CH,FS,LTS have been simulated by MathSBML.

RS: a=0.02, b=0.2, c=-65, d=8.

IB: a=0.02,b=0.2,c=-55,d=4

CH: a=0.02,b=0.2,c=-50,d=2

FS:a=0.1b=0.2c=-65,d=2

LTS:a=0.02,b=0.25,c=-65,d=2


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3415, + "tag": "Action potential" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3416, + "tag": "BioModels:BIOMD0000000127" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 3417, + "tag": "Regulation of membrane potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:53.967823+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000127", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2475": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2475, + "name": "Bertram2006_Endothelin", + "repository_type": "biomodels", + "summary": "

The model is according to the paper Endothelin Action on Pituitary Lactotrophs: One Receptor, Many GTP-Binding Proteins Figure 1 has been simulated by MathSBML. The figure for the [Ca2+]i and [Ca2+]ER have been normalized in the paper.Original model comes from http://www.math.fsu.edu/~bertram/software/pituitary

The units for parameters and species are varied from one to another, so I omit the unit definition here . Conductances in pS; currents in fA; Ca concentrations in uM; time in ms


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3418, + "tag": "Adenylate cyclase-modulating G-protein coupled receptor signaling pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3419, + "tag": "BioModels:BIOMD0000000128" + }, + { + "id": 3420, + "tag": "Endothelin receptor activity" + }, + { + "id": 3421, + "tag": "G-protein coupled receptor binding" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:54.496658+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000128", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2476": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2476, + "name": "Izhikevich2004_SpikingNeurons_inhibitionInducedSpiking", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(S) inhibition-induced spiking has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=-0.02; b=-1; c=-60; d=8; V=-63.8; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3422, + "tag": "BioModels:BIOMD0000000129" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:54.984366+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000129", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2477": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2477, + "name": "Izhikevich2004_SpikingNeurons_integrator", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich, EM Neural Networks, IEEE Transactions on2004:15(5):1063-1070 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(L) integrator has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.02; b=-0.1; c=-55; d=6; V=-60; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3423, + "tag": "BioModels:BIOMD0000000130" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:55.486308+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000130", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2478": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2478, + "name": "Izhikevich2004_SpikingNeurons_reboundBurst", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(N) rebound burst has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.03; b=0.25; c=-52; d=0;V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3424, + "tag": "BioModels:BIOMD0000000131" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:55.998421+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000131", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2479": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2479, + "name": "Izhikevich2004_SpikingNeurons_reboundSpike", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(M) rebound spike has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.03; b=0.25; c=-60; d=4; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3425, + "tag": "BioModels:BIOMD0000000132" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:56.531357+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000132", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2480": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2480, + "name": "Izhikevich2004_SpikingNeurons_resonator", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(K) resonator has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.1; b=0.26; c=-60; d=-1; V=-62; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3426, + "tag": "BioModels:BIOMD0000000133" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:57.003627+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000133", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2481": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2481, + "name": "Izhikevich2004_SpikingNeurons_SpikeLatency", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(I) spike latency has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. In this model a=0.02; b=0.2; c=-65; d=6; V=-70; u=b*V=0.2*(-70);

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3427, + "tag": "BioModels:BIOMD0000000134" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:57.465994+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000134", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2482": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2482, + "name": "Izhikevich2004_SpikingNeurons_subthresholdOscillations", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(J) subthreshold oscillations has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.05; b=0.26; c=-60; d=0; V=-62; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3428, + "tag": "BioModels:BIOMD0000000135" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:57.975583+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000135", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2483": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2483, + "name": "Izhikevich2004_SpikingNeurons_thresholdVariability", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(O) threshold variability has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.03; b=0.25; c=-60; d=4; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3429, + "tag": "BioModels:BIOMD0000000136" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:58.763867+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000136", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2484": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2484, + "name": "Sedaghat2002_InsulinSignalling_noFeedback", + "repository_type": "biomodels", + "summary": "

Model reproduces the various plots in Figure 6 and 7 of the paper. It was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3430, + "tag": "BioModels:BIOMD0000000137" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:59.353391+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000137", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2485": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2485, + "name": "Tabak2007_dopamine", + "repository_type": "biomodels", + "summary": "

The model is encoded according to the paper Low dose of dopamine may stimulate prolactin secretion by increasing fast potassium currents Figure5 has been reproduced by MathSBML. One need to change the value of ga in order to get the three correct results.

the xppaut file of the model is avaiable on the following address offered by the author , http://www.math.fsu.edu/%7Ebertram/software/pituitary/JCNS_07.ode


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3431, + "tag": "BioModels:BIOMD0000000138" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3432, + "tag": "Dopamine secretion" + }, + { + "id": 3433, + "tag": "Potassium channel activity" + }, + { + "id": 3434, + "tag": "Regulation of prolactin signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:45:59.962558+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000138", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2486": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2486, + "name": "Hoffmann2002_KnockOut_IkBNFkB_Signaling", + "repository_type": "biomodels", + "summary": "

The model corresponds to the knock out model of beta-/-, epsilon -/- and reproduces the upper panel in Fig 2C. In order to reproduce the other knock out models the transcription rate of the species that are not present must be set to zero and the rate of the one that is present must be set as seven times its corresponding value for the wild type model. This is done so as to compensate for the loss of other isoforms. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3435, + "tag": "BioModels:BIOMD0000000139" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:00.535015+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000139", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2487": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2487, + "name": "Hoffmann2002_WT_IkBNFkB_Signaling", + "repository_type": "biomodels", + "summary": "

This model corresponds to the IkB-NFkB signaling in wild type cells and reproduces the dynamics of the species as depicted in Figure 2 F of the paper. The authors mention that the simulation is carried out in three phases, where the steady state values of the species in one phase are fed to the succeding phase. This model captures the simulation dynamics of two phases and makes use of the event section to introduce the stimulus and thereby transition to the next phase. Accordingly, a few terms have been introduced that make this transition possible, this in no way compromises the original model. Also, the simulation plots are not an exact reproduction of the figures in the paper, they do however match the simulation results that the authors shared with us. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3436, + "tag": "BioModels:BIOMD0000000140" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:01.080590+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000140", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2488": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2488, + "name": "Izhikevich2004_SpikingNeurons_Class1Excitable", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(G) Class 1 excitable has been reproduced by MathSBML. The ODE and the parameters values are originally taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.02; b=-0.1; c=-55; d=6; V=-60; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3437, + "tag": "BioModels:BIOMD0000000141" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:01.606322+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000141", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2489": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2489, + "name": "Izhikevich2004_SpikingNeurons_Class2Excitable", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(H) Class 2 excitable has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.2; b=0.26; c=-65; d=0; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3438, + "tag": "BioModels:BIOMD0000000142" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 576, + "tag": "Neuronal action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:02.124611+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000142", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2490": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2490, + "name": "Olsen2003_neutrophil_oscillatory_metabolism", + "repository_type": "biomodels", + "summary": "
Olsen2003_neutrophil_oscillatory_metabolism

This model is described in the article:

Olsen LF, Kummer U, Kindzelskii AL, Petty HR.
Biophys. J. 2003 Jan; 84(1): 69-81

Abstract:

We present a two-compartment model to explain the oscillatory behavior observed experimentally in activated neutrophils. Our model is based mainly on the peroxidase-oxidase reaction catalyzed by myeloperoxidase with melatonin as a cofactor and NADPH oxidase, a major protein in the phagosome membrane of the leukocyte. The model predicts that after activation of a neutrophil, an increase in the activity of the hexose monophosphate shunt and the delivery of myeloperoxidase into the phagosome results in oscillations in oxygen and NAD(P)H concentration. The period of oscillation changes from >200 s to 10-30 s. The model is consistent with previously reported oscillations in cell metabolism and oxidant production. Key features and predictions of the model were confirmed experimentally. The requirement of the hexose monophosphate pathway for 10 s oscillations was verified using 6-aminonicotinamide and dexamethasone, which are inhibitors of glucose-6-phosphate dehydrogenase. The role of the NADPH oxidase in promoting oscillations was confirmed by dose-response studies of the effect of diphenylene iodonium, an inhibitor of the NADPH oxidase. Moreover, the model predicted an increase in the amplitude of NADPH oscillations in the presence of melatonin, which was confirmed experimentally. Successful computer modeling of complex chemical dynamics within cells and their chemical perturbation will enhance our ability to identify new antiinflammatory compounds.

This model is hosted on BioModels Database and identified by: BIOMD0000000143.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3439, + "tag": "BioModels:BIOMD0000000143" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3440, + "tag": "Melatonin metabolic process" + }, + { + "id": 3158, + "tag": "NADH peroxidase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:02.643304+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000143", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2491": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2491, + "name": "Calzone2007_CellCycle", + "repository_type": "biomodels", + "summary": "

This is the Dynamical model of nuclear division cycles during early embryogenesis of Drosophila, without StringT regulation. so ksstg=kdstg=0. Figure1B has been simulated by MathSBML. Curator changed model from only one compartment into two compartments according to the paper. Detail explaination of the models are in the supplement information of the paper.The author didn't specify which compartment Xm, Stgm, Xp are located, we assume that they locate in cytoplasm.

Some of the parameter values for the equations are dimensionless parameters.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3441, + "tag": "BioModels:BIOMD0000000144" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 3442, + "tag": "Regulation of nuclear division" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:03.230980+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000144", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2492": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2492, + "name": "Wang2007 - ATP induced intracellular Calcium Oscillation", + "repository_type": "biomodels", + "summary": "
Wang2007 - ATP induced intracellular Calicum Oscillation

The model simulate the ATP-induced intracellular Ca2+ oscillations and the quantitative effect of ATP concentration on the oscillation characteristics such as the duration, peak concentration of intracellular Ca2+ and average interval.

This model is described in the article:

Wang J, Huang X, Huang W.
J. Theor. Biol. 2007 Apr; 245(3): 510-519

Abstract:

A quantitative kinetic model is proposed to simulate the ATP-induced intracellular Ca(2+) oscillations. The quantitative effect of ATP concentration upon the oscillations was successfully simulated. Our simulation results support previous experimental explanations that the Ca(2+) oscillations are mainly due to interaction of Ca(2+) release from the endoplasmic reticulum (ER) and the ATP-dependent Ca(2+) pump back into the ER, and the oscillations are prolonged by extracellular Ca(2+) entry that maintains the constant Ca(2+) supplies to its intracellular stores. The model is also able to simulate the sudden disappearance phenomenon of the Ca(2+) oscillations observed in some cell types by taking into account of the biphasic characteristic of the Ca(2+) release from the endoplasmic reticulum (ER). Moreover, the model simulation results for the Ca(2+) oscillations characteristics such as duration, peak [Ca(2+)](cyt), and average interval, etc., lead to prediction of some possible factors responsible for the variations of Ca(2+) oscillations in different types of cells.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000145.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3443, + "tag": "BioModels:BIOMD0000000145" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:46:03.741030+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000145", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2493": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2493, + "name": "Hatakeyama2003_MAPK", + "repository_type": "biomodels", + "summary": "

Figure4 and Figure5 can be simulated by Copasi. Figure4 can be simulated in MathSBML as well. There are some typos in the paper:K29=234, is it should k_29; Table2, reaction17, is there are \"slash\" missing in between the rate equation; reaction 33,\"Akt-PI-PP\" in the last term of denominator instead of \"AktPI-P\" . For plotting figure4, we create another extra parameter *_percent, and use assignment rule calculate percentage of each species.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3444, + "tag": "BioModels:BIOMD0000000146" + }, + { + "id": 3445, + "tag": "Cricetinae" + }, + { + "id": 3446, + "tag": "ERBB signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:04.230198+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000146", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2494": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2494, + "name": "ODea2007_IkappaB", + "repository_type": "biomodels", + "summary": "

O'Dea, E.L., Barken, D., Peralta, R.Q., Tran K.T., Werner, S.L., Kearns, J.D., Levchenko, A., Hoffmann, A. A homeostatic model of IkB metabolism to control constitutive activity. Molecular Systems Biology, 3:111, pp. 1-7. 2007

Questions concerning the paper should be addressed to the corresponding author. Alexander Hoffmann (ahoffmann@ucsd.edu)


The original model was written and simulated within MathWorks MatLab 2006a using the ode15s (stiff/NDF) solver. It is highly recommended that those wanting to model this system use the MatLab version which we will freely provide upon request. As always, simulation results vary according to the numerical solver used.

Translation to SBML Level 2.1 was performed via reconstruction of the model within MathWorks SimBiology Desktop (version 2.1) followed by an Export to SBML. Please address questions about this SBML model to Jeff Kearns (jkearns@ucsd.edu).

BioModels DB curation: The model reproduces the values of diffferent species depicted in Fig 3A and 3B (wt) of the paper corresponding to Model1.1. To depict the the total IkB alpha, beta epsilon species, three additional parameters and their corresponding assignment rules have been introduced in this model by the creator. Model succesfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3447, + "tag": "BioModels:BIOMD0000000147" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:04.710598+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000147", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2495": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2495, + "name": "Komarova2003_BoneRemodeling", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.
Komarova SV, Smith RJ, Dixon SJ, Sims SM, Wahl LM Bone2003 Aug;33(2):206-15 14499354,
Abstract:
Bone remodeling occurs asynchronously at multiple sites in the adult skeleton and involves resorption by osteoclasts, followed by formation of new bone by osteoblasts. Disruptions in bone remodeling contribute to the pathogenesis of disorderssuch as osteoporosis, osteoarthritis, and Paget's disease. Interactions among cells of osteoblast and osteoclast lineages are critical in the regulation of bone remodeling. We constructed a mathematical model of autocrine and paracrine interactions among osteoblasts and osteoclasts that allowed us to calculate cell population dynamics and changes in bone mass at a discrete site of bone remodeling. Themodel predicted different modes of dynamic behavior: a single remodeling cycle in response to an external stimulus, a series of internally regulated cycles of bone remodeling, or unstable behavior similar to pathological bone remodeling in Paget's disease. Parametric analysis demonstrated that the mode of dynamic behaviorin the system depends strongly on the regulation of osteoclasts by autocrine factors, such as transforming growth factor beta. Moreover, simulations demonstratedthat nonlinear dynamics of the system may explain the differing effects of immunosuppressants on bone remodeling in vitro and in vivo. In conclusion, the mathematical model revealed that interactions among osteoblasts and osteoclasts result in complex, nonlinear system behavior, which cannot be deduced from studies of each cell type alone. The model will be useful in future studies assessing the impact of cytokines, growth factors, and potential therapies on the overall process ofremodeling in normal bone and in pathological conditions such as osteoporosis and Paget's disease.

The model reproduces Fig 2A and Fig 2B of the paper. Note that the Y-axis scale is not right, the osteoblast steadystate is approximatley 212 and not 0 as depicted in the figure. Also, there is atypo in the equation for x2_bar which has been corrected here. Model successfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Kim2007 - Crosstalk between Wnt and ERK pathways

Experimental studies have shown that both Wnt and the MAPK pathways are involved in the pathogenesis of various kinds of cancers (eg. colorectal cancer). The crosstalk between the two pathways have also been identified. Here, Kim et al., (2007) have integrated the experimental evidences on crosstalk mechanisms between the two pathways into a pathway model, and have identified the existence of a hidden positive feedback loop and suggest that this positive feedback loop might participate in the pathogenesis of colorectal cancer.

This model is described in the article:

Kim D, Rath O, Kolch W, Cho KH.
Oncogene 2007 Jul; 26(31): 4571-4579

Abstract:

The Wnt and the extracellular signal regulated-kinase (ERK) pathways are both involved in the pathogenesis of various kinds of cancers. Recently, the existence of crosstalk between Wnt and ERK pathways was reported. Gathering all reported results, we have discovered a positive feedback loop embedded in the crosstalk between the Wnt and ERK pathways. We have developed a plausible model that represents the role of this hidden positive feedback loop in the Wnt/ERK pathway crosstalk based on the integration of experimental reports and employing established basic mathematical models of each pathway. Our analysis shows that the positive feedback loop can generate bistability in both the Wnt and ERK signaling pathways, and this prediction was further validated by experiments. In particular, using the commonly accepted assumption that mutations in signaling proteins contribute to cancerogenesis, we have found two conditions through which mutations could evoke an irreversible response leading to a sustained activation of both pathways. One condition is enhanced production of beta-catenin, the other is a reduction of the velocity of MAP kinase phosphatase(s). This enables that high activities of Wnt and ERK pathways are maintained even without a persistent extracellular signal. Thus, our study adds a novel aspect to the molecular mechanisms of carcinogenesis by showing that mutational changes in individual proteins can cause fundamental functional changes well beyond the pathway they function in by a positive feedback loop embedded in crosstalk. Thus, crosstalk between signaling pathways provides a vehicle through which mutations of individual components can affect properties of the system at a larger scale.

Figure 6 of the reference publication has been reproduced. The model as such reproduces the plots corresponding to the normal conditions. To obtain simulations under 1) beta-cataenin mutation; set V12=0.846 (two-fold of the beta-catenin synthetic rate than the normal system. i.e. 2*0.426), 2) PP2A mutation; set Vmax4=Vmax5=33.75 (three-fourths of the PP2A activity that the normal system. i.e. (3/4)*45). The simulation was performed using Copasi 4.10 (Build 55).

This model is hosted on BioModels Database and identifiedby: BIOMD0000000149 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Notes from the original DOCQS curator:
In this version of the CDK2/Cyclin A complex activation there is discrepancy in the first curve which plots the binding reaction of CDK2 and Cyclin A expressed in E. coli. With the published rate constants the simulation does not match the published graph (Fig.1B) in Morris MC. et al. J Biol Chem. 277(26):23847-53 .

Notes from BioModels DB curator:
Although the parameters are those reported in the table I for CDK2/Cyclin A, the total fluorescence follows exactly the curve reported in the paper for CDK2/Cyclin H in figure 1B. Either the plot legend or the table is wrong.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3455, + "tag": "BioModels:BIOMD0000000150" + }, + { + "id": 3073, + "tag": "Cell cycle" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:46:06.311571+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000150", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2498": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2498, + "name": "Singh2006_IL6_Signal_Transduction", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2 , Fig3A and Fig 3B of the paper. The ODE for x1(gp180) and x3 (gp 130) is wrong and the authors have communicated to the curator that the species ought to have a constant value. There are a few other differences from the paper and these were made in consultation with the authors. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3458, + "tag": "BioModels:BIOMD0000000152" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:07.582698+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000152", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2500": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2500, + "name": "Fernandez2006_ModelB", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3459, + "tag": "BioModels:BIOMD0000000153" + }, + { + "id": 3460, + "tag": "Dopamine receptor signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3461, + "tag": "Ionotropic glutamate receptor signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:08.074345+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000153", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2501": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2501, + "name": "Zatorsky2006_p53_Model3", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 6B of the paper for model 3. The model was reproduced using XPP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3462, + "tag": "BioModels:BIOMD0000000154" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:08.571928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000154", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2502": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2502, + "name": "Zatorsky2006_p53_Model6", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 6B of the paper for model 6. The model was reproduced using XPP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3463, + "tag": "BioModels:BIOMD0000000155" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:09.193988+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000155", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2503": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2503, + "name": "Zatorsky2006_p53_Model5", + "repository_type": "biomodels", + "summary": "

The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 5. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3464, + "tag": "BioModels:BIOMD0000000156" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:09.833102+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000156", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2504": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2504, + "name": "Zatorsky2006_p53_Model4", + "repository_type": "biomodels", + "summary": "

The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 4. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3465, + "tag": "BioModels:BIOMD0000000157" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:10.391104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000157", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2505": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2505, + "name": "Zatorsky2006_p53_Model2", + "repository_type": "biomodels", + "summary": "

The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 2. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3466, + "tag": "BioModels:BIOMD0000000158" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:10.982364+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000158", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2506": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2506, + "name": "Zatorsky2006_p53_Model1", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of p53 and Mdm2 as depicted in Fig 6B of the plot for model 1. Results obtained on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3467, + "tag": "BioModels:BIOMD0000000159" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:11.503286+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000159", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2507": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2507, + "name": "Xie2007_CircClock", + "repository_type": "biomodels", + "summary": "

The model reproduces the oscillations for mRNA and protein species as depicted in Fig 3 of the plot. The model differs slightly from that given in the paper and this was made after a communication from the authors. The values of parameters tcvriclkp, tcdvpmt and dccpt are slightly different. Also, although it is not given in the paper, rate laws for reactions re20, re28, re35, re42, re43 and re45 are multiplied by a specie. Model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3468, + "tag": "BioModels:BIOMD0000000160" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:12.173615+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000160", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2508": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2508, + "name": "Eungdamrong2007_Ras_Activation", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of Golgi Ras-GTP and plasma membrane Ras-GTP, subjected to a palmitoylation rate of 0.00015849 second inverse. This is depicted in Fig 5a and 5b for various palmitolylation rates, however the value used in this model is not present in the figure in the paper but corresponds to Fig S2 of the supplement. Model successfully reproduced using MathSBML. Please note that the units of volumetric species in this model are molecules/micrometer cubed, to convert this to microMolar as given in the paper, multiply the simulation result by 1/602.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3469, + "tag": "BioModels:BIOMD0000000161" + }, + { + "id": 3470, + "tag": "MAPK signaling pathway - Homo sapiens (human)" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:46:12.693927+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000161", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2509": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2509, + "name": "Hernjak2005_Calcium_Signaling", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of Calcium in the spine and dendrites as depicted in Fig 8 and Fig 9 of the paper for CF activation.

The model was reproduced using MathSBML.

Please note that the units of volume species is molecules/micrometer cubed as against the units of microMolar given in the paper. To convert the units to microMolar multiply the species concentration by the conversion factor 1/602.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3471, + "tag": "BioModels:BIOMD0000000162" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:13.216051+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000162", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2510": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2510, + "name": "Zi2007_TGFbeta_signaling", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of Total Smad2 in the nucleus as well as the cytoplasm as depicted in 2D and also the other time profiles as depicted in Fig 2. Two parameters that are not present in the paper are introduced here for illustration purposes and they are Total Smad2n and Total Smad2c. The term kr_EE*LRC_EE has not been included in the ODE's for T1R_surf, T2R_surf and TGFbeta in the paper but is included in this model. MathSBML was used to reproduce the simulation result.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3472, + "tag": "BioModels:BIOMD0000000163" + }, + { + "id": 3473, + "tag": "Regulation of transforming growth factor beta receptor signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:46:13.707293+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000163", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2511": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2511, + "name": "SmithAE2002_RanTransport", + "repository_type": "biomodels", + "summary": "

The model reproduces the compartmental model for Ran transport as depicted in Fig 3 of the paper. Model reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3474, + "tag": "BioModels:BIOMD0000000164" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3475, + "tag": "Nucleocytoplasmic transport" + }, + { + "id": 3476, + "tag": "Ran protein signal transduction" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:14.413980+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000164", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2512": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2512, + "name": "Saucerman2006_PKA", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2B of the paper. Model successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3477, + "tag": "Activation of protein kinase A activity" + }, + { + "id": 3418, + "tag": "Adenylate cyclase-modulating G-protein coupled receptor signaling pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3478, + "tag": "BioModels:BIOMD0000000165" + }, + { + "id": 813, + "tag": "Heart disease" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:15.161996+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000165", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2513": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2513, + "name": "Zhu2007_TF_modulated_by_Calcium", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A theoretical study on activation of transcription factor modulated by intracellular Ca2+ oscillations.
Zhu CL, Zheng Y, Jia Y Biophys. Chem.[2007 Aug:129(1):49-55 17560007,
Abstract:
This work presents both deterministic and stochastic models of genetic expression modulated by intracellular calcium (Ca2+) oscillations, based on macroscopic differential equations and chemical Langevin equations, respectively. In deterministic case, the oscillations of intracellular Ca2+ decrease the effective Ca2+ threshold for the activation of transcriptional activator (TF-A). The average activation of TF-A increases with the increase of the average amplitude of intracellular Ca2+ oscillations, but decreases with the increase of the period of intracellular Ca2+ oscillations, which are qualitatively consistent with the experimental results on the gene expression in lymphocytes. In stochastic case, it is found that a large internal fluctuation of the biochemical reaction can enhance gene expression efficiency specifically at a low level of external stimulations or at a small rate of TF-A dimer phosphorylation activated by Ca2+, which reduces the threshold of the average intracellular Ca2+ concentration for gene expression.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M.(2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3479, + "tag": "BioModels:BIOMD0000000166" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:46:15.727981+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000166", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2514": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2514, + "name": "Mayya2005_STATmodule", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2B of the paper. Model successfully reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3480, + "tag": "BioModels:BIOMD0000000167" + }, + { + "id": 3481, + "tag": "Regulation of tyrosine phosphorylation of STAT protein" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:46:16.224457+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000167", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2515": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2515, + "name": "Obeyesekere1999_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of the different species depicted in Fig 3a of the paper. Model successfully reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3482, + "tag": "BioModels:BIOMD0000000168" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:16.782608+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000168", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2516": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2516, + "name": "Aguda1999_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of p27, E2F and aE/cdk2 as depicted in Figure 5 c of the paper. Model was simulated on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3483, + "tag": "BioModels:BIOMD0000000169" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3484, + "tag": "Mitotic cell cycle checkpoint" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:17.274183+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000169", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2517": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2517, + "name": "Weimann2004_CircadianOscillator", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of the species as depicted in Fig 3A of the paper. Model successfully tested on MathSBML and Jarnac.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3485, + "tag": "BioModels:BIOMD0000000170" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:17.791650+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000170", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2518": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2518, + "name": "Leloup1998_CircClock_LD", + "repository_type": "biomodels", + "summary": "

Leloup and Goldbeter, 1998

This model was created after the article by Leloup and Goldbeter, J Biol Rhythms 1998, Vol:13(1),pp70-87, pubmedID: 9486845
A Model for Circadian Rhythms in Drosophila Incorporating the Formation of a Complex between the PER and TIM Proteins
The parameters and initial concentrations are taken to reproduce figs. 4 D,E,F in the publication.
For a simulation without light dependent degradation of TIM_pp, change the the parameter v_dT_fac to 1.
The light/dark phases length can be set using the parameter l_d .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3486, + "tag": "BioModels:BIOMD0000000171" + }, + { + "id": 3093, + "tag": "Drosophila melanogaster" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:18.311127+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000171", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2519": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2519, + "name": "Pritchard2002_glycolysis", + "repository_type": "biomodels", + "summary": "

from:
Schemes of fluc control in a model of Saccharomyces cerevisiae glycolysis

Pritchard, L and Kell, DB Eur. J. Biochem. 269(2002), 3894-3904.
It represents a modified version of Teusink et al. Eur. J. Biochem. 267(2000), 5313-5329.
The model is a translation from the GEPASI file encoded by Leighton Pritchard.
This version uses the Vmaxes found by the best fit (R1) of Table 1 of the \tPritchard and Kell paper and simulates a decrease of external glucose concentration from 100 to 2 mM.
To reproduce the values in table 2 of the publication, set GLCo to 50 mM and compute the steady state.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3487, + "tag": "BioModels:BIOMD0000000172" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:46:18.836981+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000172", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2520": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2520, + "name": "Schmierer_2008_Smad_Tgfb", + "repository_type": "biomodels", + "summary": "

This sbml file describes the RECI model from:
\t \"Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system\" by Bernhard Schmierer, Alexander L. Tournier, Paul A. Bates and Caroline S. Hill, Proc Natl Acad Sci U S A. 2008 May 6;105(18):6608-13.
\t All parameter and species names are as in Figure S3 of the original publication. The original model was done in copasi.
SB-431542 addition to a concentration of 10000 nM is set at 2700 sec. The initial concentration of SB, the time point of addition and the final concentration can be set by altering the parameters SB_0, t_SB and SB_end.
This model file has been used to reproduce Figures 2D and 5A from the research paper using SBMLodesolver. To get the results for the figures, sum the corresponding concentrations:
fig 2D: nuclear EGFP-Smad2 = G_n + pG_n + G2_n + G4_n + 2* GG_n
fig 5A (either n or c for nucleus or cytosol):
monomeric Smad2 = S2_n/c + G_n/c
monomeric P-Smad2 = pS2_n/c + pG_n/c
Smad2/Smad4 complexes = S24_n/c + G4_n/c
Smad2/Smad2 complexes = S22_n/c + G2_n/c + GG_n/c

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Membrane identity and GTPase cascades regulated by toggle and cut-out switches
\t Perla Del Conte-Zerial, Lutz Brusch, Jochen C Rink, Claudio Collinet, Yannis Kalaidzidis, Marino Zerial, and Andreas Deutsch: Molecular Systems Biology4:206 15 July 2008, doi:10.1038/msb.2008.45

This is the cut-out switch model for the Rab5 - Rab7 transition, also referred to as model 2 in the original publication.
This model is not completely described in all details in the publication. Thanks go to Barbara Szomolay and Lutz Brusch for finding and clarifying this. According to Dr. Brusch this model represents the mechanism identified by the qualitative analysis in the article in the scenario deemed most useful by the authors. For the time-course simulations it was necessary to add a time dependency to one of the parameters, which is only verbally described in the article.
As argued in the publication the switch between early and late endosomes can be triggered by a parameter change. While with fixed parameter values each switch just converges to one steady state from its initial conditions and stays there, endosomes should switch between two different states. These changes would in reality of course depend on many different factors, such as cargo composition and amount in the specific endosome, its location and some additional cellular control mechanisms and encompass many different parameters. To keep the model simple the authors chose to add a time dependency to only one reaction - ke in the activation of RAB5 is multiplied with a term monotonously increasing over time from 0 to 1. They also hard coded a time dependence in this term, 100 minutes, to make the switch occur after several hundred minutes. As long as this modulating term remains monotonic all resulting time courses should look similar, with the switching behavior depending on the initial conditions and whether the term is increasing or decreasing. Monotonic increase is a reasonable assumption for the described mechanism of cargo accumulation.
Not explicitly described in the article:activation of Rab5 (time) : r*ke*time/(100+time) /(1+e(kg-R)*kf) instead of r*ke/(1+e(kg-R)*kf)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3491, + "tag": "BioModels:BIOMD0000000175" + }, + { + "id": 3446, + "tag": "ERBB signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:22.171996+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000175", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2523": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2523, + "name": "Conant2007_WGD_glycolysis_2A3AB", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Increased glycolytic flux as an outcome of whole-genome duplication in yeast.
Conant GC, Wolfe KH Mol. Syst. Biol. [2007 ; Volume: 3 (Issue: )]: 129 17667951 ,
Abstract:
After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.



The original model submitted by the authors was slightly altered and now comprises the models originally submitted as MODEL2426780967, MODEL2427021978, MODEL2427095802. It reproduces figures 2A,3A and 3B from the publication.

This model uses the glycolysis model from Pritchard and Kell (2002) with an additional parameter, WGD_E , to adjust for the differing enzyme conzentrations before the whole genome duplication (WGD) and parameters fV_xxx that adjust the Vmax of the different reactions (xxx eg. HXT or PYK).
Figure 3A from the article can be reproduced by changing the value of the parameters fV_xxx to 0.9 indiviually, with xxx signifying the different enzymes (HXT, HXK ...)
Figure 3B from the publication can be reproduced by setting the parameter WGD_E to 0.75 and individually setting the parameters fV_xxx to 1.333.
To reproduce figure 2A from the article change the parameter WGD_E in the range between 0.65 and 1.0.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3492, + "tag": "BioModels:BIOMD0000000176" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:46:22.814685+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000176", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2524": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2524, + "name": "Conant2007_glycolysis_2C", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Increased glycolytic flux as an outcome of whole-genome duplication in yeast.
Conant GC, Wolfe KH Mol. Syst. Biol. [2007 ; Volume: 3 (Issue: )]: 129 17667951 ,
Abstract:
After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.



This model reproduces fig. 2C from the corrigendum to the publication
The parameter Vmax_PDH was corrected by a factor 60 from 6.32 mM/min in the publication to 379.2 mM/min in accordance with the authors.
see the corrigendum at msb or its pubmed entry (pmid:18594520)

This model comprises the glycolysis model from Pritchard and Kell (2002) with an extension for the metabolisation of pyruvate in the mitochondria by pyruvate dehydrogenase and an additional parameter, WGD_E , to adjust for the differing enzyme concentrations before the whole genome duplication (WGD).
To switch off transport of pyruvate to the mitochondria, set the parameter t_m = 0.
Figure 2C from the article can be reproduced by manually changing the value of parameter WGD_E in the range between 0.65 and 1.0 and calculating the ratios of ratio of PDC/PDH fluxes in the altered model to the one of the model with WGD_E = 1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Lebeda2008 - BoTN Paralysis (4 stepmodel)
The onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction is described in this model. This model is the extended model of\u00a0 BIOMD0000000267 , which itself is the reduced form of the model developed by Simpson 1980; PMID\u00a0 6243359

This model is described in the article:

Lebeda FJ, Adler M, Erickson K, Chushak Y.
J Pharmacokinet Pharmacodyn 2008 Jun; 35(3): 251-267

Abstract:

Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000178.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model is from the article:
Interlinked mutual inhibitory positive feedbacks induce robust cellular memory effects.
Kim TH, Jung SH, Cho KH FEBS Lett.2007 Oct; 581(25) 17892872,
Abstract:
Mutual inhibitory positive feedback (MIPF), or double-negative feedback, is a key regulatory motif of cellular memory with the capability of maintaining switched states for transient stimuli. Such MIPFs are found in various biological systems where they are interlinked in many cases despite a single MIPF can still realize such a memory effect. An intriguing question then arises about the advantage of interlinking MIPFs instead of exploiting an isolated single MIPF to realize the memory effect. We have investigated the advantages of interlinked MIPF systems through mathematical modeling and computer simulations. Our results revealed that interlinking MIPFs expands the parameter range of achieving the memory effect, or the memory region, thereby making the system more robust to parameter perturbations. Moreover, the minimal duration and amplitude of an external stimulus required for off-to-on state transition are increased and, as a result, external noises can more effectively be filtered out. Hence, interlinked MIPF systems can realize more robust cellular memories with respect to both parameter perturbations and external noises. Our study suggests that interlinked MIPF systems might be an evolutionary consequence acquired for a more reliable memory effect by enhancing robustness against noisy cellular environments.

Note: The model reproduces the simulation result for an asymmetric model as depicted in Fig 3G of the paper. Model successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Interlinked mutual inhibitory positive feedbacks induce robust cellular memory effects.
Kim TH, Jung SH, Cho KH FEBS Lett.2007 Oct; 581(25) 17892872,
Abstract:
Mutual inhibitory positive feedback (MIPF), or double-negative feedback, is a key regulatory motif of cellular memory with the capability of maintaining switched states for transient stimuli. Such MIPFs are found in various biological systems where they are interlinked in many cases despite a single MIPF can still realize such a memory effect. An intriguing question then arises about the advantage of interlinking MIPFs instead of exploiting an isolated single MIPF to realize the memory effect. We have investigated the advantages of interlinked MIPF systems through mathematical modeling and computer simulations. Our results revealed that interlinking MIPFs expands the parameter range of achieving the memory effect, or the memory region, thereby making the system more robust to parameter perturbations. Moreover, the minimal duration and amplitude of an external stimulus required for off-to-on state transition are increased and, as a result, external noises can more effectively be filtered out. Hence, interlinked MIPF systems can realize more robust cellular memories with respect to both parameter perturbations and external noises. Our study suggests that interlinked MIPF systems might be an evolutionary consequence acquired for a more reliable memory effect by enhancing robustness against noisy cellular environments.

Note: The model reproduces the simulation result for the symmetric model as depicted in Fig 3H of the paper. Model successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the time profile of species depicted in Figure 12a and 12 b. The authors communicated to the curator that there is a typo in the paper, the values of kd1 and kd2 are reversed. Model successfully reproduced using MathSBML.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3504, + "tag": "BioModels:BIOMD0000000181" + }, + { + "id": 3070, + "tag": "Opisthokonta" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:25.626072+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000181", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2529": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2529, + "name": "Neves2008 - Role of cell shape and size in controlling intracellular signalling", + "repository_type": "biomodels", + "summary": "
Neves2008 - Role of cell shape and size in controlling intracellular signalling

The role of cell shape and size in the flow of spatial information from the cell surface receptor to downstream components within the cell has been studied on the \u03b2-adrenergic receptor to MAPK-signalling network.

This model is described in the article:

Neves SR, Tsokas P, Sarkar A, Grace EA, Rangamani P, Taubenfeld SM, Alberini CM, Schaff JC, Blitzer RD, Moraru II, Iyengar R
Cell. 2008, 133(4):666-680

Abstract:

The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells.

This model is hosted on BioModels Database and identifiedby: MODEL8609366518 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3505, + "tag": "BioModels:BIOMD0000000182" + }, + { + "id": 3506, + "tag": "Cell morphogenesis" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:26.155494+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000182", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2530": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2530, + "name": "Stefan2008 - calmodulin allostery", + "repository_type": "biomodels", + "summary": " \t
\t Stefan2008 - calmodulin allostery\t
\t
\t

An allosteric model for calmodulin activation, in which binding to calcium facilitates the transition between a low-affinity [tense (T)] and a high-affinity [relaxed (R)] state.

\t
\t
\t

This model is described in the article:

\t \t
Stefan MI, Edelstein SJ, Le Nov\u00e8re N
\t
Proc. Natl. Acad. Sci. U.S.A. 2008 Aug; 105(31): 10768-10773
\t

Abstract:

\t
\t

Calmodulin plays a vital role in mediating bidirectional synaptic plasticity by activating either calcium/calmodulin-dependent protein kinase II (CaMKII) or protein phosphatase 2B (PP2B) at different calcium concentrations. We propose an allosteric model for calmodulin activation, in which binding to calcium facilitates the transition between a low-affinity [tense (T)] and a high-affinity [relaxed (R)] state. The four calcium-binding sites are assumed to be nonidentical. The model is consistent with previously reported experimental data for calcium binding to calmodulin. It also accounts for known properties of calmodulin that have been difficult to model so far, including the activity of nonsaturated forms of calmodulin (we predict the existence of open conformations in the absence of calcium), an increase in calcium affinity once calmodulin is bound to a target, and the differential activation of CaMKII and PP2B depending on calcium concentration.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000183.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3507, + "tag": "BioModels:BIOMD0000000183" + }, + { + "id": 3508, + "tag": "Calmodulin binding" + }, + { + "id": 3509, + "tag": "Calmodulin-dependent protein kinase activity" + }, + { + "id": 3510, + "tag": "Detection of calcium ion" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3511, + "tag": "Positive regulation of synaptic plasticity" + }, + { + "id": 3512, + "tag": "Protein serine/threonine phosphatase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:26.676653+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000183", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2531": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2531, + "name": "Lavrentovich2008_Ca_Oscillations", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of cytoplasmic Calcium as depicted in Fig 3 of the paper. Model successfully reproduced using Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3513, + "tag": "BioModels:BIOMD0000000184" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:27.196732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000184", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2532": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2532, + "name": "Locke2008_Circadian_Clock", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2A of the paper. Model successfully reproduced using Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3514, + "tag": "BioModels:BIOMD0000000185" + }, + { + "id": 3515, + "tag": "Circadian behavior" + }, + { + "id": 3118, + "tag": "Circadian regulation of gene expression" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:27.685684+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000185", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2533": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2533, + "name": "Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Dissociation variant", + "repository_type": "biomodels", + "summary": "
Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Dissociation variant

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism. This model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. MCC:APC dissociation is described by two alternatives models, namely the \"Dissociation\" and the \"Convey\" model variants. Both these model are available in BioModels Database. This model corresponds to the \"Dissociation\" variant.

This model is described in the article:

Ibrahim B, Diekmann S, Schmitt E, Dittrich P
PLoS One. 2008 Feb 6;3(2):e1555.

Abstract:

BACKGROUND: The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

PRINCIPLE FINDINGS: We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

CONCLUSION: Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.

This model describes the controlled dissociation variant of the mitotic spindle assembly checkpoint. If the tool you use has problems with events, you can uncomment the assignment rules for u and u_prime and comment out the list of events.

In accordance with the authors due to typos in the original publication some initial conditions and parameters were slightly changed in the model:
article model
[O-Mad2] 1.5e-7 M 1.3e-7 M
[BubR1:Bub3] 1.30e-7 M 1.27e-7 M
k -4 0.01 M -1 s -1 0.02 M -1 s -1
k -5 0.1 M -1 s -1 0.2 M -1 s -1

This model is hosted on BioModels Database and identified by: MODEL6655615431 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3516, + "tag": "BioModels:BIOMD0000000186" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3517, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3518, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:28.176448+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000186", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2534": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2534, + "name": "Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Convey variant", + "repository_type": "biomodels", + "summary": "
Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Convey variant

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism. This model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. MCC:APC dissociation is described by two alternatives models, namely the \"Dissociation\" and the \"Convey\" model variants. Both these model are available in BioModels Database. This model corresponds to the \"Convey\" variant.

This model is described in the article:

Ibrahim B, Diekmann S, Schmitt E, Dittrich P
PLoS One. 2008 Feb 6;3(2):e1555.

Abstract:

BACKGROUND: The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

PRINCIPLE FINDINGS: We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

CONCLUSION: Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.

This model describes the controlled dissociation variant of the mitotic spindle assembly checkpoint. If the tool you use has problems with events, you can uncomment the assignment rules for u and u_prime and comment out the list of events.

In accordance with the authors due to typos in the original publication some initial conditions and parameters were slightly changed in the model:
article model
[O-Mad2] 1.5e-7 M 1.3e-7 M
[BubR1:Bub3] 1.30e-7 M 1.27e-7 M
k -4 0.01 M -1 s -1 0.02 M -1 s -1
k -5 0.1 M -1 s -1 0.2 M -1 s -1

This model is hosted on BioModels Database and identified by: MODEL6655578762 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3519, + "tag": "BioModels:BIOMD0000000187" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3517, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3518, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:28.666071+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000187", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2535": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2535, + "name": "Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM", + "repository_type": "biomodels", + "summary": "
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM

This model is described in the article:

Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75

Abstract:

BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000188.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3520, + "tag": "BioModels:BIOMD0000000188" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3521, + "tag": "Regulation of DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3522, + "tag": "Stabilization of p53" + } + ], + "timestamp_created": "2025-02-03 15:46:29.155450+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000188", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2536": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2536, + "name": "Proctor2008 - p53/Mdm2 circuit - p53 stablisation by p14ARF", + "repository_type": "biomodels", + "summary": "
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARF

This model is described in the article:

Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75

Abstract:

BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000188.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3523, + "tag": "BioModels:BIOMD0000000189" + }, + { + "id": 3326, + "tag": "DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:29.708836+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000189", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2537": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2537, + "name": "Rodriguez-Caso2006_Polyamine_Metabolism", + "repository_type": "biomodels", + "summary": "

SBML creators: Armando Reyes-Palomares * , Carlos Rodr\u00edguez-Caso +, Raul Monta\u00f1ez * , Marta Cascante $, Francisca S\u00e1nchez-Jim\u00e9nez * , Miguel A. Medina *

* ProCel Group, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga and CIBER de Enfermedades Raras (CIBER-ER). + Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB). $ Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona.

http://asp.uma.es

Metabolic modeling of polyamine metabolism in mammals.
Rodr\u00edguez-Caso,C et al.: J Biol Chem 2006 : 281:21799-812.
The model reproduces the dynamical behavior of the polyamine metabolism in mammals. In this model there are some additions and corrections to the publication. All perturbations and analysis have produced results very close to the published experiments. The model was successfully tested on CoPaSi v.4.4 (build 26).

Parameters not included in the publication:

1. Parameters for SSAT kinetic constants:

KmAcCoA = 1.5 \u00b5M

KmCoA = 40 \u00b5M

2. Parameters for equation MAT (table 1):

Vmax_MAT = 0.45 \u00b5M/min

Km_MAT = 41 \u00b5M

Ki_MET_MAT = 50 \u00b5M

3. Erratum.: The corrected ODE for time-dependent variable Antz is:

KsANTZ*(1-1/(1+Keq*0.01*([D]+[S])))-KdANTZ*[Antz]

According to these modifications the new steady-state analysis results are:

Metabolites:

[P]= 104.681 \u00b5M

[D]= 76.7492 \u00b5M

[S]= 58.0135 \u00b5M

[SAM]= 52.327 \u00b5M

[A]= 0.0101962 \u00b5M

[aS]= 0.0245375 \u00b5M

[aD]= 0.832236 \u00b5M

Time-dependent global parameters:

[Antz] = 0.574038 \u00b5M

Vmaxodc = 1.28315 \u00b5M/min

Vmaxssat = 0.673814 \u00b5M/min

Vmaxsamdc = 0.36829 \u00b5M/min


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3524, + "tag": "BioModels:BIOMD0000000190" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3525, + "tag": "Polyamine metabolic process" + }, + { + "id": 3526, + "tag": "Rodentia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:30.394245+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000190", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2538": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2538, + "name": "Monta\u00f1ez2008_Arginine_catabolism", + "repository_type": "biomodels", + "summary": "

SBML creators: Armando Reyes-Palomares * , Raul Monta\u00f1ez *, Carlos Rodriguez-Caso +, Francisca Sanchez-Jimenez * , Miguel A. Medina *

* ProCel Group, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga and CIBER de Enfermedades Raras (CIBER-ER). + Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB).

http://asp.uma.es

In silico analysis of arginine catabolism as a source of nitric oxide or polyamines in endothelial cells.
Monta\u00f1ez, R et al.: Amino Acids. 2008 Feb;34(2):223-9.
The model reproduces the dynamical behavior of the arginine catabolism and transport in relation to the nitric oxide production. In this model there are some additions and corrections to the publication. All perturbations and analysis have produced results very close to the published experiments. The model was successfully tested on CoPaSi v.4.4 (build 26).

Erratum: parameters values modificated respect to the publication to reach the steady-state:

Kmodc=90 \u00b5M (60 \u00b5M in the paper)

Kiornhat (is equivalent to the parameter Kmefflhat Eq ) = 360 \u00b5M (380 \u00b5M in the paper)


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3527, + "tag": "BioModels:BIOMD0000000191" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:31.271901+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000191", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2539": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2539, + "name": "G\u00f6rlich2003_RanGTP_gradient", + "repository_type": "biomodels", + "summary": " \t This model represents a concentration gradient of RanGTP across the nuclear envelope. This gradient is generated by distribution of regulators of RanGTPase. We have taken a log linear plot of graphs generated by GENESIS and compared with the experimental graphs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2008 The BioModels Team.
For more information see the terms of use.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3528, + "tag": "BioModels:BIOMD0000000192" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3529, + "tag": "Ran GTPase binding" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:32.046980+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000192", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2540": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2540, + "name": "Ibrahim2008_MCC_assembly_model_KDM", + "repository_type": "biomodels", + "summary": " BioSystems (2007), doi:10.1016/j.biosystems.2008.06.007

In-silico study of kinetochore control, amplification, and inhibition effects in MCC assembly


Bashar Ibrahim, Eberhard Schmitt, Peter Dittrich, Stephan Diekmann
This is the kinetochore dependent MCC model (KDM) from the article. For the kinetochore independent MCC model (KIM) replace u*k4f in R4 by k4f and u*k5f in R5 by k5f .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3530, + "tag": "BioModels:BIOMD0000000193" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3517, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3518, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:32.948860+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000193", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2541": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2541, + "name": "Ibrahim2008_Cdc20_Sequestring_Template_Model", + "repository_type": "biomodels", + "summary": " Biophysical Chemistry 134 (2008) 93-100

Mad2 binding is not sufficient for complete Cdc20 sequestering in mitotic transition control (an in silico study)


Bashar Ibrahim, Peter Dittrich, Stephan Diekmann, Eberhard Schmitt

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3531, + "tag": "BioModels:BIOMD0000000194" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3517, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3518, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:33.492908+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000194", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2542": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2542, + "name": "Tyson2001_Cell_Cycle_Regulation", + "repository_type": "biomodels", + "summary": "

This model describes the budding yeast cell cycle model used in fig 8 a in
Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions.
Tyson JJ and Novak B., J Theor Biol 2001 May;210(2):249-63.
It consitsts of the equations (2)-(8), with mu=0.005 min -1 . It was taken from Cell Cycle DB ( file ) and only slightly altered.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3532, + "tag": "BioModels:BIOMD0000000195" + }, + { + "id": 3195, + "tag": "Cell Cycle, Mitotic" + }, + { + "id": 3533, + "tag": "Cell cycle - yeast" + }, + { + "id": 3534, + "tag": "Regulation of cell cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:46:34.011638+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000195", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2543": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2543, + "name": "Srividhya2006_CellCycle", + "repository_type": "biomodels", + "summary": "

In this model the values of \"free CDK\" (Id: x2), \"cdc25_P\" (x4) \"Wee1_P\" (Id: y5) and \"APC\" (Id: y6) are assigned using the parameters describing the total concentrations totcdk (Id: c)), totcdc5, totwee1 and totAPC. So if you want to change the levels of these proteins, you need to change the values ofthese parameters.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3535, + "tag": "BioModels:BIOMD0000000196" + }, + { + "id": 3195, + "tag": "Cell Cycle, Mitotic" + }, + { + "id": 3536, + "tag": "Cell cycle process" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:34.597796+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000196", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2544": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2544, + "name": "Bartholome2007_MDCKII", + "repository_type": "biomodels", + "summary": "

SBML model exported from PottersWheel on 2007-09-19 15:35:47.

The values for parameters and the inital concentrations of this model where directly provided by the main author:
Parameter values
parameter value unit
p1 0.0025 1/min
p2 0.0784 1/min
p3 0.0013 1/min
p4 0.0827 1/min
p5 0.0091 1/min
p6 0.000064 1/(nmole*min)
p7 0.0397 1/min
p8 1000 nmole
p9 0.0098 1/(nmole*min)
p10 1.6 1/min
p11 1000 nmole
p12 0.0003 ml/min
The basal chamber volume was taken as 1 ml, the apical as 1.5. As starting values x1 was set to 88 nmole, all other species to 0.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3537, + "tag": "BioModels:BIOMD0000000197" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3538, + "tag": "Regulation of cellular localization" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:35.129340+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000197", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2545": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2545, + "name": "Stone1996 - activation of soluble guanylate cyclase by nitric oxide", + "repository_type": "biomodels", + "summary": "
Stone1996 - activation of soluble guanylatecyclase by nitric oxide
This features the two step binding ofNO to soluble Guanylyl Cyclase as proposed by StoneJR, Marletta MA. Biochemistry (1996) 35(4):1093-9 . There is afast step binding scheme and a slow step binding scheme. Thedifference lies in the binding of a NO to a non-heme site on sGC,which may not necessarily be the same site of binding during theinitial binding. The rates have been directly used models.

This model is described in the article:

Stone JR, Marletta MA.
Biochemistry 1996 Jan; 35(4): 1093-1099

Abstract:

The soluble form of guanylate cyclase (sGC) is the only definitive receptor for the signaling agent nitric oxide (.NO). The enzyme is a heterodimer of homologous subunits in which each subunit binds 1 equiv of 5-coordinate high-spin heme. .NO increases the Vmax of sGC up to 400-fold and has previously been shown to bind to the heme to form a 5-coordinate complex. Using stopped-flow spectrophotometry, it is demonstrated that the binding of .NO to the heme of sGC is a complex process. .NO first binds to the heme to form a 6-coordinate nitrosyl complex, which then converts to a 5-coordinate nitrosyl complex through one of two ways. For 28 +/- 4% of the heme, the 6-coordinate nitrosyl complex rapidly (approximately 20 s-1) converts to the 5-coordinate complex. For the remaining 72 +/- 4% of the heme, the conversion of the 6-coordinate nitrosyl complex to a 5-coordinate nitrosyl complex is slow (0.1-1.0 s-1) and is dependent upon the interaction of .NO with an unidentified non-heme site on the protein. The heme (200 nM) was completely converted to the 5-coordinate state with as little as 500 nM .NO, and the equilibrium dissociation constant of .NO for activating the enzyme was determined to be < or = 250 nM. Gel-filtration analysis indicates that the binding of .NO to the heme has no effect on the native molecular mass of the protein. Correlation of electronic absorption spectra with activity measurements indicates that the 5-coordinate nitrosyl form of the enzyme is activated relative to the resting 5-coordinate ferrous form of the enzyme.

This model is hosted on BioModels Database and identified by: BIOMD0000000198.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3539, + "tag": "BioModels:BIOMD0000000198" + }, + { + "id": 3309, + "tag": "Bos taurus" + }, + { + "id": 3540, + "tag": "Nitric oxide mediated signal transduction" + }, + { + "id": 3541, + "tag": "Regulation of guanylate cyclase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:35.604308+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000198", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2546": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2546, + "name": "Santolini2001_nNOS_Mechanism_Regulation", + "repository_type": "biomodels", + "summary": " \t This is a model of neuronal Nitric Oxide Synthase expressed in Escherichia coli based on Santolini J. et al. J Biol Chem. (2001) 276(2):1233-43.
Differing from the article, oxygen explicitly included in the reaction 2, 5 and 10 (numbers as in scheme 1 in the article). In the article the assumed oxygen concentration of 140 uM was included in the pseudo first order rate constant.
Fig 2E in the article shows different time courses for citrulline and NO than the ones produced by this model. Dr. Santolini, one of the authors of the article, wrote that the legends in fig. 2E might be mixed up and should rather denote NO and NO3 instead of citrulline and NO.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3542, + "tag": "BioModels:BIOMD0000000199" + }, + { + "id": 3543, + "tag": "Nitric-oxide synthase activity" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:36.177221+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000199", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2547": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2547, + "name": "Bray1995_chemotaxis_receptorlinkedcomplex", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3544, + "tag": "BioModels:BIOMD0000000200" + }, + { + "id": 3545, + "tag": "Chemotaxis" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:36.673290+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000200", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2548": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2548, + "name": "Goldbeter2008_Somite_Segmentation_Clock_Notch_Wnt_FGF", + "repository_type": "biomodels", + "summary": "

This is a model of the coupled Natch, Wnt and FGF modules as described in:
A. Goldbeter and O. Pourqui\u00e9 , Modeling the segmentation clock as a network of coupled oscillations in the Notch, Wnt and FGF signaling pathways. J Theor Biol. 2008 Jun 7;252(3):574-85, pubmed ID: 18308339
To uncouple the modules remove the reaction MAx_trans_Xa and set vsFK=vsF .
The SBML version of the model was converted from the CellML version by Catherine Lloyd for the CellML repository .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3546, + "tag": "Amniota" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3547, + "tag": "BioModels:BIOMD0000000201" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:37.401880+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000201", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2549": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2549, + "name": "ChenXF2008_CICR", + "repository_type": "biomodels", + "summary": "

The model reproduces the plots in Figures 1 and 2. Note that the units of the time scale \"A\" are not right in the paper, it was corrected by the curator. Model successfully tested on MathSBML.

", + "tags": [ + { + "id": 3548, + "tag": "Activation of store-operated calcium channel activity" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3549, + "tag": "BioModels:BIOMD0000000202" + }, + { + "id": 3550, + "tag": "Release of sequestered calcium ion into cytosol" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 15:46:37.954538+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000202", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2550": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2550, + "name": "Chickarmane2006 - Stem cell switch reversible", + "repository_type": "biomodels", + "summary": "
Chickarmane2006 - Stem cell switch reversible

Kinetic modeling approach of the transcriptional dynamics of the embryonic stem cell switch.

This model is described in the article:

Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C
PLoS Computational Biology. 2006; 2(9):e123

Abstract:

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.

This model is hosted on BioModels Database and identified by: MODEL7957907314 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3551, + "tag": "BioModels:BIOMD0000000203" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 15:46:38.435763+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000203", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2551": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2551, + "name": "Chickarmane2006 - Stem cell switch irreversible", + "repository_type": "biomodels", + "summary": "
Chickarmane2006 - Stem cell switch irreversible

Kinetic modeling approach of the transcriptional dynamics of the embryonic stem cell switch.

This model is described in the article:

Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C
PLoS Computational Biology. 2006; 2(9):e123

Abstract:

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.

This model is hosted on BioModels Database and identified by: MODEL7957942740 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3553, + "tag": "BioModels:BIOMD0000000204" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 15:46:39.078377+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000204", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2552": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2552, + "name": "Ung2008_EGFR_Endocytosis", + "repository_type": "biomodels", + "summary": "

Model reproduces the various plots in the publication for \"Control\" concentrations. Model successfully tested on MathSBML.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3554, + "tag": "BioModels:BIOMD0000000205" + }, + { + "id": 3555, + "tag": "Endocytosis" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:40.067623+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000205", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2553": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2553, + "name": "Wolf2000_Glycolytic_Oscillations", + "repository_type": "biomodels", + "summary": "

Model reproduces the dynamics of ATP and NADH as depicted in Fig 4 of the paper. Model successfully tested on Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3556, + "tag": "BioModels:BIOMD0000000206" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:46:40.944174+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000206", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2554": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2554, + "name": "Romond1999_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 3 of the paper. Model successfully reproduced using MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3557, + "tag": "BioModels:BIOMD0000000207" + }, + { + "id": 3065, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:42.051895+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000207", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2555": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2555, + "name": "Deineko2003_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 3 of the paper corresponding to the transition to S phase. Units have not been defined for this model because the paper mentions the use of arbitrary units for the various species and parameters. Model reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3558, + "tag": "BioModels:BIOMD0000000208" + }, + { + "id": 3065, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:42.597222+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000208", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2556": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2556, + "name": "Chickarmane2008 - Stem cell lineage determination", + "repository_type": "biomodels", + "summary": "
Chickarmane2008 - Stem cell lineage determination

In this work, a dynamical model of lineage determination based upon a minimal circuit, as discussed in PMID: 17215298 , which contains the Oct4/Sox2/Nanog core as well its interaction with a few other key genes is discussed.

This model is described in the article:

Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478

Abstract:

BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/

PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.

This model is hosted on BioModels Database and identifiedby: MODEL8390025091 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3559, + "tag": "BioModels:BIOMD0000000209" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3560, + "tag": "Regulation of endodermal cell fate specification" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + }, + { + "id": 3561, + "tag": "Trophectodermal cell fate commitment" + } + ], + "timestamp_created": "2025-02-03 15:46:43.435522+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000209", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2557": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2557, + "name": "Chickarmane2008 - Stem cell lineage - NANOG GATA-6 switch", + "repository_type": "biomodels", + "summary": "
Chickarmane2008 - Stem cell lineage - NANOG GATA-6 switch

In this work, a dynamical model of lineage determination based upon a minimal circuit, as discussed in PMID: 17215298 , which contains the Oct4/Sox2/Nanog core as well its interaction with a few other key genes is discussed.

This model is described in the article:

Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478

Abstract:

BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/

PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.

This model is hosted on BioModels Database and identifiedby: MODEL8389825246 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3562, + "tag": "BioModels:BIOMD0000000210" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3560, + "tag": "Regulation of endodermal cell fate specification" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + }, + { + "id": 3561, + "tag": "Trophectodermal cell fate commitment" + } + ], + "timestamp_created": "2025-02-03 15:46:44.576062+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000210", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2558": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2558, + "name": "Albert2005_Glycolysis", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Experimental and in silico analyses of glycolytic flux control in bloodstream form Trypanosoma brucei.
Albert MA, Haanstra JR, Hannaert V, Van Roy J, Opperdoes FR, Bakker BM, Michels PA. J Biol Chem2005 Aug 5;280(31):28306-15. 15955817,
Abstract:
A mathematical model of glycolysis in bloodstream form Trypanosoma brucei was developed previously on the basis of all available enzyme kinetic data (Bakker, B. M., Michels, P. A. M., Opperdoes, F. R., and Westerhoff, H. V. (1997) J. Biol. Chem. 272, 3207-3215). The model predicted correctly the fluxes and cellular metabolite concentrations as measured in non-growing trypanosomes and the major contribution to the flux control exerted by the plasma membrane glucose transporter. Surprisingly, a large overcapacity was predicted for hexokinase (HXK), phosphofructokinase (PFK), and pyruvate kinase (PYK). Here, we present our further analysis of the control of glycolytic flux in bloodstream form T. brucei. First, the model was optimized and extended with recent information about the kinetics of enzymes and their activities as measured in lysates of in vitro cultured growing trypanosomes. Second, the concentrations of five glycolytic enzymes (HXK, PFK, phosphoglycerate mutase, enolase, and PYK) in trypanosomes were changed by RNA interference. The effects of the knockdown of these enzymes on the growth, activities, and levels of various enzymes and glycolytic flux were studied and compared with model predictions. Data thus obtained support the conclusion from the in silico analysis that HXK, PFK, and PYK are in excess, albeit less than predicted. Interestingly, depletion of PFK and enolase had an effect on the activity (but not, or to a lesser extent, expression) of some other glycolytic enzymes. Enzymes located both in the glycosomes (the peroxisome-like organelles harboring the first seven enzymes of the glycolytic pathway of trypanosomes) and in the cytosol were affected. These data suggest the existence of novel regulatory mechanisms operating in trypanosome glycolysis.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model described in the article:
Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters.
Curien G, Bastien O, Robert-Genthon M, Cornish-Bowden A, C\u00e1rdenas ML, Dumas R. Mol Syst Biol. 2009;5:271. Epub 2009 May 19. PMID: 19455135 , doi: 10.1038/msb.2009.29
Abstract:
The aspartate-derived amino-acid pathway from plants is well suited for analysing the function of the allosteric network of interactions in branched pathways. For this purpose, a detailed kinetic model of the system in the plant model Arabidopsis was constructed on the basis of in vitro kinetic measurements. The data, assembled into a mathematical model, reproduce in vivo measurements and also provide non-intuitive predictions. A crucial result is the identification of allosteric interactions whose function is not to couple demand and supply but to maintain a high independence between fluxes in competing pathways. In addition, the model shows that enzyme isoforms are not functionally redundant, because they contribute unequally to the flux and its regulation. Another result is the identification of the threonine concentration as the most sensitive variable in the system, suggesting a regulatory role for threonine at a higher level of integration.

The limiting rates for the tRNA synthetase reactions, V_Lys_RS, V_Thr_RS and V_Ile_RS, are all assigned a joined value, Vmax_AA_RS, to facilitate reproduction of the results in the publication. To alter these rates seperately these assignments have to be changed or removed.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is an SBML version of the folate cycle model model from:
A mathematical model of the folate cycle: new insights into folate homeostasis.
Nijhout HF, Reed MC, Budu P, Ulrich CM J. Biol. Chem.,2004, 279 (53),55008-16
pubmedID: 15496403
Abstract:
A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the \"methyl trap\"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased.
This model was encoded by Michal Galdzicki from a MatLab file send to him by Prof. Michael Reed. There some differences in this model compared to the one described in the article, possible due to typos in the publication:
1) reaction NE (THF + H2CO <=> 5,10-CH2-THF) in the article has H2C=O as areactant and is mentioned to display pseudo first order mass actionkinetics, while in the matlab file formic acid, also used in reaction FTS, is included in the rate law for the forward reaction.
2) the reaction MS is modeled after Reed et al. 2004, which is notexplicitly mentioned in the article, although Kd and the parametersfrom Reed et al. 2004 are given.
3) in the kinetic law of the SHTM reaction (THF + Ser <=>5,10-CH2-THF + Gly), there are separate values given for Km,Glyand Km,5,10-CH2-THF in the article. in the matlab file and the SBMLmodel Km,Ser and Km,THF are used instead of Km,Gly and Km,5,10-CH2-THFfor the backwards reaction.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model 2 described in the supplement of the article below. It is parameterized for the WT at 24\u00b0C. To reproduce figure 6 the results have to be rescaled to circadian time by multiplying time by 24/tau, with tau being the period of the free-running oscillator. For the wild-type parameter set tau is equal to 22.7149.
Article:
Isoform switching facilitates period control in the Neurospora crassa circadian clock.
Akman OE, Locke JC, Tang S, Carr\u00e9 I, Millar AJ, Rand DA. Mol Syst Biol. 2008;4:164. Epub 2008 Feb 12. PMID: 18277380, doi:10.1038/msb.2008.5
Abstract:
A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Sequential polarization and imprinting of type 1 T helper lymphocytes by interferon-gamma and interleukin-12.
Schulz EG, Mariani L, Radbruch A, H\u00f6fer T. Immunity.2009;30(5):666-8. 19409816,
Abstract:
Differentiation of naive T lymphocytes into type I T helper (Th1) cells requires interferon-gamma and interleukin-12. It is puzzling that interferon-gamma induces the Th1 transcription factor T-bet, whereas interleukin-12 mediates Th1 cell lineage differentiation. We use mathematical modeling to analyze the expression kinetics of T-bet, interferon-gamma, and the IL-12 receptor beta2 chain (IL-12Rbeta2) during Th1 cell differentiation, in the presence or absence of interleukin-12 or interferon-gamma signaling. We show that interferon-gamma induced initial T-bet expression, whereas IL-12Rbeta2 was repressed by T cell receptor (TCR) signaling. The termination of TCR signaling permitted upregulation of IL-12Rbeta2 by T-bet and interleukin-12 signaling that maintained T-bet expression. This late expression of T-bet, accompanied by the upregulation of the transcription factors Runx3 and Hlx, was required to imprint the Th cell for interferon-gamma re-expression. Thus initial polarization and subsequent imprinting of Th1 cells are mediated by interlinked, sequentially acting positive feedback loops of TCR-interferon-gamma-Stat1-T-bet and interleukin-12-Stat4-T-bet signaling.


The original model was created by:
Edda G. Schulz
schulz@drfz.de
Theoretical Biophysics, Institute of Biology, Humboldt Universit\u00e4t, Invalidenstrasse 42, 10115 Berlin, Germany.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Minimum criteria for DNA damage-induced phase advances in circadian rhythms.
Hong CI, Z\u00e1mborszky J, Csik\u00e1sz-Nagy A. PLoS Comput Biol. 2009 May;5(5):e1000384. 19424508,
Abstract:
Robust oscillatory behaviors are common features of circadian and cell cycle rhythms. These cyclic processes, however, behave distinctively in terms of their periods and phases in response to external influences such as light, temperature, nutrients, etc. Nevertheless, several links have been found between these two oscillators. Cell division cycles gated by the circadian clock have been observed since the late 1950s. On the other hand, ionizing radiation (IR) treatments cause cells to undergo a DNA damage response, which leads to phase shifts (mostly advances) in circadian rhythms. Circadian gating of the cell cycle can be attributed to the cell cycle inhibitor kinase Wee1 (which is regulated by the heterodimeric circadian clock transcription factor, BMAL1/CLK), and possibly in conjunction with other cell cycle components that are known to be regulated by the circadian clock (i.e., c-Myc and cyclin D1). It has also been shown that DNA damage-induced activation of the cell cycle regulator, Chk2, leads to phosphorylation and destruction of a circadian clock component (i.e., PER1 in Mus or FRQ in Neurospora crassa). However, the molecular mechanism underlying how DNA damage causes predominantly phase advances in the circadian clock remains unknown. In order to address this question, we employ mathematical modeling to simulate different phase response curves (PRCs) from either dexamethasone (Dex) or IR treatment experiments. Dex is known to synchronize circadian rhythms in cell culture and may generate both phase advances and delays. We observe unique phase responses with minimum delays of the circadian clock upon DNA damage when two criteria are met: (1) existence of an autocatalytic positive feedback mechanism in addition to the time-delayed negative feedback loop in the clock system and (2) Chk2-dependent phosphorylation and degradation of PERs that are not bound to BMAL1/CLK.

The original xpp file of the model is available as a supplement of the article (Text S1).\t

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica.
Bruggeman FJ, Boogerd FC, Westerhoff HV. FEBS J. 2005 Apr;272(8):1965-85. 15819889 ,
Abstract:
Ammonium assimilation in Escherichia coli is regulated through multiple mechanisms (metabolic, signal transduction leading to covalent modification, transcription, and translation), which (in-)directly affect the activities of its two ammonium-assimilating enzymes, i.e. glutamine synthetase (GS) and glutamate dehydrogenase (GDH). Much is known about the kinetic properties of the components of the regulatory network that these enzymes are part of, but the ways in which, and the extents to which the network leads to subtle and quasi-intelligent regulation are unappreciated. To determine whether our present knowledge of the interactions between and the kinetic properties of the components of this network is complete - to the extent that when integrated in a kinetic model it suffices to calculate observed physiological behaviour - we now construct a kinetic model of this network, based on all of the kinetic data on the components that is available in the literature. We use this model to analyse regulation of ammonium assimilation at various carbon statuses for cells that have adapted to low and high ammonium concentrations. We show how a sudden increase in ammonium availability brings about a rapid redirection of the ammonium assimilation flux from GS/glutamate synthase (GOGAT) to GDH. The extent of redistribution depends on the nitrogen and carbon status of the cell. We develop a method to quantify the relative importance of the various regulators in the network. We find the importance is shared among regulators. We confirm that the adenylylation state of GS is the major regulator but that a total of 40% of the regulation is mediated by ADP (22%), glutamate (10%), glutamine (7%) and ATP (1%). The total steady-state ammonium assimilation flux is remarkably robust against changes in the ammonium concentration, but the fluxes through GS and GDH are completely nonrobust. Gene expression of GOGAT above a threshold value makes expression of GS under ammonium-limited conditions, and of GDH under glucose-limited conditions, sufficient for ammonium assimilation.

This version of the model originates from JWS online . The original model can be retrieved here .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one ofthe two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This the model used in the article:
Quantitative analysis of pathways controlling extrinsic apoptosis in single cells.
Albeck JG, Burke JM, Aldridge BB, Zhang M, Lauffenburger DA, Sorger PK. Mol Cell. 2008 Apr 11;30(1):11-25. PMID: 18406323 , doi: 10.1016/j.molcel.2008.02.012
Abstract:
Apoptosis in response to TRAIL or TNF requires the activation of initiator\tcaspases, which then activate the effector caspases that dismantle\tcells and cause death. However, little is known about the dynamics\tand regulatory logic linking initiators and effectors. Using a combination\tof live-cell reporters, flow cytometry, and immunoblotting, we find\tthat initiator caspases are active during the long and variable delay\tthat precedes mitochondrial outer membrane permeabilization (MOMP)\tand effector caspase activation. When combined with a mathematical\tmodel of core apoptosis pathways, experimental perturbation of regulatory\tlinks between initiator and effector caspases reveals that XIAP and\tproteasome-dependent degradation of effector caspases are important\tin restraining activity during the pre-MOMP delay. We identify conditions\tin which restraint is impaired, creating a physiologically indeterminate\tstate of partial cell death with the potential to generate genomic\tinstability. Together, these findings provide a quantitative picture\tof caspase regulatory networks and their failure modes.
The mitochondrial compartment is just added as a logical partition and its volume is not used in the mathematical formulas, to stick closer to the expressions used in the matlab files distributed with the original publication. There only the rate constants for bimolecular reactions are adapted by division by v , the ration of the volumes of the mitochondrial compartment and the total cell.
For BCL2 overexpression in figure 5, the initial BCL2 amount was increased by a factor 12 to 2.4*10 5 . For siRNA downregulation of XIAP its amount was multiplied by 0.13 to 1.3*10 4 .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one ofthe two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drugtargets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amountof active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t described in: Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.
\t Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN.;Mol Syst Biol. 2009;5:256. Epub 2009 Apr 7. PMID:19357636; doi:10.1038/msb.2009.19
Abstract:
\t Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues. \t

\t An extracellular compartment with 34 times the volume of the cell was added and the association rate as well as the dissociation constants for Insulin and EGF binding were altered (kon'=34*kon, KD'=KD/34). This was done to allow using the concentrations for those species given in the article and retaining the same dynamics and Ligand depletion as in the matlab file the SBML file was exported from. \t

SBML model exported from PottersWheel on 2008-10-14 16:26:44.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Calcium spiking.
Meyer T, Stryer L Annu Rev Biophys Biophys Chem1991:20:153-74 1867714,
Abstract:
No Abstract Available

The IP3-Ca2+ Crosscoupling Model (ICC) is reviewed by Meyer and Stryer in 1991, originally from Meyer and Stryer, 1988. PMID - 2455890 Parameters refer to figures 5 and 6 of the article which were reproduced by using Copasi 4.5 (Build 30).Species CaI and IP3 are buffered to 1% and 50% percent, respectively.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the basic model described in eq. 1 of the article:
A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.
Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9
Abstract:
We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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NFkB model M(14,25,28) - Lipniacky's NFkB model

This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,created to demonstrate application of model reduction methods proposed in

This a model from the article:
Robust simplifications of multiscale biochemical networks.
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A. BMC Syst Biol2008:2:86 18854041,
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

This model is originally proposed by Lipniacki 2004 (Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M.(2004). Mathematical model of NF-kappaB regulatory module. J. Theor. Biol. 228 (2): 195-215. 15094015

The models are provided in CellDesigner v3.5format. The name of the model M(x,y,z) should bedeciphered as following:

x - number of speciesy - number of reactionsz - number of parameters

Simulation protocol:The model can be simulated in CellDesignerdirectly, or in any simulator supportingevents. The simulation period should beset up in 20 hours (t=72000 sec). This model reproduces Figure 3b (M(14,25,28)) of the publication.

For additional information please contactAndrei.Zinovyev at curie.fr

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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NFkB model M(39,65,90) - most complex model

This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,created to demonstrate application of model reduction methods proposed in

Robust simplifications of multiscale biochemical networks.
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A. BMC Syst Biol2008:2:86 18854041,
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology withpotential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

The models are provided in CellDesigner v3.5format. The name of the model M(x,y,z) should bedeciphered as following:

x - number of speciesy - number of reactionsz - number of parameters

Simulation protocol:The model can be simulated in CellDesignerdirectly, or in any simulator supportingevents. The simulation period should beset up in 40 hours (t=144000 sec).The 'signal' event applies signal to thepathway at the moment t=20 hours=72000 sec. This model reproduces Figure 7c (M(39,65,90)) of the publication.

For additional information please contactAndrei.Zinovyev at curie.fr

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the extended model described the article:
Bifurcation analysis of the regulatory modules of the mammalian G1/S transition.
Swat M, Kel A, Herzel H. Bioinformatics 2004 Jul 10;20(10):1506-11. PMID: 15231543 , doi: 10.1093/bioinformatics/bth110
Abstract:
MOTIVATION: Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extremely complex interaction network.
RESULTS: We identify various positive and negative feedback loops in the network controlling the G1/S transition. It is shown that the positive feedback regulation of E2F1 and a double activator-inhibitor module can lead to bistability. Extensions of the core module preserve the essential features such as bistability. The complete model exhibits a transcritical bifurcation in addition to bistability. We relate these bifurcations to the cell cycle checkpoint and the G1/S phase transition point. Thus, core modules can explain major features of the complex G1/S network and have a robust decision taking function.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Quantifying robustness of biochemical network models.
Ma L, Iglesias PA. BMC Bioinformatics.2002 Dec 13;3:38. 12482327,
Abstract:
BACKGROUND: Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed. RESULTS: Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary. CONCLUSION: The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.


This model is originally proposed by Laub and Loomis (1998).[Laub MT, Loomis WF (1998). A molecular network that produces spontaneous oscillations in excitable cells of Dictyostelium. Mol Biol Cell. 9(12):3521-32. PubMED: 12482327.
The parameters used in this model (Ma and Iglesias, 2002), are different from that used in the original model (Laub and Loomis, 1998), because of the typographical errors in the original paper. The parameters used in the model presented by Ma and Iglesias, are obtained directly from the authors of original publication (Laub and Loomis, 1998). These parameters are also used in the website for the Laub-Loomis model, http://www-biology.ucsd.edu/labs/loomis/network/laubloomis.html.
By using this model, Kim et al., 2006 [Kim J, Bates DG, Postlethwaite I, Ma L, Iglesias PA. (2006) Robustness analysis of biochemical network models. Syst Biol (Stevenage). 153(3):96-104. PubMED: 16984084], validate and extend the analysis approach proposed by Ma and Iglesias (2002), by showing how hybrid optimisation can be used to compute worst-case parameter combinations in the model.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha.
Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB Syst Biol (Stevenage) [2004 Jun;1(1):93-103 17052119 ,
Abstract:
Analysis of cellular signalling interactions is expected to create an enormous informatics challenge, perhaps even greater than that of analysing the genome. A key step in the evolution towards a more quantitative understanding of signalling is to specify explicitly the kinetics of all chemical reaction steps in a pathway. We have reconstructed a model of the nuclear factor, kappaB (NF-kappaB) signalling pathway, containing 64 parameters and 26 variables, including steps in which the activation of the NF-kappaB transcription factor is intimately associated with the phosphorylation and ubiquitination of its inhibitor kappaB by a membrane-associated kinase, and its translocation from the cytoplasm to the nucleus. We apply sensitivity analysis to the model. This identifies those parameters in this (IkappaB)/NF-kappaB signalling system (containing only induced IkappaBalpha isoform) that most affect the oscillatory concentration of nuclear NF-kappaB (in terms of both period and amplitude). The intention is to provide guidance on which proteins are likely to be most significant as drug targets or should be exploited for further, more detailed experiments. The sensitivity coefficients were found to be strongly dependent upon the magnitude of the parameter change studied, indicating the highly non-linear nature of the system. Of the 64 parameters in the model, only eight to nine exerted a major control on nuclear NF-kappaB oscillations, and each of these involved as reaction participants either the IkappaB kinase (IKK) or IkappaBalpha, directly. This means that the dominant dynamics of the pathway can be reflected, in addition to that of nuclear NF-kappaB itself, by just two of the other pathway variables. This is conveniently observed in a phase-plane plot.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A kinetic study of a ternary cycle between adenine nucleotides.
Valero E, Var\u00f3n R, Garc\u00eda-Carmona F FEBS J. [2006 Aug;273(15):3598-613 16884499 ,
Abstract:
In the present paper, a kinetic study is made of the behavior of a moiety-conserved ternary cycle between the adenine nucleotides. The system contains the enzymes S-acetyl coenzyme A synthetase, adenylate kinase and pyruvate kinase, and converts ATP into AMP, then into ADP and finally back to ATP. L-Lactate dehydrogenase is added to the system to enable continuous monitoring of the progress of the reaction. The cycle cannot work when the only recycling substrate in the reaction medium is AMP. A mathematical model is proposed whose kinetic behavior has been analyzed both numerically by integration of the nonlinear differential equations describing the kinetics of the reactions involved, and analytically under steady-state conditions, with good agreement with the experimental results being obtained. The data obtained showed that there is a threshold value of the S-acetyl coenzyme A synthetase/adenylate kinase ratio, above which the cycle stops because all the recycling substrate has been accumulated as AMP, never reaching the steady state. In addition, the concept of adenylate energy charge has been applied to the system, obtaining the enabled values of the rate constants for a fixed adenylate energy charge value and vice versa.

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This a model from the article:
Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production.
Nazaret C, Heiske M, Thurley K, Mazat JP J. Theor. Biol. 2009 Jun;258(3):455-64 19007794 ,
Abstract:
Mitochondria play a central role in cellular energetic metabolism. The essential parts of this metabolism are the tricarboxylic acid (TCA) cycle, the respiratory chain and the adenosine triphosphate (ATP) synthesis machinery. Here a simplified model of these three metabolic components with a limited set of differential equations is presented. The existence of a steady state is demonstrated and results of numerical simulations are presented. The relevance of a simple model to represent actual in vivo behavior is discussed.

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This a model from the article:
The smallest chemical reaction system with bistability
Thomas Wilhelm BMC Systems Biology2009;Sep 8;3:90. 19737387,
Abstract:
Background
Bistability underlies basic biological phenomena, such as cell division, differentiation, cancer onset, and apoptosis. So far biologists identified two necessary conditions for bistability: positive feedback and ultrasensitivity.
Results
Biological systems are based upon elementary mono- and bimolecular chemical reactions. In order to definitely clarify all necessary conditions for bistability we here present the corresponding minimal system. According to our definition, it contains the minimal number of (i) reactants, (ii) reactions, and (iii) terms in the corresponding ordinary differential equations (decreasing importance from i-iii). The minimal bistable system contains two reactants and four irreversible reactions (three bimolecular, one monomolecular).We discuss the roles of the reactions with respect to the necessary conditions for bistability: two reactions comprise the positive feedback loop, a third reaction filters out small stimuli thus enabling a stable 'off' state, and the fourth reaction prevents explosions. We argue that prevention of explosion is a third general necessary condition for bistability, which is so far lacking discussion in the literature.Moreover, in addition to proving that in two-component systems three steady states are necessary for bistability (five for tristability, etc.), we also present a simple general method to design such systems: one just needs one production and three different degradation mechanisms (one production, five degradations for tristability, etc.). This helps modelling multistable systems and it is important for corresponding synthetic biology projects.
Conclusion
The presented minimal bistable system finally clarifies the often discussed question for the necessary conditions for bistability. The three necessary conditions are: positive feedback, a mechanism to filter out small stimuli and a mechanism to prevent explosions. This is important for modelling bistability with simple systems and for synthetically designing new bistable systems. Our simple model system is also well suited for corresponding teaching purposes.


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This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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PURPOSE: This tumor response pharmacodynamic model aims to describe primary lesion shrinkage in non-small cell lung cancer over time and determine if concentration-based exposure metrics for gemcitabine or that of its metabolites,n2',2'-difluorodeoxyuridine or gemcitabine triphosphate, are better than gemcitabine dose for prediction of individual response. EXPERIMENTAL DESIGN: Gemcitabine was given thrice weekly on days 1 and 8 in combination with carboplatin, which was given only on day 1 of every cycle. Gemcitabine amount in the body and area under the concentration-time curves of plasma gemcitabine, 2',2'-difluorodeoxyuridine, and intracellular gemcitabine triphosphate in white cells were compared to determine which best describes tumor shrinkage over time. Tumor growth kinetics were described using a Gompertz-like model.RESULTS: The apparent half-life for the effect of gemcitabine was 7.67 weeks. The tumor turnover time constant was 21.8 week.cm. Baseline tumor size and gemcitabine amount in the body to attain 50% of tumor shrinkage were estimated to be 6.66 cm and 10,600 mg. There was no evidence of relapse during treatment.

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This a model from the article:
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network.
K\u00fchn C, Wierling C, K\u00fchn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ. BMC Syst Biol.2009 Aug 23;3:83. 19698179,
Abstract:
BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.

The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3603, + "tag": "BioModels:BIOMD0000000235" + }, + { + "id": 3560, + "tag": "Regulation of endodermal cell fate specification" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3604, + "tag": "Strongylocentrotus purpuratus" + } + ], + "timestamp_created": "2025-02-03 15:46:58.250521+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000235", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2583": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2583, + "name": "Westermark2003_Pancreatic_GlycOsc_extended", + "repository_type": "biomodels", + "summary": "

This is the extended model described in eq. 2 of the article:
A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.
Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9
Abstract:
We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast.
Schaber J, Kofahl B, Kowald A, Klipp E FEBS J.2006 Aug; 273(15):3520-33 16884493,
Abstract:
Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.
Overgaard RV, Holford N, Rytved KA, Madsen H. Pharm Res.2007 Feb;24(2):298-309. PUBMED,
Abstract:
PURPOSE: To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling. METHODS: A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM. RESULTS: The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function. CONCLUSIONS: The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3608, + "tag": "BioModels:BIOMD0000000238" + }, + { + "id": 3609, + "tag": "Circadian temperature homeostasis" + }, + { + "id": 3610, + "tag": "Macaca fascicularis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:46:59.787165+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000238", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2586": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2586, + "name": "Jiang2007 - GSIS system, Pancreatic Beta Cells", + "repository_type": "biomodels", + "summary": "
Jiang2007 - GSIS system, Pancreatic Beta Cells
Description of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells.

This model is described in the article:

Jiang N, Cox RD, Hancock JM.
Mamm Genome 2007 Jul; 18(6-7):508-20.

Abstract:

The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic beta cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state.

The model reproduces Figure 2 of the paper, and is built using files 'ModelNNT11.xml' and 'changed.m' available from http://www.har.mrc.ac.uk/research/bioinformatics/research_areas/systems_biology.html .

A couple of small errors in the model (in the original SBML file 'ModelNNT11.xml') have been corrected. The errors are:

This model is hosted on BioModels Database and identified by: BIOMD0000000239 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Transient heterogeneity in extracellular protease production by Bacillus subtilis.
Veening JW, Igoshin OA, Eijlander RT, Nijland R, Hamoen LW, Kuipers OP Mol. Syst. Biol. 2008 ; Volume: 4 : 184 18414485,
Abstract:
The most sophisticated survival strategy Bacillus subtilis employs is the differentiation of a subpopulation of cells into highly resistant endospores. To examine the expression patterns of non-sporulating cells within heterogeneous populations, we used buoyant density centrifugation to separate vegetative cells from endospore-containing cells and compared the transcriptome profiles of both subpopulations. This demonstrated the differential expression of various regulons. Subsequent single-cell analyses using promoter-gfp fusions confirmed our microarray results. Surprisingly, only part of the vegetative subpopulation highly and transiently expresses genes encoding the extracellular proteases Bpr (bacillopeptidase) and AprE (subtilisin), both of which are under the control of the DegU transcriptional regulator. As these proteases and their degradation products freely diffuse within the liquid growth medium, all cells within the clonal population are expected to benefit from their activities, suggesting that B. subtilis employs cooperative or even altruistic behavior. To unravel the mechanisms by which protease production heterogeneity within the non-sporulating subpopulation is established, we performed a series of genetic experiments combined with mathematical modeling. Simulations with our model yield valuable insights into how population heterogeneity may arise by the relatively long and variable response times within the DegU autoactivating pathway.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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\t described in: Pharmacokinetic-pharmacodynamic modeling of caffeine: Tolerance to pressor effects
\t Shi J, Benowitz NL, Denaro CP and Sheiner LB. ;Clin. Pharmacol. Ther. 1993 Jan;53(1):6-14. PMID:8422743;
Abstract:
\t We propose a parametric pharmacokinetic-pharmacodynamic model for caffeine that quantifies the development of tolerance to the pressor effect of the drug and characterizes the mean behavior and inter-individual variation of both pharmacokinetics and pressor effect. Our study in a small group of subjects indicates that acute tolerance develops to the pressor effect of caffeine and that both the pressor effect and tolerance occur after some time delay relative to changes in plasma caffeine concentration. The half-life of equilibration of effect with plasma caffeine concentration is about 20 minutes. The half-life of development and regression of tolerance is estimated to be about 1 hour, and the model suggests that tolerance, at its fullest, causes more than a 90 percent reduction of initial (nontolerant) effect. Whereas tolerance to the pressor effect of caffeine develops in habitual coffee drinkers, the pressor response is regained after relatively brief periods of abstinence. Because of the rapid development and regression of tolerance, the pressor response to caffeine depends on how much caffeine is consumed, the schedule of consumption, and the elimination half-life of caffeine. \t

Caffeine intake in this version is modelled as cups of coffee drunk at regular intervals (parameter t_interval). The amount of caffeine per cup is determined by the parameter cupsize. The body weight of the person drinking is given by the parameter bodyweight.
The even coffee cup occures delayed to the drinking of each cup, as the availability of the caffeine in the digestive tract is assumed to be delayed to the ingestion by the time t_lag.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Theoretical and experimental evidence for hysteresis in cell proliferation.
Bai S, Goodrich D, Thron CD, Tecarro E, Obeyesekere M. Cell Cycle. 2003 Jan-Feb;2(1):46-52. 12695688 ,
Abstract:
We propose a mathematical model for the regulation of the G1-phase of the mammalian cell cycle taking into account interactions of cyclin D/cdk4, cyclin E/cdk2, Rb and E2F. Mathematical analysis of this model predicts that a change in the proliferative status in response to a change in concentrations of serum growth factors will exhibit the property of hysteresis: the concentration of growth factors required to induce proliferation is higher than the concentration required to maintain proliferation. We experimentally confirmed this prediction in mouse embryonic fibroblasts in vitro. In agreement with the mathematical model, this indicates that changes in proliferative mode caused by small changes in concentrations of growth factors are not easily reversible. Based on this study, we discuss the importance of proliferation hysteresis for cell cycle regulation.


The original model was taken from the Cell Cycle DataBase (CCDB).

Variable added: assignment rule for denoting phosphorylated Rb (Rb_phosphorylated i.e(RT-RS-R)) created.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This is the reduced model (model 8) described in: Dynamics within the CD95 death-inducing signaling complex decide life and death of cells.
Leo Neumann, Carina Pforr, Joel Beaudouin, Alexander Golks, Peter H. Krammer, Inna N. Lavrik and Roland Eils (German Cancer Research Center (DKFZ), http://www.dkfz.de);Mol Sys Biol2010;6:352. doi:10.1038/msb.2010.6;

Abstract:
This study explores the dilemma in cellular signaling that triggering of CD95 (Fas/APO-1) in some situations results in cell death and in others leads to the activation of NF-\u03baB. We established an integrated kinetic mathematical model for CD95-mediated apoptotic and NF-\u03baB signaling. Systematic model reduction resulted in a surprisingly simple model well approximating experimentally observed dynamics. The model postulates a new link between c-FLIPL cleavage in the death-inducing signaling complex (DISC) and the NF-\u03baB pathway. We validated experimentally that CD95 stimulation resulted in an interaction of p43-FLIP with the IKK complex followed by its activation. Furthermore, we showed that the apoptotic and NF-\u03baB pathways diverge already at the DISC. Model and experimental analysis of DISC formation showed that a subtle balance of c-FLIPL and procaspase-8 determines life/death decisions in a nonlinear manner. We present an integrated model describing the complex dynamics of CD95-mediated apoptosis and NF-\u03baB signaling.

The original was taken from the MSB article supplementary material site msb20106-s2.xml. All the species ids were changed since the model was not a valid SBML with its original ids - Lukas.

Notes added to the species [L] (the initial concentration of Anti-CD95), regarding changes to be made in the initial concentration of [L], to obtain figure 5D.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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\t This is the model described in: Bacterial adaptation through distributed sensing of metabolic fluxes
\tOliver Kotte, Judith B Zaugg and Matthias Heinemann;Mol Sys Biol2010;6:355. doi:10.1038/msb.2010.10;
Abstract:
\t The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.\t

In its current form this SBML model is parametrized for the glucose to acetate transition and to simulate the extended diauxic shift as shown in figure 3 and scenario 6 of the attached matlab file. In this scenario the cells first are grown from an OD600 (BM) of 0.03 with a starting glucose concentration of 0.5 g/l for 8.15 h (29340 sec). Then a medium containing 5 g/l acetate is inoculated with these cells to an OD600 of 0.03 and grown for another 19.7 hours (70920 sec). Finally the cells are shifted to a medium containing both glucose and acetate at a concentration of 3 g/l with a starting OD600 of 0.0005.
The shifts where implemented using events triggering at the times determined by the parameters shift1 and shift2 (in hours). To simulate other scenarios the initial conditions need to be changed as described in the supplemental materials (supplement 1)
The original SBML model and the MATLAB file used for the calculations can be down loaded as supplementary materials of the publication from the MSB website. (supplement 2).

The units of the external metabolites are in [g/l], those of the biomass in optical density,OD600, taken as dimensionless, and [micromole/(gramm dry weight)] for all intracellular metabolites. As the latter cannot be implemented in SBML, it was chosen to be micromole only and the units of the parameters are left mostly undefined.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This the model from the article:
A biochemically structured model for Saccharomyces cerevisiae.
Lei F, Rotb\u00f8ll M, J\u00f8rgensen SB. J Biotechnol. 2001 Jul 12;88(3):205-21. \t PMID: 11434967 ,DOI: 10.1016/S0168-1656(01)00269-3

Abstract:
A biochemically structured model for the aerobic growth of Saccharomyces cerevisiae on glucose and ethanol is presented. The model focuses on the pyruvate and acetaldehyde branch points where overflow metabolism occurs when the growth changes from oxidative to oxido-reductive. The model is designed to describe the onset of aerobic alcoholic fermentation during steady-state as well as under dynamical conditions, by triggering an increase in the glycolytic flux using a key signalling component which is assumed to be closely related to acetaldehyde. An investigation of the modelled process dynamics in a continuous cultivation revealed multiple steady states in a region of dilution rates around the transition between oxidative and oxido-reductive growth. A bifurcation analysis using the two external variables, the dilution rate, D, and the inlet concentration of glucose, S(f), as parameters, showed that a fold bifurcation occurs close to the critical dilution rate resulting in multiple steady-states. The region of dilution rates within which multiple steady states may occur depends strongly on the substrate feed concentration. Consequently a single steady state may prevail at low feed concentrations, whereas multiple steady states may occur over a relatively wide range of dilution rates at higher feed concentrations.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This the single cell model from the article:
A multiscale model to investigate circadian rhythmicity of pacemaker neurons in the suprachiasmatic nucleus.
Vasalou C, Henson MA. PLoS Comput Biol 2010 Mar 12;6(3):e1000706.\t PMID: 20300645 , DOI: 10.1371/journal.pcbi.1000706 ;

Abstract:
The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels.

Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the model with unfitted parameters described in the article
Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress
Markus Ralser, Mirjam M Wamelink, Axel Kowald, Birgit Gerisch, Gino Heeren, Eduard A Struys, Edda Klipp, Cornelis Jakobs, Michael Breitenbach, Hans Lehrach and Sylvia Krobitsch, J Biol 2007 6(4):10; PMID: 18154684 , doi: 10.1186/jbiol61
Abstract:
BACKGROUND: Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.
RESULTS: We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.
CONCLUSION: The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions.

Different realtive enzyme velocities can be simulated by varying the parameters k_rel_TPI and k_rel_GAPDH .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This file describes the SBML version of the mathematical model in the following journal article: Linking Pulmonary Oxygen Uptake, Muscle Oxygen Utilization and Cellular Metabolism during Exercise, Ann Biomed Eng. 2007 Jun;35(6):956-69. (Pubmed ID: 17380394). This mathematical model simulates oxygen transport and metabolism in skeletal muscle in response to a step change from a warm-up steady state to a higher work rate corresponding to exercise at different levels of intensity: moderate (M), heavy (H) and very heavy (VH). The model parameter values are listed in the tables of this article. The parameter values that are independent of the exercise level are reported in Table 2. The parameter values that depend on the exercise level are reported in Tables 1A, 3 and 4. The model simulations (Figures 2, 3, 4 and 5) were obtained for a representative subject with a set of parameter values different from those in Table 1A, 3 and 4. In the sbml model, these model parameters are used to simulate exercise at a very heavy (VH) intensity for the representative subject. Additionally, the parameter values needed to simulate exercise at moderate (M) and heavy (H) intensity are reported in the list of parameters of the file. The model simulates dynamics of (1) the concentrations of free (F) and total (T) oxygen concentration in blood (CFcap, CTcap) and tissue (CFtis, CTtis), Adenosine Triphosphate (ATP), Adenosine Diphosphate (ADP), Phosphocreatine (PCr) and Creatine (Cr); (2) the metabolic flux of oxidative phosphorylation, creatine kinase and ATPase; (3) the oxygen uptake in blood and oxygen transport rate from blood to tissue during exercise. The simulation also computes muscle oxygen saturation (StO2m) and relative muscle oxygen saturation (RStO2m) in order to compare simulated and experimental responses of human muscle oxygenation during exercise. The model was successfully tested with Roadrunner of the Systems Biology Workbench (SBW). The model simulations obtained with Roadrunner match those obtained with the mathematical model represented in Fortran and Matlab for relative and absolute tolerance smaller than 10-7.

To allow for simulations at varying levels of exercise, the parameter exercise_level was introduced. A value of 1 means medium, 2 heavy and 3 very heavy exercise. Setting this parameter assigns the parameters Vmax, KatpaseE, dQMm and tauQm with the relevant parameters. The warmup steady state is influenced by the parameter changes for this representative subject and the model has to be brought into steady state after each change of exercise level.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\ufffd\ufffdre N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Restif2006 - Whooping cough

This model is described in the article:

Restif O, Grenfell BT.
Proc. Biol. Sci. 2006 Feb; 273(1585): 409-416

Abstract:

Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), interfere through a given level of cross-immunity. We used whooping cough as a potential example, but the framework proposed here could be applied to other acute infectious diseases. Specifically, we analysed the effects of these parameters on the invasion dynamics of one strain into a population, where the second strain is endemic. Whereas the deterministic version of the model converges towards stable coexistence of the two strains in most cases, stochastic simulations showed that transient epidemic dynamics can cause the extinction of either strain. Thus ecological dynamics, modulated by the immune parameters, eventually determine the adaptive value of different pathogen genotypes. We advocate an integrative view of pathogen dynamics at the crossroads of immunology, epidemiology and evolution, as a way towards efficient control of infectious diseases.

This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size\u00a0 N\u00a0= 1. The model reproduces the deterministic time courses. Stochastic interpretation with Copasi UI gave results similar to the article, but was not extensively tested. The initial conditions for competition simulations can be derived by equilibrating the system for one pathogen and then adding a starting concentration for the other.

Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1)

This model is hosted on BioModels Database and identified by: BIOMD0000000249.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. This mechanistic model comprises the explicit reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. In the below article, two different mechanistic models have been studied, the first one based on previously known interactions but failing to account for the experimental data and the second one including additional interactions which were discovered and confirmed by new experiments. The mechanistic model encoded here is the second one, the extended and at the time of creation most complete model of cell fate decision making in response to different doses of EGF or HRG stimulation. The encoded parameter set corresponds to 10mM HRG stimulation as shown in Fig.1 of the article. The Supplementary Methods of the article provide further parameter sets that allow simulations for different ligands and different doses. A corresponding core model is available from http://www.ebi.ac.uk/biomodels/ as MODEL1003170000.

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010 , doi: 10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

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This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. The model comprises lumped reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. The parameter set implemented here corresponds to that used for generating Figs. 4 B,C,D (red curves for 10nM HRG) of the below article in Cell (2010). Moreover, we found that the same model described well the dynamics in different cell types (MCF-7 and PC-12), of different ligands (EGF and HRG) and at different doses (0.1nM, 1nM, 10nM) for a unique set of parameter values (as implemented here and reported in Table SD4_1 of the article) except for four parameters characterising the input, cytoplasmic ppERK. These four parameters K1, K2, tau1 and tau2 are used in the two equations involving species x1 and x2. These two equations define a phenomenological input module to describe the ligand-, dose- and cell type-dependent dynamics of ppERKc which are not modelled in mechanistic detail here. The four parameter values can be adjusted to model a specific ligand, dose and cell type. 8 parameter sets for different experiments are given in Table SD4_2 of the article. This SBML file, however, carries just one such parameter set. We have chosen that of MCF-7 cells stimulated by 10nM of HRG. To reproduce all simulations from the article, please replace the parameter values for K1, K2, tau1, tau2 as needed.

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010, doi:10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Stress-specific response of the p53-Mdm2 feedback loop
Alexander Hunziker, Mogens H Jensen and Sandeep Krishna BMC Systems Biology 2010, Jul 12;4(1):94 20624280,
Abstract:
ABSTRACT: BACKGROUND: The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses. RESULTS: We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour -- steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels. CONCLUSIONS: We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms.

The parameters of the model corresponds to the resting state, with delta = 11hr-1, gamma = 0.2hr-1, kt = 0.03nM-1hr-1 and kf = 5000nM-1hr-1.

To simulate different stress conditions as in figure 2A (also look at the curation figure of this model) of the reference publication, the above parameter should be changed. The parameter values corresponding to different stress conditions are shown in the following table.


Stress Condition/Parameter delta gamma kt kf
Nutlin 11hr-1 0.2hr-1 0.03nM-1hr-1 500nM-1hr-1
Oncogene 2hr-10.2hr-10.03nM-1hr-15000nM-1hr-1
DNA damage 2hr-10.5hr-10.03nM-1hr-12500nM-1hr-1
Hypoxia 2hr-10.2hr-10.01nM-1hr-15000nM-1hr-1

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the model described in the article:
The danger of metabolic pathways with turbo design
Teusink B, Walsh MC, van Dam K, Westerhoff HV Trends Biochem. Sci. 1998 May; Volume: 23 (Issue: 5 ): 162-9 9612078 ,
Abstract:
Many catabolic pathways begin with an ATP-requiring activation step, after which further metabolism yields a surplus of ATP. Such a 'turbo' principle is useful but also contains an inherent risk. This is illustrated by a detailed kinetic analysis of a paradoxical Saccharomyces cerevisiae mutant; the mutant fails to grow on glucose because of overactive initial enzymes of glycolysis, but is defective only in an enzyme (trehalose 6-phosphate synthase) that appears to have little relevance to glycolysis. The ubiquity of pathways that possess an initial activation step, suggests that there might be many more genes that, when deleted, cause rather paradoxical regulation phenotypes (i.e. growth defects caused by enhanced utilization of growth substrate).

The model represents the wild-type cell: 'guarded' glycolysis, which is the inhibition of the HK module by hexose monophosphate. The model reproduces figures 3c and 3d of the reference publication.

To reproduce unguarded glycolysis, set parameter wild_type to '0'.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
How yeast cells synchronize their glycolytic oscillations: a perturbation analytic treatment
Bier M, Bakker BM, Westerhoff HV. Biophys. J2000 Mar;78(3):1087-93. 10692299,
Abstract:
Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating metabolite at the bottom of the anaerobic part of the glycolytic chain. Here we address the issue of how a metabolite remote from the pacemaking origin of the oscillation may nevertheless control the synchronization. We present a quantitative model for glycolytic oscillations and their synchronization in terms of chemical kinetics. We show that, in essence, the common acetaldehyde concentration can be modeled as a small perturbation on the \"pacemaker\" whose effect on the period of the oscillations of cells in the same suspension is indeed such that a synchronization develops.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3647, + "tag": "BioModels:BIOMD0000000254" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 15:47:08.613716+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000254", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2602": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2602, + "name": "Chen2009 - ErbB Signaling", + "repository_type": "biomodels", + "summary": "

This is A431 IERMv1.0 model described in the article
Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.
William W Chen, Birgit Schoeberl, Paul J Jasper, Mario Niepel, Ulrik B Nielsen, Douglas A Lauffenburger and Peter K Sorger. Molecular Systems Biology 2009; 5:239. PMID: 19156131 , DOI: 10.1038/msb.2008.74

Abstract:
The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.

The sbml model is available as supplemental material to the article and at http://www.cdpcenter.org/resources/models/chen-et-al-2008/ . It was slightly changed to make it valid SBML and to incorporate the step functions, described in the readme file and needed for inhibitor preincubation. the equilibration processes end at 1800 sec, so to reproduce the dynamics shown in the publication and supplemental material, only the time points after 1800 need to be considered. The parameter set is the hand fitted one used for Sfigure 3 in the supplemental materials. All species are in molecules, apart from HRG, EGF and Inh, which are in M.

The results shown in SFigure 3 can be calculated dividing the parameters ERK_PP , AKT_PP and ERB_B1_P_tot by ERK_t , AKT_t and EGFR_t , respectively. Somehow we did not find the right scaleing factor for the phosphorylated ErbB1 receptor. Therefore the model does only qualitatively reproduces the timecourses shown in the first row of Sfigure 3.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\ufffd\ufffd\ufffd\ufffd\ufffd\ufffdre N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the standard model described in the article:
Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein.
Rehm M, Huber HJ, Dussmann H, Prehn JH. EMBO J. 2006 Sep 20;25(18):4338-49. Epub 2006 Aug 24. PMID:16932741, doi:10.1038/sj.emboj.7601295;
Abstract:
Activation of effector caspases is a final step during apoptosis. Single-cell imaging studies have demonstrated that this process may occur as a rapid, all-or-none response, triggering a complete substrate cleavage within 15 min. Based on biochemical data from HeLa cells, we have developed a computational model of apoptosome-dependent caspase activation that was sufficient to remodel the rapid kinetics of effector caspase activation observed in vivo. Sensitivity analyses predicted a critical role for caspase-3-dependent feedback signalling and the X-linked-inhibitor-of-apoptosis-protein (XIAP), but a less prominent role for the XIAP antagonist Smac. Single-cell experiments employing a caspase fluorescence resonance energy transfer substrate verified these model predictions qualitatively and quantitatively. XIAP was predicted to control this all-or-none response, with concentrations as high as 0.15 microM enabling, but concentrations >0.30 microM significantly blocking substrate cleavage. Overexpression of XIAP within these threshold concentrations produced cells showing slow effector caspase activation and submaximal substrate cleavage. Our study supports the hypothesis that high levels of XIAP control caspase activation and substrate cleavage, and may promote apoptosis resistance and sublethal caspase activation in vivo.\t

This model is slightly altered from the description in the article. Cytochrome C and SMAC release from the mitochondrion is modelled as simple first order kinetics, giving the same form as the (integrated) equations in the supplement of the article. The apoptosome formation is modelled equally - and independent of the Cytochrome C release. The speed is either limited by the Apaf1 or ProCaspase9 concentration, whichever is higher, symbolised via the parameter with the ID apolim.
Also, once the substrate concentration falls below 1 percent, the event Production_Breakdown is triggered, leading to a breakdown of XIAP and procaspase3 production and turning off of the enhanced/proteosomal degradation (degradation rate for reactions 38,39,40,43,44,46,48,50,51 changes from 0.0347 to 0.0058).

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the self maintaining metabolism model described in the article:
A Simple Self-Maintaining Metabolic System: Robustness, Autocatalysis, Bistability.
Piedrafita G, Montero F, Mor\u00e1n F, C\u00e1rdenas ML, Cornish-Bowden A, PLoS Computational Biology 2010, 6(8):e1000872. doi:10.1371/journal.pcbi.1000872
Abstract:
A living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. We show here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation. As long as at least one catalyst is present at a sufficient concentration in the initial state, the others can be produced and maintained. The system shows bistability, because if the amount of catalyst in the initial state is insufficient to reach the non-trivial steady state the system collapses to a trivial steady state in which all fluxes are zero. It is also robust, because if one catalyst is catastrophically lost when the system is in steady state it can recreate the same state. There are three elementary flux modes, but none of them is an enzyme-maintaining mode, the entire network being necessary to maintain the two catalysts

As this is a theoretical model and no units are given in the article, the standard units (mol, seconds and litre) are used for the parameters. k8 and k11 are set equal to k4.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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Ortega2006 - bistability from doublephosphorylation in signal transduction

This model is described in the article:

Ortega F, Garc\u00e9s JL, Mas F, Kholodenko BN, Cascante M.
FEBS J. 2006 Sep; 273(17): 3915-3926

Abstract:

Previous studies have suggested that positive feedback loops and ultrasensitivity are prerequisites for bistability in covalent modification cascades. However, it was recently shown that bistability and hysteresis can also arise solely from multisite phosphorylation. Here we analytically demonstrate that double phosphorylation of a protein (or other covalent modification) generates bistability only if: (a) the two phosphorylation (or the two dephosphorylation) reactions are catalyzed by the same enzyme; (b) the kinetics operate at least partly in the zero-order region; and (c) the ratio of the catalytic constants of the phosphorylation and dephosphorylation steps in the first modification cycle is less than this ratio in the second cycle. We also show that multisite phosphorylation enlarges the region of kinetic parameter values in which bistability appears, but does not generate multistability. In addition, we conclude that a cascade of phosphorylation/dephosphorylation cycles generates multiple steady states in the absence of feedback or feedforward loops. Our results show that bistable behavior in covalent modification cascades relies not only on the structure and regulatory pattern of feedback/feedforward loops, but also on the kinetic characteristics of their component proteins.

This model is hosted on BioModels Database and identified by: BIOMD0000000258.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which cancatalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Deficient condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which cancatalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Adequate condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Loaded condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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EGF dependent Akt pathway model

made by Kazuhiro A. Fujita.

This is the EGF dependent Akt pathway model described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters EGF_conc_pulse , EGF_conc_step and EGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value EGF_conc_pulse , a 60 second step with EGF_conc_step or a signal increasing from 0 to EGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter are set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3662, + "tag": "BioModels:BIOMD0000000262" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:13.262277+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000262", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2610": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2610, + "name": "Fujita2010_Akt_Signalling_NGF", + "repository_type": "biomodels", + "summary": "

NGF dependent Akt pathway model

made by Kazuhiro A. Fujita.

This is the NGF dependent Akt pathway model described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters NGF_conc_pulse , NGF_conc_step and NGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value NGF_conc_pulse , a 60 second step with NGF_conc_step or a signal increasing from 0 to NGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter is set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3663, + "tag": "BioModels:BIOMD0000000263" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:13.732138+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000263", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2611": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2611, + "name": "Fujita2010_Akt_Signalling_EGFRinhib", + "repository_type": "biomodels", + "summary": "

Akt pathway model with EGFR inhibitor

made by Kazuhiro A. Fujita.

This is the Akt pathway model with an EGFR inhibitor described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters EGF_conc_pulse , EGF_conc_step and EGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value EGF_conc_pulse , a 60 second step with EGF_conc_step or a signal increasing from 0 to EGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter are set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3664, + "tag": "BioModels:BIOMD0000000264" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:14.305980+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000264", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2612": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2612, + "name": "Conradie2010_RPControl_CellCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex.
Conradie R, Bruggeman FJ, Ciliberto A, Csik\u00e1sz-Nagy A, Nov\u00e1k B, Westerhoff HV, Snoep JL FEBS J.2010 Jan; 277(2): 357-67 20015233,
Abstract:
Numerous top-down kinetic models have been constructed to describe the cell cycle. These models have typically been constructed, validated and analyzed using model species (molecular intermediates and proteins) and phenotypic observations, and therefore do not focus on the individual model processes (reaction steps). We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. The reactions in the model with the highest RP control were those involved in: (a) the interplay between retinoblastoma protein and E2F transcription factor; (b) those synthesizing the delayed response genes and cyclin D/Cdk4 in response to growth signals; (c) the E2F-dependent cyclin E/Cdk2 synthesis reaction; as well as (d) p27 formation reactions. Nine of the 23 intermediates were shown to have a good correlation between their concentration control and RP control. Sensitivity and co-control analysis indicated that the strongest control of the RP is mediated via the cyclin E/Cdk2:p27 complex concentration. Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP.

The rate constants presented in the paper have units [per tenth of an hour] and have been changed here to [per hour] (e.g. k16 = 0.25 not 0.025); for further confirmation of the correctness of this change, see the original model (Novak, J Theor Biol 2004 230:563).

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Voit2003 - Trehalose Cycle

This model is described in the article:

Voit EO.
J. Theor. Biol. 2003 Jul; 223(1): 55-78

Abstract:

The physiological hallmark of heat-shock response in yeast is a rapid, enormous increase in the concentration of trehalose. Normally found in growing yeast cells and other organisms only as traces, trehalose becomes a crucial protector of proteins and membranes against a variety of stresses, including heat, cold, starvation, desiccation, osmotic or oxidative stress, and exposure to toxicants. Trehalose is produced from glucose 6-phosphate and uridine diphosphate glucose in a two-step process, and recycled to glucose by trehalases. Even though the trehalose cycle consists of only a few metabolites and enzymatic steps, its regulatory structure and operation are surprisingly complex. The article begins with a review of experimental observations on the regulation of the trehalose cycle in yeast and proposes a canonical model for its analysis. The first part of this analysis demonstrates the benefits of the various regulatory features by means of controlled comparisons with models of otherwise equivalent pathways lacking these features. The second part elucidates the significance of the expression pattern of the trehalose cycle genes in response to heat shock. Interestingly, the genes contributing to trehalose formation are up-regulated to very different degrees, and even the trehalose degrading trehalases show drastically increased activity during heat-shock response. Again using the method of controlled comparisons, the model provides rationale for the observed pattern of gene expression and reveals benefits of the counterintuitive trehalase up-regulation.

Toinduce a heat shock, set the parameter heat_shock from 0 to 1. Thischanges the parameter values of X8 to X19 from 1 to the valuesgiven in table 3 of the original publication.
Asthis is an S-systems model, it does not contain any reactionsencoded in SBML.

This model is hosted on BioModels Database and identified by: BIOMD0000000266.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Lebeda2008 - BoNT paralysis (3 step model)
The onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction is describing in the model. This is the 3-step model described in the paper. This model is the reduced form of the model developed my Simpson 1980; PMID:\u00a0\u00a0 6243359 \u00a0, i.e., it omits three unknown parameters that represents the binding sites for each species of the toxin. The extension to this model,\u00a0i.e. the 4-step model described in the paper is BIOMD0000000178.

This model is described in the article:

Lebeda FJ, Adler M, Erickson K, Chushak Y.
J Pharmacokinet Pharmacodyn 2008 Jun; 35(3): 251-267

Abstract:

Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000267.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3668, + "tag": "BioModels:BIOMD0000000267" + }, + { + "id": 3495, + "tag": "Botulism" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3496, + "tag": "Metalloendopeptidase activity" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3497, + "tag": "Negative regulation of synaptic transmission, cholinergic" + }, + { + "id": 3498, + "tag": "Neurotoxicity of clostridium toxins" + }, + { + "id": 3499, + "tag": "Pathogenesis" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 3500, + "tag": "Receptor-mediated endocytosis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:15.812779+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000267", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2615": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2615, + "name": "Reed2008_Glutathione_Metabolism", + "repository_type": "biomodels", + "summary": "

This is the model described in the article:
A mathematical model of glutathione metabolism.
Michael C Reed, Rachel L Thomas, Jovana Pavisic, S. Jill James, Cornelia M Ulrich and H. Frederik Nijhout, Theor Biol Med Model 2008,5:8; PubmedID:18442411 ; DOI:10.1186/1742-4682-5-8;
Abstract:
BACKGROUND: Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.
APPROACH: We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.
CONCLUSION: We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.\t

parameter orig. article this model
Vm_CBS 700000 420000
Vm_GNMT 245 260
K_sam_GNMT 32 63
Vr_MTD(mito) 600000 595000
V_CBS kinetic law rearranged
V_bmetc 913 913.4
Vm_GR 8925 892.5

This version of the model contains a feeding rhythm as used in figure 5 of the original article. Four parameters, breakfast, lunch dinner and fasting, describe the relative level of amino acids, described by the parameter aa_input or Aminoacid_input, in the blood. To remove the daily feeding rhythm, either set the parameters for meals and fasting to 1 (or for figure 3 to 0.333), or remove the assignment rule for the Aminoacid_input. For the steady state evaluations for figure 6, the mealtime parameters were set to one, which, while making Copasi complain about explicit time dependency, still gives valid results.

This version of the model differs slightly from the version described in the supplement, in which contains some typos. It was corrected using the version of JWS-online, created using the original matlab files, thankfully provided by the articles authors. Many thanks to Jacky Snoep for his help and support.

In the SBML version of the model the volumes of the mitochondrion, the cytoplasm and the cell were all set to one to obtain the same equations as described in the supplemental materials of the article. The total folate is equally split between the cytosol and the mitochondrion and divided by 3/4 for the cytosol and 1/4 for the mitochondrion, respectively. To obtain an SBML model in which the volumes of the compartments, cytosol and mito, are used, the model needs to be altered as follows:

While the concentrations stay the same after these alteration, the reaction fluxes change by a factor of cytosol and mito for cytosolic and mitchondrial reactions, respectively.

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3669, + "tag": "Autistic disorder" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3670, + "tag": "BioModels:BIOMD0000000268" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3672, + "tag": "Cardiovascular system disease" + }, + { + "id": 3368, + "tag": "Down syndrome" + }, + { + "id": 3673, + "tag": "Glutathione metabolic process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3674, + "tag": "hepatocyte" + } + ], + "timestamp_created": "2025-02-03 15:47:16.481966+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000268", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2616": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2616, + "name": "Liu2010_Hormonal_Crosstalk_Arabidopsis", + "repository_type": "biomodels", + "summary": "

This is the single cell model for analysis of hormonal crosstalk in Arabidopsis described in the article:
Modelling and experimental analysis of hormonal crosstalk in Arabidopsis.
Liu J, Mehdi S, Topping J, Tarkowski P and Lindsey K. Mol Syst Biol. 2010 Jun 8;6:373; PmID: 20531403 , DOI: 10.1038/msb.2010.26
Abstract:
An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS-ethylene pathway interactions is developed, and used to make quantitative predictions about PLS-hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose-response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants.

This model was originally created using Copasi and taken from the supplementary materials of the MSB article. It uses equation 5 for the auxin biosynthesis and was altered to also contain the reactions for ACC, IAA and cytokinine import. Different from the supplementary material, the parameters for the auxin synthesis, v2, are set to k2c = 0.01 uM and k2=0.2 uM_per_sec and for the WT PLS transcription k6=0.3 . To obtain the model described in the first table of the supplementary materials, set k2c=k2=0 and k6=0.9 . For the pls and PLSox mutants, k6 should be set to 0 and 0.45, respectively.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3675, + "tag": "Auxin-activated signaling pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3676, + "tag": "BioModels:BIOMD0000000269" + }, + { + "id": 3677, + "tag": "Cytokinin-activated signaling pathway" + }, + { + "id": 3678, + "tag": "Ethylene-activated signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:17.114196+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000269", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2617": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2617, + "name": "Schilling2009 - ERK distributive", + "repository_type": "biomodels", + "summary": "
Schilling2009 - ERK distributive

This model has been exported from PottersWheel on 2009-04-20 18:57:44.\u00a0 The PottersWheel Model Definition file can be obtained from the curation tab.

This model is described in the article:

Schilling M, Maiwald T, Hengl S, Winter D, Kreutz C, Kolch W, Lehmann WD, Timmer J, Klingm\u00fcller U.
Mol. Syst. Biol. 2009; 5: 334

Abstract:

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.

This model is hosted on BioModels Database and identified by: BIOMD0000000270.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3679, + "tag": "BioModels:BIOMD0000000270" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:17.629005+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000270", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2618": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2618, + "name": "Becker2010_EpoR_CoreModel", + "repository_type": "biomodels", + "summary": "

This is the core model described in the article:
Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor
Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingm\u00fcller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988
Abstract:
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

SBML model exported from PottersWheel.

% PottersWheel model definition filefunction m = BeckerSchilling2010_EpoR_CoreModel()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'BeckerSchilling2010_EpoR_CoreModel';m.name        = 'BeckerSchilling2010_EpoR_CoreModel';m.description = 'BeckerSchilling2010_EpoR_CoreModel';m.authors     = {'Verena Becker',' Marcel Schilling'};m.dates       = {'2010'};m.type        = 'PW-2-0-42';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'EpoR'     ,     516, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo'      , 2030.19, 'global', 1890,  2310,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo_EpoR' ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo_EpoRi',       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dEpoi'    ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dEpoe'    ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {            }, {'EpoR'      }, {  }, 'C' , [] , 'k1*k2', {'kt','Bmax'}, [], 'reaction0001');m = pwAddR(m, {'EpoR'      }, {            }, {  }, 'MA', [] , []     , {'kt'       }, [], 'reaction0002');m = pwAddR(m, {'Epo','EpoR'}, {'Epo_EpoR'  }, {  }, 'MA', [] , []     , {'kon'      }, [], 'reaction0003');m = pwAddR(m, {'Epo_EpoR'  }, {'Epo','EpoR'}, {  }, 'MA', [] , []     , {'koff'     }, [], 'reaction0004');m = pwAddR(m, {'Epo_EpoR'  }, {'Epo_EpoRi' }, {  }, 'MA', [] , []     , {'ke'       }, [], 'reaction0005');m = pwAddR(m, {'Epo_EpoRi' }, {'Epo','EpoR'}, {  }, 'MA', [] , []     , {'kex'      }, [], 'reaction0006');m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoi'     }, {  }, 'MA', [] , []     , {'kdi'      }, [], 'reaction0007');m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoe'     }, {  }, 'MA', [] , []     , {'kde'      }, [], 'reaction0008');%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'kt'  , 0.0329366 , 'global', 1e-007, 1000);m = pwAddK(m, 'Bmax', 516       , 'fix'   , 492   , 540 );m = pwAddK(m, 'kon' , 0.00010496, 'global', 1e-007, 1000);m = pwAddK(m, 'koff', 0.0172135 , 'global', 1e-007, 1000);m = pwAddK(m, 'ke'  , 0.0748267 , 'global', 1e-007, 1000);m = pwAddK(m, 'kex' , 0.00993805, 'global', 1e-007, 1000);m = pwAddK(m, 'kdi' , 0.00317871, 'global', 1e-007, 1000);m = pwAddK(m, 'kde' , 0.0164042 , 'global', 1e-007, 1000);%% Default sampling time pointsm.t = 0:3:99;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Epo + dEpoe'      , 'Epo_extracellular_obs');m = pwAddY(m, 'Epo_EpoR'         , 'Epo_cellsurface_obs'  );m = pwAddY(m, 'Epo_EpoRi + dEpoi', 'Epo_intracellular_obs');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_Epo_extracellular_obs', 1, 'fix', 0, 100);m = pwAddS(m, 'scale_Epo_cellsurface_obs'  , 1, 'fix', 0, 100);m = pwAddS(m, 'scale_Epo_intracellular_obs', 1, 'fix', 0, 100);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3680, + "tag": "BioModels:BIOMD0000000271" + }, + { + "id": 3681, + "tag": "Erythropoietin receptor activity" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3682, + "tag": "hematopoietic cell line" + } + ], + "timestamp_created": "2025-02-03 15:47:18.122662+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000271", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2619": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2619, + "name": "Becker2010_EpoR_AuxiliaryModel", + "repository_type": "biomodels", + "summary": "

This is the auxiliary model described in the article:
Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor
Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingm\u00fcller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988
Abstract:
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

SBML model exported from PottersWheel.

% PottersWheel model definition filefunction m = BeckerSchilling2010_EpoR_AuxiliaryMode()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'BeckerSchilling2010_EpoR_AuxiliaryMode';m.name        = 'BeckerSchilling2010_EpoR_AuxiliaryModel';m.description = 'BeckerSchilling2010_EpoR_AuxiliaryModel';m.authors     = {'Verena Becker',' Marcel Schilling'};m.dates       = {'2010'};m.type        = 'PW-2-0-42';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'EpoR'     ,      76, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv'      , 999.293, 'global', 900,  1100,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv_EpoR' ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv_EpoRi',       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dSAvi'    ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dSAve'    ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {            }, {'EpoR'      }, {  }, 'C' , [] , 'k1*k2', {'kt','Bmax_SAv'}, [], 'reaction0001');m = pwAddR(m, {'EpoR'      }, {            }, {  }, 'MA', [] , []     , {'kt'           }, [], 'reaction0002');m = pwAddR(m, {'SAv','EpoR'}, {'SAv_EpoR'  }, {  }, 'MA', [] , []     , {'kon_SAv'      }, [], 'reaction0003');m = pwAddR(m, {'SAv_EpoR'  }, {'SAv','EpoR'}, {  }, 'MA', [] , []     , {'koff_SAv'     }, [], 'reaction0004');m = pwAddR(m, {'SAv_EpoR'  }, {'SAv_EpoRi' }, {  }, 'MA', [] , []     , {'kt'           }, [], 'reaction0005');m = pwAddR(m, {'SAv_EpoRi' }, {'SAv'       }, {  }, 'MA', [] , []     , {'kex_SAv'      }, [], 'reaction0006');m = pwAddR(m, {'SAv_EpoRi' }, {'dSAvi'     }, {  }, 'MA', [] , []     , {'kdi'          }, [], 'reaction0007');m = pwAddR(m, {'SAv_EpoRi' }, {'dSAve'     }, {  }, 'MA', [] , []     , {'kde'          }, [], 'reaction0008');%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'kt'      , 0.0329366   , 'global', 1e-007, 1000);m = pwAddK(m, 'Bmax_SAv', 76          , 'fix'   , 61    , 91  );m = pwAddK(m, 'kon_SAv' , 2.29402e-006, 'global', 1e-007, 1000);m = pwAddK(m, 'koff_SAv', 0.00679946  , 'global', 1e-007, 1000);m = pwAddK(m, 'kex_SAv' , 0.01101     , 'global', 1e-007, 1000);m = pwAddK(m, 'kdi'     , 0.00317871  , 'global', 1e-007, 1000);m = pwAddK(m, 'kde'     , 0.0164042   , 'global', 1e-007, 1000);%% Default sampling time pointsm.t = 0:3:99;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'SAv + dSAve'      , 'SAv_extracellular_obs');m = pwAddY(m, 'SAv_EpoR'         , 'SAv_cellsurface_obs'  );m = pwAddY(m, 'SAv_EpoRi + dSAvi', 'SAv_intracellular_obs');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_SAv_extracellular_obs', 1, 'fix', 0, 100);m = pwAddS(m, 'scale_SAv_cellsurface_obs'  , 1, 'fix', 0, 100);m = pwAddS(m, 'scale_SAv_intracellular_obs', 1, 'fix', 0, 100);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3683, + "tag": "BioModels:BIOMD0000000272" + }, + { + "id": 3681, + "tag": "Erythropoietin receptor activity" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3682, + "tag": "hematopoietic cell line" + } + ], + "timestamp_created": "2025-02-03 15:47:18.627611+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000272", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2620": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2620, + "name": "Pokhilko2010_CircClock", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model.
Pokhilko A, Hodge SK, Stratford K, Knox K, Edwards KD, Thomson AW, Mizuno T, Millar AJ. Mol Syst Biol.2010 Sep 21;6:416. 20865009,
Abstract:
Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3684, + "tag": "BioModels:BIOMD0000000273" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:19.119877+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000273", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2621": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2621, + "name": "Rattanakul2003_BoneFormationModel", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling of bone formation and resorption mediated by parathyroid hormone: response to estrogen/PTH therapy.
\tRattanakul C, Lenbury Y, Krishnamara N, Wollkind DJ. Biosystems 2003:70(1):55-72. 12753937,
Abstract:
Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: rattananakul, lenbury, krishnamara, wollkind. (2003) - version01
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3685, + "tag": "BioModels:BIOMD0000000274" + }, + { + "id": 3686, + "tag": "Bone resorption" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3687, + "tag": "Ossification" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:19.769469+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000274", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2622": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2622, + "name": "Goldbeter2007_Somitogenesis_Switch", + "repository_type": "biomodels", + "summary": "

This is the simple model without diffusion described in th epublication
Sharp developmental thresholds defined through bistability by antagonistic gradients of retinoic acid and FGF signaling.
Goldbeter A, Gonze D, Pourqui\u00e9 O. Dev Dyn. 2007 Jun;236(6):1495-508. PMID: 17497689, doi:10.1016/j.jtbi.2008.01.006
Abstract:
The establishment of thresholds along morphogen gradients in the embryo is poorly understood. Using mathematical modeling, we show that mutually inhibitory gradients can generate and position sharp morphogen thresholds in the embryonic space. Taking vertebrate segmentation as a paradigm, we demonstrate that the antagonistic gradients of retinoic acid (RA) and Fibroblast Growth Factor (FGF) along the presomitic mesoderm (PSM) may lead to the coexistence of two stable steady states. Here, we propose that this bistability is associated with abrupt switches in the levels of FGF and RA signaling, which permit the synchronized activation of segmentation genes, such as mesp2, in successive cohorts of PSM cells in response to the segmentation clock, thereby defining the future segments. Bistability resulting from mutual inhibition of RA and FGF provides a molecular mechanism for the all-or-none transitions assumed in the \"clock and wavefront\" somitogenesis model. Given that mutually antagonistic signaling gradients are common in development, such bistable switches could represent an important principle underlying embryonic patterning.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3688, + "tag": "BioModels:BIOMD0000000275" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3689, + "tag": "Somitogenesis" + }, + { + "id": 3690, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-02-03 15:47:21.064211+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000275", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2623": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2623, + "name": "Shrestha2010_HypoCalcemia_PTHresponse", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.
Shrestha RP, Hollot CV, Chipkin SR, Schmitt CP, Chait Y. Math Biosci.2010 Jul;226(1):46-57. 20406649,
Abstract:
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.

The model corresponds to hypocalcemic clamp test explained in the paper and parameter values used in the model are that of \"subject 1\". In order to obtain the plots corresponding to \"subject 2\" and \"subject 3\" the following parameters to be changed: lambda_1, lambda_2, m1, m2, R, beta, x1_n, x2_n, x2_min, x2_max, Ca0, Ca1, t0 and alpha.

parameter Subject 1 Subject 2 Subject 3
lambda_1 0.0125 0.0122 0.0269
lambda_2 0.5595 0.4642 0.4935
m1 112.5200 150.0000 90.8570
m2 15.0000 15.0000 15.0000
R 1.2162 1.1627 1.1889
beta 10e+06 10e+06 10e+06
x1_n 490.7800 452.8200 298.8200
x2_n 6.6290 9.5894 5.4600
x2_min 0.6697 1.4813 0.8287
x2_max 14.0430 17.8710 15.1990
Ca0 1.2550 1.2369 1.2475
Ca1 0.1817 0.2211 0.1985
t0 575 577 575
alpha 0.0442 0.0488 0.0472

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3691, + "tag": "BioModels:BIOMD0000000276" + }, + { + "id": 3692, + "tag": "Calcium metabolism disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3693, + "tag": "Response to parathyroid hormone" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:22.244691+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000276", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2624": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2624, + "name": "Shrestha2010_HyperCalcemia_PTHresponse", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.
Shrestha RP, Hollot CV, Chipkin SR, Schmitt CP, Chait Y. Math Biosci.2010 Jul;226(1):46-57. 20406649,
Abstract:
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.

The model corresponds to hypercalcemic clamp test explained in the paper and parameter values used in the model are that of \"subject 1\". In order to obtain the plots corresponding to \"subject 2\" and \"subject 3\" the following parameters to be changed: lambda_1, lambda_2, m1, m2, R, beta, x1_n, x2_n, x2_min, x2_max, t0, Ca0, Ca1 and alpha.

parameter Subject 1 Subject 2 Subject 3
lambda_1 0.0125 0.0122 0.0269
lambda_2 0.5595 0.4642 0.4935
m1 112.5200 150.0000 90.8570
m2 15.0000 15.0000 15.0000
R 1.2162 1.1627 1.1889
beta 10e+06 10e+06 10e+06
x1_n 490.7800 452.8200 298.8200
x2_n 6.6290 9.5894 5.4600
x2_min 0.6697 1.4813 0.8287
x2_max 14.0430 17.8710 15.1990
Ca0 1.2200 1.2513 1.2480
Ca1 0.2624 0.2267 0.2132
t0 575 575 575
alpha 0.0569 0.0563 0.0421

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3694, + "tag": "BioModels:BIOMD0000000277" + }, + { + "id": 3692, + "tag": "Calcium metabolism disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3693, + "tag": "Response to parathyroid hormone" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:22.995751+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000277", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2625": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2625, + "name": "Lemaire2004 - Role of RANK/RANKL/OPG pathway in bone remodelling process", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.
Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ. J Theor Biol.2004 Aug 7;229(3):293-309. 15234198,
Abstract:
We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling.

This model corresponds to the core model published in the paper. There is no corresponding plot to reproduce for this model.To obtain each of the 9 plots in the Figure 2 of the reference publication, there are some changes to be made to the core model.The curation figure reproduces figure 2 of the reference publication. There is a corresponding SBML and Copasi files for each of the plot. See curation tab for more details.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3695, + "tag": "BioModels:BIOMD0000000278" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3696, + "tag": "Glucocorticoid-remediable aldosteronism" + }, + { + "id": 3452, + "tag": "Regulation of bone remodeling" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:23.580095+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000278", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2626": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2626, + "name": "Komarova2005_PTHaction_OsteoclastOsteoblastCoupling", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Mathematical model of paracrine interactions between osteoclasts and osteoblasts predicts anabolic action of parathyroid hormone on bone.
Komarova SV. Endocrinology.2005 Aug;146(8):3589-95. 15860557,
Abstract:
To restore falling plasma calcium levels, PTH promotes calcium liberation from bone. PTH targets bone-forming cells, osteoblasts, to increase expression of the cytokine receptor activator of nuclear factor kappaB ligand (RANKL), which then stimulates osteoclastic bone resorption. Intriguingly, whereas continuous administration of PTH decreases bone mass, intermittent PTH has an anabolic effect on bone, which was proposed to arise from direct effects of PTH on osteoblastic bone formation. However, antiresorptive therapies impair the ability of PTH to increase bone mass, indicating a complex role for osteoclasts in the process. We developed a mathematical model that describes the actions of PTH at a single site of bone remodeling, where osteoclasts and osteoblasts are regulated by local autocrine and paracrine factors. It was assumed that PTH acts only to increase the production of RANKL by osteoblasts. As a result, PTH stimulated osteoclasts upon application, followed by compensatory osteoblast activation due to the coupling of osteoblasts to osteoclasts through local paracrine factors. Continuous PTH administration resulted in net bone loss, because bone resorption preceded bone formation at all times. In contrast, over a wide range of model parameters, short application of PTH resulted in a net increase in bone mass, because osteoclasts were rapidly removed upon PTH withdrawal, enabling osteoblasts to rebuild the bone. In excellent agreement with experimental findings, increase in the rate of osteoclast death abolished the anabolic effect of PTH on bone. This study presents an original concept for the regulation of bone remodeling by PTH, currently the only approved anabolic treatment for osteoporosis.

The model reproduces Figures 1B and 2A of the reference publication. To obtain the figures 1B, the parameter g21 needs changes. To obtain the figures 1A, the parameters g21, g12 and k2 need to changed. For details look at the curation tab.

The initial concentration of Osteoclasts (x1) is corrected to 1.06066 from 10.06066.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: CellMLdetails
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3697, + "tag": "BioModels:BIOMD0000000279" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3696, + "tag": "Glucocorticoid-remediable aldosteronism" + }, + { + "id": 3452, + "tag": "Regulation of bone remodeling" + }, + { + "id": 3693, + "tag": "Response to parathyroid hormone" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:24.100788+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000279", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2627": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2627, + "name": "Morris1981_MuscleFibre_Voltage_reduced", + "repository_type": "biomodels", + "summary": "

This is the reduced model of the voltage oscillations in barnacle muscle fibers, generally known as the Morris-Lecar model (eg. wikipedia), described in the article:
Voltage oscillations in the barnacle giant muscle fiber.
\tMorris C, Lecar H. Biophys J. 1981 Jul;35(1):193-213. PubmedID:7260316; DOI:10.1016/S0006-3495(81)84782-0
\tAbstract:
\tBarnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.

The model consists of the differential equations (9) and (2) given on pages 205 and 196 of the article. There seems to be a typo in the figure caption of figure 9. Using V2 = 15 instead of -15 allows to reproduce the results.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This model is described inthe article:
Metabolic control mechanisms. 5. A solution for the equations representing interaction between glycolysis and respiration in ascites tumor cells.
Britton Chance, David Garfinkel, Joseph Higgins and Benno Hess, J Biol Chem. 1960 35:2426-2439. PubmedID: 13692276
Abstract:
The other papers of this series present experimentalevidence for possible relationships between the kinetics of oxygen,glucose, adenosine diphosphate, adenosine triphosphate, andphosphate and those of the cytochromes and pyridine nucleotidesof the ascites tumor cell. From these general experiments weare able to formulate, under the law of mass action, a minimumhypothesis under which the four metabolic regulations previouslydescribed can be observed. In brief, the system can be represented by the known enzyme systems, a relatively higher ADP affinity in respiration than in glycolysis, the mitochondrial membrane, a segregation of ATP into two compartments, and an ATP-utilizing system that is responsive to small decreases of the intracellular ADP level. The chemical equations for the pathway from glucose to oxygen are solved by a digital computer method so that the responses of the chemical equations and of the living cell can be accurately compared.
For reasons already described, we greatly prefer a com-puter representation based upon a physical or chemical lawrepresenting the action of the system to a model simulating theoperation of the chemical system but not based upon funda-mental laws for the reactions involved; such a representationwould not adequately represent the kinetics of the system, as inan electric circuit network or in some types of hydraulic ana-logues.

The model gives solutions of the reaction kinetics for three types of metabolism:

  1. 0 - 64s, metabolism of endogenous substrate
  2. 64s - 119s, metabolism of added glucose, illustrating the activated and inhibited aspects of glucose metabolism
  3. 119s - 153s, relief of glucose and oxygen inhibition by the addition of an uncoupling agent

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is described in the article:
The mechanism of catalase action. II. Electric analog computer studies.
Britton Chance, David S Greenstein, Joseph Higgins, CC Yang, Arch Biochem. 1952 37:322-39. PubmedID:14953444
Summary:
An electric analog computer has been constructed for a study of the kinetics of catalase action. This computer gives results for the formation and disappearance of the catalase-hydrogen peroxide complex that are in good agreement with the experimental data. The computer study verifies an approximate method for the computation of the velocity constant for the combination of hydrogen peroxide and catalase and justifies the simple formula used previously to compute the velocity constant for the reaction of the catalase-hydrogen peroxide complex with donor molecules. Finally, the computer data show that the binding of peroxide to catalase is a practically irreversible reaction.

The reaction of the enzyme-substrate complex, p, with the electron donor, a, is bimolecular, although in the article, as a is assumed to be constant, it is modelled using an apparent rate constant consisting of the product of the rate constant, k4, and the concentration of a. In this implementation, the concentration of a is set to 1 and the value of k4 just adapted so that the product equals the values given for k4*a in the article. The specific parameter values are taken from Fig 3. The graphs do not exactly match those in the paper, this may be due to the different simulators used.

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Default parameter values are those in the right hand panel of Fig 12. The other panels may be obtained by setting X to 1, 2 or 4, and K3 to 0, 1/2 or 1.

This model is described in:
The kinetics of the enzyme-substrate compound of peroxidase.
Britton Chance, Journal of Biological Chemistry, 151, 553-577, 1943. PDF at JBC
reprinted in: Adv Enzymol Relat Areas Mol Biol. 1999;73:3-23. PubmedID:10218104>
Abstract:
Under the narrow range of experimental conditions, and at a temperature of approximately 25 degrees, the following data were obtained. 1. The equilibrium constant of peroxidase and hydrogen peroxide has a minimum value of 2 x 10(-8). 2. The velocity constant for the formation of peroxidase-H2O2 Complex I is 1.2 x 10(7) liter mole-1 sec.-1, +/- 0.4 x 10(7). 3. The velocity constant for the reversible breakdown of peroxidase-H2O2 Complex I is a negligible factor in the enzyme-substrate kinetics and is calculated to be less than 0.2 sec.-1. 4. The velocity constant, k3, for the enzymatic breakdown of peroxidase-H2O2 Complex I varies from nearly zero to higher than 5 sec.-1, depending upon the acceptor and its concentration. The quotient of k3 and the leucomalachite green concentration is 3.0 x 10(4) liter mole-1 sec.-1. For ascorbic acid this has a value of 1.8 x 10(5) liter mole-1 sec.-1. 5. For a particular acceptor concentration, k3 is determined solely from the enzyme-substrate kinetics and is found to be 4.2 sec.-1. 6. For the same conditions, k3 is determined from a simple relationship derived from mathematical solutions of the Michaelis theory and is found to be 5.2 sec.-1. 7. For the same conditions, k3 is determined from the over-all enzyme action and is found to be 5.1 sec.-1. 8. The Michaelis constant determined from kinetic data alone is found to be 0.44 x 10(-6). 9. The Michaelis constant determined from steady state measurements is found to be 0.41 x 10(-6). 10. The Michaelis constant determined from measurement of the overall enzyme reaction is found to be 0.50 x 10(-6). 11. The kinetics of the enzyme-substrate compound closely agree with mathematical solutions of an extension of the Michaelis theory obtained for experimental values of concentrations and reaction velocity constants. 12. The adequacy of the criteria by which experiment and theory were correlated has been examined critically and the mathematical solutions have been found to be sensitive to variations in the experimental conditions. 13. The critical features of the enzyme-substrate kinetics are Pmax, and curve shape, rather than t1/2. t1/2 serves as a simple measure of dx/dt. 14. A second order combination of enzyme and substrate to form the enzyme-substrate compound, followed by a first order breakdown of the compound, describes the activity of peroxidase for a particular acceptor concentration. 15. The kinetic data indicate a bimolecular combination of acceptor and enzyme-substrate compound.

This model is the one described in the appendix of the article. It reproduces, amongst others, figure 12. The parameters and concentrations used are rescaled as stated in the article. K2 and K3 stand for k2 and k3, respectively, divided by k1.

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This model is the reaction sequence SEQFB, a model pathway of a branched system with sequential feedback interactions found in bacterial amino acid synthesis. Its steady state is presented in Fig 4.

The model is described in:
METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer.
JHS Hofmeyr and KJ van der Merwe, Comput Appl Biosci 1986 2:243-9; PubmedID: 3450367
Abstract:
METAMOD, a BBC microcomputer-based software package for steady-state modelling and control analysis of model metabolic pathways, is described, The package consists of two programs. METADEF allows the user to define the pathway in terms of reactions, rate equations and initial concentrations of metabolites. METACAL uses one of two algorithms to calculate the steady-state concentrations and fluxes. One algorithm uses the current ratio of production and consumption rates of variable metabolites to adjust iteratively their concentrations in such a way that they converge towards the steady state. The other algorithm solves the roots of the system equations by means of a quasi-Newtonian procedure. Control analysis allows the calculation of elasticity, control and response coefficients, by means of finite difference approximation. METAMOD is interactive and easy to use, and suitable for teaching and research purposes.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novere N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Experimental and computational analysis of polyglutamine-mediated cytotoxicity.
Tang MY, Proctor CJ, Woulfe J, Gray DA. PLoS Comput Biol.2010 Sep 23;6(9). 20885783,
Abstract:
Expanded polyglutamine (polyQ) proteins are known to be the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activation of stress kinases such as p38MAPK, which can be cytotoxic. The relationships of these systems are not well understood. We have used fluorescent reporter systems imaged in living cells, and stochastic computer modeling to explore the relationships of polyQ, p38MAPK activation, generation of reactive oxygen species (ROS), proteasome inhibition, and inclusion body formation. In cells expressing a polyQ protein inclusion, body formation was preceded by proteasome inhibition but cytotoxicity was greatly reduced by administration of a p38MAPK inhibitor. Computer simulations suggested that without the generation of ROS, the proteasome inhibition and activation of p38MAPK would have significantly reduced toxicity. Our data suggest a vicious cycle of stress kinase activation and proteasome inhibition that is ultimately lethal to cells. There was close agreement between experimental data and the predictions of a stochastic computer model, supporting a central role for proteasome inhibition and p38MAPK activation in inclusion body formation and ROS-mediated cell death.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3709, + "tag": "BioModels:BIOMD0000000285" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3362, + "tag": "Neurodegenerative disease" + }, + { + "id": 3499, + "tag": "Pathogenesis" + }, + { + "id": 3710, + "tag": "Protein polyglutamylation" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:27.412606+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000285", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2633": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2633, + "name": "Proctor2010 - a link between GSK3 and p53 in Alzheimer's Disease", + "repository_type": "biomodels", + "summary": "

This is the model described the article:
GSK3 and p53 - is there a link in Alzheimer's disease?
Carole J Proctor and Douglas A Gray Molecular Neurodegeneration 2010, 5:7; doi: 10.1186/1750-1326-5-7
Abstract:
Background: Recent evidence suggests that glycogen synthase kinase-3beta (GSK3beta) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3beta and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3beta. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model.
Results: The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3beta and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3beta and a vicious cycle ensues.
Conclusions: Since p53 and GSK3beta are both involved in the apoptotic pathway, and GSK3beta overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration.


Notes: The original model submitted by the author had events in it. Since, this model is intended for Stochastic Simulation run and Copasi cannot handle events in Stochastic run, I have replaced the events with piecewise assignment rule. -Viji

This model is an extension of Proctor_p53_Mdm2_ATM ( BIOMD0000000188 ).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This is the model described in: Feedback between p21 and reactive oxygen production is necessary for cell senescence.
\tPassos JF, Nelson G, Wang C, Richter T, Simillion C, Proctor CJ, Miwa S, Olijslagers S, Hallinan J, Wipat A, Saretzki G, Rudolph KL, Kirkwood TB, von Zglinicki T. ;Mol Sys Biol2010;6:347. Epub 2010 Feb 16. PMID:20160708 doi:10.1038/msb.2010.5;
Abstract:
\t Cellular senescence--the permanent arrest of cycling in normally proliferating cells such as fibroblasts--contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of 'deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFbeta. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype. \t

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
PI3K-dependent cross-talk interactions converge with Ras as quantifiable inputs integrated by Erk.
Wang CC, Cirit M, Haugh JM Mol. Syst. Biol. 2009;5:246. 19225459 ,
Abstract:
Although it is appreciated that canonical signal-transduction pathways represent dominant modes of regulation embedded in larger interaction networks, relatively little has been done to quantify pathway cross-talk in such networks. Through quantitative measurements that systematically canvas an array of stimulation and molecular perturbation conditions, together with computational modeling and analysis, we have elucidated cross-talk mechanisms in the platelet-derived growth factor (PDGF) receptor signaling network, in which phosphoinositide 3-kinase (PI3K) and Ras/extracellular signal-regulated kinase (Erk) pathways are prominently activated. We show that, while PI3K signaling is insulated from cross-talk, PI3K enhances Erk activation at points both upstream and downstream of Ras. The magnitudes of these effects depend strongly on the stimulation conditions, subject to saturation effects in the respective pathways and negative feedback loops. Motivated by those dynamics, a kinetic model of the network was formulated and used to precisely quantify the relative contributions of PI3K-dependent and -independent modes of Ras/Erk activation.


This model is parameterized with the median of the estimated parameters given in the supplementary material of the original publication's (doi: 10.1038/msb.2009.4 ) supplement on pages 8 and 9.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3715, + "tag": "BioModels:BIOMD0000000288" + }, + { + "id": 3129, + "tag": "ERK1 and ERK2 cascade" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3716, + "tag": "Phosphatidylinositol 3-kinase signaling" + }, + { + "id": 3717, + "tag": "Platelet-derived growth factor receptor signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 15:47:29.134171+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000288", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2639": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2639, + "name": "Alexander2010_Tcell_Regulation_Sys1", + "repository_type": "biomodels", + "summary": "

This is system 1, the model with linear antigen uptake by pAPCs, described in the article:
Self-tolerance and Autoimmunity in a Regulatory T Cell Model.
Alexander HK, Wahl LM. Bull Math Biol. 2010 Mar 2. PMID:20195912, doi:10.1007/s11538-010-9519-2;
Abstract:
The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R (0) for each system. When R (0) < 1, only the trivial equilibrium exists and is stable; when R (0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3727, + "tag": "BioModels:BIOMD0000000289" + }, + { + "id": 3728, + "tag": "Hypersensitivity reaction type II disease" + }, + { + "id": 3729, + "tag": "Metazoa" + }, + { + "id": 3730, + "tag": "Regulation of T cell anergy" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:13:50.083841+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000289", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2640": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2640, + "name": "Alexander2010_Tcell_Regulation_Sys2", + "repository_type": "biomodels", + "summary": "

This is system 2, the model with Michelis Menten type antigen uptake by pAPCs, described in the article:
Self-tolerance and Autoimmunity in a Regulatory T Cell Model.
Alexander HK, Wahl LM. Bull Math Biol. 2010 Mar 2. PMID: 20195912 , doi: 10.1007/s11538-010-9519-2 ;
Abstract:
The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R (0) for each system. When R (0) < 1, only the trivial equilibrium exists and is stable; when R (0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This a model from the article:
Mathematical model of binding of albumin-bilirubin complex to the surface of carbon pyropolymer.
Nikolaev AV, Rozhilo YA, Starozhilova TK, Sarnatskaya VV, Yushko LA, Mikhailovskii SV, Kholodov AS, Lobanov AI. Bull Exp Biol Med2005 Sep;140(3):365-9. 16307060,
Abstract:
We proposed a mathematical model and estimated the parameters of adsorption of albumin-bilirubin complex to the surface of carbon pyropolymer. Design data corresponded to the results of experimental studies. Our findings indicate that modeling of this process should take into account fractal properties of the surface of carbon pyropolymer.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3732, + "tag": "Bilirubin conjugation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3733, + "tag": "BioModels:BIOMD0000000291" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 3734, + "tag": "Liver disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:13.287616+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000291", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2642": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2642, + "name": "Rovers1995_Photsynthetic_Oscillations", + "repository_type": "biomodels", + "summary": "

This is the model described in the article:
Photosynthetic oscillations and the interdependence of photophosphorylation and electron transport as studied by a mathematical model.
Rovers W, Giersch C. Biosystems. 1995;35(1):63-73. PMID: 7772723
Abstract:
A simple mathematical model of photosynthetic carbon metabolism as driven by ATP and NADPH has been formulated to analyse photosynthetic oscillations. Two essential assumptions of this model are: (i) reduction of 3-phosphoglycerate to triosephosphate in the Clavin cycle is limited by ATP, not by NADPH, and (ii) photophosphorylation is affected by the availability of both ADP and NADP, while electron transport is limited by NADP only. The model produces oscillations of observed damping and period in ATP and NADP concentrations which are about 180 degrees out of phase, while three alternative proposals regarding coupling of electron transport and photophosphorylation do not produce oscillatory model solutions. The phases of ATP and NADPH are in reasonable agreement with the available experimental data. The model (which assumes that redox control of photophosphorylation is part of the oscillatory mechanism) is compared with an alternative proposal (that oscillations are due to interdependence of turnover of adenylates and Calvin cycle intermediates). From the similarity of the mathematical structures of both models it is inviting to speculate that both models are partial aspects of the oscillatory mechanism.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3735, + "tag": "BioModels:BIOMD0000000292" + }, + { + "id": 3736, + "tag": "Carbon fixation in photosynthetic organisms" + }, + { + "id": 3086, + "tag": "Reductive pentose-phosphate cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:14:13.781894+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000292", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2643": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2643, + "name": "Proctor2010 - UCHL1 Protein Aggregation", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modelling the Role of UCH-L1 on Protein Aggregation inAge-Related Neurodegeneration.
Proctor CJ, Tangeman PJ, Ardley HC. PLoS One. 2010 Oct 6;5(10):e13175 20949132 ,
Abstract:
Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and \u03b1-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3737, + "tag": "BioModels:BIOMD0000000293" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 3738, + "tag": "Negative regulation of proteasomal ubiquitin-dependent protein catabolic process" + }, + { + "id": 3362, + "tag": "Neurodegenerative disease" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3739, + "tag": "Thiol-dependent ubiquitin-specific protease activity" + } + ], + "timestamp_created": "2025-02-03 16:14:14.304854+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000293", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2644": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2644, + "name": "Restif2007 - Vaccination invasion", + "repository_type": "biomodels", + "summary": "
Restif2007 - Vaccination invasion

This model is described in the article:

Restif O, Grenfell BT.
J R Soc Interface 2007 Feb; 4(12): 143-153

Abstract:

Vaccines exert strong selective pressures on pathogens, favouring the spread of antigenic variants. We propose a simple mathematical model to investigate the dynamics of a novel pathogenic strain that emerges in a population where a previous strain is maintained at low endemic level by a vaccine. We compare three methods to assess the ability of the novel strain to invade and persist: algebraic rate of invasion; deterministic dynamics; and stochastic dynamics. These three techniques provide complementary predictions on the fate of the system. In particular, we emphasize the importance of stochastic simulations, which account for the possibility of extinctions of either strain. More specifically, our model suggests that the probability of persistence of an invasive strain (i) can be minimized for intermediate levels of vaccine cross-protection (i.e. immune protection against the novel strain) and (ii) is lower if cross-immunity acts through a reduced infectious period rather than through reduced susceptibility.

This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size\u00a0 N\u00a0= 1. The model does reproduces the deterministic time course. The initial values are set to the steady state values for a latent infection with strain 1 with an invading infection of strain 2 (I2=1e-06), 100 percent vaccination with a susceptibility reduction \u03c4=0.7 at birth (p=1), and all other parameters as in figure 3 of the publication.\u00a0

To be compatible with older software tools, the english letter names instead of the greek symbols were used for parameter names:

parameter symbol name
transmission rate \u03b2 beta
recovery rate \u03b3 gamma
birth/death rate \u03bc mu
rate of loss of natural immunity \u03c3 sigma
rate of loss of vaccine immunity \u03c3 v sigmaV
reduction of susceptibility by primary infection \u03b8 theta
reduction of infection period by primary infection \u03bd nu
reduction of susceptibility by vaccination \u03c4 tau
reduction of infection period by vaccination \u03b7 eta

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model is hosted on BioModels Database and identified by: BIOMD0000000294.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3740, + "tag": "BioModels:BIOMD0000000294" + }, + { + "id": 3636, + "tag": "Bordetella pertussis" + }, + { + "id": 3273, + "tag": "Defense response, incompatible interaction" + }, + { + "id": 3614, + "tag": "Entry of bacterium into host cell" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3637, + "tag": "Pertussis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:14.809101+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000294", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2645": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2645, + "name": "Akman2008_Circadian_Clock_Model1", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Isoform switching facilitates period control in the Neurospora crassa circadian clock.
Akman OE, Locke JC, Tang S, Carr\u00e9 I, Millar AJ, Rand DA Mol. Syst. Biol. 2008;Vol 4: 164 18277380 ,
Abstract:
A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the reduced model described in the article:
A synthetic Escherichia coli predator\u2013prey ecosystem
Balagadd\u00e9 FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L.Mol Syst Biol. 2008;4:187. Epub 2008 Apr 15. PMID: 18414488; DOI:10.1038/msb.2008.24

Abstract:
We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator\u2013prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner.

In the article the cell density is given in per 103 cells per microlitre. To evade a conversion factor in the SBML implementation, the unit for the cell densities was just left the same as for the AHLs A and A2 (nM).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Mathematical model of the morphogenesis checkpoint in budding yeast.
Ciliberto A, Novak B, Tyson JJ J. Cell Biol. [2003 Dec; Volume: 163 (Issue: 6 )] Page info: 1243-54 14691135 ,
Abstract:
The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next.

The model reproduces Fig 3 of the paper.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.
Leloup JC, Gonze D, Goldbeter A. J Biol Rhythms.1999 Dec;14(6):433-48. 10643740,
Abstract:
We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.

This particular version of the model has been translated from equations 1a-1j (Drosophila).

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Leloup JC, Gonze D, Goldbeter A. (1999) - version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.
Leloup JC, Gonze D, Goldbeter A. J Biol Rhythms. 1999 Dec;14(6):433-48. 10643740 ,
Abstract:
We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.

This particular version of the model has been translated from equations 4a-4c (Neurospora).

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Leloup JC, Gonze D, Goldbeter A. (1999) - version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Hypoxia-dependent sequestration of an oxygen sensor by a widespread structural motif can shape the hypoxic response - a predictive kinetic model
Bernhard Schmierer, B\u00e9la Nov\u00e1k1 and Christopher J Schofield BMC Systems Biology2010, 4:139 20955552,
Abstract:
Background
The activity of the heterodimeric transcription factor hypoxia inducible factor (HIF) is regulated by the post-translational, oxygen-dependent hydroxylation of its \u03b1-subunit by members of the prolyl hydroxylase domain (PHD or EGLN)-family and by factor inhibiting HIF (FIH). PHD-dependent hydroxylation targets HIF\u03b1 for rapid proteasomal degradation; FIH-catalysed asparaginyl-hydroxylation of the C-terminal transactivation domain (CAD) of HIF\u03b1 suppresses the CAD-dependent subset of the extensive transcriptional responses induced by HIF. FIH can also hydroxylate ankyrin-repeat domain (ARD) proteins, a large group of proteins which are functionally unrelated but share common structural features. Competition by ARD proteins for FIH is hypothesised to affect FIH activity towards HIF\u03b1; however the extent of this competition and its effect on the HIF-dependent hypoxic response are unknown.
Results
To analyse if and in which way the FIH/ARD protein interaction affects HIF-activity, we created a rate equation model. Our model predicts that an oxygen-regulated sequestration of FIH by ARD proteins significantly shapes the input/output characteristics of the HIF system. The FIH/ARD protein interaction is predicted to create an oxygen threshold for HIF\u03b1 CAD-hydroxylation and to significantly sharpen the signal/response curves, which not only focuses HIF\u03b1 CAD-hydroxylation into a defined range of oxygen tensions, but also makes the response ultrasensitive to varying oxygen tensions. Our model further suggests that the hydroxylation status of the ARD protein pool can encode the strength and the duration of a hypoxic episode, which may allow cells to memorise these features for a certain time period after reoxygenation.
Conclusions
The FIH/ARD protein interaction has the potential to contribute to oxygen-range finding, can sensitise the response to changes in oxygen levels, and can provide a memory of the strength and the duration of a hypoxic episode. These emergent properties are predicted to significantly shape the characteristics of HIF activity in animal cells. We argue that the FIH/ARD interaction should be taken into account in studies of the effect of pharmacological inhibition of the HIF-hydroxylases and propose that the interaction of a signalling sensor with a large group of proteins might be a general mechanism for the regulation of signalling pathways.

There are there models described in the paper. 1) Skeleton Model 1 (SKM1) - HIF\u03b1 CAD-hydroxylation in the absence of the FIH/AR-interaction. 2) Skeleton Model 2 (SKM2) - FIG sequestration by ARD proteins and oxygen-dependent FIH-release. 3) Full Model (Fusion of SKM1 and SKM2) - the effects of the FIH/ARD proteins interaction on HIF\u03b1 CAD-hydroxylation.

This model corresponds to the \"Full Model\" described in the paper. The model reproduces figure 5 of the publication.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This is the model of the RTC3 counter described in the article:
Synthetic gene networks that count.
\t Friedland AE, Lu TK, Wang X, Shi D, Church G, Collins JJ. Science. 2009 May 29;324(5931):1199-202. PMID:19478183, DOI:10.1126/science.1172005

\t Abstract:
\t Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here, we report two complementary synthetic genetic counters in Escherichia coli that can count up to three induction events: the first, a riboregulated transcriptional cascade, and the second, a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.\t

\t The 3 arabinose pulses are implemented using events, one for the start of pulses and one for the end. The variable pulse_flag changes arabinose consumption to fit behaviour during pulses and in between. To simulate two pulses only, set the pulse length of the third pulse to a negative value (though with an absolute value smaller than the pulse intervall length). \t

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is a model of one presynaptic and one postsynaptic cell, as described in the article:
Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.
Wang XJ, Buzs\u00e1ki G. J Neurosci. 1996 Oct 15;16(20):6402-13. PMID:8815919;\t

Abstract:
Fast neuronal oscillations (gamma, 20-80 Hz) have been observed in the neocortex and hippocampus during behavioral arousal. Using computer simulations, we investigated the hypothesis that such rhythmic activity can emerge in a random network of interconnected GABAergic fast-spiking interneurons. Specific conditions for the population synchronization, on properties of single cells and the circuit, were identified. These include the following: (1) that the amplitude of spike afterhyperpolarization be above the GABAA synaptic reversal potential; (2) that the ratio between the synaptic decay time constant and the oscillation period be sufficiently large; (3) that the effects of heterogeneities be modest because of a steep frequency-current relationship of fast-spiking neurons. Furthermore, using a population coherence measure, based on coincident firings of neural pairs, it is demonstrated that large-scale network synchronization requires a critical (minimal) average number of synaptic contacts per cell, which is not sensitive to the network size. By changing the GABAA synaptic maximal conductance, synaptic decay time constant, or the mean external excitatory drive to the network, the neuronal firing frequencies were gradually and monotonically varied. By contrast, the network synchronization was found to be high only within a frequency band coinciding with the gamma (20-80 Hz) range. We conclude that the GABAA synaptic transmission provides a suitable mechanism for synchronized gamma oscillations in a sparsely connected network of fast-spiking interneurons. In turn, the interneuronal network can presumably maintain subthreshold oscillations in principal cell populations and serve to synchronize discharges of spatially distributed neurons.\t

\t The presynaptic and postsynaptic cell have identical parameters and the variables in each cell are identified by using _pre or _post as a postfix to their names. The presynaptic cell influences the postsynaptic one via the synapse (variables and parameters: I_syn, E_syn, g_syn, F, theta_syn, alpha, beta). The applied current to the presynaptic cell, I_app_pre, is set to 2 microA/cm2 for 10 ms as in figure 1C of the article. The dependence of the postsynaptic cell on directly applied current can be investigated in isolation by setting I_app_pre to 0 and altering I_app_post. \t

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This is the continuous deterministic (ODE) model of the complement system described in the article:
Computational and Experimental Study of the Regulatory Mechanisms of the Complement System.
Liu B, Zhang J, Tan PY, Hsu D, Blom AM, Leong B, Sethi S, Ho B, Ding JL and Thiagarajan PS. PLoS Comp. Bio. 2011 Jan. 7:1; doi:10.1371/journal.pcbi.1001059

Abstract:
The complement system is key to innate immunity and its activation is necessary for the clearance of bacteria and apoptotic cells. However, insufficient or excessive complement activation will lead to immune-related diseases. It is so far unknown how the complement activity is up- or down- regulated and what the associated pathophysiological mechanisms are. To quantitatively understand the modulatory mechanisms of the complement system, we built a computational model involving the enhancement and suppression mechanisms that regulate complement activity. Our model consists of a large system of Ordinary Differential Equations (ODEs) accompanied by a dynamic Bayesian network as a probabilistic approximation of the ODE dynamics. Applying Bayesian inference techniques, this approximation was used to perform parameter estimation and sensitivity analysis. Our combined computational and experimental study showed that the antimicrobial response is sensitive to changes in pH and calcium levels, which determines the strength of the crosstalk between CRP and L-ficolin. Our study also revealed differential regulatory effects of C4BP. While C4BP delays but does not decrease the classical complement activation, it attenuates but does not significantly delay the lectin pathway activation. We also found that the major inhibitory role of C4BP is to facilitate the decay of C3 convertase. In summary, the present work elucidates the regulatory mechanisms of the complement system and demonstrates how the bio-pathway machinery maintains the balance between activation and inhibition. The insights we have gained could contribute to the development of therapies targeting the complement system.

Comment:
Reproduction of figures in the article:
Figure 5: the effects of C4BP
Fig 5A: set initial concentrations PC=0.0327796, GlcNac=0, vary the initial concentration of C4BP from 2.6 to 2600 using parameter scan
Fig 5B: set initial concentrations PC=0, GlcNac=0.0327796, vary the initial concentration of C4BP from 2.6 to 2600 using parameter scan
Figure 6: knockout simulations
Set PC=0.0327796, GlcNac=0
Fig 6A: kf01=0, kf02=0
Fig 6B: kf04=0, kf06=0, kf07=0
Fig 6C: kf05=0
Fig 6D: kf03=0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Bifurcation and resonance in a model for bursting nerve cells.
Plant RE J Math Biol1981 Jan; 11(1): 15-32 7252375,
Abstract:
In this paper we consider a model for the phenomenon of bursting in nerve cells. Experimental evidence indicates that this phenomenon is due to the interaction of multiple conductances with very different kinetics, and the model incorporates this evidence. As a parameter is varied the model undergoes a transition between two oscillatory waveforms; a corresponding transition is observed experimentally. After establishing the periodicity of the subcritical oscillatory solution, the nature of the transition is studied. It is found to be a resonance bifurcation, with the solution branching at the critical point to another periodic solution of the same period. Using this result a comparison is made between the model and experimental observations. The model is found to predict and allow an interpretation of these observations.

Also, look athttp://www.scholarpedia.org/article/Plant_model

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the 2 state model of Myosin V movement described in the article:
A simple kinetic model describes the processivity of myosin-v.
Kolomeisky AB , Fisher ME Biophys. J. 84(3):1642-50 (2003); PubmedID: 12609867

Abstract:
Myosin-V is a motor protein responsible for organelle and vesicle transport in cells. Recent single-molecule experiments have shown that it is an efficient processive motor that walks along actin filaments taking steps of mean size close to 36 nm. A theoretical study of myosin-V motility is presented following an approach used successfully to analyze the dynamics of conventional kinesin but also taking some account of step-size variations. Much of the present experimental data for myosin-V can be well described by a two-state chemical kinetic model with three load-dependent rates. In addition, the analysis predicts the variation of the mean velocity and of the randomness-a quantitative measure of the stochastic deviations from uniform, constant-speed motion-with ATP concentration under both resisting and assisting loads, and indicates a substep of size d(0) approximately 13-14 nm (from the ATP-binding state) that appears to accord with independent observations.

The model differs slightly from the published version. The ATP and ADP bound forms of myosin are called S0 and S1. The state transition and binding constants are called k_1, k_2, k_3 and k_4 instead of k0 0, u0 1, k' 0 and w0 1. Similarly the state loading factors are named th_1, th_2, th_3 and th_4 instead of \u03b8+ 0, \u03b8+ 1, \u03b8- 0 and \u03b8- 1. The species fwd_step1, fwd_step2, back_step1 and back_step2 count the number of state changes of each kind the myosine molecules have taken over time.
The model can be evaluated in a deterministic continuous or stochastic discreet fashion. The parameter V holds the (forward) speed at each time point, the V_avg the overall way divided by the simulation time and the amount of myosine molecules.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3762, + "tag": "BioModels:BIOMD0000000305" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 3763, + "tag": "Microfilament motor activity" + }, + { + "id": 3764, + "tag": "Regulation of actin filament length" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:21.513444+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000305", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2656": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2656, + "name": "Tyson2003_Activator_Inhibitor", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of the activator inhibitor oscillator (figure 2b) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3765, + "tag": "BioModels:BIOMD0000000306" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:22.090323+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000306", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2657": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2657, + "name": "Tyson2003_Substrate_Depletion_Osc", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of the substrate depletion oscillator (figure 2c) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3766, + "tag": "BioModels:BIOMD0000000307" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:22.600710+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000307", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2658": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2658, + "name": "Tyson2003_NegFB_Oscillator", + "repository_type": "biomodels", + "summary": "

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This is an SBML implementation the model of negative feedback oscillator (figure 2a) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3767, + "tag": "BioModels:BIOMD0000000308" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:23.146237+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000308", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2659": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2659, + "name": "Tyson2003_NegFB_Homeostasis", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of homeostastis by negative feedback (figure 1g) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3768, + "tag": "BioModels:BIOMD0000000309" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:23.601840+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000309", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2660": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2660, + "name": "Tyson2003_Mutual_Inhibition", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of mutual inhibition (figure 1f) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3769, + "tag": "BioModels:BIOMD0000000310" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:24.061675+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000310", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2661": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2661, + "name": "Tyson2003_Mutual_Activation", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of mutual activation (figure 1e) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

The article has a typo: the expression k2*X*R most likely should be k2*R

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3770, + "tag": "BioModels:BIOMD0000000311" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:24.539732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000311", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2662": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2662, + "name": "Tyson2003_Perfect_Adaption", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of the perfect adaptor (figure 1d) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3771, + "tag": "BioModels:BIOMD0000000312" + }, + { + "id": 3258, + "tag": "Regulation of binding" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:25.021740+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000312", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2663": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2663, + "name": "Raia2010 - IL13 Signalling MedB1", + "repository_type": "biomodels", + "summary": "

This is the model of IL13 induced signalling in MedB-1 cell described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, B\u00f6hm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, M\u00f6ller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingm\u00fcller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID:21127196; DOI:10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 () and the conversion factor 2.265 molecules IL13/cell = 1 ng/ml to be around 60 fl.

SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:

% PottersWheel model definition filefunction m = Raia2010_IL13_MedB1()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'Raia2010_IL13_MedB1';m.name        = 'Raia2010_IL13_MedB1';m.description = '';m.authors     = {'Raia et al'};m.dates       = {'2010'};m.type        = 'PW-2-0-47';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'Rec'         ,              1.3, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Rec_i'       , 113.193916718733, 'global',  0.001, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_Rec'    ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec'  ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec_i',                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'JAK2'        ,              2.8, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pJAK2'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SHP1'        ,               91, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'STAT5'       ,              165, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pSTAT5'      ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SOCS3mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'DecoyR'      ,             0.34, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_DecoyR' ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SOCS3'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'CD274mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {'Rec'         }, {'IL13_Rec'    }, {'IL13stimulation'   }, 'C' , [] , 'k1 * m1 * r1 * 2.265'        , {'Kon_IL13Rec'                             });m = pwAddR(m, {'Rec'         }, {'Rec_i'       }, {                    }, 'MA', [] , []                            , {'Rec_intern'                              });m = pwAddR(m, {'Rec_i'       }, {'Rec'         }, {                    }, 'MA', [] , []                            , {'Rec_recycle'                             });m = pwAddR(m, {'IL13_Rec'    }, {'p_IL13_Rec'  }, {'pJAK2'             }, 'E' , [] , []                            , {'Rec_phosphorylation'                     });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'IL13_Rec','SOCS3'  }, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'p_IL13_Rec','SOCS3'}, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});m = pwAddR(m, {'p_IL13_Rec'  }, {'p_IL13_Rec_i'}, {                    }, 'MA', [] , []                            , {'pRec_intern'                             });m = pwAddR(m, {'p_IL13_Rec_i'}, {              }, {                    }, 'MA', [] , []                            , {'pRec_degradation'                        });m = pwAddR(m, {'pJAK2'       }, {'JAK2'        }, {'SHP1'              }, 'E' , [] , []                            , {'pJAK2_dephosphorylation'                 });m = pwAddR(m, {'STAT5'       }, {'pSTAT5'      }, {'pJAK2'             }, 'E' , [] , []                            , {'STAT5_phosphorylation'                   });m = pwAddR(m, {'pSTAT5'      }, {'STAT5'       }, {'SHP1'              }, 'E' , [] , []                            , {'pSTAT5_dephosphorylation'                });m = pwAddR(m, {'DecoyR'      }, {'IL13_DecoyR' }, {'IL13stimulation'   }, 'C' , [] , 'k1 * m1 * r1 * 2.265'        , {'DecoyR_binding'                          });m = pwAddR(m, {              }, {'SOCS3mRNA'   }, {'pSTAT5'            }, 'C' , [] , 'm1*k1'                       , {'SOCS3mRNA_production'                    });m = pwAddR(m, {              }, {'SOCS3'       }, {'SOCS3mRNA'         }, 'C' , [] , 'm1*k1/(k2+m1)'               , {'SOCS3_translation','SOCS3_accumulation'  });m = pwAddR(m, {'SOCS3'       }, {              }, {                    }, 'MA', [] , []                            , {'SOCS3_degradation'                       });m = pwAddR(m, {              }, {'CD274mRNA'   }, {'pSTAT5'            }, 'C' , [] , 'm1*k1'                       , {'CD274mRNA_production'                    });%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'Kon_IL13Rec'             , 0.00341992477561527  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_phosphorylation'     , 999.630699390459     , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_intern'             , 0.152540135862128    , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_degradation'        , 0.17292753960894     , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_intern'              , 0.103345784175639    , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_recycle'             , 0.00135598001330518  , 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_phosphorylation'    , 0.157057142470047    , 'global', 1e-009, 1000);m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.000621906059346898 , 'global', 1e-009, 1000);m = pwAddK(m, 'STAT5_phosphorylation'   , 0.0382596267705733   , 'global', 1e-009, 1000);m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.000343391620492938 , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3mRNA_production'    , 0.00215826062955433  , 'global', 1e-009, 1000);m = pwAddK(m, 'DecoyR_binding'          , 0.000124391087466499 , 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_p_inhibition'       , 0.0168267797836881   , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3_translation'       , 11.9086462945188     , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3_accumulation'      , 3.70803336415341     , 'global', 1     , 1000);m = pwAddK(m, 'SOCS3_degradation'       , 0.0429185935645562   , 'global', 1e-009, 1000);m = pwAddK(m, 'CD274mRNA_production'    , 8.21752278733562e-005, 'global', 1e-009, 1000);%% U: Driving input% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0]  , [0 1]  , [], [], [], [], {}, [], 'protein.generic');%% Default sampling time pointsm.t = 0:1:120;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec'                       , 'RecSurf_obs'  , 'scale_RecSurf'  , '0.10 * y + 0.1 * max(y)');m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i + IL13_DecoyR', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i'                         , 'pIL4Ra_obs'   , 'scale_pIL4Ra'   , '0.1 * y + 0.15 * max(y)');m = pwAddY(m, 'pJAK2'                                             , 'pJAK2_obs'    , 'scale_pJAK2'    , '0.15 * y + 0.1 * max(y)');m = pwAddY(m, 'SOCS3mRNA'                                         , 'SOCS3mRNA_obs', 'scale_SOCS3mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'CD274mRNA'                                         , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'SOCS3'                                             , 'SOCS3_obs'    , 'scale_SOCS3'    , '0.1 * y + 0.15 * max(y)');m = pwAddY(m, 'pSTAT5'                                            , 'pSTAT5_obs'   , 'scale_pSTAT5'   , '0.15 * y + 0.1 * max(y)');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_pJAK2'    , 1.39039557075997, 'global', 0.001, 10000);m = pwAddS(m, 'scale_pIL4Ra'   , 1.88700484471494, 'global', 0.001, 10000);m = pwAddS(m, 'scale_RecSurf'  ,                1,    'fix', 0.001, 10000);m = pwAddS(m, 'scale_IL13-cell', 5.56750251420935, 'global', 0.001, 10000);m = pwAddS(m, 'scale_SOCS3mRNA', 17.6699101927908, 'global', 0.001, 10000);m = pwAddS(m, 'scale_CD274mRNA', 2.48547378765387, 'global', 0.001, 10000);m = pwAddS(m, 'scale_pSTAT5'   ,                1,    'fix', 0.001, 10000);m = pwAddS(m, 'scale_SOCS3'    ,                1,    'fix', 0.001, 10000);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\ufffd\ufffdre N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3772, + "tag": "BioModels:BIOMD0000000313" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3773, + "tag": "Mature T-cell and NK-cell lymphoma" + }, + { + "id": 3774, + "tag": "Regulation of JAK-STAT cascade" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:25.506305+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000313", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2664": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2664, + "name": "Raia2011 - IL13 L1236", + "repository_type": "biomodels", + "summary": "

This is the model of IL13 induced signalling in L1236 cells described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, B\u00f6hm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, M\u00f6ller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingm\u00fcller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID: 21127196 ; DOI: 10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 (15.8 kDa) and the conversion factor 3.776 molecules IL13/cell = 1 ng/ml to be around 100 fl.

SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:

% PottersWheel model definition filefunction m = Raia2010_IL13_L1236()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'Raia2010_IL13_L1236';m.name        = 'Raia2010_IL13_L1236';m.description = '';m.authors     = {'Raia et al'};m.dates       = {'2010'};m.type        = 'PW-2-0-47';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'Rec'         ,              1.8, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Rec_i'       , 118.598421286338, 'global',  0.001, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_Rec'    ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec'  ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec_i',                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'JAK2'        ,               24, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pJAK2'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SHP1'        ,              9.4, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'STAT5'       ,              209, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pSTAT5'      ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'CD274mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {'Rec'         }, {'IL13_Rec'    }, {'IL13stimulation'}, 'C' , [] , 'k1 * m1 * r1 * 3.776', {'Kon_IL13Rec'             });m = pwAddR(m, {'Rec'         }, {'Rec_i'       }, {                 }, 'MA', [] , []                    , {'Rec_intern'              });m = pwAddR(m, {'Rec_i'       }, {'Rec'         }, {                 }, 'MA', [] , []                    , {'Rec_recycle'             });m = pwAddR(m, {'IL13_Rec'    }, {'p_IL13_Rec'  }, {'pJAK2'          }, 'E' , [] , []                    , {'Rec_phosphorylation'     });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'IL13_Rec'       }, 'E' , [] , []                    , {'JAK2_phosphorylation'    });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'p_IL13_Rec'     }, 'E' , [] , []                    , {'JAK2_phosphorylation'    });m = pwAddR(m, {'p_IL13_Rec'  }, {'p_IL13_Rec_i'}, {                 }, 'MA', [] , []                    , {'pRec_intern'             });m = pwAddR(m, {'p_IL13_Rec_i'}, {              }, {                 }, 'MA', [] , []                    , {'pRec_degradation'        });m = pwAddR(m, {'pJAK2'       }, {'JAK2'        }, {'SHP1'           }, 'E' , [] , []                    , {'pJAK2_dephosphorylation' });m = pwAddR(m, {'STAT5'       }, {'pSTAT5'      }, {'pJAK2'          }, 'E' , [] , []                    , {'STAT5_phosphorylation'   });m = pwAddR(m, {'pSTAT5'      }, {'STAT5'       }, {'SHP1'           }, 'E' , [] , []                    , {'pSTAT5_dephosphorylation'});m = pwAddR(m, {              }, {'CD274mRNA'   }, {'pSTAT5'         }, 'C' , [] , 'm1*k1'               , {'CD274mRNA_production'    });%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'Kon_IL13Rec'             , 0.00174086832237195, 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_phosphorylation'     , 9.07540737285078   , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_intern'             , 0.324132341358502  , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_degradation'        , 0.417538218767296  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_intern'              , 0.259685756311325  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_recycle'             , 0.00392430355501153, 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_phosphorylation'    , 0.300019047540849  , 'global', 1e-009, 1000);m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.0981610557569751 , 'global', 1e-009, 1000);m = pwAddK(m, 'STAT5_phosphorylation'   , 0.00426766529531612, 'global', 1e-009, 1000);m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.0116388587580445 , 'global', 1e-009, 1000);m = pwAddK(m, 'CD274mRNA_production'    , 0.0115927572109515 , 'global', 1e-009, 1000);%% U: Driving input% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0]  , [0 1]  , [], [], [], [], {}, [], 'protein.generic');%% Default sampling time pointsm.t = 0:1:120;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec'         , 'RecSurf_obs'  , 'scale_RecSurf'  , '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i'           , 'pIL4Ra_obs'   , 'scale_pIL4Ra'   , '0.10 * y + 0.15 * max(y)');m = pwAddY(m, 'pJAK2'                               , 'pJAK2_obs'    , 'scale_pJAK2'    , '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'CD274mRNA'                           , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'pSTAT5'                              , 'pSTAT5_obs'   , 'scale_pSTAT5'   , '0.1 * y + 0.1 * max(y)');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_pJAK2'    , 0.469836894150194, 'global',  0.001, 10000);m = pwAddS(m, 'scale_pIL4Ra'   ,  1.80002942264669, 'global',  0.001, 10000);m = pwAddS(m, 'scale_RecSurf'  ,                 1,    'fix', 0.0001, 10000);m = pwAddS(m, 'scale_IL13-cell',  174.726805005048, 'global',  0.001, 10000);m = pwAddS(m, 'scale_CD274mRNA', 0.110568221201943, 'global',  0.001, 10000);m = pwAddS(m, 'scale_pSTAT5'   ,                 1,    'fix',  0.001, 10000);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3775, + "tag": "BioModels:BIOMD0000000314" + }, + { + "id": 3776, + "tag": "Hodgkin's lymphoma" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3777, + "tag": "JAK-STAT cascade" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:26.449679+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000314", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2665": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2665, + "name": "Montagne2011_Oligator_optimised", + "repository_type": "biomodels", + "summary": "

This is the model of the in vitro DNA oscillator called oligator with the optmized set of parameters described in the article:
Programming an in vitro DNA oscillator using a molecular networking strategy.
Montagne K, Plasson R, Sakai Y, Fujii T, Rondelez Y. Mol Syst Biol. 2011 Feb 1;7:466. PubmedID:21283142, Doi:10.1038/msb.2010.120

Abstract:
Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules.

The model reproduces the time courses in fig 2B. The parameter identifiers of the reaction constants are not the same as in the supplemental material, but are just called kXd and kXr for the forward and backwards constant of reaction X respectively.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3778, + "tag": "BioModels:BIOMD0000000315" + }, + { + "id": 3779, + "tag": "DNA metabolic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:27.042873+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000315", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2666": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2666, + "name": "Shen-Orr2002_FeedForward_AND_gate", + "repository_type": "biomodels", + "summary": "

This is the coherent feed forward loop with an AND-gate like control of the response operon described in the article:
Network motifs in the transcriptional regulation network of Escherichia coli
Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon, Nat Genet 2002 31:64-68; PMID: 11967538 ; DOI: 10.1038/ng881 ;

Abstract:
Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

This model reproduces the timecourse presented in Figure 2a. All species and parameters in the model are dimensionless.

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This is the single input module, SIM, described in the article:
Network motifs in the transcriptional regulation network of Escherichia coli
Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon, Nat Genet 2002 31:64-68; PMID:11967538; DOI:10.1038/ng881;

Abstract:
Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

This model reproduces the SIM timecourse presented in Figure 2b. All species and parameters in the model are dimensionless.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3782, + "tag": "BioModels:BIOMD0000000317" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:28.076245+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000317", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2668": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2668, + "name": "Yao2008_Rb_E2F_Switch", + "repository_type": "biomodels", + "summary": "

This is the model described in the article:
A bistable Rb-E2F switch underlies the restriction point
Guang Yao, Tae Jun Lee, Seiichi Mori, Joseph R. Nevins, Lingchong You, Nat Cell Biol 2008 10:476-482; PMID: 18364697 ; DOI: 10.1038/ncb1711 .

Abstract:
The restriction point (R-point) marks the critical event when a mammalian cell commits to proliferation and becomes independent of growth stimulation. It is fundamental for normal differentiation and tissue homeostasis, and seems to be dysregulated in virtually all cancers. Although the R-point has been linked to various activities involved in the regulation of G1-S transition of the mammalian cell cycle, the underlying mechanism remains unclear. Using single-cell measurements, we show here that the Rb-E2F pathway functions as a bistable switch to convert graded serum inputs into all-or-none E2F responses. Once turned ON by sufficient serum stimulation, E2F can memorize and maintain this ON state independently of continuous serum stimulation. We further show that, at critical concentrations and duration of serum stimulation, bistable E2F activation correlates directly with the ability of a cell to traverse the R-point.

This model reproduces the serum-pulse stimulation-protocol in Figure 3(b).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novere N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3783, + "tag": "BioModels:BIOMD0000000318" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:28.577948+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000318", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2669": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2669, + "name": "Decroly1982_Enzymatic_Oscillator", + "repository_type": "biomodels", + "summary": "

This is the scaled model described in the article:
Birhythmicity, chaos, and other patterns of temporal self-organization in a multiply regulated biochemical system
Olivier Decroly, Albert Goldbeter, Proc Natl Acad Sci USA 1982 79:6917-6921; PMID:6960354;

Abstract:
We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable steady state (hard excitation) or with chaos. The relationship between these patterns of temporal self-organization is analyzed as a function of the control parameters of the model. Chaos and birhythmicity appear to be rare events in comparison with simple periodic behavior. We discuss the relevance of these results with respect to the regularity of most biological rhythms.

The parameters q1 = 50 and q2 = 0.02 are explicitely included as the stoichiometric coefficients of beta and gamma in the reactions r2 and r3, respectively. Parameter values and initial conditions [ks=1.99/sec, alpha(0)=29.19988, beta(0)=188.8, gamma(0)=0.3367] are for the chaotic regime presented in the upper-curve of Figure 3b.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3784, + "tag": "BioModels:BIOMD0000000319" + }, + { + "id": 3143, + "tag": "Rhythmic behavior" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:29.047776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000319", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2670": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2670, + "name": "Grange2001 - PK interaction of L-dopa and benserazide", + "repository_type": "biomodels", + "summary": "
Grange2001 - PK interaction of L-dopa and benserazide

A pharmacokinetics of L-dopa in rats after administration of L-dopa alone (BIOMD0000000321) or L-dopa combined with a peripheral AADC (amino-acid-decarboxylase) inhibitor (this model: BIOMD0000000320) has been studied using noncompartmental analysis.

This model is described in the article:

Grange S, Holford NH, Guentert TW
Pharmaceutical Research [2001, 18(8):1174-1184]

Abstract:

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The volumes and variables in this model are taken for a rat with 0.25 kg. The inital dose for L_Dopa (L_Dopa_per_kg_rat) and Benserazide (Benserazide_per_kg_rat) are to be given in umole per kg. 80 mg/kg L-Dopa correspond to 404 umol/kg, 20 mg/kg benserazide to 78 umol/kg. To change the model to a different mass of rat the compartment volumes, and the parameters rat_body_mass and Q have to changed accordingly.

The model has three species (A-dopa, A_B, A_M) whose initial concentrations are calculated from a listOfInitialAssignments . While running for the first time the time-course (24hrs) for this model in COPASI (up to version 4.6, Build 33), the resulting graph displays only straight lines for all the species. Any subsequent runs should provide proper plots (i.e. without making any change to the model, just by clicking the \"run\" button again).

The above issue is caused by some initial assignments which are not calculated when COPASI imports the file. This issue should not be present in newer releases of COPASI.

This model is hosted on BioModels Database and identified by: MODEL0910130001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3785, + "tag": "BioModels:BIOMD0000000320" + }, + { + "id": 3786, + "tag": "Dopamine uptake involved in synaptic transmission" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:29.596075+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000320", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2671": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2671, + "name": "Grange2001 - L Dopa PK model", + "repository_type": "biomodels", + "summary": "
Grange2001 - L-dopa PK model

A pharmacokinetics of L-dopa in rats after administration of L-dopa alone (this model: BIOMD0000000321) or L-dopa combined with a peripheral AADC (amino-acid-decarboxylase) inhibitor (BIOMD0000000320) has been studied using noncompartmental analysis.

This model is described in the article:

Grange S, Holford NH, Guentert TW
Pharmaceutical Research [2001, 18(8):1174-1184]

Abstract:

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The model has a species (A-dopa) whose initial concentration is calculated from a listOfInitialAssignments . While running for the first time the time-course (24hrs) for this model in COPASI (up to version 4.6, Build 33), the resulting graph displays only straight lines for all the species. Any subsequent runs should provide proper plots (i.e. without making any change to the model, just by clicking the \"run\" button again).

The above issue is caused by some initial assignments which are not calculated when COPASI imports the file. This issue should not be present in newer releases of COPASI.

This model is hosted on BioModels Database and identified by: MODEL1103250000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3787, + "tag": "BioModels:BIOMD0000000321" + }, + { + "id": 3786, + "tag": "Dopamine uptake involved in synaptic transmission" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:30.107778+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000321", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2672": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2672, + "name": "Kim2011_Oscillator_SimpleI", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Synthetic in vitro transcriptional oscillators.
Kim J, Winfree E Mol. Syst. Biol. 2011 Feb 1;7:465. 21283141 ,
Abstract:
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplifiedanalog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platformfor the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally,a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.

Note:

The paper describes 7 models (MODEL1012090000-6) and all these are submitted by the authors. Thismodel (MODEL1012090000) corresponds to the Simple model for both mode I and II (Design I and II). The model reproduces timecourse figure plotted in the supplementary material (page 10 of Supplementary material) of the reference publication.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3788, + "tag": "BioModels:BIOMD0000000322" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:14:30.637740+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000322", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2673": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2673, + "name": "Kim2011_Oscillator_SimpleIII", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Synthetic in vitro transcriptional oscillators.
Kim J, Winfree E Mol. Syst. Biol. 2011 Feb 1;7:465. 21283141 ,
Abstract:
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.

Notes:

The paper describes 7 models (MODEL1012090000-6) and all these are submitted by the authors. This model (MODEL1012090001) corresponds to the Simple model of the three-switch ring oscillator (Design III). The model reproduces figure 6 (central figures) of the reference publication. The time is rescaled by s=v_d/K_I*t where K_I=0.333 and v_d=1 (for alpha = 1) and v_d=0.5 (for alpha = 0.5). i.e. For alpha = 1, s = 0.003 * t (roughly 10 unitless time = 1hr; the time-course should be run for 60 timeunits (6hrs) to get figure 6a). For alpha = 2, s= 0.0015 * t (roughly 5 unitless time = 1hr; the time-course shoue be run for 100 timesunits (20hrs) to get figure 6b).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the full model (eq. 1 and 2) of the voltage oscillations in barnacle muscle fibers described in the article:
Voltage oscillations in the barnacle giant muscle fiber.
\tMorris C, Lecar H. Biophys J. 1981 Jul;35(1):193-213. PubmedID:7260316; DOI:10.1016/S0006-3495(81)84782-0
\tAbstract:
\tBarnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.

The model consists of the differential equations (1) and (2) given on pages 195 and 196 of the article. There is one typo in the equation for I in (1), gL(VL) should be gL(V - VL). This was changed in the SBML file. As there are no current values given, for reproducing the time courses in figure 6 an applied current of 50 uA was assumed. The legend for the broken and the full line in this figure seems to be confounded in the article.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This is the model of the minmal 2 feedback switch described in the article:
Synthetic conversion of a graded receptor signal into a tunable, reversible switch.
Santhosh Palani and Casim A. Sarkar, 2011, Molecular Systems Biology 7:480; doi: 10.1038/msb.2011.13

The ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network switches have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (nH420), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties.Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways.

This model is parametrised for a transcription factor and receptor feedback strength of 3, TFs = 3 and Rs = 3. To reproduce figure 1 E, the parameters TFs and Rs have to be varied accordingly.

Nomenclature for the model:
L : Ligand
R : Receptor
C : Ligand-Receptor Complex
I : Inactive Transcription Factor
X : C bound to I
A : Active Transcription Factor

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Network-level analysis of light adaptation in rod cells under normal and altered conditions.
Dell'Orco D, Schmidt H, Mariani S, Fanelli F Mol Biosyst2009 Oct; 5(10):1232-46 19756313,
Abstract:
Photoreceptor cells finely adjust their sensitivity and electrical response according to changes in light stimuli as a direct consequence of the feedback and regulation mechanisms in the phototransduction cascade. In this study, we employed a systems biology approach to develop a dynamic model of vertebrate rod phototransduction that accounts for the details of the underlying biochemistry. Following a bottom-up strategy, we first reproduced the results of a robust model developed by Hamer et al. (Vis. Neurosci., 2005, 22(4), 417), and then added a number of additional cascade reactions including: (a) explicit reactions to simulate the interaction between the activated effector and the regulator of G-protein signalling (RGS); (b) a reaction for the reformation of the G-protein from separate subunits; (c) a reaction for rhodopsin (R) reconstitution from the association of the opsin apoprotein with the 11-cis-retinal chromophore; (d) reactions for the slow activation of the cascade by opsin. The extended network structure successfully reproduced a number of experimental conditions that were inaccessible to prior models. With a single set of parameters the model was able to predict qualitative and quantitative features of rod photoresponses to light stimuli ranging over five orders of magnitude, in normal and altered conditions, including genetic manipulations of the cascade components. In particular, the model reproduced the salient dynamic features of the rod from Rpe65(-/-) animals, a well established model for Leber congenital amaurosis and vitamin A deficiency. The results of this study suggest that a systems-level approach can help to unravel the adaptation mechanisms in normal and in disease-associated conditions on a molecular basis.


Note:

Figure 7 of the reference is reproduced here. Each plot is obtained by increasing flash strength. More details about generating the plots can be obtained from the comments in the curation figure (go to curation tab).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A mathematical model of the pancreatic duct cell generating high bicarbonate concentrations in pancreatic juice
David C Whitcomb, G Bard Ermentrout, Pancreas 2004 29:e30-40; PubMedID:15257112

Abstract:
OBJECTIVE:To develop a simple, physiologically based mathematical model of pancreatic duct cell secretion using experimentally derived parameters that generates pancreatic fluid bicarbonate concentrations of >140 mM after CFTR activation.
METHODS:A new mathematical model was developed simulating a duct cell within a proximal pancreatic duct and included a sodium-2-bicarbonate cotransporter (NBC) and sodium-potassium pump (NaK pump) on a chloride-impermeable basolateral membrane, CFTR on the luminal membrane with 0.2 to 1 bicarbonate to chloride permeability ratio. Chloride-bicarbonate antiporters (Cl/HCO3 AP) were added or subtracted from the basolateral (APb) and luminal (APl) membranes. The model was integrated over time using XPPAUT.
RESULTS:This model predicts robust, NaK pump-dependent bicarbonate secretion with opening of the CFTR, generates and maintains pancreatic fluid secretion with bicarbonate concentrations >140 mM, and returns to basal levels with CFTR closure. Limiting CFTR permeability to bicarbonate, as seen in some CFTR mutations, markedly inhibited pancreatic bicarbonate and fluid secretion.
CONCLUSIONS:A simple CFTR-dependent duct cell model can explain active, high-volume, high-concentration bicarbonate secretion in pancreatic juice that reproduces the experimental findings. This model may also provide insight into why CFTR mutations that predominantly affect bicarbonate permeability predispose to pancreatic dysfunction in humans.

This SBML version of the model was created directly from the XPPAUT code found in the appendix with the exception of the parameter vr, the ratio between the duct cell volume and the duct lumen, which is defined inversely to the main text in the XPPAUT code. vr was defined as the ratio of the duct cell volume to the duct lumen volume as in the main text. The model reproduces the figures found in the article. The model uses initial assignments for the lumen volume and events to trigger CFTR opening, so only tools supporting these features can be used to simulate it (eg. Copasi and SBW/Roadrunner).

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This is the model of atorvastatin metabolism in hepaitc cells described in the article:
A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes
Joachim Bucher , Stephan Riedmaier , Anke Schnabel , Katrin Marcus , Gabriele Vacun , Thomas S Weiss , Wolfgang E Thasler , Andreas K Nussler , Ulrich M Zanger and Matthias Reuss. BMC Systems Biology 2011, 5:66. DOI:10.1186/1752-0509-5-66

Abstract:
Background:
The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.
Results:
In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated in primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n=150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.
Conclusions:
A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.

The model is parameterized for patient 1 and reproduces the time courses in figure 2 of the article.

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Kummer2000 - Oscillations in CalciumSignalling

Simplified (3-variable) calcium oscillation model Kummer et al. (2000) Biophys. J. 79, 1188-1195 This model is defined in a small compartment with low concentrations. You can run it first with the LSODA ODE solver and then with the Gillespie Monte Carlo method (in Time Course widget). This illustrates that at low particle numbers, as here, the stochastic simulation and the ODE approach produce different results (the stochastic approach is more correct in these circumstances). This file also demonstrates the use of several different plots to visualize results, including a histogram.

This model is described in the article:

Kummer U, Olsen LF, Dixon CJ, Green AK, Bornberg-Bauer E, Baier G.
Biophys. J. 2000 Sep; 79(3): 1188-1195

Abstract:

We present a new model for calcium oscillations based on experiments in hepatocytes. The model considers feedback inhibition on the initial agonist receptor complex by calcium and activated phospholipase C, as well as receptor type-dependent self-enhanced behavior of the activated G(alpha) subunit. It is able to show simple periodic oscillations and periodic bursting, and it is the first model to display chaotic bursting in response to agonist stimulations. Moreover, our model offers a possible explanation for the differences in dynamic behavior observed in response to different agonists in hepatocytes.

This model is hosted on BioModels Database and identified by: BIOMD0000000329.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model is from the article:
On the encoding and decoding of calcium signals in hepatocytes
Ann Zahle Larsen, Lars Folke Olsen and Ursula Kummera Biophysical ChemistryVolume 107, Issue 1, 1 January 2004, Pages 83-99 14871603,
Abstract:
Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.

Note:

This model corresponds to the 5 variable receptor-operated model, as described by Larsen et al., 2004. This model is a modified version of the model described in Kummer 2000 (PMID:10968983)

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This a model from the article:
On the encoding and decoding of calcium signals in hepatocytes
Ann Zahle Larsen, Lars Folke Olsen and Ursula Kummera Biophysical ChemistryVolume 107, Issue 1, 1 January 2004, Pages 83-99 14871603,
Abstract:
Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.

Note:

This model corresponds to the improved model eqn 1-7, as described by Larsen et al., 2004 implemented to investigate how the cell can decode different oscillations. This is done by introducing 2 more variables Enzyme and Product in addition to the 5 variables G-alpha, PLC, Ca_cyt, Ca_ER and Ca_mit receptor-operated model described in the first part of the paper. The receptor-operated model is itself a modified version of the model described in Kummer 2000 (PMID:10968983)

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This model is from the article:
Modelling thrombin generation in human ovarian follicular fluid
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Bulletin of Mathematical BiologyVolume 68, Issue 8, 12 July 2006, Pages 2283-302 16838084,
Abstract:
A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.

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This model is from the article:
Modelling thrombin generation in human ovarian follicular fluid
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Bulletin of Mathematical BiologyVolume 68, Issue 8, 12 July 2006, Pages 2283-302 16838084,
Abstract:
A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.

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This model is from the article:
A mathematical model of lipid-mediated thrombin generation
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Mathematical Medicine and BiologyVolume 20, Issue 1, 1 March 2003, Pages 105-29 12974500,
Abstract:
Thrombin is an enzyme that is generated in both vascular and non-vascular systems. In blood coagulation, a fundamental process in all species, thrombin induces the formation of a fibrin clot. A dynamical model of thrombin generation in the presence of lipid surfaces is presented. This model also includes the self-regulating thrombin feedback reactions, the thrombomodulin-protein C-protein S inhibitory system, tissue factor pathway inhibitor (TFPI), and the inhibitor, antithrombin (AT). The dynamics of this complex system were found to be highly lipid dependent, as would be expected from experimental studies. Simulations of this model indicate that a threshold lipid level is required to generate physiologically relevant amounts of thrombin. The dependence of the onset, the peak levels, and the duration of thrombin generation on lipid was saturable. The lipid concentration affects the way in which the inhibitors modulate thrombin production. A novel feature of this model is the inclusion of the dynamical protein C pathway, initiated by thrombin feedback. This inhibitory system exerts its effects on the lipid surface, where its substrates are formed. The maximum impact of TFPI occurs at intermediate vesicle concentrations. Inhibition by AT is only indirectly affected by the lipid since AT irreversibly binds only to solution phase proteins. In a system with normal plasma concentrations of the proteins involved in thrombin formation, the combination of these three inhibitors is sufficient both to effectively stop thrombin generation prior to the exhaustion of its precursor, prothrombin, and to inhibit all thrombin formed. This model can be used to predict thrombin generation under extreme lipid conditions that are difficult to implement experimentally and to examine thrombin generation in non-vascular systems.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
A model for the stoichiometric regulation of blood coagulation.
Hockin MF, Jones KC, Everse SJ, Mann KG. Journal of Biological ChemistryVolume 277, Issue 21, 24 May 2002, Pages 18322 -18333 11893748,
Abstract:
We have developed a model of the extrinsic blood coagulation system that includes the stoichiometric anticoagulants. The model accounts for the formation, expression, and propagation of the vitamin K-dependent procoagulant complexes and extends our previous model by including: (a) the tissue factor pathway inhibitor (TFPI)-mediated inactivation of tissue factor (TF).VIIa and its product complexes; (b) the antithrombin-III (AT-III)-mediated inactivation of IIa, mIIa, factor VIIa, factor IXa, and factor Xa; (c) the initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; (d) factor VIIIa dissociation/activity loss; (e) the binding competition and kinetic activation steps that exist between TF and factors VII and VIIa; and (f) the activation of factor VII by IIa, factor Xa, and factor IXa. These additions to our earlier model generate a model consisting of 34 differential equations with 42 rate constants that together describe the 27 independent equilibrium expressions, which describe the fates of 34 species. Simulations are initiated by \"exposing\" picomolar concentrations of TF to an electronic milieu consisting of factors II, IX, X, VII, VIIa, V, and VIIII, and the anticoagulants TFPI and AT-III at concentrations found in normal plasma or associated with coagulation pathology. The reaction followed in terms of thrombin generation, proceeds through phases that can be operationally defined as initiation, propagation, and termination. The generation of thrombin displays a nonlinear dependence upon TF, AT-III, and TFPI and the combination of these latter inhibitors displays kinetic thresholds. At subthreshold TF, thrombin production/expression is suppressed by the combination of TFPI and AT-III; for concentrations above the TF threshold, the bolus of thrombin produced is quantitatively equivalent. A comparison of the model with empirical laboratory data illustrates that most experimentally observable parameters are captured, and the pathology that results in enhanced or deficient thrombin generation is accurately described.

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Jones1994_BloodCoagulation
This model is built based on theexperimental findings described in Lawson et al., 1994(PMID:8083241)

This model is described in the article:

Jones KC, Mann KG.
J. Biol. Chem. 1994 Sep; 269(37): 23367-23373

Abstract:

A mathematical simulation of the tissue factor pathway to the generation of thrombin has been developed using a combination of empirical, estimated, and deduced rate constants for reactions involving the activation of factor IX, X, V, and VIII, in the formation of thrombin, as well as rate constants for the assembly of the coagulation enzyme complexes which involve factor VIIIa-factor IXa (intrinsic tenase) and factor Va-Xa (prothrombinase) assembled on phospholipid membrane. Differential equations describing the fate of each species in the reaction were developed and solved using an interactive procedure based upon the Runge-Kutta technique. In addition to the theoretical considerations involving the reactions of the tissue factor pathway, a physical constraint associated with the stability of the factor VIIIa-factor IXa complex has been incorporated into the model based upon the empirical observations associated with the stability of this complex. The model system provides a realistic accounting of the fates of each of the proteins in the coagulation reaction through a range of initiator (factor VIIa-tissue factor) concentrations ranging from 5 pM to 5 nM. The model is responsive to alterations in the concentrations of factor VIII, factor V, and their respective activated species, factor VIIIa and factor Va, and overall provides a reasonable approximation of empirical data. The computer model permits the assessment of the reaction over a broad range of conditions and provides a useful tool for the development and management of reaction studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000336.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model is from the article:
Cooperation and Competition in the Evolution of ATP-Producing Pathways
Thomas Pfeiffer, Stefan Schuster, Sebastian Bonhoeffer Science 2001 Apr; Volume:292 (Issue:5516); Page info:504-7 11283355 ,
Abstract:
Heterotrophic organisms generally face a trade-off between rate and yield of adenosine triphosphate (ATP) production. This trade-off may result in an evolutionary dilemma, because cells with a higher rate but lower yield of ATP production may gain a selective advantage when competing for shared energy resources. Using an analysis of model simulations and biochemical observations, we show that ATP production with a low rate and high yield can be viewed as a form of cooperative resource use and may evolve in spatially structured environments. Furthermore, we argue that the high ATP yield of respiration may have facilitated the evolutionary transition from unicellular to undifferentiated multicellular organisms.


Note:

This model reproduces the competition and invasion described in Supplemental Figure 2.

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This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3814, + "tag": "BioModels:BIOMD0000000338" + }, + { + "id": 3815, + "tag": "Hemophilia B" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:38.772612+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000338", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2689": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2689, + "name": "Wajima2009_BloodCoagulation_PTtest", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3816, + "tag": "BioModels:BIOMD0000000339" + }, + { + "id": 3815, + "tag": "Hemophilia B" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:39.557220+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000339", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2690": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2690, + "name": "Wajima2009_BloodCoagulation_warfarin_heparin", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3817, + "tag": "BioModels:BIOMD0000000340" + }, + { + "id": 3815, + "tag": "Hemophilia B" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:40.256178+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000340", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2691": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2691, + "name": "Topp2000_BetaCellMass_Diabetes", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes.
Topp B, Promislow K, deVries G, Miura RM, Finegood DT. J Theor Biol.2000 Oct 21;206(4):605-19. 11013117,
Abstract:
Diabetes is a disease of the glucose regulatory system that is associated with increased morbidity and early mortality. The primary variables of this system are beta-cell mass, plasma insulin concentrations, and plasma glucose concentrations. Existing mathematical models of glucose regulation incorporate only glucose and/or insulin dynamics. Here we develop a novel model of beta -cell mass, insulin, and glucose dynamics, which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics. For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point. Mild hyperglycemia leads to the growth of the beta -cell mass (negative feedback) while extreme hyperglycemia leads to the reduction of the beta-cell mass (positive feedback). The model predicts that there are three pathways in prolonged hyperglycemia: (1) the physiological fixed point can be shifted to a hyperglycemic level (regulated hyperglycemia), (2) the physiological and saddle points can be eliminated (bifurcation), and (3) progressive defects in glucose and/or insulin dynamics can drive glucose levels up at a rate faster than the adaptation of the beta -cell mass which can drive glucose levels down (dynamical hyperglycemia).

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3818, + "tag": "BioModels:BIOMD0000000341" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3314, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:40.779964+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000341", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2692": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2692, + "name": "Zi2011_TGF-beta_Pathway", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Quantitative analysis of transient and sustained transforming growth factor-\u03b2 signaling dynamics.
Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 ,
Abstract:
Mammalian cells can decode the concentration of extracellular transforming growth factor-\u03b2 (TGF-\u03b2) and transduce this cue into appropriate cell fate decisions. How variable TGF-\u03b2 ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-\u03b2 stimulation. The TGF-\u03b2 pathway elicits a transient signaling response to a single pulse of TGF-\u03b2 stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-\u03b2 pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-\u03b2 levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-\u03b2 pathway might be critical for cell fate determination.

Note:

Developer of the model: Zhike Zi

Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011

1. The global parameter that set the type of stimulation

(a) for sustained TGF-beta stimulation: set stimulation_type = 1.

(b) for single pulse of TGF-beta stimulation: set stimulation_type = 2.

parameter \"single_pulse_duration\" is for the duration of stimulation, for example,

single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation.

*Note: make sure that the time course cover the time point when the event is triggered.

(c) for single pulse of TGF-beta stimulation in COPASI

change the trigger of event \"single_pulse_TGF_beta_washout\"

from

\"and(eq(stimulation_type, 2), eq(time, single_pulse_duration))\" (for SBML-SAT)

to

\"and(eq(stimulation_type, 2), gt(time, single_pulse_duration))\" (for COPASI)

2. Notes for TGF-beta dose in terms of molecules per cell

(a) The following equation applies for conversion of TGF-beta dose in molecules per cell

TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23

(b) for standard experimental setup 1e6 cells in 2 mL medium

0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell

0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell

(c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed.

initial Vmed = 1e-8 (1e6 cells in 10 mL medium)

Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium)

3. Please note that this model contains events and the medium compartment size is varied.

4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.

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This model is from the article:
Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework.
Brannmark C, Palmer R, Glad ST, Cedersund G, Stralfors P. J Biol Chem.2010 Jun 25;285(26):20171-9. 20421297,
Abstract:
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between co-existing submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.

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This model is from the article:
Modelling the Role of the Hsp70/Hsp90 System in the Maintenance of Protein Homeostasis
Proctor CJ, Lorimer IAJ PLoS ONE2011; 6(7): e22038. doi:10.1371/journal.pone.0022038,
Abstract:
Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis.

Note:

Simulations were performed under three different conditions: 1) normal condition (no stress), 2) moderate stress due to an increase in reactive oxygen species (ROS) i.e. ROS levels were increased by a factor of 4 at time=4hours for a period of 1 hour (not 2 hours as mentioned in the figure 5 legend of the reference publication. This is a typo in the paper and is clarified by the author) and 3) high stress due to sustained increase in reactive oxygen species (ROS) (here ROS increases with time).

The model that corresponds to the normal condition is submitted as a main model in the BioModels Database. The other two models, that corresponds to the moderate stress conditions and high stress conditions are available in SBML format as supporting files [go to Curation tab].

Supplementary figures S3 (normal condition), S4 (moderate stress condition) and S6 (high stress condition) are reproduced here.

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This model is from the article:
Mathematical modeling and analysis of insulin clearance in vivo.
Koschorreck M, Gilles ED. BMC Syst Biol. 2008 May 13;2:43. 18477391,
Abstract:
BACKGROUND:Analyzing the dynamics of insulin concentration in the blood is necessary for a comprehensive understanding of the effects of insulin in vivo. Insulin removal from the blood has been addressed in many studies. The results are highly variable with respect to insulin clearance and the relative contributions of hepatic and renal insulin degradation.RESULTS:We present a dynamic mathematical model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. The model describes renal and hepatic insulin degradation, pancreatic insulin secretion and nonspecific insulin binding in the liver. Hepatic insulin receptor activation by insulin binding, receptor internalization and autophosphorylation is explicitly included in the model. We present a detailed mathematical analysis of insulin degradation and insulin clearance. Stationary model analysis shows that degradation rates, relative contributions of the different tissues to total insulin degradation and insulin clearance highly depend on the insulin concentration.CONCLUSION:This study provides a detailed dynamic model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. Experimental data sets from literature are used for the model validation. We show that essential dynamic and stationary characteristics of insulin degradation are nonlinear and depend on the actual insulin concentration.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the original model from Richard FitzHugh, which led the famous FitzHugh\u2013Nagumo model, still used for instance in computational neurosciences.
Impulses and Physiological States in Theoretical Models of Nerve Membrane
FitzHugh R Biophysical Journal, 1961 July:1(6):445-466 doi:10.1016/S0006-3495(61)86902-6 ,
Abstract:
Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting BVP model has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a physiological state diagram, with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3827, + "tag": "BioModels:BIOMD0000000346" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3061, + "tag": "Transmission of nerve impulse" + }, + { + "id": 3130, + "tag": "cellular organisms" + }, + { + "id": 3828, + "tag": "nerve" + } + ], + "timestamp_created": "2025-02-03 16:14:43.307540+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000346", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2697": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2697, + "name": "Bachmann2011_JAK2-STAT5_FeedbackControl", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.
Bachmann J, Raue A, Schilling M, B\u00f6hm ME, Kreutz C, Kaschek D, Busch H, Gretz N, Lehmann WD, Timmer J, Klingm\u00fcller U. Mol Syst Biol. 2011 Jul 19;7:516. 21772264 ,
Abstract:
Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3829, + "tag": "BioModels:BIOMD0000000347" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3774, + "tag": "Regulation of JAK-STAT cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3830, + "tag": "erythroid progenitor cell" + } + ], + "timestamp_created": "2025-02-03 16:14:43.866792+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000347", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2698": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2698, + "name": "Fridlyand2010_GlucoseSensitivity_A", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Glucose sensing in the pancreatic beta cell: a computational systems analysis.
Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556,
Abstract:
BACKGROUND:Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.METHODS:We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.RESULTS:The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.CONCLUSION:This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.

This model was taken from the Vcell MathModel directory and was converted to SBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3831, + "tag": "BioModels:BIOMD0000000348" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3314, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3832, + "tag": "Type B pancreatic cell proliferation" + } + ], + "timestamp_created": "2025-02-03 16:14:44.445672+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000348", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2699": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2699, + "name": "Fridlyand2010_GlucoseSensitivity_B", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Glucose sensing in the pancreatic beta cell: a computational systems analysis.
Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556,
Abstract:
BACKGROUND:Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.METHODS:We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.RESULTS:The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.CONCLUSION:This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.

This model was taken from the Vcell MathModel directory and was converted to SBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Multiple light inputs to a simple clock circuit allow complex biological rhythms
Troein C, Corellou F, Dixon LE, van Ooijen G, O'Neill JS, Bouget FY, Millar AJ. Plant J.2011 Apr;66(2):375-85. 21219507,
Abstract:
Circadian clocks are biological timekeepers that allow living cells to time their activity in anticipation of predictable environmental changes. Detailed understanding of the circadian network of higher plants, such as Arabidopsis thaliana, is hampered by the high number of partially redundant genes. However, the picoeukaryotic alga Ostreococcus tauri, which was recently shown to possess a small number of non-redundant clock genes, presents an attractive alternative target for detailed modelling of circadian clocks in the green lineage. Based on extensive time-series data from in vivo reporter gene assays, we developed a model of the Ostreococcus clock as a feedback loop between the genes TOC1 and CCA1. The model reproduces the dynamics of the transcriptional and translational reporters over a range of photoperiods. Surprisingly, the model is also able to predict the transient behaviour of the clock when the light conditions are altered. Despite the apparent simplicity of the clock circuit, it displays considerable complexity in its response to changing light conditions. Systematic screening of the effects of altered day length revealed a complex relationship between phase and photoperiod, which is also captured by the model. The complex light response is shown to stem from circadian gating of light-dependent mechanisms. This study provides insights into the contributions of light inputs to the Ostreococcus clock. The model suggests that a high number of light-dependent reactions are important for flexible timing in a circadian clock with only one feedback loop.

Note: Two-gene model of the Ostreococcus circadian clock

This is a model of the circadian clock of Ostreococcus tauri, with a negative feedback loop between TOC1 and CCA1 (a.k.a. LHY) and multiple light inputs. It was used and described in Troein et al., Plant Journal (2011).

The model incorporates luciferase reporters, and in this SBML model the four different versions of the model for transcriptional and translational reporter lines (pTOC1::LUC, pCCA1::LUC, TOC1-LUC and CCA1-LUC) are all accessible by setting one of the rep_X parameters to 1 and the others to 0. You can also set all four to 0 to only simulate the non-reporter core of the system.

Input to the system should be provided by modifying the \"light\" function. An implementation of LD 12:12 is provided as an example, but the model was also used with more complicated light regimes that vary between data sets and are not convenient to express directly in SBML.

The functions \"ox_cca1\" and \"ox_toc1\" can be altered to add overexpression of CCA1 and TOC1. Setting either to x gives additional, constitutive transcription at x times the maximal (and typically not realizable) transcription rate of the native gene. The overexpression mutant fits in Figure 7 of Troein et al. (2011) used ox_cca1 = 0.115 and oc_toc1 = 0.0584, respectively.

The functions \"copies_toc1\" and \"copies_cca1\" are normally 1 but can be lowered to simulate knockdown experiments. The functions \"transcription\", \"translation\" and \"proteasome\" can be modified to simulate the effects of altering the overall rate of transcription, translation and protein degradation.

The parameters were fitted specifically to data from transgenic reporter lines TOC8, pTOC3, LHY7 and pLHY7 (Corellou et al., Plant Cell 2009). Parameters that begin with \"effcopies\" describe the effective number of copies of CCA1 or TOC1 in the respective translational fusion lines, with anything above 1 due to the fusion proteins.

For the model fitting, the initial values were fitted to the data in the various time courses. The initial values given here correspond to the limit cycle of the system in LD 12:12. The system converges to the limit cycle in just a few days under most light conditions, so these initial values are biologically meaningful.

The species cca1luc_c and cca1luc_n have been merged into cca1luc (which corresponds to the observable luminescence signal), because Copasi refused to run the system otherwise. For TOC1-LUC, the predicted output signal is the sum of toc1luc_1 and toc1luc_2.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
The auxin signalling network translates dynamic input into robust patterning at the shoot apex.
Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Gu\u00e9don Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 ,
Abstract:
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong bufferingcapacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Note:

Figure 3 of the supplementary material of the reference article has been reproduced here. Time evolution of all the variables in the model are plotted, under the influence of a step input of auxin level (auxin=5, when time>1000; 0.11, otherwise). pi_A is varied between 0 and 2 by steps of 0.1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3675, + "tag": "Auxin-activated signaling pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3836, + "tag": "BioModels:BIOMD0000000351" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:45.940073+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000351", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2702": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2702, + "name": "Vernoux2011_AuxinSignaling_AuxinFluctuating", + "repository_type": "biomodels", + "summary": "

This model is from the article:
The auxin signalling network translates dynamic input into robust patterning at the shoot apex.
Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Gu\u00e9don Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 ,
Abstract:
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Note:

Figure 4 of the supplementary material of the reference article has been reproduced here. In this model, the fluctuations of auxin level is represented using sinux function. Time evolution of the variables AUX/IAA (I) and mRNA (R) are plotted, under the influence of fluctuations of auxin level. pi_A is varied between 0 and 2 by steps of 0.1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3675, + "tag": "Auxin-activated signaling pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3837, + "tag": "BioModels:BIOMD0000000352" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:46.422367+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000352", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2703": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2703, + "name": "Nag2011_ChloroplasticStarchDegradation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.
Nag A, Lunacek M, Graf PA, Chang CH. BMC Syst Biol.2011 Jun 18;5:94. 21682905,
Abstract:
BACKGROUND:Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.RESULTS:Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing \u03b2-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter kcat and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and \u03b2-amylase kcat and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.CONCLUSIONS:We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3838, + "tag": "BioModels:BIOMD0000000353" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3839, + "tag": "Starch catabolic process" + } + ], + "timestamp_created": "2025-02-03 16:14:46.951031+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000353", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2704": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2704, + "name": "Abell2011_CalciumSignaling_WithoutAdaptation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.
Abell E, Ahrends R, Bandara S, Park BO, Teruel MN. Proc Natl Acad Sci U S A. 2011 Aug 15. 21844332 ,
Abstract:
Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.

Note:

There are two models described in the paper to simulate basal and receptor stimulated Ca 2+ signaling. 1) No adaptive feedback (this model: MODEL1108050000) and 2) with three slow adaptive feedback loops (MODEL1108050001).

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3840, + "tag": "BioModels:BIOMD0000000354" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:47.508050+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000354", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2705": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2705, + "name": "Abell2011_CalciumSignaling_WithAdaptation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.
Abell E, Ahrends R, Bandara S, Park BO, Teruel MN. Proc Natl Acad Sci U S A. 2011 Aug 15. 21844332 ,
Abstract:
Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.

Note:

There are two models described in the paper to simulate basal and receptor stimulated Ca 2+ signaling. 1) No adaptive feedback (MODEL1108050000) and 2) with three slow adaptive feedback loops (this model: MODEL1108050001).

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3841, + "tag": "BioModels:BIOMD0000000355" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:47.970456+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000355", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2706": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2706, + "name": "Nyman2011_M3Hierarachical_InsulinGlucosedynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis.
Nyman E, Brannmark C, Palmer R, Brugard J, Nystrom FH, Stralfors P, Cedersund G.J Biol Chem.2011 Jul 22;286(29):26028-41. 21572040,
Abstract:
Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3842, + "tag": "BioModels:BIOMD0000000356" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3314, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:48.432437+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000356", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2707": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2707, + "name": "Lee2010_ThrombinActivation_OneForm_reduced", + "repository_type": "biomodels", + "summary": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. A structural model is consistent with the data-derived finding that mechanisms that feature channeling or ratcheting are not necessary to describe thrombin production.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3843, + "tag": "BioModels:BIOMD0000000357" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3844, + "tag": "Serine-type endopeptidase activity" + } + ], + "timestamp_created": "2025-02-03 16:14:48.912667+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000357", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2708": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2708, + "name": "Stortelder1997 - Thrombin Generation Amidolytic Activity", + "repository_type": "biomodels", + "summary": "
Stortelder1997 - Thrombin Generation Amidolytic Activity

Mathematical modelling of a part of the blood coagulation mechanism.

This model is described in the article:

Stortelder W.J.H., Hemker P.W., Hemker, H.C.
CWI. Modelling, Analysis and Simulation, No. R 9720, p.1-11.

Abstract:

This paper describes the mathematical modelling of a part of the blood coagulation mechanism. The model includes the activation of factor X by a purified enzyme from Russel's Viper Venom (RVV), factor V and prothrombin, and also comprises the inactivation of the products formed. In this study we assume that in principle the mechanism of the process is known. However, the exact structure of the mechanism is unknown, and the process still can be described by different mathematical models. These models are put to test by measuring their capacity to explain the course of thrombin generation as observed in plasma after recalcification in presence of RVV. The mechanism studied is mathematically modelled as a system of differential-algebraic equations (DAEs). Each candidate model contains some freedom, which is expressed in the model equations by the presence of unknown parameters. For example, reaction constants or initial concentrations are unknown. The goal of parameter estimation is to determine these unknown parameters in such a way that the theoretical (i.e., computed) results fit the experimental data within measurement accuracy and to judge which modifications of the chemical reaction scheme allow the best fit. We present results on model discrimination and estimation of reaction constants, which are hard to obtain in another way.

This model is hosted on BioModels Database and identified by: BIOMD0000000358 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3845, + "tag": "BioModels:BIOMD0000000358" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:49.360547+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000358", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2709": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2709, + "name": "Panteleev2002_TFPImechanism_schmema3", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3846, + "tag": "BioModels:BIOMD0000000359" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:49.828797+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000359", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2710": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2710, + "name": "Panteleev2002_TFPImechanism_schmema2", + "repository_type": "biomodels", + "summary": "Mikhail A. Panteleev, Veronica I. Zarnitsina & Fazoil I. Ataullakhanov. Tissue factor pathway inhibitor: a possible mechanism of action. European Journal of Biochemistry 269, 8 (2002).

We have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3847, + "tag": "BioModels:BIOMD0000000360" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:50.288523+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000360", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2711": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2711, + "name": "Panteleev2002_TFPImechanism_schmema1", + "repository_type": "biomodels", + "summary": "Mikhail A. Panteleev, Veronica I. Zarnitsina & Fazoil I. Ataullakhanov. Tissue factor pathway inhibitor: a possible mechanism of action. European Journal of Biochemistry 269, 8 (2002).

We have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3848, + "tag": "BioModels:BIOMD0000000361" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:50.803345+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000361", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2712": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2712, + "name": "Butenas2004_BloodCoagulation", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3849, + "tag": "BioModels:BIOMD0000000362" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:51.275800+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000362", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2713": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2713, + "name": "Lee2010_ThrombinActivation_OneForm_minimal", + "repository_type": "biomodels", + "summary": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. A structural model is consistent with the data-derived finding that mechanisms that feature channeling or ratcheting are not necessary to describe thrombin production.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3850, + "tag": "BioModels:BIOMD0000000363" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:51.904430+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000363", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2714": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2714, + "name": "Lee2010_ThrombinActivation_OneForm", + "repository_type": "biomodels", + "summary": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. A structural model is consistent with the data-derived finding that mechanisms that feature channeling or ratcheting are not necessary to describe thrombin production.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3851, + "tag": "BioModels:BIOMD0000000364" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:52.362439+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000364", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2715": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2715, + "name": "Hockin1999_BloodCoagulation_VaInactivation", + "repository_type": "biomodels", + "summary": "Matthew F. Hockin, Kevin M. Cawthern, Michael Kalafatis & Kenneth G. Mann. A model describing the inactivation of factor Va by APC: bond cleavage, fragment dissociation, and product inhibition. Biochemistry 38, 21 (1999).

The inactivation of factor Va is a complex process which includes bond cleavage (at three sites) and dissociation of the A2N.A2C peptides, with intermediate activity in each species. Quantitation of the functional consequences of each step in the reaction has allowed for understanding of the presentation of disease in individuals possessing the factor V polymorphism factor VLEIDEN. APC cleavage of membrane-bound bovine factor Va (Arg306, Arg505, Arg662) leads to the dissociation of fragments of the A2 domain, residues 307-713 (A2N.A2C + A2C-peptide), leaving behind the membrane-bound A1.LC species. Evaluation of the dissociation process by light scattering yields invariant mass loss estimates as a function of APC concentration. The rate constant for A2 fragment dissociation varies with [APC], reaching a maximal value of k = 0.028 s-1, the unimolecular rate constant for A2 domain fragment dissociation. The APC binding site resides in the factor Va light chain (LC) (Kd = 7 nM), suggesting that the membrane-bound LC.A1 product would act to sequester APC. This inhibitory interaction (LC.A1.APC) is demonstrated to exist with either purified factor Va LC or the products of factor Va inactivation. Utilizing these experimental data and the reported rates of bond cleavage, binding constants, and product activity values for factor Va partial inactivation products, a model is developed which describes factor Va inactivation and accounts for the defect in factor VLEIDEN. The model accurately predicts the rates of inactivation of factor Va and factor VaLEIDEN, and the effect of product inhibition. Modeled reaction progress diagrams and activity profiles (from either factor Va or factor VaLEIDEN) are coincident with experimentally derived data, providing a mechanistic and kinetic explanation for all steps in the inactivation of normal factor Va and the pathology associated with factor VLEIDEN.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3852, + "tag": "BioModels:BIOMD0000000365" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 3853, + "tag": "Inherited blood coagulation disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:52.880301+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000365", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2716": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2716, + "name": "Orfao2008_ThrombinGeneration_AmidolyticActivity", + "repository_type": "biomodels", + "summary": "S. Cunha Orfao, G. Jank, K. Mottaghy, S. Walcher & E. Zerz. Qualitative properties and stabilizability of a model for blood thrombin formation. Journal of Mathematical Analysis and Applications 346, 1 (2008).

We perform a qualitative analysis of a differential equation that was originally introduced by Stortelder, Hemker and Hemker to model the formation of thrombin, and discuss issues of controllability and stabilizability. Results include a general proof of convergence to equilibrium, and of local exponential stabilizability.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3854, + "tag": "BioModels:BIOMD0000000366" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3672, + "tag": "Cardiovascular system disease" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:53.343029+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000366", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2717": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2717, + "name": "Mueller2008_ThrombinGeneration_minimal", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3855, + "tag": "BioModels:BIOMD0000000367" + }, + { + "id": 3856, + "tag": "Coronary artery disease" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:53.816358+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000367", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2718": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2718, + "name": "Beltrami1995_ThrombinGeneration_C", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3857, + "tag": "BioModels:BIOMD0000000368" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:54.276326+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000368", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2719": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2719, + "name": "Beltrami1995_ThrombinGeneration_D", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3858, + "tag": "BioModels:BIOMD0000000369" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:14:54.729954+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000369", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2720": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2720, + "name": "Vinod2011_MitoticExit", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc14 endocycles
Vinod PK, Freire P, Rattani A, Ciliberto A, Uhlmann F, Novak B. J R Soc Interface. 2011 Aug 7;8(61):1128-41. Epub 2011 Feb 2. 21288956 ,
Abstract:
The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20-APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3859, + "tag": "BioModels:BIOMD0000000370" + }, + { + "id": 3066, + "tag": "Mitotic cell cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:14:55.260022+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000370", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2721": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2721, + "name": "DeVries2000_PancreaticBetaCells_InsulinSecretion", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Channel sharing in pancreatic beta -cells revisited: enhancement of emergentbursting by noise.
De Vries G, Sherman A. J Theor Biol2000 Dec 21;207(4):513-30 11093836,
Abstract:
Secretion of insulin by electrically coupled populations of pancreatic beta-cells is governed by bursting electrical activity. Isolated beta -cells,however, exhibit atypical bursting or continuous spike activity. We studybursting as an emergent property of the population, focussing on interactionsamong the subclass of spiking cells. These are modelled by equipping the fastsubsystem with a saddle-node-loop bifurcation, which makes it monostable. Suchcells can only spike tonically or remain silent when isolated, but can beinduced to burst with weak diffusive coupling. With stronger coupling, the cellsrevert to tonic spiking. We demonstrate that the addition of noise dramaticallyincreases, via a phenomenon like stochastic resonance, the coupling range overwhich bursting is seen. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: De Vries G, Sherman A. (2000) - version01

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3860, + "tag": "BioModels:BIOMD0000000371" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3861, + "tag": "Insulin secretion" + }, + { + "id": 3862, + "tag": "Regulation of type B pancreatic cell proliferation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3832, + "tag": "Type B pancreatic cell proliferation" + } + ], + "timestamp_created": "2025-02-03 16:14:55.734514+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000371", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2722": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2722, + "name": "Tolic2000_InsulinGlucoseFeedback", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling the insulin-glucose feedback system: the significance of pulsatileinsulin secretion.
Tolic IM, Mosekilde E, Sturis J. J Theor Biol2000 Dec 7;207(3):361-75 11082306,
Abstract:
A mathematical model of the insulin-glucose feedback regulation in man is usedto examine the effects of an oscillatory supply of insulin compared to aconstant supply at the same average rate. We show that interactions between theoscillatory insulin supply and the receptor dynamics can be of minutesignificance only. It is possible, however, to interpret seemingly conflictingresults of clinical studies in terms of their different experimental conditionswith respect to the hepatic glucose release. If this release is operating nearan upper limit, an oscillatory insulin supply will be more efficient in loweringthe blood glucose level than a constant supply. If the insulin level is highenough for the hepatic release of glucose to nearly vanish, the opposite effectis observed. For insulin concentrations close to the point of inflection of theinsulin-glucose dose-response curve an oscillatory and a constant insulininfusion produce similar effects. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Tolic IM, Mosekilde E, Sturis J. (2000) - version=1.0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Calcium and glycolysis mediate multiple bursting modes in pancreatic islets.
Bertram R, Satin L, Zhang M, Smolen P, Sherman A. Biophys J2004 Nov;87(5):3074-87 15347584,
Abstract:
Pancreatic islets of Langerhans produce bursts of electrical activity whenexposed to stimulatory glucose levels. These bursts often have a regularrepeating pattern, with a period of 10-60 s. In some cases, however, the burstsare episodic, clustered into bursts of bursts, which we call compound bursting.Consistent with this are recordings of free Ca2+ concentration, oxygenconsumption, mitochondrial membrane potential, and intraislet glucose levelsthat exhibit very slow oscillations, with faster oscillations superimposed. Wedescribe a new mathematical model of the pancreatic beta-cell that can accountfor these multimodal patterns. The model includes the feedback of cytosolic Ca2+onto ion channels that can account for bursting, and a metabolic subsystem thatis capable of producing slow oscillations driven by oscillations in glycolysis.This slow rhythm is responsible for the slow mode of compound bursting in themodel. We also show that it is possible for glycolytic oscillations alone todrive a very slow form of bursting, which we call \"glycolytic bursting.\"Finally, the model predicts that there is bistability between stationary andoscillatory glycolysis for a range of parameter values. We provide experimentalsupport for this model prediction. Overall, the model can account for adiversity of islet behaviors described in the literature over the past 20 years.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Satin L, Zhang M, Smolen P, Sherman A. (2004) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A role for calcium release-activated current (CRAC) in cholinergic modulation ofelectrical activity in pancreatic beta-cells.
Bertram R, Smolen P, Sherman A, Mears D, Atwater I, Martin F, Soria B. Biophys J1995 Jun;68(6):2323-32 7647236,
Abstract:
S. Bordin and colleagues have proposed that the depolarizing effects ofacetylcholine and other muscarinic agonists on pancreatic beta-cells aremediated by a calcium release-activated current (CRAC). We support thishypothesis with additional data, and present a theoretical model which accountsfor most known data on muscarinic effects. Additional phenomena, such as thebiphasic responses of beta-cells to changes in glucose concentration and thedepolarizing effects of the sarco-endoplasmic reticulum calcium ATPase pumppoison thapsigargin, are also accounted for by our model. The ability of thissingle hypothesis, that CRAC is present in beta-cells, to explain so manyphenomena motivates a more complete characterization of this current.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Smolen P, Sherman A, Mears D, Atwater I, Martin F, Soria B. (1995) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Evidence that calcium release-activated current mediates the biphasic electricalactivity of mouse pancreatic beta-cells.
Mears D, Sheppard NF Jr, Atwater I, Rojas E, Bertram R, Sherman A. J Membr Biol1997 Jan 1;155(1):47-59 9002424,
Abstract:
The electrical response of pancreatic beta-cells to step increases in glucoseconcentration is biphasic, consisting of a prolonged depolarization with actionpotentials (Phase 1) followed by membrane potential oscillations known asbursts. We have proposed that the Phase 1 response results from the combineddepolarizing influences of potassium channel closure and an inward, nonselectivecation current (ICRAN) that activates as intracellular calcium stores emptyduring exposure to basal glucose (Bertram et al., 1995). The stores refillduring Phase 1, deactivating ICRAN and allowing steady-state bursting tocommence. We support this hypothesis with additional simulations andexperimental results indicating that Phase 1 duration is sensitive to thefilling state of intracellular calcium stores. First, the duration of the Phase1 transient increases with duration of prior exposure to basal (2.8 mM) glucose,reflecting the increased time required to fill calcium stores that have beenemptying for longer periods. Second, Phase 1 duration is reduced when islets areexposed to elevated K+ to refill calcium stores in the presence of basalglucose. Third, when extracellular calcium is removed during the basal glucoseexposure to reduce calcium influx into the stores, Phase 1 duration increases.Finally, no Phase 1 is observed following hyperpolarization of the beta-cellmembrane with diazoxide in the continued presence of 11 mm glucose, a conditionin which intracellular calcium stores remain full. Application of carbachol toempty calcium stores during basal glucose exposure did not increase Phase 1duration as the model predicts. Despite this discrepancy, the good agreementbetween most of the experimental results and the model predictions providesevidence that a calcium release-activated current mediates the Phase 1electrical response of the pancreatic beta-cell.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Mears D, Sheppard NF Jr, Atwater I, Rojas E, Bertram R, Sherman A. (1997) - version=1.0
The original CellML model was created by:
Tessa Paris
tpar054@aucklanduni.ac.uk
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the model described in the article:
Interaction of glycolysis and mitochondrial respiration in metabolic oscillations of pancreatic islets.
Bertram R, Satin LS, Pedersen MG, Luciani DS, Sherman A. Biophys J. 2007 Mar 1;92(5):1544-55. Pubmed ID: 17172305, doi: 10.1529/biophysj.106.097154.
Abstract:
Insulin secretion from pancreatic beta-cells is oscillatory, with a typical period of 2-7 min, reflecting oscillations in membrane potential and the cytosolic Ca(2+) concentration. Our central hypothesis is that the slow 2-7 min oscillations are due to glycolytic oscillations, whereas faster oscillations that are superimposed are due to Ca(2+) feedback onto metabolism or ion channels. We extend a previous mathematical model based on this hypothesis to include a more detailed description of mitochondrial metabolism. We demonstrate that this model can account for typical oscillatory patterns of membrane potential and Ca(2+) concentration in islets. It also accounts for temporal data on oxygen consumption in islets. A recent challenge to the notion that glycolytic oscillations drive slow Ca(2+) oscillations in islets are data showing that oscillations in Ca(2+), mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization. We demonstrate that these data are in fact compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity. Finally, we use the model to address the recent finding that the activity of islets from some mice is uniformly fast, whereas that from islets of other mice is slow. We propose a mechanism for this dichotomy.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram, Satin, Pedersen, Luciani, Sherman, 2007 version 02
The original CellML model was created and curated by:
Catherine May Lloyd
c.lloyd(at)auckland.ac.nz
The University of Auckland, Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
The phantom burster model for pancreatic beta-cells.
Bertram R, Previte J, Sherman A, Kinard TA, Satin LS. Biophys J2000 Dec;79(6):2880-92 11106596,
Abstract:
Pancreatic beta-cells exhibit bursting oscillations with a wide range ofperiods. Whereas periods in isolated cells are generally either a few seconds ora few minutes, in intact islets of Langerhans they are intermediate (10-60 s).We develop a mathematical model for beta-cell electrical activity capable ofgenerating this wide range of bursting oscillations. Unlike previous models,bursting is driven by the interaction of two slow processes, one with arelatively small time constant (1-5 s) and the other with a much larger timeconstant (1-2 min). Bursting on the intermediate time scale is generated withoutneed for a slow process having an intermediate time constant, hence phantombursting. The model suggests that isolated cells exhibiting a fast pattern maynonetheless possess slower processes that can be brought out by injectingsuitable exogenous currents. Guided by this, we devise an experimental protocolusing the dynamic clamp technique that reliably elicits islet-like, mediumperiod oscillations from isolated cells. Finally, we show that strong electricalcoupling between a fast burster and a slow burster can produce synchronizedmedium bursting, suggesting that islets may be composed of cells that areintrinsically either fast or slow, with few or none that are intrinsicallymedium.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Previte J, Sherman A, Kinard TA, Satin LS. (2000) - version02

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Effects of extracellular calcium on electrical bursting and intracellular and luminal calcium oscillations in insulin secreting pancreatic beta-cells.
Chay TR Biophys J.1997 Sep;73(3):1673-88. 9284334,
Abstract:
The extracellular calcium concentration has interesting effects on bursting of pancreatic beta-cells. The mechanism underlying the extracellular Ca2+ effect is not well understood. By incorporating a low-threshold transient inward current to the store-operated bursting model of Chay, this paper elucidates the role of the extracellular Ca2+ concentration in influencing electrical activity, intracellular Ca2+ concentration, and the luminal Ca2+ concentration in the intracellular Ca2+ store. The possibility that this inward current is a carbachol-sensitive and TTX-insensitive Na+ current discovered by others is discussed. In addition, this paper explains how these three variables respond when various pharmacological agents are applied to the store-operated model.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Chay TR (1997) - version05
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3871, + "tag": "BioModels:BIOMD0000000378" + }, + { + "id": 3151, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3862, + "tag": "Regulation of type B pancreatic cell proliferation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3832, + "tag": "Type B pancreatic cell proliferation" + } + ], + "timestamp_created": "2025-02-03 16:14:59.252444+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000378", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2729": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2729, + "name": "DallaMan2007_MealModel_GlucoseInsulinSystem", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Meal simulation model of the glucose-insulin system.
Dalla Man C, Rizza RA, Cobelli C.IEEE Trans Biomed Eng.2007 Oct;54(10):1740-9. 17926672,
Abstract:
A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae.
Smallbone K, Malys N, Messiha HL, Wishart JA, Simeonidis E. Methods Enzymol. 2011;500:355-70. 21943906 ,
Abstract:
In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed.

This SBML model is made available under the Creative Commons Attribution-Share Alike 3.0 Unported Licence (see www.creativecommons.org ).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3873, + "tag": "BioModels:BIOMD0000000380" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3874, + "tag": "Trehalose biosynthetic process" + } + ], + "timestamp_created": "2025-02-03 16:15:00.233450+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000380", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2731": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2731, + "name": "Maree2006_DuCa_Type1DiabetesModel", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modelling the onset of Type 1 diabetes: can impaired macrophage phagocytosis make the difference between health and disease?
Maree AF, Kublik R, Finegood DT, Edelstein-Keshet L.Philos Transact A Math Phys Eng Sci.2006 May 15;364(1842):1267-82. 16608707,
Abstract:
A wave of apoptosis (programmed cell death) occurs normally in pancreatic beta-cells of newborn mice. We previously showed that macrophages from non-obese diabetic (NOD) mice become activated more slowly and engulf apoptotic cells at a lower rate than macrophages from control (Balb/c) mice. It has been hypothesized that this low clearance could result in secondary necrosis, escalating inflammation and self-antigen presentation that later triggers autoimmune, Type 1 diabetes (T1D). We here investigate whether this hypothesis could offer a reasonable and parsimonious explanation for onset of T1D in NOD mice. We quantify variants of the Copenhagen model (Freiesleben De Blasio et al. 1999 Diabetes 48, 1677), based on parameters from NOD and Balb/c experimental data. We show that the original Copenhagen model fails to explain observed phenomena within a reasonable range of parameter values, predicting an unrealistic all-or-none disease occurrence for both strains. However, if we take into account that, in general, activated macrophages produce harmful cytokines only when engulfing necrotic (but not apoptotic) cells, then the revised model becomes qualitatively and quantitatively reasonable. Further, we show that known differences between NOD and Balb/c mouse macrophage kinetics are large enough to account for the fact that an apoptotic wave can trigger escalating inflammatory response in NOD, but not Balb/c mice. In Balb/c mice, macrophages clear the apoptotic wave so efficiently, that chronic inflammation is prevented.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This a model from the article:\t
Computer model for mechanisms underlying ultradian oscillations of insulin and glucose.\t
\t Sturis J, Polonsky KS, Mosekilde E, Van Cauter E.\t Am J Physiol.1991 May;260(5 Pt 1):E801-9.\t 2035636,\t
Abstract:
Oscillations in human insulin secretion have been observed in two distinct period ranges, 10-15 min (i.e. rapid) and 100-150 min (i.e., ultradian). The cause of the ultradian oscillations remains to be elucidated. To determine whether the oscillations could result from the feedback loops between insulin and glucose, a parsimonious mathematical model including the major mechanisms involved in glucose regulation was developed. This model comprises two major negative feedback loops describing the effects of insulin on glucose utilization and glucose production, respectively, and both loops include the stimulatory effect of glucose on insulin secretion. Model formulations and parameters are representative of results from published clinical investigations. The occurrence of sustained insulin and glucose oscillations was found to be dependent on two essential features: 1) a time delay of 30-45 min for the effect of insulin on glucose production and 2) a sluggish effect of insulin on glucose utilization, because insulin acts from a compartment remote from plasma. When these characteristics were incorporated in the model, numerical simulations mimicked all experimental findings so far observed for these ultradian oscillations, including 1) self-sustained oscillations during constant glucose infusion at various rates; 2) damped oscillations after meal or oral glucose ingestion; 3) increased amplitude of oscillation after increased stimulation of insulin secretion, without change in frequency; and 4) slight advance of the glucose oscillation compared with the insulin oscillation.(ABSTRACT TRUNCATED AT 250 WORDS)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Farquhar et al. (1980, DOI:10.1007/BF00386231 ).

The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3881, + "tag": "BioModels:BIOMD0000000383" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:02.211028+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000383", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2734": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2734, + "name": "Arnold2011_Medlyn2002_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ).

The parameter values are widely taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713) . A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3882, + "tag": "BioModels:BIOMD0000000384" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:02.731096+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000384", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2735": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2735, + "name": "Arnold2011_Schultz2003_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Schultz (2003, DOI:10.1071/FP02146 ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3883, + "tag": "BioModels:BIOMD0000000385" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:03.319615+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000385", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2736": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2736, + "name": "Arnold2011_Sharkey2007_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Sharkey et al. (2007, DOI:10.1111/j.1365-3040.2007.01710.x ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3884, + "tag": "BioModels:BIOMD0000000386" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:04.939113+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000386", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2737": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2737, + "name": "Arnold2011_Damour2007_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Damour and Urban (2007, [for PDF click here] ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Urban et al. (2003, DOI:10.1093/treephys/23.5.289 ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle by Zhu et al. (2009, DOI:10.1016/j.nonrwa.2008.01.021 ).

The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ).A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3886, + "tag": "BioModels:BIOMD0000000388" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:06.000657+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000388", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2739": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2739, + "name": "Arnold2011_Hahn1986_CalvinCycle_Starch_Sucrose", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis by Hahn (1986, [click here for abstract] ).

The parameter values are taken from Hahn (1984, [click here for abstract] ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3887, + "tag": "BioModels:BIOMD0000000389" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:06.460108+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000389", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2740": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2740, + "name": "Arnold2011_Giersch1990_CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle by Giersch et al. (1990, DOI:10.1007/BF00032595).

The parameter values are taken from Figure 4 and 5. The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathway to starch synthesis by Poolman et al. (2000, DOI:10.1093/jexbot/51.suppl_1.319).

The parameter values are widely taken from Pettersson and Ryde-Pettersson (1988, DOI:10.1111/j.1432-1033.1988.tb14242.x) and Poolman (1999, [click here for PDF]). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3889, + "tag": "BioModels:BIOMD0000000391" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:07.423173+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000391", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2742": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2742, + "name": "Arnold2011_Laisk2006_CalvinCycle_Starch_Sucrose", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis by Laisk et al. (2006, DOI:10.1007/s11120-006-9109-1) and the personally provided implementation to Laisk et al. (2009, DOI:10.1007/978-1-4020-9237-4_13).

A reduced version of the published model is implemented (light-dependent reactions are taken out). The parameter values are widely taken from Laisk et al. (1989, [click here for PDF]). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3890, + "tag": "BioModels:BIOMD0000000392" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:07.901201+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000392", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2743": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2743, + "name": "Arnold2011_Zhu2007_CalvinCycle_Starch_Sucrose_Photorespiration", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis and photorespiration by Zhu et al. (2007, DOI:10.1104/pp.107.103713) and the personally provided implementation.

The parameter values are partly taken from Pettersson and Ryde-Pettersson (1988, DOI:10.1111/j.1432-1033.1988.tb14242.x). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3891, + "tag": "BioModels:BIOMD0000000393" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3336, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-02-03 16:15:08.443274+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000393", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2744": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2744, + "name": "Sivakumar2011 - EGF Receptor Signaling Pathway", + "repository_type": "biomodels", + "summary": " \t
\t Sivakumar2011 - EGF Receptor Signaling Pathway\t
\t
\t

EGFR belongs to the human epidermal receptor (HER) family of receptor tyrosine kinases, which consists of four closely related receptors (EGFR (HER1, erbB1), HER2 (neu, erbB2), HER3 (erbB3), and HER4 (erbB4)) that mediate cellular signaling pathways involved in growth and proliferation in response to the binding of a variety of growth factor ligands. There are currently six known endogenous ligands for EGFR: EGF, transforming growth factor- (TGF-), amphiregulin, betacellulin, heparin-binding EGF (HB-EGF), and epiregulin.Upon ligand binding, the EGFR forms homo- or heterodimeric complexes (usually with HER2), which leads to activation of the receptor tyrosine kinase, via autophosphorylation.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000394.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3892, + "tag": "BioModels:BIOMD0000000394" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:08.945776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000394", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2745": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2745, + "name": "Sivakumar2011 - Hedgehog Signaling Pathway", + "repository_type": "biomodels", + "summary": " \t
\t Sivakumar2011 - Hedgehog Signaling Pathway\t
\t
\t

This is the current model for the Hedgehog signaling pathway. The best data for mechanism of signaling has been worked out in Drosophila, so this model is based largely on Drosophila data. Hedgehog target genes vary from tissue to tissue, so the identities of individual target genes have not been listed. The main difference between the Drosophila and mammalian Hedgehog signaling pathways is the fact that there are three mammalian homologs of Cubitus interruptus, Gli1 Gli2 and Gli3. Some or all of the mammalian homologs may be proteolytically processed, but the data are controversial. There are two mammalian Ptc genes and three mammalian Hedgehog genes as well. The pathway for Sonic Hedgehog appears to be most similar to the Drosophila hedgehog pathway.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000395.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3893, + "tag": "BioModels:BIOMD0000000395" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3894, + "tag": "Smoothened signaling pathway" + } + ], + "timestamp_created": "2025-02-03 16:15:09.421653+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000395", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2746": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2746, + "name": "Sivakumar2011 - Notch Signaling Pathway", + "repository_type": "biomodels", + "summary": " \t
\t Sivakumar2011 - Notch Signaling Pathway\t
\t
\t

Notch is a transmembrane receptor that mediates local cell-cell communication and coordinates a signaling cascade. It plays a key role in modulating cell fate decisions throughout the development of invertebrate and vertebrate species and the misregulation leads to a number of human diseases.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000396.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3895, + "tag": "BioModels:BIOMD0000000396" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3896, + "tag": "Notch signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:09.967866+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000396", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2747": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2747, + "name": "Sivakumar2011_WntSignalingPathway", + "repository_type": "biomodels", + "summary": "
Sivakumar2011_WntSignalingPathway
The secreted protein Wnt activates theheptahelical receptor Frizzled on nieghboring cells. Activation ofFrizzled causes the recruitment of additional membrane proteinswhich in turn result in 1) the activation of the proteinDishevelled via phosphorylation and 2) the activation of aheterotrimeric G protein of unknown type. Activation of Dishevelledresults in the down-regulation of the Beta-Catenin destructioncomplex which causes ubiquitination of Beta-Catenin and itsultimate degradation via the proteasome. Inhibition of theBeta-Catenin destruction complex yields a higher cytosolicconcentration of Beta-Catenin, which enters the nucleus, bindsvarious transcriptional regulatory molecules including the TCF/LEFclass of proteins, and results in the transcription of TCF/LEFtarget genes. Activation of the heterotrimeric G-protein pathway inturn activates Phospholipase C which in turn catalyzes thecatalysis of PI(4,5)P2 into DAG and IP3.

Reference:

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
OMICS 2011 Oct; 15(10): 729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000397.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3897, + "tag": "BioModels:BIOMD0000000397" + }, + { + "id": 3898, + "tag": "Canonical Wnt signaling pathway" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:10.490196+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000397", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2748": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2748, + "name": "Sivakumar2011_NeuralStemCellDifferentiation_Crosstalk", + "repository_type": "biomodels", + "summary": "
Sivakumar2011_NeuralStemCellDifferentiation_Crosstalk
This model is generated by integrating BIOMD0000000394(EGFR), BIOMD0000000395(Hedgehog), BIOMD0000000396(Notch) and BIOMD0000000397(Wnt), to investigate the signalling crosstalk between the fourpathways.

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
OMICS 2011 Oct; 15(10): 729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000398.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3899, + "tag": "BioModels:BIOMD0000000398" + }, + { + "id": 3898, + "tag": "Canonical Wnt signaling pathway" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 3896, + "tag": "Notch signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3894, + "tag": "Smoothened signaling pathway" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 16:15:11.033384+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000398", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2749": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2749, + "name": "Jenkinson2011_EGF_MAPK", + "repository_type": "biomodels", + "summary": "

This is a model described in the article:
Thermodynamically Consistent Model Calibration in Chemical Kinetics.
Garrett Jenkinson and John Goutsias, BMC Systems Biology 2011 May 6;5(1):64.; PMID:21548948.

ABSTRACT:
BACKGROUND:
The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function.
RESULTS:
We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database.
CONCLUSIONS:
TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.

This model is a thermodynamically feasible version of a previous modelin the BioModels database,BIOMD0000000019, described in Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Schoeberl et al (2002), PMID:11923843.
The only difference between the present model and the model listed under BIOMD0000000019 are the values of the parameters.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3900, + "tag": "BioModels:BIOMD0000000399" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3103, + "tag": "Signaling by EGFR" + } + ], + "timestamp_created": "2025-02-03 16:15:11.650733+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000399", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2750": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2750, + "name": "Cooling2007_IP3transients_CardiacMyocyte", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling hypertrophic IP3 transients in the cardiac myocyte.
Cooling M, Hunter P, Crampin EJ. Biophys J2007 Nov 15;93(10):3421-33 17693463,
Abstract:
Cardiac hypertrophy is a known risk factor for heart disease, and at thecellular level is caused by a complex interaction of signal transductionpathways. The IP3-calcineurin pathway plays an important role in stimulating thetranscription factor NFAT which binds to DNA cooperatively with otherhypertrophic transcription factors. Using available kinetic data, we construct amathematical model of the IP3 signal production system after stimulation by ahypertrophic alpha-adrenergic agonist (endothelin-1) in the mouse atrial cardiacmyocyte. We use a global sensitivity analysis to identify key controllingparameters with respect to the resultant IP3 transient, including thephosphorylation of cell-membrane receptors, the ligand strength and bindingkinetics to precoupled (with G(alpha)GDP) receptor, and the kinetics associatedwith precoupling the receptors. We show that the kinetics associated with thereceptor system contribute to the behavior of the system to a great extent, withprecoupled receptors driving the response to extracellular ligand. Finally, byreparameterizing for a second hypertrophic alpha-adrenergic agonist,angiotensin-II, we show that differences in key receptor kinetic and membranedensity parameters are sufficient to explain different observed IP3 transientsin essentially the same pathway.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Cooling M, Hunter P, Crampin EJ. (2007) - version02
The original CellML model was created by:
Cooling, Mike,
m.cooling@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model without Tumour.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

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This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3906, + "tag": "BioModels:BIOMD0000000402" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3905, + "tag": "Multiple myeloma" + }, + { + "id": 3452, + "tag": "Regulation of bone remodeling" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:13.167578+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000402", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2753": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2753, + "name": "Ayati2010_BoneRemodelingDynamics_WithTumour+DrugTreatment", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour and Drug Treatment.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3907, + "tag": "BioModels:BIOMD0000000403" + }, + { + "id": 3412, + "tag": "Chordata" + }, + { + "id": 3905, + "tag": "Multiple myeloma" + }, + { + "id": 3452, + "tag": "Regulation of bone remodeling" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:13.638349+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000403", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2754": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2754, + "name": "Bray1993_chemotaxis", + "repository_type": "biomodels", + "summary": "

This version of the model is very close to the version described in the paper with one exception: the binding of aspartate to the various receptor complexes, as well as the formation of the different complexes are modeled using chemical kinetics (mass action law), rather than instant equilibrium. The qualitative behaviour of the model is unchanged. Note that in order to quantitatively replicate the figure 8b, and in particular to have a basal bias of 0.7, we have to change the rate constant of the aspartate-triggered dephosphorylation of CheY from 59000 to 70000. The peaks have then slightly different values.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3908, + "tag": "Bacterial chemotaxis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3909, + "tag": "BioModels:BIOMD0000000404" + }, + { + "id": 3545, + "tag": "Chemotaxis" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:14.160747+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000404", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2755": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2755, + "name": "Cookson2011_EnzymaticQueueingCoupling", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Queueing up for enzymatic processing: correlated signaling through coupled degradation.
Natalie A Cookson, William H Mather, Tal Danino, Octavio Mondrag\u00f3n-Palomino, Ruth J Williams, Lev S Tsimring, & Jeff Hasty Molecular Systems Biology2011; 7:561; DOI:10.1038/msb.2011.94
Abstract:
High-throughput technologies have led to the generation of complex wiring diagrams as a post-sequencing paradigm for depicting the interactions between vast and diverse cellular species. While these diagrams are useful for analyzing biological systems on a large scale, a detailed understanding of the molecular mechanisms that underlie the observed network connections is critical for the further development of systems and synthetic biology. Here, we use queueing theory to investigate how \u2018waiting lines\u2019 can lead to correlations between protein \u2018customers\u2019 that are coupled solely through a downstream set of enzymatic \u2018servers\u2019. Using the E. coli ClpXP degradation machine as a model processing system, we observe significant cross-talk between two networks that are indirectly coupled through a common set of processors. We further illustrate the implications of enzymatic queueing using a synthetic biology application, in which two independent synthetic networks demonstrate synchronized behavior when common ClpXP machinery is overburdened. Our results demonstrate that such post-translational processes can lead to dynamic connections in cellular networks and may provide a mechanistic understanding of existing but currently inexplicable links.

Note:
Individual stochastic trajectories for a queueing system in three different conditions, 1) Underloaded, 2) Balanced and 3) Overloaded, demonstrate correlation resonance. The parameter values in this model correspond to the Balanced Condition.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3910, + "tag": "BioModels:BIOMD0000000405" + }, + { + "id": 3653, + "tag": "Regulation of catalytic activity" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:14.672758+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000405", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2756": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2756, + "name": "Moriya2011_CellCycle_FissionYeast", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Overexpression limits of fission yeast cell-cycle regulators in vivo and in silico.
Moriya H, Chino A, Kapuy O, Csik\u00e1sz-Nagy A, Nov\u00e1k B. Mol Syst Biol. 2011 Dec 6;7:556. 22146300 ,
Abstract:
Cellular systems are generally robust against fluctuations of intracellular parameters such as gene expression level. However, little is known about expression limits of genes required to halt cellular systems. In this study, using the fission yeast Schizosaccharomyces pombe, we developed a genetic 'tug-of-war' (gTOW) method to assess the overexpression limit of certain genes. Using gTOW, we determined copy number limits for 31 cell-cycle regulators; the limits varied from 1 to >100. Comparison with orthologs of the budding yeast Saccharomyces cerevisiae suggested the presence of a conserved fragile core in the eukaryotic cell cycle. Robustness profiles of networks regulating cytokinesis in both yeasts (septation-initiation network (SIN) and mitotic exit network (MEN)) were quite different, probably reflecting differences in their physiologic functions. Fragility in the regulation of GTPase spg1 was due to dosage imbalance against GTPase-activating protein (GAP) byr4. Using the gTOW data, we modified a mathematical model and successfully reproduced the robustness of the S. pombe cell cycle with the model.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3911, + "tag": "BioModels:BIOMD0000000406" + }, + { + "id": 3534, + "tag": "Regulation of cell cycle" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3074, + "tag": "Schizosaccharomyces pombe" + } + ], + "timestamp_created": "2025-02-03 16:15:15.208227+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000406", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2757": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2757, + "name": "Schliemann2011_TNF_ProAntiApoptosis", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Heterogeneity Reduces Sensitivity of Cell Death for TNF-Stimuli
Schliemann M, Bullinger E, Borchers S, Allgower F, Findeisen R, Scheurich P. BMC Syst Biol. 2011 Dec 28;5(1):204. 22204418 ,
Abstract:
BACKGROUND:Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor kappaB (NF-kappaB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner.RESULTS:This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-kappaB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability.CONCLUSIONS:Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-kappaB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity.

Note: SBML model generated from Matlab system description on 12-July-2011 21:08:15 by exportSBML Copyright Eric Bullinger 2007-2011

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3912, + "tag": "BioModels:BIOMD0000000407" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3913, + "tag": "rhabdomyosarcoma cell line" + } + ], + "timestamp_created": "2025-02-03 16:15:15.730531+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000407", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2758": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2758, + "name": "Hettling2011_CreatineKinase", + "repository_type": "biomodels", + "summary": "

This model is from the article:
\tAnalyzing the functional properties of the creatine kinase system with multiscale 'sloppy' modeling.
Hettling H, van Beek JH PLoS Comput Biol.2011 Aug;7(8):e1002130. PMEDID,
Abstract:
In this study the function of the two isoforms of creatine kinase (CK; EC 2.7.3.2) in myocardium is investigated. The 'phosphocreatine shuttle' hypothesis states that mitochondrial and cytosolic CK plays a pivotal role in the transport of high-energy phosphate (HEP) groups from mitochondria to myofibrils in contracting muscle. Temporal buffering of changes in ATP and ADP is another potential role of CK. With a mathematical model, we analyzed energy transport and damping of high peaks of ATP hydrolysis during the cardiac cycle. The analysis was based on multiscale data measured at the level of isolated enzymes, isolated mitochondria and on dynamic response times of oxidative phosphorylation measured at the whole heart level. Using 'sloppy modeling' ensemble simulations, we derived confidence intervals for predictions of the contributions by phosphocreatine (PCr) and ATP to the transfer of HEP from mitochondria to sites of ATP hydrolysis. Our calculations indicate that only 15\u00b18% (mean\u00b1SD) of transcytosolic energy transport is carried by PCr, contradicting the PCr shuttle hypothesis. We also predicted temporal buffering capabilities of the CK isoforms protecting against high peaks of ATP hydrolysis (3750 \u00b5M*s(-1)) in myofibrils. CK inhibition by 98% in silico leads to an increase in amplitude of mitochondrial ATP synthesis pulsation from 215\u00b123 to 566\u00b131 \u00b5M*s(-1), while amplitudes of oscillations in cytosolic ADP concentration double from 77\u00b111 to 146\u00b11 \u00b5M. Our findings indicate that CK acts as a large bandwidth high-capacity temporal energy buffer maintaining cellular ATP homeostasis and reducing oscillations in mitochondrial metabolism. However, the contribution of CK to the transport of high-energy phosphate groups appears limited. Mitochondrial CK activity lowers cytosolic inorganic phosphate levels while cytosolic CK has the opposite effect.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in budding yeast.
Queralt E, Lehane C, Novak B, Uhlmann F. Cell. 2006 May 19;125(4):719-32. 16713564 ,
Abstract:
After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the \"mitotic exit network\" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Dynamics and feedback loops in the transforming growth factor \u03b2 signaling pathway.
Wegner K, Bachmann A, Schad JU, Lucarelli P, Sahle S, Nickel P, Meyer C, Klingm\u00fcller U, Dooley S, Kummer U. Biophys Chem. 2012 Jan 5. 22284904 ,
Abstract:
Transforming growth factor \u03b2 (TGF-\u03b2) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-\u03b2 target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3916, + "tag": "BioModels:BIOMD0000000410" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3352, + "tag": "Transforming growth factor beta receptor signaling pathway" + }, + { + "id": 3674, + "tag": "hepatocyte" + } + ], + "timestamp_created": "2025-02-03 16:15:17.193904+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000410", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2761": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2761, + "name": "Heiland2012_CircadianClock_C.reinhardtii", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Modeling temperature entrainment of circadian clocks using the Arrhenius equation and a reconstructed model from Chlamydomonas reinhardtii
Ines Heiland, Christian Bodenstein, Thomas Hinze, Olga Weisheit, Oliver Ebenhoeh, Maria Mittag and Stefan Schuster Journal of Biological Physics 4 March 2012; pp 1-16; doi: 10.1007/s10867-012-9264-x ,
Abstract:
Endogenous circadian rhythms allow living organisms to anticipate daily variations in their natural environment. Temperature regulation and entrainment mechanisms of circadian clocks are still poorly understood. To better understand the molecular basis of these processes, we built a mathematical model based on experimental data examining temperature regulation of the circadian RNA-binding protein CHLAMY1 from the unicellular green alga Chlamydomonas reinhardtii , simulating the effect of temperature on the rates by applying the Arrhenius equation. Using numerical simulations, we demonstrate that our model is temperature-compensated and can be entrained to temperature cycles of various length and amplitude. The range of periods that allow entrainment of the model depends on the shape of the temperature cycles and is larger for sinusoidal compared to rectangular temperature curves. We show that the response to temperature of protein (de)phosphorylation rates play a key role in facilitating temperature entrainment of the oscillator in Chlamydomonas reinhardtii . We systematically investigated the response of our model to single temperature pulses to explain experimentally observed phase response curves.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3917, + "tag": "BioModels:BIOMD0000000411" + }, + { + "id": 3918, + "tag": "Chlamydomonas reinhardtii" + }, + { + "id": 3609, + "tag": "Circadian temperature homeostasis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:17.673599+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000411", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2762": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2762, + "name": "Pokhilko2012_CircClock_RepressilatorFeedbackloop", + "repository_type": "biomodels", + "summary": "

This model is from the article:
The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops
Pokhilko A, Fern\u00e1ndez AP, Edwards KD, Southern MM, Halliday KJ, Millar AJ. Mol Syst Biol.2012 Mar 6;8:574. 22395476,
Abstract:
Circadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene PSEUDO-RESPONSE REGULATOR 9 was repressed by an evening gene, previously identified with TIMING OF CAB EXPRESSION1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes LATE ELONGATED HYPOCOTYL and CIRCADIAN CLOCK ASSOCIATED1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3919, + "tag": "BioModels:BIOMD0000000412" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:18.163608+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000412", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2763": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2763, + "name": "Band2012_DII-Venus_FullModel", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.
Band LR, Wells DM, Larrieu A, Sun J, Middleton AM, French AP, Brunoud G, Sato EM, Wilson MH, P\u00e9ret B, Oliva M, Swarup R, Sairanen I, Parry G, Ljung K, Beeckman T, Garibaldi JM, Estelle M, Owen MR, Vissenberg K, Hodgman TC, Pridmore TP, King JR, Vernoux T, Bennett MJ. Proc Natl Acad Sci U S A.2012 Mar 20;109(12):4668-73 22393022,
Abstract:
Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

This model corresponds to the full model described in the article.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3920, + "tag": "BioModels:BIOMD0000000413" + }, + { + "id": 3921, + "tag": "Positive gravitropism" + }, + { + "id": 3922, + "tag": "Regulation of auxin metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:18.843579+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000413", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2764": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2764, + "name": "Band2012_DII-Venus_ReducedModel", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.
Band LR, Wells DM, Larrieu A, Sun J, Middleton AM, French AP, Brunoud G, Sato EM, Wilson MH, P\u00e9ret B, Oliva M, Swarup R, Sairanen I, Parry G, Ljung K, Beeckman T, Garibaldi JM, Estelle M, Owen MR, Vissenberg K, Hodgman TC, Pridmore TP, King JR, Vernoux T, Bennett MJ. Proc Natl Acad Sci U S A.2012 Mar 20;109(12):4668-73 22393022,
Abstract:
Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

This model corresponds to the simplified model described in the article. It is assumed that, on the timescale of DII-VENUS degradation, the concentrations of auxin, TIR1/AFB, and their complexes can be approximated by quasi-steady-state expressions. This reduced the full model to a single ODE that describes how the DII-VENUS dynamics depend on the auxin influx and four parameter groupings.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3923, + "tag": "BioModels:BIOMD0000000414" + }, + { + "id": 3921, + "tag": "Positive gravitropism" + }, + { + "id": 3922, + "tag": "Regulation of auxin metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:19.379301+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000414", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2765": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2765, + "name": "Mellor2012_LipooxygenasePathway", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Reduction of off-flavor generation in soybean homogenates: a mathematical model.
Mellor N , Bligh F , Chandler I , Hodgman C \tJ. Food Sci.2010 Sep; 75(7): R131-8; PMID: 2153556,
Abstract:
The generation of off-flavors in soybean homogenates such as n-hexanal via the lipoxygenase (LOX) pathway can be a problem in the processed food industry. Previous studies have examined the effect of using soybean varieties missing one or more of the 3 LOX isozymes on n-hexanal generation. A dynamic mathematical model of the soybean LOX pathway using ordinary differential equations was constructed using parameters estimated from existing data with the aim of predicting how n-hexanal generation could be reduced. Time-course simulations of LOX-null beans were run and compared with experimental results. Model L(2), L(3), and L(12) beans were within the range relative to the wild type found experimentally, with L(13) and L(23) beans close to the experimental range. Model L(1) beans produced much more n-hexanal relative to the wild type than those in experiments. Sensitivity analysis indicates that reducing the estimated K(m) parameter for LOX isozyme 3 (L-3) would improve the fit between model predictions and experimental results found in the literature. The model also predicts that increasing L-3 or reducing L-2 levels within beans may reduce n-hexanal generation. PRACTICAL APPLICATION: This work describes the use of mathematics to attempt to quantify the enzyme-catalyzed conversions of compounds in soybean homogenates into undesirable flavors, primarily from the compound n-hexanal. The effect of different soybean genotypes and enzyme kinetic constants was also studied, leading to recommendations on which combinations might minimize off-flavor levels and what further work might be carried out to substantiate these conclusions.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3924, + "tag": "BioModels:BIOMD0000000415" + }, + { + "id": 3925, + "tag": "Glycine max" + }, + { + "id": 3926, + "tag": "Lipoxygenase pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:20.156910+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000415", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2766": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2766, + "name": "Muraro2011_Cytokinin-Auxin_CrossRegulation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
The influence of cytokinin-auxin cross-regulation on cell-fate determination in Arabidopsis thaliana root development
Muraro D, Byrne H, King J, Voss U, Kieber J, Bennett M. J Theor Biol.2011 Aug 21;283(1):152-67.PMID: 21640126,
Abstract:
Root growth and development in Arabidopsis thaliana are sustained by a specialised zone termed the meristem, which contains a population of dividing and differentiating cells that are functionally analogous to a stem cell niche in animals. The hormones auxin and cytokinin control meristem size antagonistically. Local accumulation of auxin promotes cell division and the initiation of a lateral root primordium. By contrast, high cytokinin concentrations disrupt the regular pattern of divisions that characterises lateral root development, and promote differentiation. The way in which the hormones interact is controlled by a genetic regulatory network. In this paper, we propose a deterministic mathematical model to describe this network and present model simulations that reproduce the experimentally observed effects of cytokinin on the expression of auxin regulated genes. We show how auxin response genes and auxin efflux transporters may be affected by the presence of cytokinin. We also analyse and compare the responses of the hormones auxin and cytokinin to changes in their supply with the responses obtained by genetic mutations of SHY2, which encodes a protein that plays a key role in balancing cytokinin and auxin regulation of meristem size. We show that although shy2 mutations can qualitatively reproduce the effect of varying auxin and cytokinin supply on their response genes, some elements of the network respond differently to changes in hormonal supply and to genetic mutations, implying a different, general response of the network. We conclude that an analysis based on the ratio between these two hormones may be misleading and that a mathematical model can serve as a useful tool for stimulate further experimental work by predicting the response of the network to changes in hormone levels and to other genetic mutations.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3927, + "tag": "BioModels:BIOMD0000000416" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:20.787117+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000416", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2767": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2767, + "name": "Ratushny2012_NF", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3928, + "tag": "BioModels:BIOMD0000000417" + }, + { + "id": 3929, + "tag": "Regulation of transcription, DNA-templated" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:15:21.304074+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000417", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2768": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2768, + "name": "Ratushny2012_SPF", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3930, + "tag": "BioModels:BIOMD0000000418" + }, + { + "id": 3929, + "tag": "Regulation of transcription, DNA-templated" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:15:21.832910+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000418", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2769": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2769, + "name": "Ratushny2012_SPF_I", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3931, + "tag": "BioModels:BIOMD0000000419" + }, + { + "id": 3929, + "tag": "Regulation of transcription, DNA-templated" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:15:22.299869+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000419", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2770": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2770, + "name": "Ratushny2012_ASSURE_I", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3932, + "tag": "BioModels:BIOMD0000000420" + }, + { + "id": 3929, + "tag": "Regulation of transcription, DNA-templated" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:15:22.820920+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000420", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2771": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2771, + "name": "Ratushny2012_ASSURE_II", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3933, + "tag": "BioModels:BIOMD0000000421" + }, + { + "id": 3929, + "tag": "Regulation of transcription, DNA-templated" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:15:23.300454+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000421", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2772": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2772, + "name": "Middleton2012_GibberellinSignalling", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Mathematical modeling elucidates the role of transcriptional feedback in gibberellin signaling.
Middleton AM , \u00dabeda-Tom\u00e1s S , Griffiths J , Holman T , Hedden P , Thomas SG , Phillips AL , Holdsworth MJ , Bennett MJ , King JR, Owen MR Proc. Natl. Acad. Sci. U.S.A. 2012 May; 109(19): 7571-6 22523240 ,
Abstract:
The hormone gibberellin (GA) is a key regulator of plant growth. Many of the components of the gibberellin signal transduction [e.g., GIBBERELLIN INSENSITIVE DWARF 1 (GID1) and DELLA], biosynthesis [e.g., GA 20-oxidase (GA20ox) and GA3ox], and deactivation pathways have been identified. Gibberellin binds its receptor, GID1, to form a complex that mediates the degradation of DELLA proteins. In this way, gibberellin relieves DELLA-dependent growth repression. However, gibberellin regulates expression of GID1, GA20ox, and GA3ox, and there is also evidence that it regulates DELLA expression. In this paper, we use integrated mathematical modeling and experiments to understand how these feedback loops interact to control gibberellin signaling. Model simulations are in good agreement with in vitro data on the signal transduction and biosynthesis pathways and in vivo data on the expression levels of gibberellin-responsive genes. We find that GA-GID1 interactions are characterized by two timescales (because of a lid on GID1 that can open and close slowly relative to GA-GID1 binding and dissociation). Furthermore, the model accurately predicts the response to exogenous gibberellin after a number of chemical and genetic perturbations. Finally, we investigate the role of the various feedback loops in gibberellin signaling. We find that regulation of GA20ox transcription plays a significant role in both modulating the level of endogenous gibberellin and generating overshoots after the removal of exogenous gibberellin. Moreover, although the contribution of other individual feedback loops seems relatively small, GID1 and DELLA transcriptional regulation acts synergistically with GA20ox feedback.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3934, + "tag": "BioModels:BIOMD0000000422" + }, + { + "id": 3935, + "tag": "Gibberellin mediated signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:23.811034+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000422", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2773": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2773, + "name": "Nyman2012_InsulinSignalling", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes.
Nyman E, Fagerholm S, Jullesson D, Str\u00e5lfors P, Cedersund G. FEBS J. 2012 Jan 16. 22248283 ,
Abstract:
Insulin signaling through insulin receptor (IR) and insulin receptor substrate-1 (IRS1) is important for insulin control of target cells. We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling. Database The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/nyman/index.html free of charge.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3936, + "tag": "BioModels:BIOMD0000000423" + }, + { + "id": 3937, + "tag": "Insulin receptor signaling pathway" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3938, + "tag": "adipocyte" + } + ], + "timestamp_created": "2025-02-03 16:15:24.284004+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000423", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2774": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2774, + "name": "Faratian2009 - Role of PTEN in Trastuzumab resistance", + "repository_type": "biomodels", + "summary": "
Faratian2009 - Role of PTEN in Trastuzumabresistance

This model is described in the article:

Faratian D, Goltsov A, Lebedeva G, Sorokin A, Moodie S, Mullen P, Kay C, Um IH, Langdon S, Goryanin I, Harrison DJ.
Cancer Res. 2009 Aug; 69(16): 6713-6720

Abstract:

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000424.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3939, + "tag": "BioModels:BIOMD0000000424" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3716, + "tag": "Phosphatidylinositol 3-kinase signaling" + }, + { + "id": 3940, + "tag": "Response to drug" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:24.855473+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000424", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2775": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2775, + "name": "Tan2012 - Antibiotic Treatment, Inoculum Effect", + "repository_type": "biomodels", + "summary": "
Tan2012 - Antibiotic Treatment, Inoculum Effect

The efficacy of many antibiotics decreases with increasing bacterial density, a phenomenon called the \u2018inoculum effect\u2019 (IE). This study reveals that, for ribosome-targeting antibiotics, IE is due to bistable inhibition of bacterial growth, which reduces the efficacy of certain treatment frequencies.

This model is described in the article:

Tan C, Phillip Smith R, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L.
Mol Syst Biol. 2012 Oct 9; 8:617

Abstract:

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.

This model is hosted on BioModels Database and identified by: MODEL1208300000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3250, + "tag": "Bacterial infectious disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3941, + "tag": "BioModels:BIOMD0000000425" + }, + { + "id": 3942, + "tag": "Cellular response to antibiotic" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:25.352353+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000425", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2776": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2776, + "name": "Mosca2012 - Central Carbon Metabolism Regulated by AKT", + "repository_type": "biomodels", + "summary": "
Mosca2012 - Central Carbon Metabolism Regulated by AKT

The role of the PI3K/Akt/PKB signalling pathway in oncogenesis has been extensively investigated and altered expression or mutations of many components of this pathway have been implicated in human cancers. Indeed, expression of constitutively active forms of Akt/PKB can prevent cell death upon growth factor withdrawal. PI3K/Akt/mTOR-mediated survival relies on a profound metabolic adaptation, including aerobic glycolysis. Here, the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis has been modelled, considering two states - high and low PI3K/Akt/mTOR activity. The high PI3K/Akt/mTOR activity represents cancer cell line where PI3K/Akt/mTOR promotes a high rate of glucose metabolism (condition H) and the low PI3K/Akt/mTOR activity is characterised by a lower glycolytic rate due to a reduced PI3K/Akt/mTOR signal (condition L). This model corresponds to the high PI3K/Akt/mTOR signal (condition H).

This model is described in the article:

Mosca E, Alfieri R, Maj C, Bevilacqua A, Canti G, Milanesi L.
Frontiers in Systems Biology. 2012 Oct 13

Abstract:

Signal transduction pathways and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modelled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis. We used a computational model in order to compare two metabolic states generated by the specific variation of the metabolic fluxes regulated by the activity of the PI3K/Akt/mTOR pathway. One of the two states represented the metabolism of a growing cancer cell characterised by aerobic glycolysis and cellular biosynthesis, while the other state represented the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism, as reported in literature in relation to the activity of the PI3K/Akt/mTOR. Some steps that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.

This model is hosted on BioModels Database and identified by: MODEL1210150000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3943, + "tag": "BioModels:BIOMD0000000426" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3651, + "tag": "HeLa cell" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3944, + "tag": "Protein kinase B signaling" + }, + { + "id": 3945, + "tag": "Regulation of metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:26.492526+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000426", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2777": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2777, + "name": "Bianconi2012 - EGFR and IGF1R pathway in lung cancer", + "repository_type": "biomodels", + "summary": "
Bianconi2012 - EGFR and IGF1R pathway in lung cancer

EGFR and IGF1R pathways play a key role in various human cancers and are crucial for tumour transformation and survival of malignant cells. High EGFR and IGF1R expression and activity has been associated with multiple aspects of cancer progression including tumourigenesis, metastasis, resistance to chemotherapeutics and other molecularly targeted drugs. Here, the biological relationship between the proteins involved in EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks has been modelled to study the time behaviour of the overall system, and the functional interdependencies among the receptors, the proteins and kinases involved.

This model is described in the article:

Bianconi F, Baldelli E, Ludovini V, Crin\u00f2 L, Flacco A, Valigi P.
Biotechnol Adv. 2012 Jan-Feb;30(1):142-53.

Abstract:

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome.

This model is hosted on BioModels Database and identified by: MODEL1209230000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3946, + "tag": "BioModels:BIOMD0000000427" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3256, + "tag": "Non-small cell lung carcinoma" + }, + { + "id": 3947, + "tag": "Regulation of ERK1 and ERK2 cascade" + }, + { + "id": 3948, + "tag": "Regulation of phosphatidylinositol 3-kinase signaling" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:27.105419+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000427", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2778": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2778, + "name": "Achcar2012 - Glycolysis in bloodstream form T. brucei", + "repository_type": "biomodels", + "summary": "
Achcar2012 - Glycolysis in bloodstream form T. brucei

Kinetic models of metabolism require quantitative knowledge of detailed kinetic parameters. However, the knowledge about these parameters is often uncertain. An analysis of the effect of parameter uncertainties on a particularly well defined example of a quantitative metablic model, the model of glycolysis in bloodstream form Trypanosoma brucei , has been presented here.

This model is described in the article:

Achcar F, Kerkhoven EJ; SilicoTryp Consortium, Bakker BM, Barrett MP, Breitling R.
PLoS Comput Biol. 2012 Jan; 8(1):e1002352.

Abstract:

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

This model is hosted on BioModels Database and identified by: MODEL1209130000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3949, + "tag": "BioModels:BIOMD0000000428" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:15:27.827261+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000428", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2779": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2779, + "name": "Schaber2012 - Hog pathway in yeast", + "repository_type": "biomodels", + "summary": "
Schaber2012 - Hog pathway in yeast

The high osmolarity glycerol (HOG) pathway in the yeast Saccharomyces cerevisiae is one of the best-studied mitogen-activated protein kinase (MAPK) pathways and serves as a prototype signalling system for eukaryotes. This pathway is necessary and sufficient to adapt to high external osmolarity. A key component of this pathway is the stress-activated protein kinase (SAPK) Hog1, which is rapidly phosphorylated by the SAPK kinase Pbs2 upon hyper-osmotic shock, and which is the terminal kinase of two parallel signalling pathways, subsequently called the Sho1 branch and the Sln1 branch, respectively. Ensemble modelling (192 models) is used to study the yeast HOG pathway, a prototype for eukaryotic mitogen-activated kinase signalling systems. The best fit model (Model Nr.22: described here) provides new insights into the function of this system, some of which are then experimentally validated.

This model is described in the article:

Schaber J, Baltanas R, Bush A, Klipp E, Colman-Lerner A.
Mol Syst Biol. 2012 Nov 13;8:622.

Abstract:

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.

This model is hosted on BioModels Database and identified by: MODEL1209110001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3950, + "tag": "BioModels:BIOMD0000000429" + }, + { + "id": 3387, + "tag": "MAPK cascade involved in osmosensory signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:15:28.402615+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000429", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2780": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2780, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-40). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280020 - M4_K2_USEQ] correspond to type M4 with mass-action kinetics K2, in USEQ (Unsequestrated ) condition.

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280020 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3951, + "tag": "BioModels:BIOMD0000000430" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:29.106908+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000430", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2781": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2781, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280024 - M4_K2_PSEQ] correspond to type M4 with mass-action kinetics K2, in PSEQ (sequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280024 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3952, + "tag": "BioModels:BIOMD0000000431" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:30.641939+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000431", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2782": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2782, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280036 - M4_K2_QSS_USEQ] correspond to type M4 with mass-action kinetics K2, in QSS (quasi steady state) and USEQ (Unsequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280036 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3953, + "tag": "BioModels:BIOMD0000000432" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:31.306236+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000432", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2783": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2783, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280040 - M4_K2_QSS_PSEQ] correspond to type M4 with mass-action kinetics K2, in QSS (quasi steady state) and USEQ (Unsequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280040 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3954, + "tag": "BioModels:BIOMD0000000433" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:32.002085+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000433", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2784": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2784, + "name": "McAuley2012 - Whole-body Cholesterol Metabolism", + "repository_type": "biomodels", + "summary": "
McAuley2012 - Whole-body Cholesterol Metabolism

Lipid metabolism has a key role to play in human longevity and healthy aging. A whole-body mathematical model of cholesterol metabolism that explores the changes in both the rate of intestinal cholesterol absorption and the hepatic rate of clearance of LDL-C from the plasma, has been presented here. The model showed that of these two mechanisms, changes to the rate of LDL-C removal from the plasma with age had the most significant effect on cholesterol metabolism.

The original SBML model file was generated using MathSBML 2.5.1.

This model is described in the article:

Mc Auley MM, Wilkinson DJ, Jones JJ, Kirkwood TT.
BMC Syst Biol. 2012 Oct 10;6(1):130.

Abstract:

BACKGROUND: Global demographic changes have stimulated marked interest in the process of ageing. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.

RESULTS: The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116mg/dL.

CONCLUSIONS: Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism.

This model is hosted on BioModels Database and identified by: MODEL1206010000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3955, + "tag": "Age-dependent general metabolic decline" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3956, + "tag": "BioModels:BIOMD0000000434" + }, + { + "id": 3957, + "tag": "Cholesterol ester storage disease" + }, + { + "id": 3958, + "tag": "Cholesterol metabolic process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:32.503643+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000434", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2785": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2785, + "name": "deBack2012 - Lineage Specification in Pancreas Development", + "repository_type": "biomodels", + "summary": "
deBack2012 - Lineage Specification in Pancreas Development

This model of two neighbouring pancreas precursor cells, describes the exocrine versus endocrine lineage specification process. To account for the tissue scale patterns, this couplet model has been extended to hundreds of coupled cells.

This model is described in the article:

de Back W., Zhou JX, Brusch L
J. R. Soc. Interface 6 February 2013 vol. 10 no. 79 20120766

Abstract:

The cell fate decision of multi-potent pancreatic progenitor cells between the exocrine and endocrine lineages is regulated by Notch signalling, mediated by cell\u2013cell interactions. However, canonical models of Notch-mediated lateral inhibition cannot explain the scattered spatial distribution of endocrine cells and the cell-type ratio in the developing pancreas. Based on evidence from acinar-to-islet cell transdifferentiation in vitro, we propose that lateral stabilization, i.e. positive feedback between adjacent progenitor cells, acts in parallel with lateral inhibition to regulate pattern formation in the pancreas. A simple mathematical model of transcriptional regulation and cell\u2013cell interaction reveals the existence of multi-stability of spatial patterns whose simultaneous occurrence causes scattering of endocrine cells in the presence of noise. The scattering pattern allows for control of the endocrine-to-exocrine cell-type ratio by modulation of lateral stabilization strength. These theoretical results suggest a previously unrecognized role for lateral stabilization in lineage specification, spatial patterning and cell-type ratio control in organ development.

This model is hosted on BioModels Database and identified by: MODEL1211010000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3959, + "tag": "BioModels:BIOMD0000000435" + }, + { + "id": 3960, + "tag": "Cell-cell signaling" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:33.042536+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000435", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2786": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2786, + "name": "Gupta2009 - Eicosanoid Metabolism", + "repository_type": "biomodels", + "summary": "
Gupta2009 - Eicosanoid Metabolism

Integrated model of eicosanoid metabolism and signaling based on lipidomics flux analysis.

This model is described in the article:

Gupta S, Maurya MR, Stephens DL, Dennis EA, Subramaniam S.
Biophys. J. 2009 Jun; 96(11):4542-51.

Abstract:

There is increasing evidence for a major and critical involvement of lipids in signal transduction and cellular trafficking, and this has motivated large-scale studies on lipid pathways. The Lipid Metabolites and Pathways Strategy consortium is actively investigating lipid metabolism in mammalian cells and has made available time-course data on various lipids in response to treatment with KDO(2)-lipid A (a lipopolysaccharide analog) of macrophage RAW 264.7 cells. The lipids known as eicosanoids play an important role in inflammation. We have reconstructed an integrated network of eicosanoid metabolism and signaling based on the KEGG pathway database and the literature and have developed a kinetic model. A matrix-based approach was used to estimate the rate constants from experimental data and these were further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data well for all species, and simulated enzyme activities were similar to their literature values. The quantitative model for eicosanoid metabolism that we have developed can be used to design experimental studies utilizing genetic and pharmacological perturbations to probe fluxes in lipid pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000436 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3961, + "tag": "BioModels:BIOMD0000000436" + }, + { + "id": 3962, + "tag": "Eicosanoid signaling pathway" + }, + { + "id": 3963, + "tag": "Icosanoid metabolic process" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:33.554350+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000436", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2787": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2787, + "name": "Tseng2012 - Circadian clock of N.crassa", + "repository_type": "biomodels", + "summary": "
Tseng2012 - Circadian clock of N.crassa

A comprehensive model of the circardian clock of fungal Neurospora crassa , which encompasses existing knowledge of the biochemistry of Neurospora clock, is described by Tseng et al. (2012). The model is validated against a wide range of experimental phenotypes and has been used to investigate possible molecular explanations of temperature compensation.

This model is described in the article:

Tseng YY, Hunt SM, Heintzen C, Crosthwaite SK, Schwartz JM
PLoS Comput. Biol. [2012 ; Volume: 8 (Issue: 3 )] Page info: e1002437

Abstract:

Circadian clocks provide an internal measure of external time allowing organisms to anticipate and exploit predictable daily changes in the environment. Rhythms driven by circadian clocks have a temperature compensated periodicity of approximately 24 hours that persists in constant conditions and can be reset by environmental time cues. Computational modelling has aided our understanding of the molecular mechanisms of circadian clocks, nevertheless it remains a major challenge to integrate the large number of clock components and their interactions into a single, comprehensive model that is able to account for the full breadth of clock phenotypes. Here we present a comprehensive dynamic model of the Neurospora crassa circadian clock that incorporates its key components and their transcriptional and post-transcriptional regulation. The model accounts for a wide range of clock characteristics including: a periodicity of 21.6 hours, persistent oscillation in constant conditions, arrhythmicity in constant light, resetting by brief light pulses, and entrainment to full photoperiods. Crucial components influencing the period and amplitude of oscillations were identified by control analysis. Furthermore, simulations enabled us to propose a mechanism for temperature compensation, which is achieved by simultaneously increasing the translation of frq RNA and decreasing the nuclear import of FRQ protein.

Figure 3 of the reference publication has been reproduced using Copasi 4.8 (Build 35).

This model is hosted on BioModels Database and identifiedby: MODEL1212150000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3964, + "tag": "BioModels:BIOMD0000000437" + }, + { + "id": 3568, + "tag": "Neurospora crassa" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3965, + "tag": "Temperature compensation of the circadian clock" + } + ], + "timestamp_created": "2025-02-03 16:15:34.010537+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000437", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2788": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2788, + "name": "Saeidi2012 - Quorum sensing device that produces GFP", + "repository_type": "biomodels", + "summary": "
Saeidi2012 - Quorum sensing device that produces GFP

Saeidi et al. (2012) has modelled a quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL).

This model is described in the article:

Nazanin Saeidi, Mohamed Arshath, Matthew Wook Chang, Chueh Loo Poh
Chemical Engineering Science. December 2012.

Abstract:

Modeling of biological parts is of crucial importance as it enables the in silico study of synthetic biological systems prior to the actual construction of genetic circuits, which can be time consuming and costly. Because standard biological parts are utilized to build the synthetic systems, it is important that each of these standard parts is well characterized and has a corresponding mathematical model that could simulate the characteristics of the part. These models could be used in computer aided design (CAD) tools during the design stage to facilitate the building of the model of biological systems. This paper describes the development of a mathematical model that is able to simulate both the dynamic and static performance of a biological device created using standard parts. We modeled an example quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL). The parameters of the model were estimated using experimental results. The simulation results show that the model was able to simulate behavior similar to experimental results. Since it is important that these models and the content in the models can be searchable and readable by machines, standard SBML (system biology markup language) format was used to store the models. All parts and reactions are fully annotated to enable easy searching, and the models follow the Minimum Information Requested In the Annotation of Models (MIRIAM) compliance as well as the Minimum Information About a Simulation Experiment (MIASE).

Figure 4a of the reference publication has been reproduced as curation figure. The plot shows the performance of the model at different concentrations of the inducer (3OC12HSL=5E-10, 5E-07, 5E-07).

This model is hosted on BioModels Database and identifiedby: MODEL1301110000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3966, + "tag": "BioModels:BIOMD0000000438" + }, + { + "id": 3967, + "tag": "Quorum sensing" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:34.475082+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000438", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2789": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2789, + "name": "Smith2009 - RGS mediated GTP hydrolysis", + "repository_type": "biomodels", + "summary": "
Smith2009 - RGS mediated GTP hydrolysis

This model is described in the article:

Smith B, Hill C, Godfrey EL, Rand D, van den Berg H, Thornton S, Hodgkin M, Davey J, Ladds G.
Cell Signal. 2009 Jul;21(7):1151-60.

Abstract:

G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increases the intrinsic GTPase activity of Galpha-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where Galpha(GTP) enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the Galpha produces this unimodal relationship.

To reproduce dose-response plots in the publication, the model is simulated with 12 different concentrations (see parameter Ligand_conc). For each concentration, a single value must be obtained from the integral of the trajectory of species z3 from time=0 to time=30. These values are then used to build a dose-response plot (authors used GraphPad Prism). Mutant strains are simulated with alternative parameter values or initial conditions in Table S3.

This model is hosted on BioModels Database and identified by: MODEL1212040001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3968, + "tag": "BioModels:BIOMD0000000439" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3074, + "tag": "Schizosaccharomyces pombe" + } + ], + "timestamp_created": "2025-02-03 16:15:34.949744+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000439", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2790": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2790, + "name": "Sarma2012 - Oscillations in MAPK cascade (S1)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S1)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S1 that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3969, + "tag": "BioModels:BIOMD0000000440" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:35.414922+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000440", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2791": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2791, + "name": "Sarma2012 - Oscillations in MAPK cascade (S2)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S2)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S2 that comprises negative feedback from MK-PP to MKK_PP layer coupled to positive feedback from MK-PP to MKKK-P layer.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190004 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3970, + "tag": "BioModels:BIOMD0000000441" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:35.888187+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000441", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2792": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2792, + "name": "Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling module", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling module

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S2 that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer. In addition, a positive feedback loop from S2 to the phosphorylation step of Kinase X has been introduced.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190005 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3971, + "tag": "BioModels:BIOMD0000000442" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:36.356394+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000442", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2793": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2793, + "name": "Sarma2012 - Oscillations in MAPK cascade (S1n)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S1n)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. Further these models were extended to S1n and S2n, to incorporate the nuclear-cytoplasmic translocation of the MK layer components of the cascade. This model corresponds to model S1n that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer, with the inclusion of nuclear-cytoplasmic translocation.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190006 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3972, + "tag": "BioModels:BIOMD0000000443" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:36.828172+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000443", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2794": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2794, + "name": "Sarma2012 - Oscillations in MAPK cascade (S2n)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S2n)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. Further these models were extended to S1n and S2n, to incorporate the nuclear-cytoplasmic translocation of the MK layer components of the cascade. This model corresponds to model S2n that comprises negative feedback from MK-PP to MKK-PP layer coupled to positive feedback from MK-PP to MKKK-PP layer, with the inclusion of nuclear-cytoplasmic translocation.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190007 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3973, + "tag": "BioModels:BIOMD0000000444" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:37.303989+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000444", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2795": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2795, + "name": "Pokhilko2013 - TOC1 signalling in Arabidopsis circadian clock", + "repository_type": "biomodels", + "summary": "
Pokhilko2013 - TOC1 signalling in Arabidopsiscircadian clock

In this model, Pokhilko et al. has incorporated the negative transcriptional regulations of the core clock genes by TOC1 and the up-regulation of TOC1 expression by ABA signalling, to their previous model BIOMD0000000412

This model is described in the article:

Pokhilko A, Mas P, Millar AJ.
BMC Syst Biol 2013; 7: 23

Abstract:

BACKGROUND: 24-hour biological clocks are intimately connected to the cellular signalling network, which complicates the analysis of clock mechanisms. The transcriptional regulator TOC1 (TIMING OF CAB EXPRESSION 1) is a founding component of the gene circuit in the plant circadian clock. Recent results show that TOC1 suppresses transcription of multiple target genes within the clock circuit, far beyond its previously-described regulation of the morning transcription factors LHY (LATE ELONGATED HYPOCOTYL) and CCA1 (CIRCADIAN CLOCK ASSOCIATED 1). It is unclear how this pervasive effect of TOC1 affects the dynamics of the clock and its outputs. TOC1 also appears to function in a nested feedback loop that includes signalling by the plant hormone Abscisic Acid (ABA), which is upregulated by abiotic stresses, such as drought. ABA treatments both alter TOC1 levels and affect the clock's timing behaviour. Conversely, the clock rhythmically modulates physiological processes induced by ABA, such as the closing of stomata in the leaf epidermis. In order to understand the dynamics of the clock and its outputs under changing environmental conditions, the reciprocal interactions between the clock and other signalling pathways must be integrated. RESULTS: We extended the mathematical model of the plant clock gene circuit by incorporating the repression of multiple clock genes by TOC1, observed experimentally. The revised model more accurately matches the data on the clock's molecular profiles and timing behaviour, explaining the clock's responses in TOC1 over-expression and toc1 mutant plants. A simplified representation of ABA signalling allowed us to investigate the interactions of ABA and circadian pathways. Increased ABA levels lengthen the free-running period of the clock, consistent with the experimental data. Adding stomatal closure to the model, as a key ABA- and clock-regulated downstream process allowed to describe TOC1 effects on the rhythmic gating of stomatal closure. CONCLUSIONS: The integrated model of the circadian clock circuit and ABA-regulated environmental sensing allowed us to explain multiple experimental observations on the timing and stomatal responses to genetic and environmental perturbations. These results crystallise a new role of TOC1 as an environmental sensor, which both affects the pace of the central oscillator and modulates the kinetics of downstream processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000445.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3974, + "tag": "BioModels:BIOMD0000000445" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:37.787216+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000445", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2796": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2796, + "name": "Erguler2013 - Unfolded protein stress response", + "repository_type": "biomodels", + "summary": "
Erguler2013 - Unfolded protein stress response

The model investigates the mechanism by which UPR (unfolded protein response) outcome switches between survival and death.

This model is described in the article:

Erguler K, Pieri M, Deltas C.
BMC Syst Biol. 2013 Feb 21;7(1):16.

Abstract:

BACKGROUND: The unfolded protein response (UPR) is a major signalling cascade acting in the quality control ofprotein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory rolein a range of genetic and environmental disorders including neurodegenerative and cardiovasculardiseases, diabetes and kidney diseases. The three major receptors of the ER stress involved withthe UPR, i.e. IRE1a, PERK and ATF6, signal through a complex web of pathways to convey anappropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on theseverity of unfolded protein accumulation in the ER; however, the decision mechanism for the switchand its timing have so far been poorly understood.

RESULTS:Here, we propose a mechanism by which the UPR outcome switches between survival and death.We compose a mathematical model integrating the three signalling branches, and perform a comprehensivebifurcation analysis to investigate possible responses to stimuli. The analysis reveals threedistinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance,and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understoodas a dynamic process where the balance between the stress and the folding capacity of the ER playsa pivotal role in managing the delivery of the most appropriate response. The model demonstratesfor the first time that the UPR is capable of generating oscillations in translation attenuation and theapoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set ofparameters controlling this behaviour.

CONCLUSIONS:This work contributes largely to the understanding of one of the most ubiquitous signalling pathwaysinvolved in protein folding quality control in the metazoan ER. The insights gained have direct consequenceson the management of many UPR-related diseases, revealing, in addition, an extended listof candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioningmight be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the wayto novel approaches for the treatment of many UPR-related conditions.

In the paper, PERKA refers to the amount of phosphorylated PERK monomer. However, it refers to the active complex in the model. The complex with the model parameterization is formed of 4 monomers (n=4). So, the value of PERKA should be multiplied by 4, in order to generate the figures in the paper (eg. Figure 12).

An additional parameter (tmr=10)) is used in the model. This parameter is not mentioned in the paper. The model values of kf(=10) and kr(=1) are not consistent with that of the paper (kf=100, kr=10, in the paper). However, this is corrected by the introduction of \"tmr\" in the model, which is multiplied with kf and kr to get the resulting values.

The term \"tmr\" was missing in the kinetic laws of the reactions reu7 and reu8, in the original model. This has been corrected as per the author's request.

This model is hosted on BioModels Database and identifiedby: MODEL1302180000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3975, + "tag": "BioModels:BIOMD0000000446" + }, + { + "id": 3976, + "tag": "Endoplasmic reticulum unfolded protein response" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:15:38.255764+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000446", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2797": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2797, + "name": "Venkatraman2012 - Interplay between PLS and TSP1 in TGF-\u03b21 activation", + "repository_type": "biomodels", + "summary": "
Venkatraman2012 - Interplay between PLS and TSP1 in TGF-\u03b21 activation

The interplay between PLS (Plasmin) and TSP1 (Thrombospondin-1) in TGF-\u03b21 (Transforming growth factor-\u03b21)is shown using mathematical modelling and in vitro experimentents.

This model is described in the article:

Venkatraman L, Chia SM, Narmada BC, White JK, Bhowmick SS, Forbes Dewey C Jr, So PT, Tucker-Kellogg L, Yu H.
Biophys J. 2012 Sep 5;103(5):1060-8.

Abstract:

Transforming growth factor-\u03b21 (TGF-\u03b21) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-\u03b21 from its latent form provides spatiotemporal control over TGF-\u03b21 signaling, but the current understanding of TGF-\u03b21 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-\u03b21, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-\u03b21 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-\u03b21, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-\u03b21 positive feedback loop and causes an unexpected net decrease in TGF-\u03b21 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-\u03b21. The TGF-\u03b21 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-\u03b21 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-\u03b21 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-\u03b21 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-\u03b2 dysregulation.

This model is hosted on BioModels Database and identifiedby: MODEL1303130000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3977, + "tag": "BioModels:BIOMD0000000447" + }, + { + "id": 3175, + "tag": "Rattus rattus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3978, + "tag": "Thrombospondin receptor activity" + }, + { + "id": 3979, + "tag": "Transforming growth factor beta activation" + } + ], + "timestamp_created": "2025-02-03 16:15:38.783718+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000447", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2798": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2798, + "name": "Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (normal condition)", + "repository_type": "biomodels", + "summary": "
Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (normal condition)

The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under normal condtion

This model is described in the article:

Br\u00e4nnmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Str\u00e5lfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.

Abstract:

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

This model is hosted on BioModels Database and identifiedby: MODEL1304190000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3980, + "tag": "BioModels:BIOMD0000000448" + }, + { + "id": 3981, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3982, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-02-03 16:15:39.670857+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000448", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2799": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2799, + "name": "Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (diabetic condition)", + "repository_type": "biomodels", + "summary": "
Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (diabetic condition)

The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under diabetic condtion

This model is described in the article:

Br\u00e4nnmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Str\u00e5lfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.

Abstract:

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

This model is hosted on BioModels Database and identifiedby: MODEL1304160000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3983, + "tag": "BioModels:BIOMD0000000449" + }, + { + "id": 3981, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3982, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-02-03 16:15:40.889272+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000449", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2800": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2800, + "name": "Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version2", + "repository_type": "biomodels", + "summary": "
Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version2

Mammalian polyamine metabolism consists of a bi-cycle with two required entrances, omithine and S-adenosyl methionine (SAM), and several alternative exists. The relevant regulatory roles of the short half-life enzymes ornithine decarboxylase (ODC), S-adenosyl methione decarboxylase (SAMDC) and spermindine/spermine acetyl transferase (SSAT) in polyamine metabolism are well studied, and has been modelled here.

This model is described in the article:

Reyes-Palomares A, Monta\u00f1ez R, S\u00e1nchez-Jim\u00e9nez F, Medina MA
Amino Acids, February 2012, Volume 42, Issue 2-3, pp 597-610

Abstract:

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

Notes by the author:

This model combines BIOMD0000000190 and BIOMD0000000268 from BioModels Database, both models include corrections respect to their originals publications.

To simulate a MATI/MATIII switch to MATII in proliferating liver:

We set to 0 the Vmax parameters of MATI and MATIII

We included MATII reaction equation.

We add a regulation factor dependent of SAM levels in ODC and SAMDCe rates of synthesis (66.5/[SAM]).

H2O2 was increased in a 50 % according to an initial state ofproliferating and regenerating liver.

This model is hosted on BioModels Database and identifiedby: MODEL1305060001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3984, + "tag": "BioModels:BIOMD0000000450" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:15:41.401583+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000450", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2801": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2801, + "name": "Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer model", + "repository_type": "biomodels", + "summary": "
Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer model

The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely \"single GAS polymer model\" and \"double GAS polymer model\" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the \"double GAS polymer model\".

This model is described in the article:

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U
Nat Immunol. 2014 Feb;15(2):168-76.

Abstract:

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000501 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3985, + "tag": "BioModels:BIOMD0000000501" + }, + { + "id": 3354, + "tag": "Interferon-gamma-mediated signaling pathway" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:42:59.821667+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000501", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2802": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2802, + "name": "Messiha2013 - Pentose phosphate pathway model", + "repository_type": "biomodels", + "summary": "
Messiha2013 - Pentose phosphate pathway model

This model describes the dynamic behaviour of the pentose phosphate pathway with the inclusion of various enzymes involved in the pathway. The model's predictions are compared with experimental observations of transient metabolite concentrations following a glucose pulse.

This model is described in the article:

Hanan L. Messiha, Edward Kent, Naglis Malys, Kathleen M. Carroll, Pedro Mendes, Kieran Smallbone
PeerJ PrePrints 1:e146v2

Abstract:

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000502 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3986, + "tag": "BioModels:BIOMD0000000502" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:00.400260+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000502", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2803": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2803, + "name": "Messiha2013 - combined glycolysis and pentose phosphate pathway model", + "repository_type": "biomodels", + "summary": "
Messiha2013 - combined glycolysis and pentose phosphate pathway model

BIOMD0000000502 and MODEL1303260018 are combined to examine the response to oxidative stress.

This model is described in the article:

Hanan L. Messiha, Edward Kent, Naglis Malys, Kathleen M. Carroll, Pedro Mendes, Kieran Smallbone
PeerJ PrePrints 1:e146v2

Abstract:

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000503 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3987, + "tag": "BioModels:BIOMD0000000503" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:00.947878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000503", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2804": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2804, + "name": "Proctor2013 - Cartilage breakdown, interventions to reduce collagen release", + "repository_type": "biomodels", + "summary": "
Proctor2013 - Cartilage breakdown, interventions to reduce collagen release

The molecular pathways involved in cartilage breakdown is studied using this model to examine possible interventions to reduce cartilage collagen release. The model contains three separate submodels, one which describes the IL-1/JNK signalling pathway, secondly the OSM/STAT3 signalling pathway, and lastly a module which includes proMMP (Matrix matalloproteinase) activation, and aggrecan and collagen release.

This model is described in the article:

Proctor CJ, Macdonald C, Milner JM, Rowan AD, Cawston TE.
Arthritis Rheum. 2013 Nov 27.

Abstract:

Objective. To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions to reduce collagen release. Methods. We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language (SBML), a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein and activity and initiate cartilage collagen breakdown. Simulations were performed in the COPASI software package. Results. The model predicted that simulated inhibition of c-Jun N-terminal kinase (JNK) or p38 mitogen-activated protein kinase, and over-expression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Over-expression of TIMP-1 was much less effective than TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of Janus kinase 1 (JAK1), the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets which are downstream, such as the JNK pathway, rather than close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion. Our study shows the value of computer modelling as a tool for examining possible interventions to reduce cartilage collagen breakdown. The model predicts interventions that either prevent transcription or inhibit activity of collagenases are promising strategies and should be investigated further in an experimental setting. \u00a9 2013 American College of Rheumatology.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000504 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3988, + "tag": "BioModels:BIOMD0000000504" + }, + { + "id": 3989, + "tag": "Cartilage disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3990, + "tag": "Regulation of collagen catabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:01.432890+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000504", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2805": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2805, + "name": "vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (steady-state model)", + "repository_type": "biomodels", + "summary": "
vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (steady-state model)

Lipid metabolism plays an important role in the development of metabolic syndrome, a major risk factor for cardiovascular disease and diabetes. This model gives insights into the response of lipid oxidation to dietart and medical interventions. The model predicts the rate of lipid oxidation and the time course of most acyl carnitines. There are two models described in the paper, (i) steady-state model [ BIOMD0000000505 ], (ii) time-course model [ BIOMD0000000506 ]. This model corresponds to the steady-state model.

This model is described in the article:

van Eunen K, Simons SM, Gerding A, Bleeker A, den Besten G, Touw CM, Houten SM, Groen BK, Krab K, Reijngoud DJ, Bakker BM.
PLoS Comput Biol. 2013;9(8):e1003186.

Abstract:

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000505 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3991, + "tag": "BioModels:BIOMD0000000505" + }, + { + "id": 3992, + "tag": "Fatty acid oxidation" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:01.956124+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000505", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2806": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2806, + "name": "vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (time-course model)", + "repository_type": "biomodels", + "summary": "
vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (time-course model)

Lipid metabolism plays an important role in the development of metabolic syndrome, a major risk factor for cardiovascular disease and diabetes. This model gives insights into the response of lipid oxidation to dietart and medical interventions. The model predicts the rate of lipid oxidation and the time course of most acyl carnitines. There are two models described in the paper, (i) steady-state model [ BIOMD0000000505 ], (ii) time-course model [ BIOMD0000000506 ]. This model corresponds to the time-course model.

This model is described in the article:

van Eunen K, Simons SM, Gerding A, Bleeker A, den Besten G, Touw CM, Houten SM, Groen BK, Krab K, Reijngoud DJ, Bakker BM.
PLoS Comput Biol. 2013;9(8):e1003186.

Abstract:

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000506 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3993, + "tag": "BioModels:BIOMD0000000506" + }, + { + "id": 3992, + "tag": "Fatty acid oxidation" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:02.516000+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000506", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2807": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2807, + "name": "Gardner2000 - genetic toggle switch in E.coli", + "repository_type": "biomodels", + "summary": "
Gardner2000 - genetic toggle switch in E.coli

The behaviour of the genetic toggle switch and the conditions for bistability has been studies using a synthetic, bistable gene circuit.

This model is described in the article:

Gardner TS, Cantor CR, Collins JJ
Nature. 2000 Jan 20;403(6767):339-42.

Abstract:

It has been proposed' that gene-regulatory circuits with virtually any desired property can be constructed from networks of simple regulatory elements. These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000507 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3994, + "tag": "BioModels:BIOMD0000000507" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:03.087371+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000507", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2808": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2808, + "name": "Barrack2014 - Calcium/cell cycle coupling - Cyclin D dependent ATP release", + "repository_type": "biomodels", + "summary": "
Barrack2014 - Calcium/cell cycle coupling - Cyclin D dependent ATP release

This model is designed based on the hypothesis that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescents cells onto the cell cycle. The model describes the ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell.

This model is described in the article:

Barrack DS, Thul R, Owen MR.
J Theor Biol. 2014 Jan 13;347C:17-32.

Abstract:

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000508 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3995, + "tag": "BioModels:BIOMD0000000508" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:03.551902+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000508", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2809": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2809, + "name": "Barrack2014 - Calcium/cell cycle coupling - Rs dependent ATP release", + "repository_type": "biomodels", + "summary": "
Barrack2014 - Calcium/cell cycle coupling - Rs dependent ATP release

This model is designed based on the hypothesis that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescents cells onto the cell cycle. The model describes the ATP mediated calcium-cell cycle coupling via Rs (retinoblastoma tumour suppressor protein bound to the E2F transcription factor) in a single radial glial cell.

This model is described in the article:

Barrack DS, Thul R, Owen MR.
J Theor Biol. 2014 Jan 13;347C:17-32.

Abstract:

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000509 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3996, + "tag": "BioModels:BIOMD0000000509" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:04.108387+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000509", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2810": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2810, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C (with glucosomal ribokinase)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei -MODEL C (with glucosomal ribokinase)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model C of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000510 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3997, + "tag": "BioModels:BIOMD0000000510" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:43:04.635211+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000510", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2811": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2811, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D (with ATP:ADP antiporter)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D (with ATP:ADP antiporter)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model D of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000511 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3998, + "tag": "BioModels:BIOMD0000000511" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:43:05.269903+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000511", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2812": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2812, + "name": "Benson2014 - FAAH inhibitors for the treatment of osteoarthritic pain", + "repository_type": "biomodels", + "summary": "
Benson2014 - FAAH inhibitors for the treatment of osteoarthritic pain

Evaluation of fatty acid amide hydrolase (FAAH) as a target for osteoarthritic pain in humans, using an integrated systems pharmacology model.

The SBML version of the model is obtained from the supplementary material of the corresponding paper (see below).

This model is described in the article:

Benson N, Metelkin E, Demin O, Li GL, Nichols D, van der Graaf PH.
CPT Pharmacometrics Syst Pharmacol. 2014 Jan 15;3:e91.

Abstract:

The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). In 2011, PF-04457845, an irreversible inhibitor of FAAH, was progressed to phase II clinical trials for osteoarthritic pain. This article discusses a prospective, integrated systems pharmacology model evaluation of FAAH as a target for pain in humans, using physiologically based pharmacokinetic and systems biology approaches. The model integrated physiological compartments; endocannabinoid production, degradation, and disposition data; PF-04457845 pharmacokinetics and pharmacodynamics, and cannabinoid receptor CB1-binding kinetics. The modeling identified clear gaps in our understanding and highlighted key risks going forward, in particular relating to whether methods are in place to demonstrate target engagement and pharmacological effect. The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.

This model is hosted on BioModels Database and identifiedby: MODEL1402030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 3999, + "tag": "BioModels:BIOMD0000000512" + }, + { + "id": 4000, + "tag": "Endocannabinoid signaling pathway" + }, + { + "id": 4001, + "tag": "Fatty acid amide hydrolase activity" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3449, + "tag": "Osteoarthritis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:05.779736+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000512", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2813": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2813, + "name": "Kerkhoven2013 - Glycolysis in T.brucei - MODEL A", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis in T.brucei - MODEL A

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model A of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000513 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4002, + "tag": "BioModels:BIOMD0000000513" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:43:06.246646+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000513", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2814": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2814, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL B", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL B

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model B of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000514 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4003, + "tag": "BioModels:BIOMD0000000514" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:43:06.735747+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000514", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2815": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2815, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C in fructose medium (with glucosomal ribokinase)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C in fructose medium (with glucosomal ribokinase)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model C-fruc of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000515 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4004, + "tag": "BioModels:BIOMD0000000515" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:43:07.216431+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000515", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2816": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2816, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D in fructose medium (with ATP:ADP antiporter)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D in fructose medium (with ATP:ADP antiporter)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model D-fruc of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000516 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4005, + "tag": "BioModels:BIOMD0000000516" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3198, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-02-03 16:43:07.791246+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000516", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2817": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2817, + "name": "Smallbone2013 - Colon Crypt cycle - Version 3", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 3

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4006, + "tag": "BioModels:BIOMD0000000517" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 16:43:08.251648+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000517", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2818": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2818, + "name": "Smallbone2013 - Colon Crypt cycle - Version 2", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 2

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190002 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4007, + "tag": "BioModels:BIOMD0000000518" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 16:43:08.729597+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000518", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2819": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2819, + "name": "Smallbone2013 - Colon Crypt cycle - Version 1", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 1

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4008, + "tag": "BioModels:BIOMD0000000519" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 16:43:09.238102+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000519", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2820": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2820, + "name": "Smallbone2013 - Colon Crypt cycle - Version 0", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 0

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4009, + "tag": "BioModels:BIOMD0000000520" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3552, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-02-03 16:43:09.758695+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000520", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2821": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2821, + "name": "Ribba2012 - Low-grade gliomas, tumour growth inhibition model", + "repository_type": "biomodels", + "summary": "
Ribba2012 - Low-grade gliomas, tumour growth inhibition model

Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy.

This model is described in the article:

Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F.
Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080

Abstract:

PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.

EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.

RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.

CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000521 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 4010, + "tag": "Benign glioma" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4011, + "tag": "BioModels:BIOMD0000000521" + }, + { + "id": 3602, + "tag": "Defense response to tumor cell" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:10.281819+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000521", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2822": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2822, + "name": "Muraro2014 - Vascular patterning in Arabidopsis roots", + "repository_type": "biomodels", + "summary": "
Muraro2014 - Vascular patterning in Arabidopsis roots

Using a multicellular model, maintanence of vascular patterning in Arabidopsis roots has been studied. The model that is provided here is the single-cell version of the model. The two-cell and multicellular models described in the paper can be downloaded as python scripts (follow the curation tab to get these files).

This model is described in the article:

Muraro D, Mellor N, Pound MP, Help H, Lucas M, Chopard J, Byrne HM, Godin C, Hodgman TC, King JR, Pridmore TP, Helariutta Y, Bennett MJ, Bishopp A.
Proc Natl Acad Sci U S A. 2014 Jan 14;111(2):857-62.

Abstract:

As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000522 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4012, + "tag": "BioModels:BIOMD0000000522" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4013, + "tag": "Xylem and phloem pattern formation" + } + ], + "timestamp_created": "2025-02-03 16:43:10.780502+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000522", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2823": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2823, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000523 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4014, + "tag": "BioModels:BIOMD0000000523" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:11.256583+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000523", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2824": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2824, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000524 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4015, + "tag": "BioModels:BIOMD0000000524" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:11.812225+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000524", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2825": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2825, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans-cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans-cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000525 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4016, + "tag": "BioModels:BIOMD0000000525" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:12.287732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000525", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2826": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2826, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans-cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans-cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000526 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3356, + "tag": "Apoptotic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4017, + "tag": "BioModels:BIOMD0000000526" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:13.104861+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000526", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2827": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2827, + "name": "Kaiser2014 - Salmonella persistence after ciprofloxacin treatment", + "repository_type": "biomodels", + "summary": "
Kaiser2014 - Salmonella persistence after ciprofloxacin treatment

The model describes the bacterial tolerance to antibiotics. Using a mouse model for Salmonella diarrhea, the authors have found that bacterial persistence occurs in the presence of the antibiotic ciprofloxacin because Salmonella can exist in two different states. One, the fast-growing population that spreads in the host's tissues and the other, slow-growing \"persister\" population that hide out inside dendritic cells of the host's immune system and cannot be attacked by the antibiotics. However, this can be killed by adding agents that directly stimulate the host's immune defense.

This model is described in the article:

Kaiser P, Regoes RR, Dolowschiak T, Wotzka SY, Lengefeld J, Slack E, Grant AJ, Ackermann M, Hardt WD.
PLoS Biol. 2014 Feb 18;12(2):e1001793.

Abstract:

In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103\u207aCX\u2083CR1\u207bCD11c\u207a dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

This model is hosted on BioModels Database and identifiedby: MODEL1312170001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4018, + "tag": "BioModels:BIOMD0000000527" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 4019, + "tag": "Primary bacterial infectious disease" + }, + { + "id": 4020, + "tag": "Response to antibiotic" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4021, + "tag": "Salmonella enterica subsp. enterica serovar Typhimurium str. DT104" + } + ], + "timestamp_created": "2025-02-03 16:43:13.674231+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000527", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2828": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2828, + "name": "Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - Cal/09)", + "repository_type": "biomodels", + "summary": "
Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - Cal/09)

The dynamics of the interplay between the viral antagonism and the innate immune response has been studied using modelling approaches. The responses of human monocyte-derived dendritic cells infected by two influenza A H1N1 strains (the pandemic swine-origin A/California/4/2009 (Cal/09) and the seasonal A/New Caledonia/20/1999 (NC/99)) that have different clinical outcomes have been modelled. From the time course gene expression measurements of a set of selected genes, the dynamic features of viral antagonism and innate immune response are extracted. It is found that the strength and the time scale of action of viral antagonism is significantly different between the two viruses. This model describes the viral infection by seasonal Cal/09.

This model is described in the article:

Fribourg M, Hartmann B, Schmolke M, Marjanovic N, Albrecht RA, Garc\u00eda-Sastre A, Sealfon SC, Jayaprakash C, Hayot F.
J Theor Biol. 2014 Mar 2;351C:47-57.

Abstract:

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

This model is hosted on BioModels Database and identifiedby: MODEL1403310002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4022, + "tag": "BioModels:BIOMD0000000528" + }, + { + "id": 3273, + "tag": "Defense response, incompatible interaction" + }, + { + "id": 4023, + "tag": "H1N1 swine influenza virus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4024, + "tag": "Swine influenza" + } + ], + "timestamp_created": "2025-02-03 16:43:14.176209+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000528", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2829": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2829, + "name": "Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - NC/99)", + "repository_type": "biomodels", + "summary": "
Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - NC/99)

The dynamics of the interplay between the viral antagonism and the innate immune response has been studied using modelling approaches. The responses of human monocyte-derived dendritic cells infected by two influenza A H1N1 strains (the pandemic swine-origin A/California/4/2009 (Cal/09) and the seasonal A/New Caledonia/20/1999 (NC/99)) that have different clinical outcomes have been modelled. From the time course gene expression measurements of a set of selected genes, the dynamic features of viral antagonism and innate immune response are extracted. It is found that the strength and the time scale of action of viral antagonism is significantly different between the two viruses. This model describes the viral infection by seasonal NC/99.

This model is described in the article:

Fribourg M, Hartmann B, Schmolke M, Marjanovic N, Albrecht RA, Garc\u00eda-Sastre A, Sealfon SC, Jayaprakash C, Hayot F.
J Theor Biol. 2014 Mar 2;351C:47-57.

Abstract:

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

This model is hosted on BioModels Database and identifiedby: MODEL1403310001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4025, + "tag": "BioModels:BIOMD0000000529" + }, + { + "id": 3273, + "tag": "Defense response, incompatible interaction" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3274, + "tag": "Influenza" + }, + { + "id": 4026, + "tag": "Influenza A virus (A/New Caledonia/20/1999(H1N1))" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:14.668738+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000529", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2830": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2830, + "name": "Schmitz2014 - RNA triplex formation", + "repository_type": "biomodels", + "summary": "
Schmitz2014 - RNA triplex formation
The model is parameterized using theparameters for gene CCDC3 from Supplementary Table S1. The twomiRNAs which form the triplex together with CCDC3 are miR-551b andmiR-138.

This model is described in the article:

Schmitz U, Lai X, Winter F, Wolkenhauer O, Vera J, Gupta SK.
Nucleic Acids Res. 2014 Jul; 42(12): 7539-7552

Abstract:

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.

This model is hosted on BioModels Database and identified by: BIOMD0000000530.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4027, + "tag": "BioModels:BIOMD0000000530" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:15.132779+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000530", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2831": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2831, + "name": "Crespo2012 - Kinetics of Amyloid Fibril Formation", + "repository_type": "biomodels", + "summary": "
Crespo2012 - Kinetics of Amyloid FibrilFormation

This model is described in the article:

Crespo R, Rocha FA, Damas AM, Martins PM.
J. Biol. Chem. 2012 Aug; 287(36): 30585-30594

Abstract:

Associated with neurodegenerative disorders such as Alzheimer, Parkinson, or prion diseases, the conversion of soluble proteins into amyloid fibrils remains poorly understood. Extensive \"in vitro\" measurements of protein aggregation kinetics have been reported, but no consensus mechanism has emerged until now. This contribution aims at overcoming this gap by proposing a theoretically consistent crystallization-like model (CLM) that is able to describe the classic types of amyloid fibrillization kinetics identified in our literature survey. Amyloid conversion represented as a function of time is shown to follow different curve shapes, ranging from sigmoidal to hyperbolic, according to the relative importance of the nucleation and growth steps. Using the CLM, apparently unrelated data are deconvoluted into generic mechanistic information integrating the combined influence of seeding, nucleation, growth, and fibril breakage events. It is notable that this complex assembly of interdependent events is ultimately reduced to a mathematically simple model, whose two parameters can be determined by little more than visual inspection. The good fitting results obtained for all cases confirm the CLM as a good approximation to the generalized underlying principle governing amyloid fibrillization. A perspective is presented on possible applications of the CLM during the development of new targets for amyloid disease therapeutics.

This model is hosted on BioModels Database and identified by: BIOMD0000000531.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4029, + "tag": "BioModels:BIOMD0000000531" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:15.600743+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000531", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2832": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2832, + "name": "Vazquez2014 - Chemical inhibition from amyloid protein aggregation kinetics", + "repository_type": "biomodels", + "summary": "
Vazquez2014 - Chemical inhibition fromamyloid protein aggregation kinetics

This model is described in the article:

V\u00e1zquez JA.
BMC Pharmacol Toxicol 2014; 15(1): 9

Abstract:

BACKGROUNDS: The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. RESULTS: In this manuscript, experimental data of insulin, A?42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH?=?7.4/T?=?37\u00b0C) and taiwaniaflavone for A?42 were the compounds studied that shown the greatest inhibition capacity. CONCLUSIONS: An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

This model is hosted on BioModels Database and identified by: BIOMD0000000532.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4030, + "tag": "BioModels:BIOMD0000000532" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:16.128186+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000532", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2833": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2833, + "name": "Steckmann2012 - Amyloid beta-protein fibrillogenesis (kinetics of secondary structure conversion)", + "repository_type": "biomodels", + "summary": "
Steckmann2012 - Amyloid beta-proteinfibrillogenesis (kinetics of secondary structure conversion)

This model is described in the article:

Steckmann T, Awan Z, Gerstman BS, Chapagain PP.
J. Theor. Biol. 2012 May; 301: 95-102

Abstract:

Amyloid fibrils are a common component in many debilitating human neurological diseases such as Alzheimer's (AD), Parkinson's, and Creutzfeldt-Jakob, and in animal diseases such as BSE. The role of fibrillar ?? proteins in AD has stimulated interest in the kinetics of ?? fibril formation. Kinetic models that include reaction pathways and rate parameters for the various stages of the process can be helpful towards understanding the dynamics on a molecular level. Based upon experimental data, we have developed a mathematical model for the reaction pathways and determined rate parameters for peptide secondary structural conversion and aggregation during the entire fibrillogenesis process from random coil to mature fibrils, including the molecular species that accelerate the conversions. The model and the rate parameters include different molecular structural stages in the nucleation and polymerization processes and the numerical solutions yield graphs of concentrations of different molecular species versus time that are in close agreement with experimental results. The model also allows for the calculation of the time-dependent increase in aggregate size. The calculated results agree well with experimental results, and allow differences in experimental conditions to be included in the calculations. The specific steps of the model and the rate constants that are determined by fitting to experimental data provide insight on the molecular species involved in the fibril formation process.

This model is hosted on BioModels Database and identified by: BIOMD0000000533.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4031, + "tag": "Amyloid-beta formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4032, + "tag": "BioModels:BIOMD0000000533" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:16.609830+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000533", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2834": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2834, + "name": "Dwivedi2014 - Healthy Volunteer IL6 Model", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Healthy Volunteer IL6Model
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Healthy Volunteer model are 2c and2d.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000534.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4033, + "tag": "BioModels:BIOMD0000000534" + }, + { + "id": 4034, + "tag": "Crohn's disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4035, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:17.084792+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000534", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2835": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2835, + "name": "Dwivedi2014 - Crohns IL6 Disease model - Anti-IL6 Antibody", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Crohns IL6 Disease model -Anti-IL6 Antibody
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Crohn's Disease model are 4a, 4b,4c and 5a.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000535.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4036, + "tag": "BioModels:BIOMD0000000535" + }, + { + "id": 4034, + "tag": "Crohn's disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4035, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:17.626790+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000535", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2836": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2836, + "name": "Dwivedi2014 - Crohns IL6 Disease model - sgp130 activity", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Crohns IL6 Disease model -sgp130 activity
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
The figure referring to this Crohn's Disease model is 6b.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000536.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4037, + "tag": "BioModels:BIOMD0000000536" + }, + { + "id": 4034, + "tag": "Crohn's disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4035, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:18.100883+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000536", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2837": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2837, + "name": "Dwivedi2014 - Crohns IL6 Disease model - Anti-IL6R Antibody", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Crohns IL6 Disease model -Anti-IL6R Antibody
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Crohn's Disease model are 3a, 4d,4e, 4f and 5b.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000537.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4038, + "tag": "BioModels:BIOMD0000000537" + }, + { + "id": 4034, + "tag": "Crohn's disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4035, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:18.868075+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000537", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2838": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2838, + "name": "Clarke2000 - One-hit model of cell death in neuronal degenerations", + "repository_type": "biomodels", + "summary": "
Clarke2000 - One-hit model of cell death inneuronal degenerations
This one-hit model fits differentneuronal-death associated diseases for different animalmodels.\u00a0

This model is described in the article:

Clarke G, Collins RA, Leavitt BR, Andrews DF, Hayden MR, Lumsden CJ, McInnes RR.
Nature 2000 Jul; 406(6792): 195-199

Abstract:

In genetic disorders associated with premature neuronal death, symptoms may not appear for years or decades. This delay in clinical onset is often assumed to reflect the occurrence of age-dependent cumulative damage. For example, it has been suggested that oxidative stress disrupts metabolism in neurological degenerative disorders by the cumulative damage of essential macromolecules. A prediction of the cumulative damage hypothesis is that the probability of cell death will increase over time. Here we show in contrast that the kinetics of neuronal death in 12 models of photoreceptor degeneration, hippocampal neurons undergoing excitotoxic cell death, a mouse model of cerebellar degeneration and Parkinson's and Huntington's diseases are all exponential and better explained by mathematical models in which the risk of cell death remains constant or decreases exponentially with age. These kinetics argue against the cumulative damage hypothesis; instead, the time of death of any neuron is random. Our findings are most simply accommodated by a 'one-hit' biochemical model in which mutation imposes a mutant steady state on the neuron and a single event randomly initiates cell death. This model appears to be common to many forms of neurodegeneration and has implications for therapeutic strategies.

This model is hosted on BioModels Database and identified by: BIOMD0000000538.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4039, + "tag": "BioModels:BIOMD0000000538" + }, + { + "id": 4040, + "tag": "Canis lupus familiaris" + }, + { + "id": 4041, + "tag": "Cerebral degeneration" + }, + { + "id": 4042, + "tag": "Felis catus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4043, + "tag": "Huntington's disease" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 4044, + "tag": "Neuron apoptotic process" + }, + { + "id": 4045, + "tag": "Ocular albinism" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 4046, + "tag": "Retinal degeneration" + }, + { + "id": 4047, + "tag": "Retinal detachment" + }, + { + "id": 4048, + "tag": "Retinal disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:19.553038+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000538", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2839": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2839, + "name": "Fran\u00e7ois2005 - Mixed Feedback Loop (two-gene network)", + "repository_type": "biomodels", + "summary": "Paul Fran\u00e7ois & Vincent Hakim. Core genetic module: the mixed feedback loop. Physical Review E 72, 3 Pt 1 (2005).

The so-called mixed feedback loop (MFL) is a small two-gene network where protein A regulates the transcription of protein B and the two proteins form a heterodimer. It has been found to be statistically over-represented in statistical analyses of gene and protein interaction databases and to lie at the core of several computer-generated genetic networks. Here, we propose and mathematically study a model of the MFL and show that, by itself, it can serve both as a bistable switch and as a clock (an oscillator) depending on kinetic parameters. The MFL phase diagram as well as a detailed description of the nonlinear oscillation regime are presented and some biological examples are discussed. The results emphasize the role of protein interactions in the function of genetic modules and the usefulness of modeling RNA dynamics explicitly.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4049, + "tag": "BioModels:BIOMD0000000539" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:43:20.836134+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000539", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2840": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2840, + "name": "Yugi2014 - Insulin induced signalling (PFKL phosphorylation) - model 1", + "repository_type": "biomodels", + "summary": "
Yugi2014 - Insulin induced signalling (PFKLphosphorylation) - model 1

Insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, which thereby controls a key reaction in glycolysis. This mechanism is revealed using the mathematical model. In this model, the PFKL phosphorylation time courses are obtained from experimental data.

Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio University, Japan) for his kind advice in converting the model from MATLAB to SBML.

This model is described in the article:

Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4): 1171-1183

Abstract:

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

This model is hosted on BioModels Database and identified by: BIOMD0000000540.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4050, + "tag": "BioModels:BIOMD0000000540" + }, + { + "id": 4051, + "tag": "Regulation of glycolytic process" + }, + { + "id": 4052, + "tag": "Response to insulin" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:43:21.659817+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000540", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2841": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2841, + "name": "Yugi2014 - Insulin induced signalling (PFKL phosphorylation) - model 2", + "repository_type": "biomodels", + "summary": "
Yugi2014 - Insulin induced signalling (PFKLphosphorylation) - model 2

Insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, which thereby controls a key reaction in glycolysis. This mechanism is revealed using the mathematical model. In this model, the PFKL phosphorylation time courses are calculation from the signalling pathway model developed by Kubata et al. (2012) ( MODEL1204060000 - Kubota2012_InsulinAction_AKTpathway).

Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio University, Japan) for his kind advice in converting the model from MATLAB to SBML.

This model is described in the article:

Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4): 1171-1183

Abstract:

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

This model is hosted on BioModels Database and identified by: BIOMD0000000541.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4053, + "tag": "BioModels:BIOMD0000000541" + }, + { + "id": 4051, + "tag": "Regulation of glycolytic process" + }, + { + "id": 4052, + "tag": "Response to insulin" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:43:22.161527+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000541", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2842": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2842, + "name": "Yuraszeck2010 - Vulnerabilities in the Tau Network in Tau Pathophysiology", + "repository_type": "biomodels", + "summary": "
Yuraszeck2010 - Vulnerabilities in the TauNetwork in Tau Pathophysiology

This model is described in the article:

Yuraszeck TM, Neveu P, Rodriguez-Fernandez M, Robinson A, Kosik KS, Doyle FJ 3rd.
PLoS Comput. Biol. 2010; 6(11): e1000997

Abstract:

The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's.

This model is hosted on BioModels Database and identified by: BIOMD0000000542.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4054, + "tag": "BioModels:BIOMD0000000542" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4055, + "tag": "Tauopathy" + } + ], + "timestamp_created": "2025-02-03 16:43:22.718250+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000542", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2843": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2843, + "name": "Qi2013 - IL-6 and IFN crosstalk model (non-competitive)", + "repository_type": "biomodels", + "summary": "
Qi2013 - IL-6 and IFN crosstalk model(non-competitive)
This model [BIOMD0000000543]describes the crosstalk between IFN-gamma and IL-6 inducedsignalling; it aims to outline mechanisms and factors that maycontrol the interaction between both signalling pathways,discussing a role of heterodimer formation in signallingdysfunction.

To account for the possibility of different IFNR and gp130binding sites for STAT1 and STAT3, model 1 [BIOMD0000000543]assumes that there is no competition between STAT1 and STAT3 forthe receptor complexes (includes two extra reactions).
The reverse of this is true in model 2 [BIOMD0000000544]where it generally is assumed that there is competition betweenSTAT1 and STAT3 for the receptor complexes.

This model is described in the article:

Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX.
BMC Bioinformatics 2013; 14: 41

Abstract:

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000543.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4056, + "tag": "BioModels:BIOMD0000000543" + }, + { + "id": 4057, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:43:23.272133+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000543", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2844": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2844, + "name": "Qi2013 - IL-6 and IFN crosstalk model", + "repository_type": "biomodels", + "summary": "
Qi2013 - IL-6 and IFN crosstalk model
This model [BIOMD0000000544]describes the crosstalk between IFN-gamma and IL-6 inducedsignalling; it aims to outline mechanisms and factors that maycontrol the interaction between both signalling pathways,discussing a role of heterodimer formation in signallingdysfunction.

To account for the possibility of different IFNR and gp130binding sites for STAT1 and STAT3, model 1
[BIOMD0000000543]assumes that there is no competition between STAT1 and STAT3 forthe receptor complexes (includes two extra reactions).
The reverse of this is true in model 2 [BIOMD0000000544]where it generally is assumed that there is competition betweenSTAT1 and STAT3 for the receptor complexes.

This model is described in the article:

Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX.
BMC Bioinformatics 2013; 14: 41

Abstract:

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000544.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4058, + "tag": "BioModels:BIOMD0000000544" + }, + { + "id": 4057, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:43:23.881863+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000544", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2845": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2845, + "name": "Ouyang2014 - photomorphogenic UV-B signalling network", + "repository_type": "biomodels", + "summary": "
Ouyang2014 - photomorphogenic UV-B signallingnetwork

This model is described in the article:

Ouyang X, Huang X, Jin X, Chen Z, Yang P, Ge H, Li S, Deng XW.
Proc. Natl. Acad. Sci. U.S.A. 2014 Aug; 111(31): 11539-11544

Abstract:

Long-wavelength and low-fluence UV-B light is an informational signal known to induce photomorphogenic development in plants. Using the model plant Arabidopsis thaliana, a variety of factors involved in UV-B-specific signaling have been experimentally characterized over the past decade, including the UV-B light receptor UV resistance locus 8; the positive regulators constitutive photomorphogenesis 1 and elongated hypocotyl 5; and the negative regulators cullin4, repressor of UV-B photomorphogenesis 1 (RUP1), and RUP2. Individual genetic and molecular studies have revealed that these proteins function in either positive or negative regulatory capacities for the sufficient and balanced transduction of photomorphogenic UV-B signal. Less is known, however, regarding how these signaling events are systematically linked. In our study, we use a systems biology approach to investigate the dynamic behaviors and correlations of multiple signaling components involved in Arabidopsis UV-B-induced photomorphogenesis. We define a mathematical representation of photomorphogenic UV-B signaling at a temporal scale. Supplemented with experimental validation, our computational modeling demonstrates the functional interaction that occurs among different protein complexes in early and prolonged response to photomorphogenic UV-B.

This model is hosted on BioModels Database and identified by: BIOMD0000000545.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4059, + "tag": "BioModels:BIOMD0000000545" + }, + { + "id": 4060, + "tag": "Photomorphogenesis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:24.397304+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000545", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2846": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2846, + "name": "Miao2010 - Innate and adaptive immune responses to primary Influenza A Virus infection_1_1", + "repository_type": "biomodels", + "summary": "
Miao2010 - Innate and adaptive immuneresponses to primary Influenza A Virus infection

This model is described in the article:

Miao H, Hollenbaugh JA, Zand MS, Holden-Wiltse J, Mosmann TR, Perelson AS, Wu H, Topham DJ.
J. Virol. 2010 Jul; 84(13): 6687-6698

Abstract:

Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is approximately 0.5 days, and the average half-life of free infectious virus is approximately 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity.

This model is hosted on BioModels Database and identified by: BIOMD0000000546.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4061, + "tag": "Adaptive immune response" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4062, + "tag": "BioModels:BIOMD0000000546" + }, + { + "id": 3274, + "tag": "Influenza" + }, + { + "id": 4063, + "tag": "Influenza A virus (strain A/X-31 H3N2)" + }, + { + "id": 4064, + "tag": "Innate immune response" + }, + { + "id": 3621, + "tag": "Murinae" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:24.888367+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000546", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2847": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2847, + "name": "Talemi2014 - Arsenic toxicity and detoxification mechanisms in yeast", + "repository_type": "biomodels", + "summary": "
Talemi2014 - Arsenic toxicity anddetoxification mechanisms in yeast
The model implements arsenite (AsIII)transport regulation, its distribution within main cellular AsIIIpools and detoxification. The intracellular As pools considered arefree AsIII (AsIIIin), protein-bound AsIII (AsIIIprot), glutathioneconjugated AsIII (AsGS3) and vacuolar sequestered AsIII (vAsGS3).

This model is described in the article:

Talemi SR, Jacobson T, Garla V, Navarrete C, Wagner A, Tam\u00e1s MJ, Schaber J.
Mol. Microbiol. 2014 Jun; 92(6): 1343-1356

Abstract:

Arsenic has a dual role as causative and curative agent of human disease. Therefore, there is considerable interest in elucidating arsenic toxicity and detoxification mechanisms. By an ensemble modelling approach, we identified a best parsimonious mathematical model which recapitulates and predicts intracellular arsenic dynamics for different conditions and mutants, thereby providing novel insights into arsenic toxicity and detoxification mechanisms in yeast, which could partly be confirmed experimentally by dedicated experiments. Specifically, our analyses suggest that: (i) arsenic is mainly protein-bound during short-term (acute) exposure, whereas glutathione-conjugated arsenic dominates during long-term (chronic) exposure, (ii) arsenic is not stably retained, but can leave the vacuole via an export mechanism, and (iii) Fps1 is controlled by Hog1-dependent and Hog1-independent mechanisms during arsenite stress. Our results challenge glutathione depletion as a key mechanism for arsenic toxicity and instead suggest that (iv) increased glutathione biosynthesis protects the proteome against the damaging effects of arsenic and that (v) widespread protein inactivation contributes to the toxicity of this metalloid. Our work in yeast may prove useful to elucidate similar mechanisms in higher eukaryotes and have implications for the use of arsenic in medical therapy.

This model is hosted on BioModels Database and identified by: BIOMD0000000547.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4065, + "tag": "BioModels:BIOMD0000000547" + }, + { + "id": 4066, + "tag": "Response to arsenic-containing substance" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:25.371144+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000547", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2848": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2848, + "name": "Sneppen2009 - Modeling proteasome dynamics in Parkinson's disease", + "repository_type": "biomodels", + "summary": "
Sneppen2009 - Modeling proteasome dynamics inParkinson's disease

This model is described in the article:

Sneppen K, Lizana L, Jensen MH, Pigolotti S, Otzen D.
Phys Biol 2009; 6(3): 036005

Abstract:

In Parkinson's disease (PD), there is evidence that alpha-synuclein (alphaSN) aggregation is coupled to dysfunctional or overburdened protein quality control systems, in particular the ubiquitin-proteasome system. Here, we develop a simple dynamical model for the on-going conflict between alphaSN aggregation and the maintenance of a functional proteasome in the healthy cell, based on the premise that proteasomal activity can be titrated out by mature alphaSN fibrils and their protofilament precursors. In the presence of excess proteasomes the cell easily maintains homeostasis. However, when the ratio between the available proteasome and the alphaSN protofilaments is reduced below a threshold level, we predict a collapse of homeostasis and onset of oscillations in the proteasome concentration. Depleted proteasome opens for accumulation of oligomers. Our analysis suggests that the onset of PD is associated with a proteasome population that becomes occupied in periodic degradation of aggregates. This behavior is found to be the general state of a proteasome/chaperone system under pressure, and suggests new interpretations of other diseases where protein aggregation could stress elements of the protein quality control system.

This model is hosted on BioModels Database and identified by: BIOMD0000000548.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4067, + "tag": "BioModels:BIOMD0000000548" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 3363, + "tag": "Proteasome-mediated ubiquitin-dependent protein catabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:25.870034+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000548", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2849": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2849, + "name": "Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis - Age Dependent", + "repository_type": "biomodels", + "summary": "
Baker2013 - Cytokine Mediated Inflammation inRheumatoid Arthritis - Age Dependant
This model by Baker M. 2013, describesthe interaction between pro and anti-inflammatory cytokinesignalling in rheumatoid arthritis.

Using two ordinary differential equations, the first model [BIOMD0000000550]analyses bifurcation and describes different pathological states byaltering inflammatory regulation parameters.
The second model [BIOMD0000000549]includes the effect that ageing has on pro-inflammatory signalling,allowing for time-dependant properties and disease progression tobe observed. The author also describes potential dosing forreversal of the disease state.

This model is described in the article:

Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR.
Math Med Biol 2013 Dec; 30(4): 311-337

Abstract:

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.

This model is hosted on BioModels Database and identified by: BIOMD0000000549.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4068, + "tag": "BioModels:BIOMD0000000549" + }, + { + "id": 4057, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4069, + "tag": "Regulation of inflammatory response" + }, + { + "id": 4070, + "tag": "Rheumatoid arthritis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:26.352937+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000549", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2850": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2850, + "name": "Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis", + "repository_type": "biomodels", + "summary": "
Baker2013 - Cytokine Mediated Inflammation inRheumatoid Arthritis
This model by Baker M. 2013, describesthe interaction between pro and anti-inflammatory cytokinesignalling in rheumatoid arthritis.

Using two ordinary differential equations, the first model [BIOMD0000000550]analyses bifurcation and describes different pathological states byaltering inflammatory regulation parameters.
The second model [BIOMD0000000549]includes the effect that ageing has on pro-inflammatory signalling,allowing for time-dependant properties and disease progression tobe observed. The author also describes potential dosing forreversal of the disease state.

This model is described in the article:

Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR.
Math Med Biol 2013 Dec; 30(4): 311-337

Abstract:

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.

This model is hosted on BioModels Database and identified by: BIOMD0000000550.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4071, + "tag": "BioModels:BIOMD0000000550" + }, + { + "id": 4057, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4069, + "tag": "Regulation of inflammatory response" + }, + { + "id": 4070, + "tag": "Rheumatoid arthritis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:26.830620+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000550", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2851": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2851, + "name": "Das2010 - Effect of a gamma-secretase inhibitor on Amyloid-beta dynamics", + "repository_type": "biomodels", + "summary": "
Das2010 - Effect of a gamma-secretaseinhibitor on Amyloid-beta dynamics

This model is described in the article:

Das R, Nachbar RB, Edelstein-Keshet L, Saltzman JS, Wiener MC, Bagchi A, Bailey J, Coombs D, Simon AJ, Hargreaves RJ, Cook JJ.
Bull. Math. Biol. 2011 Jan; 73(1): 230-247

Abstract:

Aggregation of the small peptide amyloid beta (A?) into oligomers and fibrils in the brain is believed to be a precursor to Alzheimer's disease. A? is produced via multiple proteolytic cleavages of amyloid precursor protein (APP), mediated by the enzymes ?- and ?-secretase. In this study, we examine the temporal dynamics of soluble (unaggregated) A? in the plasma and cerebral-spinal fluid (CSF) of rhesus monkeys treated with different oral doses of a ?-secretase inhibitor. A dose-dependent reduction of A? concentration was observed within hours of drug ingestion, for all doses tested. A? concentration in the CSF returned to its predrug level over the monitoring period. In contrast, A? concentration in the plasma exhibited an unexpected overshoot to as high as 200% of the predrug concentration, and this overshoot persisted as late as 72 hours post-drug ingestion. To account for these observations, we proposed and analyzed a minimal physiological model for A? dynamics that could fit the data. Our analysis suggests that the overshoot arises from the attenuation of an A? clearance mechanism, possibly due to the inhibitor. Our model predicts that the efficacy of A? clearance recovers to its basal (pretreatment) value with a characteristic time of >48 hours, matching the time-scale of the overshoot. These results point to the need for a more detailed investigation of soluble A? clearance mechanisms and their interaction with A?-reducing drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000551.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3269, + "tag": "Amyloid-beta clearance" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4072, + "tag": "BioModels:BIOMD0000000551" + }, + { + "id": 4073, + "tag": "Macaca mulatta" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:27.522192+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000551", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2852": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2852, + "name": "Ehrenstein2000 - Positive-Feedback model for the loss of acetylcholine in Alzheimer's disease", + "repository_type": "biomodels", + "summary": "
Ehrenstein2000 - Positive-Feedback model forthe loss of acetylcholine in Alzheimer's disease
Curated model derived from

BIOMD0000000553

This model is described in the article:

Ehrenstein G, Galdzicki Z, Lange GD.
Ann. N. Y. Acad. Sci. 2000; 899: 283-291

Abstract:

We describe a two-component positive-feedback system that could account for the large reduction of acetylcholine that is characteristic of patients with Alzheimer's disease (AD). One component is beta-amyloid-induced apoptosis of cholinergic cells, leading to a decrease in acetylcholine. The other component is an increase in the concentration of beta-amyloid in response to a decrease in acetylcholine. We describe each mechanism with a differential equation, and then solve the two equations numerically. The solution provides a description of the time course of the reduction of acetylcholine in AD patients that is consistent with epidemiological data. This model may also provide an explanation for the significant, but lesser, decrease of other neurotransmitters that is characteristic of AD.

This model is hosted on BioModels Database and identified by: BIOMD0000000552.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4074, + "tag": "Acetylcholine biosynthetic process" + }, + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4075, + "tag": "BioModels:BIOMD0000000552" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:28.091433+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000552", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2853": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2853, + "name": "Ehrenstein1997 - The choline-leakage hypothesis in Alzheimer's disease", + "repository_type": "biomodels", + "summary": "
Ehrenstein1997 - The choline-leakagehypothesis in Alzheimer's disease

This model is described in the article:

Ehrenstein G, Galdzicki Z, Lange GD.
Biophys. J. 1997 Sep; 73(3): 1276-1280

Abstract:

We present a hypothesis for the loss of acetylcholine in Alzheimer's disease that is based on two recent experimental results: that beta-amyloid causes leakage of choline across cell membranes and that decreased production of acetylcholine increases the production of beta-amyloid. According to the hypothesis, an increase in beta-amyloid concentration caused by proteolysis of the amyloid precursor protein results in an increase in the leakage of choline out of cells. This leads to a reduction in intracellular choline concentration and hence a reduction in acetylcholine production. The reduction in acetylcholine production, in turn, causes an increase in the concentration of beta-amyloid. The resultant positive feedback between decreased acetylcholine and increased beta-amyloid accelerates the loss of acetylcholine. We compare the predictions of the choline-leakage hypothesis with a number of experimental observations. We also approximate it with a pair of ordinary differential equations. The solutions of these equations indicate that the loss of acetylcholine is very sensitive to the initial rate of beta-amyloid production.

This model is hosted on BioModels Database and identified by: BIOMD0000000553.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4074, + "tag": "Acetylcholine biosynthetic process" + }, + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4076, + "tag": "BioModels:BIOMD0000000553" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:28.579775+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000553", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2854": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2854, + "name": "Cloutier2009 - Brain Energy Metabolism", + "repository_type": "biomodels", + "summary": "
Cloutier2009 - Brain Energy Metabolism


This model was taken from the\u00a0 CellMLrepository \u00a0andautomatically converted to SBML.\u00a0\u00a0Following thesubmission the parameters are manually encoded and annotated asspices and global quantities by BioModels curators.\u00a0
\u00a0
Theoriginal model was:\u00a0 Cloutier M, Bolger FB, Lowry JP, Wellstead P. (2009) -version=1.0\u00a0
Theoriginal CellML model was created by:\u00a0
CatherineLloyd \u00a0
c.lloyd@auckland.ac.nz\u00a0
TheUniversity of Auckland\u00a0

This model is described in the article:

Cloutier M, Bolger FB, Lowry JP, Wellstead P.
J Comput Neurosci 2009 Dec; 27(3): 391-414

Abstract:

An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50 microM propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model's predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue.

This model is hosted on BioModels Database and identified by: BIOMD0000000554.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4077, + "tag": "BioModels:BIOMD0000000554" + }, + { + "id": 4078, + "tag": "Generation of precursor metabolites and energy" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:29.064058+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000554", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2855": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2855, + "name": "Auer2010 - Correlation between lag time and aggregation rate in protein aggregation", + "repository_type": "biomodels", + "summary": "
Auer2010 - Correlation between lag time andaggregation rate in protein aggregation

This model is described in the article:

Auer S, Kashchiev D.
Proteins 2010 Aug; 78(11): 2412-2416

Abstract:

Under favorable conditions, many proteins can assemble into macroscopically large aggregates such as the amyloid fibrils that are associated with Alzheimer's, Parkinson's, and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation.

This model is hosted on BioModels Database and identified by: BIOMD0000000555.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4079, + "tag": "BioModels:BIOMD0000000555" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:29.532936+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000555", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2856": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2856, + "name": "Ortega2013 - Interplay between secretases determines biphasic amyloid-beta level", + "repository_type": "biomodels", + "summary": "
Ortega2013 - Interplay between secretasesdetermines biphasic amyloid-beta level

This model is described in the article:

Ortega F, Stott J, Visser SA, Bendtsen C.
J. Biol. Chem. 2013 Jan; 288(2): 785-792

Abstract:

Amyloid-? (A?) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by ?-secretase, generating C99, and then by ?-secretase. APP is also cleaved by ?-secretase. It is hypothesized that reducing the production of A? in the brain may slow the progression of Alzheimer disease. Therefore, different ?-secretase inhibitors have been developed to reduce A? production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in A? production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the A? rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the A? dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in A? production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by ?-secretase, leading to an additional increase in C99 that overcomes the loss in ?-secretase activity. With a minor extension, the model also describes plasma A? profiles observed in humans upon dosing with a ?-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting A? production in Alzheimer disease.

This model is hosted on BioModels Database and identified by: BIOMD0000000556.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4080, + "tag": "Amyloid precursor protein catabolic process" + }, + { + "id": 4031, + "tag": "Amyloid-beta formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4081, + "tag": "BioModels:BIOMD0000000556" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:30.010916+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000556", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2857": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2857, + "name": "Reiterer2013 - pseudophosphatase STYX role in ERK signalling", + "repository_type": "biomodels", + "summary": "
Reiterer2013 - pseudophosphatase STYX role inERK signalling

This model is described in the article:

Reiterer V, Fey D, Kolch W, Kholodenko BN, Farhan H.
Proc. Natl. Acad. Sci. U.S.A. 2013 Jul; 110(31): E2934-43

Abstract:

Serine/threonine/tyrosine-interacting protein (STYX) is a catalytically inactive member of the dual-specificity phosphatases (DUSPs) family. Whereas the role of DUSPs in cellular signaling is well explored, the function of STYX is still unknown. Here, we identify STYX as a spatial regulator of ERK signaling. We used predictive-model simulation to test several hypotheses for possible modes of STYX action. We show that STYX localizes to the nucleus, competes with nuclear DUSP4 for binding to ERK, and acts as a nuclear anchor that regulates ERK nuclear export. Depletion of STYX increases ERK activity in both cytosol and nucleus. Importantly, depletion of STYX causes an ERK-dependent fragmentation of the Golgi apparatus and inhibits Golgi polarization and directional cell migration. Finally, we show that overexpression of STYX reduces ERK1/2 activation, thereby blocking PC12 cell differentiation. Overall, our results identify STYX as an important regulator of ERK1/2 signaling critical for cell migration and PC12 cell differentiation.

This model is hosted on BioModels Database and identified by: BIOMD0000000557.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4082, + "tag": "BioModels:BIOMD0000000557" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3947, + "tag": "Regulation of ERK1 and ERK2 cascade" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:30.546069+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000557", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2858": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2858, + "name": "Cloutier2012 - Feedback motif for Parkinson's disease", + "repository_type": "biomodels", + "summary": "
Cloutier2012 - Feedback motif for Parkinson'sdisease

This model is described in the article:

Cloutier M, Middleton R, Wellstead P.
IET Syst Biol 2012 Jun; 6(3): 86-93

Abstract:

Previous article on the integrative modelling of Parkinson's disease (PD) described a mathematical model with properties suggesting that PD pathogenesis is associated with a feedback-induced biochemical bistability. In this article, the authors show that the dynamics of the mathematical model can be extracted and distilled into an equivalent two-state feedback motif whose stability properties are controlled by multi-factorial combinations of risk factors and genetic mutations associated with PD. Based on this finding, the authors propose a principle for PD pathogenesis in the form of the switch-like transition of a bistable feedback process from 'healthy' homeostatic levels of reactive oxygen species and the protein ?-synuclein, to an alternative 'disease' state in which concentrations of both molecules are stable at the damagingly high-levels associated with PD. The bistability is analysed using the rate curves and steady-state response characteristics of the feedback motif. In particular, the authors show how a bifurcation in the feedback motif marks the pathogenic moment at which the 'healthy' state is lost and the 'disease' state is initiated. Further analysis shows how known risks (such as: age, toxins and genetic predisposition) modify the stability characteristics of the feedback motif in a way that is compatible with known features of PD, and which explain properties such as: multi-factorial causality, variability in susceptibility and severity, multi-timescale progression and the special cases of familial Parkinson's and Parkinsonian symptoms induced purely by toxic stress.

Stress of 2.6 obtained by optimization(parameter S1) was imposed between days 10 and 150 in order toreproduce the Figure.

This model is hosted on BioModels Database and identified by: BIOMD0000000558.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4083, + "tag": "BioModels:BIOMD0000000558" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 4084, + "tag": "Response to reactive oxygen species" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:31.056592+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000558", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2859": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2859, + "name": "Ouzounoglou2014 - Modeling of alpha-synuclein effects on neuronal homeostasis", + "repository_type": "biomodels", + "summary": "
Ouzounoglou2014 - Modeling of alpha-synucleineffects on neuronal homeostasis

This model is described in the article:

Ouzounoglou E, Kalamatianos D, Emmanouilidou E, Xilouri M, Stefanis L, Vekrellis K, Manolakos ES.
BMC Syst Biol 2014; 8: 54

Abstract:

BACKGROUND: Alpha-synuclein (ASYN) is central in Parkinson's disease (PD) pathogenesis. Converging pieces of evidence suggest that the levels of ASYN expression play a critical role in both familial and sporadic Parkinson's disease. ASYN fibrils are the main component of inclusions called Lewy Bodies (LBs) which are found mainly in the surviving neurons of the substantia nigra. Despite the accumulated knowledge regarding the involvement of ASYN in molecular mechanisms underlying the development of PD, there is much information missing which prevents understanding the causes of the disease and how to stop its progression. RESULTS: Using a Systems Biology approach, we develop a biomolecular reactions model that describes the intracellular ASYN dynamics in relation to overexpression, post-translational modification, oligomerization and degradation of the protein. Especially for the proteolysis of ASYN, the model takes into account the biological knowledge regarding the contribution of Chaperone Mediated Autophagy (CMA), macro-autophagic and proteasome pathways in the protein's degradation. Importantly, inhibitory phenomena, caused by ASYN, concerning CMA (more specifically the lysosomal-associated membrane protein 2a, abbreviated as Lamp2a receptor, which is the rate limiting step of CMA) and the proteasome are carefully modeled. The model is validated by simulation studies of known experimental overexpression data from SH-SY5Y cells and the unknown model parameters are estimated either computationally or by experimental fitting. The calibrated model is then tested under three hypothetical intervention scenarios and in all cases predicts increased cell viability that agrees with experimental evidence. The biomodel has been annotated and is made available in SBML format. CONCLUSIONS: The mathematical model presented here successfully simulates the dynamic phenomena of ASYN overexpression and oligomerization and predicts the biological system's behavior in a number of scenarios not used for model calibration. It allows, for the first time, to qualitatively estimate the protein levels that are capable of deregulating proteolytic homeostasis. In addition, it can help form new hypotheses for intervention that could be tested experimentally.


Note: The model contains reactions of species located in different compartments. If the model is applied using volume sizes unequal to one, an extension of the model might be reasonable to guarantee mass conservation.


This model is hosted on BioModels Database and identified by: BIOMD0000000559.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4085, + "tag": "Autophagy" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4086, + "tag": "BioModels:BIOMD0000000559" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 4087, + "tag": "Protein catabolic process" + }, + { + "id": 4088, + "tag": "Regulation of dopamine metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:31.614323+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000559", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2860": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2860, + "name": "Hui2016 - Age-related changes in articular cartilage", + "repository_type": "biomodels", + "summary": "
Hui2014 - Age-related changes in articularcartilage

This model is described in the article:

Wang Hui1, David A Young1, Andrew D Rowan1, Xin Xu2, Tim E Cawston1, Carole J Proctor1,3
Annals of the Rheumatic Diseases

Abstract:

Objective: To use a computational approach to investigate the cellular and extracellular matrix changes that occur with age in the knee joints of mice. Methods: Knee joints from an inbred C57/BL1/6 (ICRFa) mouse colony were harvested at 3\u201330?months of age. Sections were stained with H&E, Safranin-O, Picro-sirius red and antibodies to matrix metalloproteinase-13 (MMP-13), nitrotyrosine, LC-3B, Bcl-2, and cleaved type II collagen used for immunohistochemistry. Based on this and other data from the literature, a computer simulation model was built using the Systems Biology Markup Language using an iterative approach of data analysis and modelling. Individual parameters were subsequently altered to assess their effect on the model. Results: A progressive loss of cartilage matrix occurred with age. Nitrotyrosine, MMP-13 and anaplastic lymphoma kinase (ALK1) staining in cartilage increased with age with a concomitant decrease in LC-3B and Bcl-2. Stochastic simulations from the computational model showed a good agreement with these data, once transforming growth factor-? signalling via ALK1/ALK5 receptors was included. Oxidative stress and the interleukin 1 pathway were identified as key factors in driving the cartilage breakdown associated with ageing. Conclusions: A progressive loss of cartilage matrix and cellularity occurs with age. This is accompanied with increased levels of oxidative stress, apoptosis and MMP-13 and a decrease in chondrocyte autophagy. These changes explain the marked predisposition of joints to develop osteoarthritis with age. Computational modelling provides useful insights into the underlying mechanisms involved in age-related changes in musculoskeletal tissues.

This model is hosted on BioModels Database and identified by: BIOMD0000000560.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4089, + "tag": "Age-dependent response to oxidative stress involved in replicative cell aging" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4090, + "tag": "BioModels:BIOMD0000000560" + }, + { + "id": 4091, + "tag": "Cartilage development" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3449, + "tag": "Osteoarthritis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:32.288557+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000560", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2861": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2861, + "name": "Martins2013 - True and apparent inhibition of amyloid fribril formation", + "repository_type": "biomodels", + "summary": "
Martins2013 - True and apparent inhibition ofamyloid fribril formation

This model is described in the article:

Martins PM.
Prion 2013 Mar-Apr; 7(2): 136-139

Abstract:

A possible therapeutic strategy for amyloid diseases involves the use of small molecule compounds to inhibit protein assembly into insoluble aggregates. According to the recently proposed Crystallization-Like Model, the kinetics of amyloid fibrillization can be retarded by decreasing the frequency of new fibril formation or by decreasing the elongation rate of existing fibrils. To the compounds that affect the nucleation and/or the growth steps we call true inhibitors. An apparent inhibition mechanism may however result from the alteration of thermodynamic properties such as the solubility of the amyloidogenic protein. Apparent inhibitors markedly influence protein aggregation kinetics measured in vitro, yet they are likely to lead to disappointing results when tested in vivo. This is because cells and tissues media are in general much more buffered against small variations in composition than the solutions prepared in lab. Here we show how to discriminate between true and apparent inhibition mechanisms from experimental data on protein aggregation kinetics. The goal is to be able to identify false positives much earlier during the drug development process.

This model is hosted on BioModels Database and identified by: BIOMD0000000561.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4092, + "tag": "BioModels:BIOMD0000000561" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 4093, + "tag": "Prion disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:32.824922+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000561", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2862": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2862, + "name": "Chaouiya2013 - EGF and TNFalpha mediated signalling pathway", + "repository_type": "biomodels", + "summary": "
Chaouiya2013 - EGF and TNFalpha mediated signalling pathway

This model is described in the article:

Chaouiya C, B\u00e9renguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, Dr\u00e4ger A, B\u00fcchel F, Cokelaer T, Kowal B, Wicks B, Gon\u00e7alves E, Dorier J, Page M, Monteiro PT, von Kamp A, Xenarios I, de Jong H, Hucka M, Klamt S, Thieffry D, Le Nov\u00e8re N, Saez-Rodriguez J, Helikar T.
BMC Syst Biol 2013; 7: 135

Abstract:

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (\"qual\"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000562.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4094, + "tag": "BioModels:BIOMD0000000562" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:33.411740+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000562", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2863": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2863, + "name": "Pritchard2014 - plant-microbe interaction", + "repository_type": "biomodels", + "summary": "
Pritchard2014 - plant-microbeinteraction
Thismodel is an abstraction of a generic interaction between microbes,and a plant host. The reactions are generally intended to berepresentative of processes, not specific molecular mechanisms(except where indicated, eg. for activation of receptors). Themodel is intended to be of a similar level of abstraction as theZig-Zag model proposed in Jones and Dangl (2006) [PMID: 17108957], but to represent a dynamic system. Jones and Dangl (2006) model is used here to illustrate the advantages of dynamic representations of systems over expository models such as the Zig-Zag model.

This model is described in the article:

Pritchard L, Birch PR.
Mol. Plant Pathol. 2014 Dec; 15(9): 865-870

Abstract:


This model is hosted on BioModels Database and identified by: BIOMD0000000563.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3171, + "tag": "Arabidopsis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4095, + "tag": "BioModels:BIOMD0000000563" + }, + { + "id": 4096, + "tag": "Immune system process" + }, + { + "id": 4097, + "tag": "Pseudomonas syringae" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:34.002837+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000563", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2864": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2864, + "name": "Gould2013 - Temperature Sensitive Circadian Clock", + "repository_type": "biomodels", + "summary": "
Gould2011 - Temperature Sensitive CircadianClock
This model is a temperature sensitiveversion of Pokhilko et al.\u00a0 2010 (PMID:20865009),which is BIOMD0000000273in BioModels.

This model is described in the article:

Gould PD, Ugarte N, Domijan M, Costa M, Foreman J, Macgregor D, Rose K, Griffiths J, Millar AJ, Finkenst\u00e4dt B, Penfield S, Rand DA, Halliday KJ, Hall AJ.
Mol. Syst. Biol. 2013; 9: 650

Abstract:

Circadian clocks exhibit 'temperature compensation', meaning that they show only small changes in period over a broad temperature range. Several clock genes have been implicated in the temperature-dependent control of period in Arabidopsis. We show that blue light is essential for this, suggesting that the effects of light and temperature interact or converge upon common targets in the circadian clock. Our data demonstrate that two cryptochrome photoreceptors differentially control circadian period and sustain rhythmicity across the physiological temperature range. In order to test the hypothesis that the targets of light regulation are sufficient to mediate temperature compensation, we constructed a temperature-compensated clock model by adding passive temperature effects into only the light-sensitive processes in the model. Remarkably, this model was not only capable of full temperature compensation and consistent with mRNA profiles across a temperature range, but also predicted the temperature-dependent change in the level of LATE ELONGATED HYPOCOTYL, a key clock protein. Our analysis provides a systems-level understanding of period control in the plant circadian oscillator.

This model is hosted on BioModels Database and identified by: BIOMD0000000564.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4098, + "tag": "BioModels:BIOMD0000000564" + }, + { + "id": 3571, + "tag": "Circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:34.565163+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000564", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2865": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2865, + "name": "Machado2014 - Curcumin production pathway in Escherichia coli", + "repository_type": "biomodels", + "summary": "
Machado2014 - Curcumin production pathway inEscherichia coli

This model is described in the article:

Machado D, Rodrigues LR, Rocha I.
BioSystems 2014 Nov; 125: 16-21

Abstract:

Curcumin is a natural compound obtained from turmeric, and is well known for its pharmacological effects. In this work, we design a heterologous pathway for industrial production of curcumin in Escherichia coli. A kinetic model of the pathway is then developed and connected to a kinetic model of the central carbon metabolism of E. coli. This model is used for optimization of the mutant strain through a rational design approach, and two manipulation targets are identified for overexpression. Dynamic simulations are then performed to compare the curcumin production profiles of the different mutant strains. Our results show that it is possible to obtain a significant improvement in the curcumin production rates with the proposed mutants. The kinetic model here developed can be an important framework to optimize curcumin production at an industrial scale and add value to its biomedical potential.

This model is hosted on BioModels Database and identified by: BIOMD0000000565.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4099, + "tag": "BioModels:BIOMD0000000565" + }, + { + "id": 4100, + "tag": "Curcumin metabolic process" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:35.044943+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000565", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2866": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2866, + "name": "Morris2009 - \u03b1-Synuclein aggregation variable temperature and pH", + "repository_type": "biomodels", + "summary": "
Morris2009 - \u03b1-Synuclein aggregationvariable temperature and pH

This model is described in the article:

Morris AM, Finke RG.
Biophys. Chem. 2009 Mar; 140(1-3): 9-15

Abstract:

The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an \"Ockham's razor\"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)x[A](0) (where k(2) is the rate constant for autocatalytic growth and [A](0) is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.

This model is hosted on BioModels Database and identified by: BIOMD0000000566.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4101, + "tag": "BioModels:BIOMD0000000566" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:35.783674+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000566", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2867": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2867, + "name": "Morris2008 - Fitting protein aggregation data via F-W 2-step mechanism", + "repository_type": "biomodels", + "summary": "
Morris2008 - Fitting protein aggregation datavia F-W 2-step mechanism

This model is described in the article:

Morris AM, Watzky MA, Agar JN, Finke RG.
Biochemistry 2008 Feb; 47(8): 2413-2427

Abstract:

The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/\"Ockham's razor\" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Sait\u00f4 and co-workers. In particular, the kinetic equation for Sait\u00f4's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Sait\u00f4 and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.

This model is hosted on BioModels Database and identified by: BIOMD0000000567.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4102, + "tag": "BioModels:BIOMD0000000567" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4043, + "tag": "Huntington's disease" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:36.299754+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000567", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2868": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2868, + "name": "Mueller2015 - Hepatocyte proliferation, T160 phosphorylation of CDK2", + "repository_type": "biomodels", + "summary": "
Mueller2015 - Hepatocyte proliferation, T160phosphorylation of CDK2

This model is described in the article:

Mueller S, Huard J, Waldow K, Huang X, D'Alessandro LA, Bohl S, B\u00f6rner K, Grimm D, Klamt S, Klingm\u00fcller U, Schilling M.
Mol. Syst. Biol. 2015; 11(3): 795

Abstract:

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

This model is hosted on BioModels Database and identified by: BIOMD0000000568.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4103, + "tag": "BioModels:BIOMD0000000568" + }, + { + "id": 4104, + "tag": "Hepatocyte proliferation" + }, + { + "id": 3734, + "tag": "Liver disease" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:36.782459+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000568", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2869": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2869, + "name": "Dutta-Roy2015 - Opening of the multiple AMPA receptor conductance states", + "repository_type": "biomodels", + "summary": "
Dutta-Roy2015 - Opening of the multiple AMPAreceptor conductance states

This model is described in the article:

Dutta-Roy R, Rosenmund C, Edelstein SJ, Le Nov\u00e8re N.
PLoS ONE 2015; 10(1): e0116616

Abstract:

Modulation of the properties of AMPA receptors at the post-synaptic membrane is one of the main suggested mechanisms underlying fast synaptic transmission in the central nervous system of vertebrates. Electrophysiological recordings of single channels stimulated with agonists showed that both recombinant and native AMPA receptors visit multiple conductance states in an agonist concentration dependent manner. We propose an allosteric model of the multiple conductance states based on concerted conformational transitions of the four subunits, as an iris diaphragm. Our model predicts that the thermodynamic behaviour of the conductance states upon full and partial agonist stimulations can be described with increased affinity of receptors as they progress to higher conductance states. The model also predicts the existence of AMPA receptors in non-liganded conductive substates. However, the probability of spontaneous openings decreases with increasing conductances. Finally, we predict that the large conductance states are stabilized within the rise phase of a whole-cell EPSC in glutamatergic hippocampal neurons. Our model provides a mechanistic link between ligand concentration and conductance states that can explain thermodynamic and kinetic features of AMPA receptor gating.

This model is hosted on BioModels Database and identified by: BIOMD0000000569.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4105, + "tag": "AMPA glutamate receptor activity" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4106, + "tag": "BioModels:BIOMD0000000569" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:37.262052+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000569", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2870": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2870, + "name": "Aubert2002 - Coupling between Brain electrical activity, Metabolism and Hemodynamics", + "repository_type": "biomodels", + "summary": "
Aubert2002 - Coupling between Brainelectrical activity, Metabolism and Hemodynamics
Felix Winter encoded this model in SBMLas part of his work at ASD GmbH

This model is described in the article:

Aubert A, Costalat R.
Neuroimage 2002 Nov; 17(3): 1162-1181

Abstract:

In order to improve the interpretation of functional neuroimaging data, we implemented a mathematical model of the coupling between membrane ionic currents, energy metabolism (i.e., ATP regeneration via phosphocreatine buffer effect, glycolysis, and mitochondrial respiration), blood-brain barrier exchanges, and hemodynamics. Various hypotheses were tested for the variation of the cerebral metabolic rate of oxygen (CMRO(2)): (H1) the CMRO(2) remains at its baseline level; (H2) the CMRO(2) is enhanced as soon as the cerebral blood flow (CBF) increases; (H3) the CMRO(2) increase depends on intracellular oxygen and pyruvate concentrations, and intracellular ATP/ADP ratio; (H4) in addition to hypothesis H3, the CMRO(2) progressively increases, due to the action of a second messenger. A good agreement with experimental data from magnetic resonance imaging and spectroscopy (MRI and MRS) was obtained when we simulated sustained and repetitive activation protocols using hypotheses (H3) or (H4), rather than hypotheses (H1) or (H2). Furthermore, by studying the effect of the variation of some physiologically important parameters on the time course of the modeled blood-oxygenation-level-dependent (BOLD) signal, we were able to formulate hypotheses about the physiological or biochemical significance of functional magnetic resonance data, especially the poststimulus undershoot and the baseline drift.

This model is hosted on BioModels Database and identified by: BIOMD0000000570.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4107, + "tag": "BioModels:BIOMD0000000570" + }, + { + "id": 4078, + "tag": "Generation of precursor metabolites and energy" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:37.802372+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000570", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2871": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2871, + "name": "Nishio2008 - Design of the phosphotransferase system for enhanced glucose uptake in E. coli.", + "repository_type": "biomodels", + "summary": "
Nishio2008 - Design of the phosphotransferasesystem for enhanced glucose uptake in E. coli.

This model is described in the article:

Nishio Y, Usuda Y, Matsui K, Kurata H.
Mol. Syst. Biol. 2008; 4: 160

Abstract:

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate.

This model is hosted on BioModels Database and identified by: BIOMD0000000571.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4108, + "tag": "BioModels:BIOMD0000000571" + }, + { + "id": 3189, + "tag": "Escherichia coli (strain K12)" + }, + { + "id": 4109, + "tag": "Protein-N(PI)-phosphohistidine-sugar phosphotransferase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:38.277801+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000571", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2872": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2872, + "name": "Costa2014 - Computational Model of L. lactis Metabolism", + "repository_type": "biomodels", + "summary": "
Costa2014 - Computational Model of L. lactisMetabolism

This model is described in the article:

Costa RS, Hartmann A, Gaspar P, Neves AR, Vinga S.
Mol Biosyst 2014 Mar; 10(3): 628-639

Abstract:

Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising possibilities to elucidate the effect of alterations in the main metabolism of L. lactis, with application in strain optimization.

This model is hosted on BioModels Database and identified by: BIOMD0000000572.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4110, + "tag": "BioModels:BIOMD0000000572" + }, + { + "id": 4111, + "tag": "Butanediol biosynthetic process" + }, + { + "id": 3097, + "tag": "Lactococcus lactis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:38.788033+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000572", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2873": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2873, + "name": "Aguilera 2014 - HIV latency. Interaction between HIV proteins and immune response", + "repository_type": "biomodels", + "summary": "
Aguilera 2014 - HIV latency. Interactionbetween HIV proteins and immune response

This model is described in the article:

Aguilera LU, Rodr\u00edguez-Gonz\u00e1lez J.
J. Theor. Biol. 2014 Nov; 360: 67-77

Abstract:

HIV infection leads to two cell fates, the viral productive state or viral latency (a reversible non-productive state). HIV latency is relevant because infected active CD4+ T-lymphocytes can reach a resting memory state in which the provirus remains silent for long periods of time. Despite experimental and theoretical efforts, the causal molecular mechanisms responsible for HIV latency are only partially understood. Studies have determined that HIV latency is influenced by the innate immune response carried out by cell restriction factors that inhibit the postintegration steps in the virus replication cycle. In this study, we present a mathematical study that combines deterministic and stochastic approaches to analyze the interactions between HIV proteins and the innate immune response. Using wide ranges of parameter values, we observed the following: (1) a phenomenological description of the viral productive and latent cell phenotypes is obtained by bistable and bimodal dynamics, (2) biochemical noise reduces the probability that an infected cell adopts the latent state, (3) the effects of the innate immune response enhance the HIV latency state, (4) the conditions of the cell before infection affect the latent phenotype, i.e., the existing expression of cell restriction factors propitiates HIV latency, and existing expression of HIV proteins reduces HIV latency.

This model is hosted on BioModels Database and identified by: BIOMD0000000573.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4112, + "tag": "BioModels:BIOMD0000000573" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4113, + "tag": "Human immunodeficiency virus infectious disease" + }, + { + "id": 4064, + "tag": "Innate immune response" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:39.592980+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000573", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2874": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2874, + "name": "Lai2014 - Hemiconcerted MWC model of intact calmodulin with two targets", + "repository_type": "biomodels", + "summary": "
Lai2014 - Hemiconcerted MWC model of intactcalmodulin with two targets

This model is described in the article:

Lai M, Brun D, Edelstein SJ, Le Nov\u00e8re N.
PLoS Comput. Biol. 2015 Jan; 11(1): e1004063

Abstract:

Calmodulin is a calcium-binding protein ubiquitous in eukaryotic cells, involved in numerous calcium-regulated biological phenomena, such as synaptic plasticity, muscle contraction, cell cycle, and circadian rhythms. It exibits a characteristic dumbell shape, with two globular domains (N- and C-terminal lobe) joined by a linker region. Each lobe can take alternative conformations, affected by the binding of calcium and target proteins. Calmodulin displays considerable functional flexibility due to its capability to bind different targets, often in a tissue-specific fashion. In various specific physiological environments (e.g. skeletal muscle, neuron dendritic spines) several targets compete for the same calmodulin pool, regulating its availability and affinity for calcium. In this work, we sought to understand the general principles underlying calmodulin modulation by different target proteins, and to account for simultaneous effects of multiple competing targets, thus enabling a more realistic simulation of calmodulin-dependent pathways. We built a mechanistic allosteric model of calmodulin, based on an hemiconcerted framework: each calmodulin lobe can exist in two conformations in thermodynamic equilibrium, with different affinities for calcium and different affinities for each target. Each lobe was allowed to switch conformation on its own. The model was parameterised and validated against experimental data from the literature. In spite of its simplicity, a two-state allosteric model was able to satisfactorily represent several sets of experiments, in particular the binding of calcium on intact and truncated calmodulin and the effect of different skMLCK peptides on calmodulin's saturation curve. The model can also be readily extended to include multiple targets. We show that some targets stabilise the low calcium affinity T state while others stabilise the high affinity R state. Most of the effects produced by calmodulin targets can be explained as modulation of a pre-existing dynamic equilibrium between different conformations of calmodulin's lobes, in agreement with linkage theory and MWC-type models.

This model is hosted on BioModels Database and identified by: BIOMD0000000574.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4114, + "tag": "BioModels:BIOMD0000000574" + }, + { + "id": 3509, + "tag": "Calmodulin-dependent protein kinase activity" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:40.470064+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000574", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2875": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2875, + "name": "Sass2009 - Approach to an \u03b1-synuclein-based BST model of Parkinson's disease", + "repository_type": "biomodels", + "summary": "
Sass2009 - Approach to an\u03b1-synuclein-based BST model of Parkinson's disease

This model is described in the article:

Sass MB, Lorenz AN, Green RL, Coleman RA.
J. Neurosci. Methods 2009 Apr; 178(2): 366-377

Abstract:

This paper presents a detailed systems model of Parkinson's disease (PD), developed utilizing a pragmatic application of biochemical systems theory (BST) intended to assist experimentalists in the study of system behavior. This approach utilizes relative values as a reasonable initial estimate for BST and provides a theoretical means of applying numerical solutions to qualitative and semi-quantitative understandings of cellular pathways and mechanisms. The approach allows for the simulation of human disease through its ability to organize and integrate existing information about metabolic pathways without having a full quantitative description of those pathways, so that hypotheses about individual processes may be tested in a systems environment. Incorporating this method, the PD model describes alpha-synuclein aggregation as mediated by dopamine metabolism, the ubiquitin-proteasome system, and lysosomal degradation, allowing for the examination of dynamic pathway interactions and the evaluation of possible toxic mechanisms in the aggregation process. Four system perturbations: elevated alpha-synuclein aggregation, impaired dopamine packaging, increased neurotoxins, and alpha-synuclein overexpression, were analyzed for correlation to qualitative PD system hypotheses present in the literature, with the model demonstrating a high level of agreement with these hypotheses. Additionally, various PD treatment methods, including levadopa and monoamine oxidase inhibition (MAOI) therapy, were applied to the disease models to examine their effects on the system. Future additions and refinements to the model may further the understanding of the emergent behaviors of the disease, helping in the identification of system sensitivities and possible therapeutic targets.

This model is hosted on BioModels Database and identified by: BIOMD0000000575.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4115, + "tag": "BioModels:BIOMD0000000575" + }, + { + "id": 4116, + "tag": "Chaperone-mediated autophagy" + }, + { + "id": 4117, + "tag": "Dopamine metabolic process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 4118, + "tag": "Macroautophagy" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 3363, + "tag": "Proteasome-mediated ubiquitin-dependent protein catabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:40.955792+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000575", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2876": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2876, + "name": "Kolodkin2013 - Nuclear receptor-mediated cortisol signalling network", + "repository_type": "biomodels", + "summary": "
Kolodkin2013 - Nuclear receptor-mediatedcortisol signalling network

This model is described in the article:

Kolodkin A, Sahin N, Phillips A, Hood SR, Bruggeman FJ, Westerhoff HV, Plant N.
Nat Commun 2013; 4: 1792

Abstract:

It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body's response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress.

This model is hosted on BioModels Database and identified by: BIOMD0000000576.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4119, + "tag": "BioModels:BIOMD0000000576" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4120, + "tag": "Response to cortisol" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:41.443083+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000576", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2877": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2877, + "name": "Zhou2015 - Circadian clock with immune regulator NPR1", + "repository_type": "biomodels", + "summary": "
Zhou2015 - Circadian clock with immuneregulator NPR1
Arabidopsis clock model modified fromP2012 (Pokhilko et al., 2013 - BIOMD0000000445)model to include the master immune regulator NPR1 coupling to LHY,TOC1 and PRR7.
Triggers: The Global Quantities contain triggers that allowone to change coupling settings, Salicyclic acid (SA) treatment andnpr1 mutants.
LHY_on: true->NPR1 couples to LHY
PRR7_on: true->NPR1 couples to PRR7
WT: true->WT plants, false->npr1 mutant plants
SA: true->SA treated plants, false->no treatment
This model has L=1, i.e. operates only under constant lightconditions and is not aiming to make preditions under diurnalconditions. Due to period overshoot only time points after 28h arerelevant.

This model is described in the article:

Zhou M, Wang W, Karapetyan S, Mwimba M, Marqu\u00e9s J, Buchler NE, Dong X.
Nature 2015 Jun;

Abstract:

Recent studies have shown that in addition to the transcriptional circadian clock, many organisms, including Arabidopsis, have a circadian redox rhythm driven by the organism's metabolic activities. It has been hypothesized that the redox rhythm is linked to the circadian clock, but the mechanism and the biological significance of this link have only begun to be investigated. Here we report that the master immune regulator NPR1 (non-expressor of pathogenesis-related gene 1) of Arabidopsis is a sensor of the plant's redox state and regulates transcription of core circadian clock genes even in the absence of pathogen challenge. Surprisingly, acute perturbation in the redox status triggered by the immune signal salicylic acid does not compromise the circadian clock but rather leads to its reinforcement. Mathematical modelling and subsequent experiments show that NPR1 reinforces the circadian clock without changing the period by regulating both the morning and the evening clock genes. This balanced network architecture helps plants gate their immune responses towards the morning and minimize costs on growth at night. Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.

This model is hosted on BioModels Database and identified by: BIOMD0000000577.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4121, + "tag": "BioModels:BIOMD0000000577" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:41.962172+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000577", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2878": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2878, + "name": "Invergo2014 - Phototransduction cascade in mouse rod cells", + "repository_type": "biomodels", + "summary": "
Invergo2014 - Phototransduction cascade inmouse rod cells

This model is described in the article:

Invergo BM, Dell'Orco D, Montanucci L, Koch KW, Bertranpetit J.
Mol Biosyst 2014 Jun; 10(6): 1481-1489

Abstract:

Vertebrate visual phototransduction is perhaps the most well-studied G-protein signaling pathway. A wealth of available biochemical and electrophysiological data has resulted in a rich history of mathematical modeling of the system. However, while the most comprehensive models have relied upon amphibian biochemical and electrophysiological data, modern research typically employs mammalian species, particularly mice, which exhibit significantly faster signaling dynamics. In this work, we present an adaptation of a previously published, comprehensive model of amphibian phototransduction that can produce quantitatively accurate simulations of the murine photoresponse. We demonstrate the ability of the model to predict responses to a wide range of stimuli and under a variety of mutant conditions. Finally, we employ the model to highlight a likely unknown mechanism related to the interaction between rhodopsin and rhodopsin kinase.

This model is hosted on BioModels Database and identified by: BIOMD0000000578.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4122, + "tag": "BioModels:BIOMD0000000578" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3793, + "tag": "Phototransduction" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:42.452264+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000578", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2879": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2879, + "name": "Sengupta2015 - Knowledge base model of human energy pool network (HEPNet)", + "repository_type": "biomodels", + "summary": "
Sengupta2015 - Knowledge base model of humanenergy pool network (HEPNet)

This model is described in the article:

Sengupta A, Grover M, Chakraborty A, Saxena S.
PLoS ONE 2015; 10(6): e0127918

Abstract:

HEPNet is an electronic representation of metabolic reactions occurring within human cellular organization focusing on inflow and outflow of the energy currency ATP, GTP and other energy associated moieties. The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. The results also highlight that energy currencies play a minor role. However, the moiety creatine phosphate has a unique character, since it is a ready-made source of phosphoryl groups for the rapid synthesis of ATP from ADP. HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders.

This model is hosted on BioModels Database and identified by: BIOMD0000000579.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4123, + "tag": "BioModels:BIOMD0000000579" + }, + { + "id": 4078, + "tag": "Generation of precursor metabolites and energy" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:42.977919+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000579", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2880": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2880, + "name": "Sonntag2012 - mTOR model - IRS dependent regulation of AMPK by insulin", + "repository_type": "biomodels", + "summary": "
Sonntag2012 - mTOR model - IRS dependent regulation of AMPK by insulin
TSC1-TSC2 complex has two states: 1)active (TSC1_TSC2_pS1387), regulated by AMPK_pT172; 2) inactive(TSC1_TSC2_pT1462) regulated by Akt_pT308. Particularly, mTORC1 isinhibited by TSC1_TSC2 in active state. AMPK is activated at T172by the species IRS1_p activated by the insulin receptor uponinsulin stimulation. Consequently, AMPK_pT172 is inhibited bymTORC1_pS2448 indirectly by the p70-S6K-negative feedback loop.

This model is described in the article:

Sonntag AG, Dalle Pezze P, Shanley DP, Thedieck K.
FEBS J. 2012 Sep; 279(18): 3314-3328

Abstract:

Mammalian target of rapamycin (mTOR) kinase responds to growth factors, nutrients and cellular energy status and is a central controller of cellular growth. mTOR exists in two multiprotein complexes that are embedded into a complex signalling network. Adenosine monophosphate-dependent kinase (AMPK) is activated by energy deprivation and shuts off adenosine 5'-triphosphate (ATP)-consuming anabolic processes, in part via the inactivation of mTORC1. Surprisingly, we observed that AMPK not only responds to energy deprivation but can also be activated by insulin, and is further induced in mTORC1-deficient cells. We have recently modelled the mTOR network, covering both mTOR complexes and their insulin and nutrient inputs. In the present study we extended the network by an AMPK module to generate the to date most comprehensive data-driven dynamic AMPK-mTOR network model. In order to define the intersection via which AMPK is activated by the insulin network, we compared simulations for six different hypothetical model structures to our observed AMPK dynamics. Hypotheses ranking suggested that the most probable intersection between insulin and AMPK was the insulin receptor substrate (IRS) and that the effects of canonical IRS downstream cues on AMPK would be mediated via an mTORC1-driven negative-feedback loop. We tested these predictions experimentally in multiple set-ups, where we inhibited or induced players along the insulin-mTORC1 signalling axis and observed AMPK induction or inhibition. We confirmed the identified model and therefore report a novel connection within the insulin-mTOR-AMPK network: we conclude that AMPK is positively regulated by IRS and can be inhibited via the negative-feedback loop.

This model is hosted on BioModels Database and identified by: BIOMD0000000580.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4124, + "tag": "BioModels:BIOMD0000000580" + }, + { + "id": 3981, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 4125, + "tag": "Regulation of cAMP-dependent protein kinase activity" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:43.455068+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000580", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2881": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2881, + "name": "DallePezze2012 - TSC-independent mTORC2 regulation", + "repository_type": "biomodels", + "summary": "
DallePezze2012 - TSC-independent mTORC2regulation

This model is described in the article:

Dalle Pezze P, Sonntag AG, Thien A, Prentzell MT, G\u00f6del M, Fischer S, Neumann-Haefelin E, Huber TB, Baumeister R, Shanley DP, Thedieck K.
Sci Signal 2012 Mar; 5(217): ra25

Abstract:

The kinase mammalian target of rapamycin (mTOR) exists in two multiprotein complexes (mTORC1 and mTORC2) and is a central regulator of growth and metabolism. Insulin activation of mTORC1, mediated by phosphoinositide 3-kinase (PI3K), Akt, and the inhibitory tuberous sclerosis complex 1/2 (TSC1-TSC2), initiates a negative feedback loop that ultimately inhibits PI3K. We present a data-driven dynamic insulin-mTOR network model that integrates the entire core network and used this model to investigate the less well understood mechanisms by which insulin regulates mTORC2. By analyzing the effects of perturbations targeting several levels within the network in silico and experimentally, we found that, in contrast to current hypotheses, the TSC1-TSC2 complex was not a direct or indirect (acting through the negative feedback loop) regulator of mTORC2. Although mTORC2 activation required active PI3K, this was not affected by the negative feedback loop. Therefore, we propose an mTORC2 activation pathway through a PI3K variant that is insensitive to the negative feedback loop that regulates mTORC1. This putative pathway predicts that mTORC2 would be refractory to Akt, which inhibits TSC1-TSC2, and, indeed, we found that mTORC2 was insensitive to constitutive Akt activation in several cell types. Our results suggest that a previously unknown network structure connects mTORC2 to its upstream cues and clarifies which molecular connectors contribute to mTORC2 activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000581.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4126, + "tag": "BioModels:BIOMD0000000581" + }, + { + "id": 3981, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:44.024799+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000581", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2882": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2882, + "name": "DallePezze2014 - Cellular senescene-induced mitochondrial dysfunction", + "repository_type": "biomodels", + "summary": "
DallePazze2014 - Cellular senescene-inducedmitochondrial dysfunction

This model is described in the article:

Dalle Pezze P, Nelson G, Otten EG, Korolchuk VI, Kirkwood TB, von Zglinicki T, Shanley DP.
PLoS Comput. Biol. 2014 Aug; 10(8): e1003728

Abstract:

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset.

This model is hosted on BioModels Database and identified by: BIOMD0000000582.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4127, + "tag": "BioModels:BIOMD0000000582" + }, + { + "id": 3981, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4128, + "tag": "Regulation of cellular senescence" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:44.599788+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000582", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2883": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2883, + "name": "Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection", + "repository_type": "biomodels", + "summary": "
Leber2015 - Mucosal immunity and gutmicrobiome interaction during C. difficile infection

This model is described in the article:

Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J.
PLoS ONE 2015; 10(7): e0134849

Abstract:

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000583.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4129, + "tag": "BioModels:BIOMD0000000583" + }, + { + "id": 4130, + "tag": "Clostridioides difficile" + }, + { + "id": 4131, + "tag": "Clostridium difficile colitis" + }, + { + "id": 4132, + "tag": "Immune response" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 4133, + "tag": "Obsolete host-pathogen interaction" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:45.137867+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000583", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2884": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2884, + "name": "Mandlik2015 - Tristable genetic circuit of Leishmania", + "repository_type": "biomodels", + "summary": "Vineetha Mandlik, Mayuri Gurav & Shailza Singh. Regulatory dynamics of network architecture and function in tristable genetic circuit of Leishmania: a mathematical biology approach. Journal of Biomolecular Structure and Dynamics 33, 12 (2015).

The emerging field of synthetic biology has led to the design of tailor-made synthetic circuits for several therapeutic applications. Biological networks can be reprogramed by designing synthetic circuits that modulate the expression of target proteins. IPCS (inositol phosphorylceramide synthase) has been an attractive target in the sphingolipid metabolism of the parasite Leishmania. In this study, we have constructed a tristable circuit for the IPCS protein. The circuit has been validated and its long-term behavior has been assessed. The robustness and evolvability of the circuit has been estimated using evolutionary algorithms. The tristable synthetic circuit has been specifically designed to improve the rate of production of phosphatidylcholine: ceramide cholinephosphotransferase 4 (SLS4 protein). Site-specific delivery of the circuit into the parasite-infected macrophages could serve as a possible therapeutic intervention of the infectious disease 'Leishmaniasis'.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4134, + "tag": "BioModels:BIOMD0000000584" + }, + { + "id": 4135, + "tag": "Leishmania" + }, + { + "id": 3301, + "tag": "Leishmaniasis" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4136, + "tag": "Sphingolipid metabolic process" + } + ], + "timestamp_created": "2025-02-03 16:43:45.632596+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000584", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2885": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2885, + "name": "Rateitschak2012 - Interferon-gamma (IFN\u03b3) induced STAT1 signalling (PC_IFNg100)", + "repository_type": "biomodels", + "summary": "
Rateitschak2012 - Interferon-gamma (IFN\u03b3) induced STAT1 signalling (PC_IFNg100)

This model is described in the article:

Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O.
PLoS Comput. Biol. 2012; 8(12): e1002815

Abstract:

The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFN\u03b3) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFN\u03b3 inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFN\u03b3 induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types.

This model is hosted on BioModels Database and identified by: BIOMD0000000585.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4137, + "tag": "BioModels:BIOMD0000000585" + }, + { + "id": 3777, + "tag": "JAK-STAT cascade" + }, + { + "id": 4138, + "tag": "Pancreatic cancer" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 4139, + "tag": "Response to interferon-gamma" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:46.185464+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000585", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2886": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2886, + "name": "Karapetyan2016 - Genetic oscillatory network - Activator Titration Circuit (ATC)", + "repository_type": "biomodels", + "summary": "
Karapetyan2016 - Genetic oscillatory network - Activator Titration Circuit (ATC)

This model is described in the article:

Karapetyan S, Buchler NE.
Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712

Abstract:

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

This model is hosted on BioModels Database and identified by: BIOMD0000000586.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4140, + "tag": "BioModels:BIOMD0000000586" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:46.732952+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000586", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2887": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2887, + "name": "Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC)", + "repository_type": "biomodels", + "summary": "
Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC)

This model is described in the article:

Karapetyan S, Buchler NE.
Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712

Abstract:

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

This model is hosted on BioModels Database and identified by: BIOMD0000000587.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4141, + "tag": "BioModels:BIOMD0000000587" + }, + { + "id": 3316, + "tag": "Regulation of gene expression" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:47.252056+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000587", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2888": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2888, + "name": "Benson2013 - Identification of key drug targets in nerve growth factor pathway", + "repository_type": "biomodels", + "summary": "
Benson2013 - Identification of key drug targets in nerve growth factor pathway

This model is described in the article:

Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P, van der Graaf PH.
Interface Focus 2013 Apr; 3(2): 20120071

Abstract:

The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000588.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4142, + "tag": "BioModels:BIOMD0000000588" + }, + { + "id": 4143, + "tag": "Negative regulation of neurotrophin TRK receptor signaling pathway" + }, + { + "id": 4144, + "tag": "Nerve growth factor signaling pathway" + }, + { + "id": 3153, + "tag": "Rattus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:47.796613+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000588", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2889": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2889, + "name": "Valero2016 - Ascorbate-Glutathione cycle in chloroplasts under light/dark conditions", + "repository_type": "biomodels", + "summary": "
Valero2016 - Ascorbate-Glutathione cycle in chloroplasts under light/dark conditions

This model is described in the article:

Valero E, Maci\u00e0 H, De la Fuente IM, Hern\u00e1ndez JA, Gonz\u00e1lez-S\u00e1nchez MI, Garc\u00eda-Carmona F.
BMC Syst Biol 2016; 10(1): 11

Abstract:

Light/dark cycles are probably the most important environmental signals that regulate plant development. Light is essential for photosynthesis, but an excess, in combination with the unavoidable presence of atmospheric oxygen inside the chloroplast, leads to excessive reactive oxygen species production. Among the defense mechanisms that activate plants to cope with environmental stress situations, it is worth noting the ascorbate-glutathione cycle, a complex metabolic pathway in which a variety of photochemical, chemical and enzymatic steps are involved.We herein studied the dynamic behavior of this pathway under light/dark conditions and for several consecutive days. For this purpose, a mathematical model was developed including a variable electron source with a rate law proportional to the intensity of solar irradiance during the photoperiod, and which is continuously turned off at night and on again the next day. The model is defined by a nonlinear system of ordinary differential equations with an on/off time-dependent input, including a parameter to simulate the fact that the photoperiod length is not constant throughout the year, and which takes into account the particular experimental kinetics of each enzyme involved in the pathway. Unlike previous models, which have only provided steady-state solutions, the present model is able to simulate diurnal fluctuations in the metabolite concentrations, fluxes and enzymatic rates involved in the network.The obtained results are broadly consistent with experimental observations and highlight the key role played by ascorbate recycling for plants to adapt to their surrounding environment. This approach provides a new strategy to in vivo studies to analyze plant defense mechanisms against oxidative stress induced by external changes, which can also be extrapolated to other complex metabolic pathways to constitute a useful tool to the scientific community in general.

This model is hosted on BioModels Database and identified by: BIOMD0000000580.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4145, + "tag": "Ascorbate glutathione cycle" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4146, + "tag": "BioModels:BIOMD0000000589" + }, + { + "id": 4147, + "tag": "Embryophyta" + }, + { + "id": 4148, + "tag": "Response to abiotic stimulus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:48.382670+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000589", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2890": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2890, + "name": "Hermansen2015 - denovo biosynthesis of pyrimidines in yeast", + "repository_type": "biomodels", + "summary": "
Hermansen2015 - denovo biosynthesis of pyrimidines in yeast

This model is described in the article:

Hermansen RA , Mannakee BK , Knecht W , Liberles DA , Gutenkunst RN
BMC Evolutionary Biology. 2015, 15:232

Abstract:

Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes.Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint.We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

This model is hosted on BioModels Database and identified by: MODEL1512160000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4149, + "tag": "BioModels:BIOMD0000000590" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:48.858978+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000590", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2891": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2891, + "name": "Boehm2014 - isoform-specific dimerization of pSTAT5A and pSTAT5B", + "repository_type": "biomodels", + "summary": "
Boehm2014 - isoform-specific dimerization of pSTAT5A and pSTAT5B
To study STAT5 activation, the authors build a dynamic model of pSTAT5 isoform dimerization. Combinatorial binding of pSTAT5A and pSTAT5B is analysed using model hypotheses and concurrent experiments. Model parameters are derived from the experiments on Ba/F3 cells. Results show that pSTAT5 heterodimerization hypothesis for STAT5 activation favours experimental results.

This model is described in the article:

Boehm ME, Adlung L, Schilling M, Roth S, Klingm\u00fcller U, Lehmann WD
J Proteome Res. 2014 Dec 5;13(12):5685-94

Abstract:

STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5.

This model is hosted on BioModels Database and identified by: BIOMD0000000591.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality, and Use..

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4150, + "tag": "BioModels:BIOMD0000000591" + }, + { + "id": 4151, + "tag": "Erythropoietin-mediated signaling pathway" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3481, + "tag": "Regulation of tyrosine phosphorylation of STAT protein" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:49.320145+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000591", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2892": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2892, + "name": "Martinez-Sanchez2015 - T CD4+ lymphocyte transcriptional regulatory network", + "repository_type": "biomodels", + "summary": "Mariana Esther Martinez-Sanchez, Luis Mendoza, Carlos Villarreal & Elena R. Alvarez-Buylla. A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity. PLOS Computational Biology 11, 6 (2015).

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-\u03b2 and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4152, + "tag": "BioModels:BIOMD0000000592" + }, + { + "id": 4153, + "tag": "Cell differentiation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3690, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-02-03 16:43:49.812568+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000592", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2893": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2893, + "name": "Martinez-Sanchez2015 - T CD4+ lymphocyte transcriptional-signaling regulatory network", + "repository_type": "biomodels", + "summary": "
Martinez-Sanchez2015 - T CD4+ lymphocytetranscriptional-signaling regulatory network

This model is described in the article:

Martinez-Sanchez ME, Mendoza L, Villarreal C, Alvarez-Buylla ER.
PLoS Comput. Biol. 2015 Jun; 11(6): e1004324

Abstract:

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-? and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000593.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4154, + "tag": "BioModels:BIOMD0000000593" + }, + { + "id": 4153, + "tag": "Cell differentiation" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3690, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-02-03 16:43:50.335553+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000593", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2894": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2894, + "name": "Capuani2015 - Binding of Cbl and Gbr2 to EGFR (Multisite Phosphorylation Model - MPM)", + "repository_type": "biomodels", + "summary": "Fabrizio Capuani, Alexia Conte, Elisabetta Argenzio, Luca Marchetti, Corrado Priami, Simona Polo, Pier Paolo Di Fiore, Sara Sigismund & Andrea Ciliberto. Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells. Nature Communications 6 (2015).

Ubiquitination of the epidermal growth factor receptor (EGFR) that occurs when Cbl and Grb2 bind to three phosphotyrosine residues (pY1045, pY1068 and pY1086) on the receptor displays a sharp threshold effect as a function of EGF concentration. Here we use a simple modelling approach together with experiments to show that the establishment of the threshold requires both the multiplicity of binding sites and cooperative binding of Cbl and Grb2 to the EGFR. While the threshold is remarkably robust, a more sophisticated model predicted that it could be modulated as a function of EGFR levels on the cell surface. We confirmed experimentally that the system has evolved to perform optimally at physiological levels of EGFR. As a consequence, this system displays an intrinsic weakness that causes--at the supraphysiological levels of receptor and/or ligand associated with cancer--uncoupling of the mechanisms leading to signalling through phosphorylation and attenuation through ubiquitination.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4155, + "tag": "BioModels:BIOMD0000000594" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:50.888123+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000594", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2895": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2895, + "name": "Capuani2015 - Binding of Cbl and Grb2 to EGFR (Early Activation Model - EAM)", + "repository_type": "biomodels", + "summary": "Fabrizio Capuani, Alexia Conte, Elisabetta Argenzio, Luca Marchetti, Corrado Priami, Simona Polo, Pier Paolo Di Fiore, Sara Sigismund & Andrea Ciliberto. Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells. Nature Communications 6 (2015).

Ubiquitination of the epidermal growth factor receptor (EGFR) that occurs when Cbl and Grb2 bind to three phosphotyrosine residues (pY1045, pY1068 and pY1086) on the receptor displays a sharp threshold effect as a function of EGF concentration. Here we use a simple modelling approach together with experiments to show that the establishment of the threshold requires both the multiplicity of binding sites and cooperative binding of Cbl and Grb2 to the EGFR. While the threshold is remarkably robust, a more sophisticated model predicted that it could be modulated as a function of EGFR levels on the cell surface. We confirmed experimentally that the system has evolved to perform optimally at physiological levels of EGFR. As a consequence, this system displays an intrinsic weakness that causes--at the supraphysiological levels of receptor and/or ligand associated with cancer--uncoupling of the mechanisms leading to signalling through phosphorylation and attenuation through ubiquitination.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4156, + "tag": "BioModels:BIOMD0000000595" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:51.389928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000595", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2896": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 2896, + "name": "Philipson2015 - Innate immune response modulated by NLRX1", + "repository_type": "biomodels", + "summary": "Casandra W. Philipson, Josep Bassaganya-Riera, Monica Viladomiu, Barbara Kronsteiner, Vida Abedi, Stefan Hoops, Pawel Michalak, Lin Kang, Stephen E. Girardin & Raquel Hontecillas. Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection. PLOS ONE 10, 9 (2015).

Helicobacter pylori colonizes half of the world's population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4157, + "tag": "BioModels:BIOMD0000000596" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:51.877296+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000596", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2897": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2897, + "name": "Flis2015 - Plant clock gene circuit (P2011.1.2 PLM_71 ver 1)", + "repository_type": "biomodels", + "summary": "
Flis2015 - Plant clock gene circuit(P2011.1.2 PLM_71 ver 1)

This model is described in the article:

Flis A, Fern\u00e1ndez AP, Zielinski T, Mengin V, Sulpice R, Stratford K, Hume A, Pokhilko A, Southern MM, Seaton DD, McWatters HG, Stitt M, Halliday KJ, Millar AJ.
Open Biol 2015 Oct; 5(10):

Abstract:

Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in Arabidopsis thaliana seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of PRR9 and ELF4, and regulation of PRR5 by GI. Absolute RNA quantification revealed low levels of PRR9 transcripts (peak approx. 50 copies cell(-1)) compared with other clock genes, and threefold higher levels of LHY RNA (more than 1500 copies cell(-1)) than of its close relative CCA1. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.


 cL_m_degr, param m1, modified to ensure light rate > dark rate.  

This model is hosted on BioModels Database and identified by: MODEL1510190000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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 cL_m_degr, param m1, modified to ensure light rate > dark rate. Parameter set from PLM_67v2_LDLLLDs_newFFT_1, with modification to m1 (= old_m1 - m2).
", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4159, + "tag": "BioModels:BIOMD0000000598" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:52.872732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000598", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2899": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2899, + "name": "Coggins2014 - CXCL12 dependent recruitment of beta arrestin", + "repository_type": "biomodels", + "summary": "Nathaniel L. Coggins, Danielle Trakimas, S. Laura Chang, Anna Ehrlich, Paramita Ray, Kathryn E. Luker, Jennifer J. Linderman & Gary D. Luker. CXCR7 controls competition for recruitment of \u03b2-arrestin 2 in cells expressing both CXCR4 and CXCR7. PLoS ONE 9, 6 (2014).

Chemokine CXCL12 promotes growth and metastasis of more than 20 different human cancers, as well as pathogenesis of other common diseases. CXCL12 binds two different receptors, CXCR4 and CXCR7, both of which recruit and signal through the cytosolic adapter protein \u03b2-arrestin 2. Differences in CXCL12-dependent recruitment of \u03b2-arrestin 2 in cells expressing one or both receptors remain poorly defined. To quantitatively investigate parameters controlling association of \u03b2-arrestin 2 with CXCR4 or CXCR7 in cells co-expressing both receptors, we used a systems biology approach combining real-time, multi-spectral luciferase complementation imaging with computational modeling. Cells expressing only CXCR4 maintain low basal association with \u03b2-arrestin 2, and CXCL12 induces a rapid, transient increase in this interaction. In contrast, cells expressing only CXCR7 have higher basal association with \u03b2-arrestin 2 and exhibit more gradual, prolonged recruitment of \u03b2-arrestin 2 in response to CXCL12. We developed and fit a data-driven computational model for association of either CXCR4 or CXCR7 with \u03b2-arrestin 2 in cells expressing only one type of receptor. We then experimentally validated model predictions that co-expression of CXCR4 and CXCR7 on the same cell substantially decreases both the magnitude and duration of CXCL12-regulated recruitment of \u03b2-arrestin 2 to CXCR4. Co-expression of both receptors on the same cell only minimally alters recruitment of \u03b2-arrestin 2 to CXCR7. In silico experiments also identified \u03b2-arrestin 2 as a limiting factor in cells expressing both receptors, establishing that CXCR7 wins the \"competition\" with CXCR4 for CXCL12 and recruitment of \u03b2-arrestin 2. These results reveal how competition for \u03b2-arrestin 2 controls integrated responses to CXCL12 in cells expressing both CXCR4 and CXCR7. These results advance understanding of normal and pathologic functions of CXCL12, which is critical for developing effective strategies to target these pathways therapeutically.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4160, + "tag": "BioModels:BIOMD0000000599" + }, + { + "id": 4161, + "tag": "CXCL12-activated CXCR4 signaling pathway" + }, + { + "id": 4162, + "tag": "Chemokine (C-X-C motif) ligand 12 signaling pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:53.355678+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000599", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2900": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2900, + "name": "Celli\u00e8re2011 - Plasticity of TGF-\u03b2 Signalling", + "repository_type": "biomodels", + "summary": "
Celli\u00e8re2011 - Plasticity of TGF-\u03b2 Signalling

Transforming growth factor beta (TGF-\u03b2) signalling has been implicated as an important regulator of almost all major cell behaviours, including proliferation, differentiation, cell death, and motility. It remains unclear that how the TGF-\u03b2 signalling pathway accomplishes the flexibility in its responses. What and how many parameters have to be altered for cells to respond differently to perform complex tasks? This canonical response has been explored in this model, by considering the core signalling architecture of TGF-\u03b2 pathway.

This model is described in the article:

Celli\u00e8re G, Fengos G, Herv\u00e9 M, Iber D.
BMC Syst Biol. 2011 Nov 3;5:184.

Abstract:

The family of TGF-\u03b2 ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-\u03b2 ligands eliciting both sustained or transient responses. Members of the TGF-\u03b2 family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration. A number of different models have been proposed to reconcile these different behaviours. We were interested to define the set of minimal modifications that are required to change the type of signal processing in the TGF-\u03b2 signaling network.\t\tRESULTS:To define the key aspects for signaling plasticity we focused on the core of the TGF-\u03b2 signaling network. With the help of a parameter screen we identified ranges of kinetic parameters and protein concentrations that give rise to transient, sustained, or oscillatory responses to constant stimuli, as well as those parameter ranges that enable a proportional response to time-varying ligand concentrations (as expected in the read-out of morphogens). A combination of a strong negative feedback and fast shuttling to the nucleus biases signaling to a transient rather than a sustained response, while oscillations were obtained if ligand binding to the receptor is weak and the turn-over of the I-Smad is fast. A proportional read-out required inefficient receptor activation in addition to a low affinity of receptor-ligand binding. We find that targeted modification of single parameters suffices to alter the response type. The intensity of a constant signal (i.e. the ligand concentration), on the other hand, affected only the strength but not the type of the response.CONCLUSIONS:The architecture of the TGF-\u03b2 pathway enables the observed signaling plasticity. The observed range of signaling outputs to TGF-\u03b2 ligand in different cell types and under different conditions can be explained with differences in cellular protein concentrations and with changes in effective rate constants due to cross-talk with other signaling pathways. It will be interesting to uncover the exact cellular differences as well as the details of the cross-talks in future work.

This model is hosted on BioModels Database and identified by: MODEL1208280000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to [CC0 Public Domain Dedication>http://creativecommons.org/publicdomain/zero/1.0/] for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4163, + "tag": "BioModels:BIOMD0000000600" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3352, + "tag": "Transforming growth factor beta receptor signaling pathway" + } + ], + "timestamp_created": "2025-02-03 16:43:53.837226+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000600", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2901": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2901, + "name": "Rosas2015 - Caffeine-induced luminal SR calcium changes", + "repository_type": "biomodels", + "summary": "
This SBML model reproduced the calcium release from SR by application of 20 mM or 2mM caffeine, described in the paper. * Ca_i_Total and Ca_SR_Total respectively represent the total calcium concentration in the sarcoplasm and in the sarcoplasmic reticulum. * Ca_i  and Ca_SR respectively represent the free calcium concentration in the sarcoplasm and  the sarcoplasmic reticulum.* J1 is the calcium  flux due to all mechanisms (except SERCA pumps) that remove the excess of calcium from the sarcoplasm.* J2 is the calcium flux from the reticulum to the sarcoplasm via the ryanodine receptors (RyR) present in the reticulum membrane.* J3 is the calcium flux from the sarcoplasm to the reticulum by the SERCA pumps located in the reticulum membrane.*The parameters are a,  b, B c, Ca_i_basal, Ca_SR_basal, caff, csq,  gamma, KC, kf, KR, Ks, nf, ns and nv.* The value of KC for the model were calculated for J2=J3, after substituting Ca_i=Ca_i_basal, Ca_SR=Ca_SR_basal and caff=0. * Po represents the RyR open probability based on CICR. * Caffeine (caff)** increases the calcium affinity of smooth muscle's RyR so they open even when calcium is at basal level.** Due to caffeine-induced calcium release, a 5 seconds  pulse of caffeine (20 mM) was applied (event called Caff_ON) at 10 seconds after the simulation starts. The event called Caff_OFF starts when the pulse of caffeine finished (caff=0).* PE denotes the concentration of calcium binding sites. * Xi=Ca_SR_Total+PE+KR*In order to reproduce the dynamics of calcium following the application of 2 mM of caffeine, the value of some parameters needs to be change: b=35, Ca_i_basal=9.257e-6, gamma=7.45, caff=0.002 and the initial condition for Ca_i_Total=9.257e-6.*The unit of the calcium concentration is mol/L.* The unit of time is second. *The original SBML code was exported from COPASI 4.12 (Build 81).
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4164, + "tag": "BioModels:BIOMD0000000601" + }, + { + "id": 3397, + "tag": "Cavia porcellus" + }, + { + "id": 4165, + "tag": "Release of sequestered calcium ion into cytosol by sarcoplasmic reticulum" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:54.625994+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000601", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2902": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2902, + "name": "Stavrum2013 - Tryptophan Metabolism in Liver", + "repository_type": "biomodels", + "summary": "
Stavrum2013 - Tryptophan Metabolism inLiver

This model is described in the article:

Stavrum AK, Heiland I, Schuster S, Puntervoll P, Ziegler M.
J. Biol. Chem. 2013 Nov; 288(48): 34555-34566

Abstract:

Tryptophan is utilized in various metabolic routes including protein synthesis, serotonin, and melatonin synthesis and the kynurenine pathway. Perturbations in these pathways have been associated with neurodegenerative diseases and cancer. Here we present a comprehensive kinetic model of the complex network of human tryptophan metabolism based upon existing kinetic data for all enzymatic conversions and transporters. By integrating tissue-specific expression data, modeling tryptophan metabolism in liver and brain returned intermediate metabolite concentrations in the physiological range. Sensitivity and metabolic control analyses identified expected key enzymes to govern fluxes in the branches of the network. Combining tissue-specific models revealed a considerable impact of the kynurenine pathway in liver on the concentrations of neuroactive derivatives in the brain. Moreover, using expression data from a cancer study predicted metabolite changes that resembled the experimental observations. We conclude that the combination of the kinetic model with expression data represents a powerful diagnostic tool to predict alterations in tryptophan metabolism. The model is readily scalable to include more tissues, thereby enabling assessment of organismal tryptophan metabolism in health and disease.

This model is hosted on BioModels Database and identified by: BIOMD0000000602.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4166, + "tag": "BioModels:BIOMD0000000602" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4167, + "tag": "Serotonin biosynthetic process from tryptophan" + }, + { + "id": 4168, + "tag": "Tryptophan metabolic process" + } + ], + "timestamp_created": "2025-02-03 16:43:55.185796+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000602", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2903": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2903, + "name": "PetelenzKuehn_osmoadaptation_WT", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4169, + "tag": "BioModels:BIOMD0000000603" + }, + { + "id": 4170, + "tag": "Canonical glycolysis" + }, + { + "id": 4171, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4172, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4173, + "tag": "Response to osmotic stress" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:55.737378+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000603", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2904": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2904, + "name": "PetelenzKuehn_osmoadaptation_pfk2627D", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4174, + "tag": "BioModels:BIOMD0000000604" + }, + { + "id": 4170, + "tag": "Canonical glycolysis" + }, + { + "id": 4171, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4172, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4173, + "tag": "Response to osmotic stress" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:56.252209+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000604", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2905": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2905, + "name": "PetelenzKuehn_osmoadaptation_HOG1att", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4175, + "tag": "BioModels:BIOMD0000000605" + }, + { + "id": 4170, + "tag": "Canonical glycolysis" + }, + { + "id": 4171, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4172, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4173, + "tag": "Response to osmotic stress" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:56.780988+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000605", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2906": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2906, + "name": "PetelenzKuehn_osmoadaptation_hog1D", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4176, + "tag": "BioModels:BIOMD0000000606" + }, + { + "id": 4170, + "tag": "Canonical glycolysis" + }, + { + "id": 4171, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4172, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4173, + "tag": "Response to osmotic stress" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:57.285566+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000606", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2907": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2907, + "name": "PetelenzKuehn_osmoadaptation_fps1D1", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4177, + "tag": "BioModels:BIOMD0000000607" + }, + { + "id": 4170, + "tag": "Canonical glycolysis" + }, + { + "id": 4171, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4172, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4173, + "tag": "Response to osmotic stress" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:57.780299+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000607", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2908": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2908, + "name": "Palsson2013 - Fully-integrated immune response model (FIRM)", + "repository_type": "biomodels", + "summary": "
Palsson2013 - Fully-integration immune response model (FIRM)

FIRM (The Fully-integrated Immune Response Modeling) is a hybrid construct incorporating multiple existing models of the immune system [De Boer et al., (1985);Bell, (1970); Marino and Kirschner, (2004) ]. FIRM used a pharmacokinetic / pharmacodynamic modelling approach to combine previously published individual models of humoral and cellular response with antigen exposure. This integrated model has a potential to simulate a range of responses under a variety of conditions, for example, the immune response against tuberculosis infection, blood borne pathogen infection, Spontaneous tumour rejection and influence of regulatory T cells (Treg) on tumour rejection.

The SBML model provided here was generated from the matlab code (provided by the authors). The matlab to SBML conversion was done using MOCCASIN version 1.1.0. This model describes the immune response against tuberculosis (TB) infection and reproduces figure 7 of the reference publication.

Note:The following minor edit to the original matlab code was done during the conversion to SBML: The model had two parameters named k3 and K3. To avoid case-insensitive issues during the conversion, K3 was changed to K3s in the original matlap code before using the conversion software. The matlab code of the model provided by the authors (with the above change) can be obtained from the curation tab.

This model is described in the article:

Palsson S, Hickling TP, Bradshaw-Pierce EL, Zager M, Jooss K, O'Brien PJ, Spilker ME, Palsson BO, Vicini P.
BMC Syst Biol. 2013 Sep 28;7:95.

Abstract:

BACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-\u03b3, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-\u03b2. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

This model is hosted on BioModels Database and identified by: MODEL1603310000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4178, + "tag": "BioModels:BIOMD0000000608" + }, + { + "id": 4132, + "tag": "Immune response" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4179, + "tag": "Tuberculosis" + } + ], + "timestamp_created": "2025-02-03 16:43:58.274922+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000608", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2909": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2909, + "name": "Reddyhoff2015 - Acetaminophen metabolism and toxicity", + "repository_type": "biomodels", + "summary": "
Reddyhoff2015 - Acetaminophen metabolism and toxicity
This model examines acetaminophen metabolism and related hepatotoxicity. Multiple pathways associated with APAP metabolism has been included in the model. Using numerical, sensitivity and timescale analysis, key parameters involved in the toxicity has been identified. The model analysis highlights a critical acetaminophen dose in terms of the model parameters.

This model is described in the article:

Reddyhoff D, Ward J, Williams D, Regan S, Webb S
J Theor Biol. 2015 Dec 7;386:132-46.

Abstract:

Acetaminophen is a widespread and commonly used painkiller all over the world. However, it can cause liver damage when taken in large doses or at repeated chronic doses. Current models of acetaminophen metabolism are complex, and limited to numerical investigation though provide results that represent clinical investigation well. We derive a mathematical model based on mass action laws aimed at capturing the main dynamics of acetaminophen metabolism, in particular the contrast between normal and overdose cases, whilst remaining simple enough for detailed mathematical analysis that can identify key parameters and quantify their role in liver toxicity. We use singular perturbation analysis to separate the different timescales describing the sequence of events in acetaminophen metabolism, systematically identifying which parameters dominate during each of the successive stages. Using this approach we determined, in terms of the model parameters, the critical dose between safe and overdose cases, timescales for exhaustion and regeneration of important cofactors for acetaminophen metabolism and total toxin accumulation as a fraction of initial dose.

This model is hosted on BioModels Database and identified by: MODEL1603080000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4180, + "tag": "BioModels:BIOMD0000000609" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4181, + "tag": "Response to paracetamol" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:58.785120+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000609", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2910": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2910, + "name": "PetelenzKuehn_osmoadaptation_gpd1D", + "repository_type": "biomodels", + "summary": "
Petelenz-kurdzeil2013 - Osmo adaptationgpd1D

This model is described in the article:

Petelenz-Kurdziel E, Kuehn C, Nordlander B, Klein D, Hong KK, Jacobson T, Dahl P, Schaber J, Nielsen J, Hohmann S, Klipp E.
PLoS Comput. Biol. 2013; 9(6): e1003084

Abstract:

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000610.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4182, + "tag": "BioModels:BIOMD0000000610" + }, + { + "id": 4171, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:43:59.260911+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000610", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2911": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2911, + "name": "Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers", + "repository_type": "biomodels", + "summary": "
Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
Note:
The SBML model is generated from SimBiology. The SimBiology (.sbproj) file is available for download from the curation tab.

This model is described in the article:

Nayak S, Lee D, Patel-Hett S, Pittman DD, Martin SW, Heatherington AC, Vicini P, Hua F.
CPT Pharmacometrics Syst Pharmacol. 2015 Jul;4(7):396-405.

Abstract:

A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.

This model is hosted on BioModels Database and identified by: MODEL1511160000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4183, + "tag": "BioModels:BIOMD0000000611" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 4184, + "tag": "Blood coagulation disease" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:43:59.828492+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000611", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2912": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2912, + "name": "Proctor2016 - Circadian rhythm of PTH and the dynamics of signaling molecules on bone remodeling", + "repository_type": "biomodels", + "summary": "
Proctor2016 - Circadian rhythm of PTH and thedynamics of signaling molecules on bone remodeling

This model is described in the article:

Proctor CJ, Gartland A.
Front Endocrinol (Lausanne) 2016; 7: 61

Abstract:

Bone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-?, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing.

This model is hosted on BioModels Database and identified by: BIOMD0000000612.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4185, + "tag": "BioModels:BIOMD0000000612" + }, + { + "id": 4186, + "tag": "Bone remodeling" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:00.305942+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000612", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2913": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2913, + "name": "Peterson2010 - Integrated calcium homeostasis and bone remodelling", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level3/version1/core"> <body xmlns="http://www.w3.org/1999/xhtml"> <div class="dc:title">Peterson2010 - integrated calcium homeostasisand bone remodelling</div><div class="dc:description"> </div><div class="dc:bibliographicCitation"> <p>This model is described in the article:</p> <div class="bibo:title"> <a href="http://identifiers.org/pubmed/19732857" title="Access to this publication">A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling.</a> </div> <div class="bibo:authorList">Peterson MC, Riggs MM.</div> <div class="bibo:Journal">Bone 2010 Jan; 46(1): 49-63</div> <p>Abstract:</p> <div class="bibo:abstract"> <p>Bone biology is physiologically complex and intimately linked to calcium homeostasis. The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research.</p> </div></div><div class="dc:publisher"> <p>This model is hosted on <a href="http://www.ebi.ac.uk/biomodels/">BioModels Database</a> and identified by: <a href="http://identifiers.org/biomodels.db/BIOMD0000000613">BIOMD0000000613</a>.</p> <p>To cite BioModels Database, please use: <a href="http://identifiers.org/pubmed/20587024" title="Latest BioModels Database publication">BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models</a>.</p></div><div class="dc:license"> <p>To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to <a href="http://creativecommons.org/publicdomain/zero/1.0/" title="Access to: CC0 1.0 Universal (CC0 1.0), Public Domain Dedication">CC0 Public Domain Dedication</a> for more information.</p></div></body> </notes>", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4187, + "tag": "BioModels:BIOMD0000000613" + }, + { + "id": 4186, + "tag": "Bone remodeling" + }, + { + "id": 4188, + "tag": "Calcium ion homeostasis" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4189, + "tag": "Hyperparathyroidism" + }, + { + "id": 4190, + "tag": "Hypoparathyroidism" + }, + { + "id": 4191, + "tag": "Regulation of parathyroid hormone secretion" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:00.814018+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000613", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2914": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2914, + "name": "Kamihira2000 - calcitonin fibrillation kinetics", + "repository_type": "biomodels", + "summary": "
Kamihira2000 - calcitonin fibrillation kinetics

This model studies the kinetics of human calcitonin fibrillation described as a two-step process. Empirical data is used to determine the parameter values. Results show that the first step in fibrillation is a slow homogenous reaction and the second step is a fast autocatalytic heterogenous reaction.

This model is described in the article:

Kamihira M, Naito A, Tuzi S, Nosaka AY, Sait\u00f4 H.
Protein Sci. 2000 May; 9(5): 867-877

Abstract:

Conformational transitions of human calcitonin (hCT) during fibril formation in the acidic and neutral conditions were investigated by high-resolution solid-state 13C NMR spectroscopy. In aqueous acetic acid solution (pH 3.3), a local alpha-helical form is present around Gly10 whereas a random coil form is dominant as viewed from Phe22, Ala26, and Ala31 in the monomer form on the basis of the 13C chemical shifts. On the other hand, a local beta-sheet form as viewed from Gly10 and Phe22, and both beta-sheet and random coil as viewed from Ala26 and Ala31 were detected in the fibril at pH 3.3. The results indicate that conformational transitions from alpha-helix to beta-sheet, and from random coil to beta-sheet forms occurred in the central and C-terminus regions, respectively, during the fibril formation. The increased 13C resonance intensities of fibrils after a certain delay time suggests that the fibrillation can be explained by a two-step reaction mechanism in which the first step is a homogeneous association to form a nucleus, and the second step is an autocatalytic heterogeneous fibrillation. In contrast to the fibril at pH 3.3, the fibril at pH 7.5 formed a local beta-sheet conformation at the central region and exhibited a random coil at the C-terminus region. Not only a hydrophobic interaction among the amphiphilic alpha-helices, but also an electrostatic interaction between charged side chains can play an important role for the fibril formation at pH 7.5 and 3.3 acting as electrostatically favorable and unfavorable interactions, respectively. These results suggest that hCT fibrils are formed by stacking antiparallel beta-sheets at pH 7.5 and a mixture of antiparallel and parallel beta-sheets at pH 3.3.

This model is hosted on BioModels Database and identified by: BIOMD0000000614.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4192, + "tag": "BioModels:BIOMD0000000614" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4193, + "tag": "Regulation of supramolecular fiber organization" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:01.406740+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000614", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2915": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2915, + "name": "Kuznetsov2016(II) - \u03b1-syn aggregation kinetics in Parkinson's Disease", + "repository_type": "biomodels", + "summary": "
Kuznetsov2016(II) - \u03b1-syn aggregationkinetics in Parkinson's
This theoretical model uses 2-step Finke-Watzky (FW) kineticstodescribe the production, misfolding, aggregation, transport anddegradation of \u03b1-syn that may lead to Parkinson's Disease(PD). Deregulated \u03b1-syn degradation is predicted to becrucialfor PD pathogenesis.   

This model is described in the article:

Kuznetsov IA, Kuznetsov AV.
Math Biosci 2016 Aug; 278: 22-29

Abstract:

The aim of this paper is to develop a minimal model describing events leading to the onset of Parkinson's disease (PD). The model accounts for \u03b1-synuclein (\u03b1-syn) production in the soma, transport toward the synapse, misfolding, and aggregation. The production and aggregation of polymeric \u03b1-syn is simulated using a minimalistic 2-step Finke-Watzky model. We utilized the developed model to analyze what changes in a healthy neuron are likely to lead to the onset of \u03b1-syn aggregation. We checked the effects of interruption of \u03b1-syn transport toward the synapse, entry of misfolded (infectious) \u03b1-syn into the somatic and synaptic compartments, increasing the rate of \u03b1-syn synthesis in the soma, and failure of \u03b1-syn degradation machinery. Our model suggests that failure of \u03b1-syn degradation machinery is probably the most likely cause for the onset of \u03b1-syn aggregation leading to PD.

This model is hosted on BioModels Database and identified by: BIOMD0000000615.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4194, + "tag": "BioModels:BIOMD0000000615" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 3015, + "tag": "Parkinson's disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:01.923876+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000615", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2916": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2916, + "name": "Dunster2014 - WBC Interactions (Model1)", + "repository_type": "biomodels", + "summary": "
Dunster2014 - WBC Interactions (Model1)
This is a sub-model of a three-stepinflammatory response modelling study. The model includes distinctpopulations of white blood cells namely, macrophages and active andapoptotic neutrophil populations. Neutrophil apoptosis rate ispredicted to be crucial for the qualitative nature of thesystem.

This model is described in the article:

Dunster JL, Byrne HM, King JR.
Bull. Math. Biol. 2014 Aug; 76(8): 1953-1980

Abstract:

There is growing interest in inflammation due to its involvement in many diverse medical conditions, including Alzheimer's disease, cancer, arthritis and asthma. The traditional view that resolution of inflammation is a passive process is now being superceded by an alternative hypothesis whereby its resolution is an active, anti-inflammatory process that can be manipulated therapeutically. This shift in mindset has stimulated a resurgence of interest in the biological mechanisms by which inflammation resolves. The anti-inflammatory processes central to the resolution of inflammation revolve around macrophages and are closely related to pro-inflammatory processes mediated by neutrophils and their ability to damage healthy tissue. We develop a spatially averaged model of inflammation centring on its resolution, accounting for populations of neutrophils and macrophages and incorporating both pro- and anti-inflammatory processes. Our ordinary differential equation model exhibits two outcomes that we relate to healthy and unhealthy states. We use bifurcation analysis to investigate how variation in the system parameters affects its outcome. We find that therapeutic manipulation of the rate of macrophage phagocytosis can aid in resolving inflammation but success is critically dependent on the rate of neutrophil apoptosis. Indeed our model predicts that an effective treatment protocol would take a dual approach, targeting macrophage phagocytosis alongside neutrophil apoptosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000616.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4195, + "tag": "BioModels:BIOMD0000000616" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3332, + "tag": "Inflammatory response" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:02.450983+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000616", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2917": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2917, + "name": "Walsh2014 - Inhibition kinetics of DAPT on APP Cleavage", + "repository_type": "biomodels", + "summary": "
Walsh2014 - Inhibition kinetics of DAPT onAPP Cleavage

This model is described in the article:

Walsh R.
PeerJ 2014; 2: e649

Abstract:

Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits ?-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000617.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4080, + "tag": "Amyloid precursor protein catabolic process" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4196, + "tag": "BioModels:BIOMD0000000617" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:03.005961+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000617", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2918": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2918, + "name": "Krohn2011 - Cerebral amyloid-\u03b2 proteostasis regulated by membrane transport protein ABCC1", + "repository_type": "biomodels", + "summary": "
Krohn2011 - Cerebral amyloid-\u03b2proteostasis regulated by membrane transport protein ABCC1

This model is described in the article:

Krohn M, Lange C, Hofrichter J, Scheffler K, Stenzel J, Steffen J, Schumacher T, Br\u00fcning T, Plath AS, Alfen F, Schmidt A, Winter F, Rateitschak K, Wree A, Gsponer J, Walker LC, Pahnke J.
J. Clin. Invest. 2011 Oct; 121(10): 3924-3931

Abstract:

In Alzheimer disease (AD), the intracerebral accumulation of amyloid-\u03b2 (A\u03b2) peptides is a critical yet poorly understood process. A\u03b2 clearance via the blood-brain barrier is reduced by approximately 30% in AD patients, but the underlying mechanisms remain elusive. ABC transporters have been implicated in the regulation of A\u03b2 levels in the brain. Using a mouse model of AD in which the animals were further genetically modified to lack specific ABC transporters, here we have shown that the transporter ABCC1 has an important role in cerebral A\u03b2 clearance and accumulation. Deficiency of ABCC1 substantially increased cerebral A\u03b2 levels without altering the expression of most enzymes that would favor the production of A\u03b2 from the A\u03b2 precursor protein. In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced A\u03b2 load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1. Thus, by altering the temporal aggregation profile of A\u03b2, pharmacological activation of ABC transporters could impede the neurodegenerative cascade that culminates in the dementia of AD.

This model is hosted on BioModels Database and identified by: BIOMD0000000618.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 4028, + "tag": "Amyloid fibril formation" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4197, + "tag": "BioModels:BIOMD0000000618" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4198, + "tag": "Transport" + } + ], + "timestamp_created": "2025-02-03 16:44:03.489293+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000618", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2919": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2919, + "name": "Sluka2016 - Acetaminophen PBPK", + "repository_type": "biomodels", + "summary": "

Basic PBPK (Physiologically Based PharmacoKinetic) model of Acetaminophen.

This is a basic model of Acetaminophen (APAP, Paracetamol) pharmacokinetics in humans. Many of the model parameters (compartment volumes, volumetric flow rates, etc.) are scaled allometrically based on the body weight (BW) raised to the\t\t\t3/4 power. Because of that, the assigned values of many of the parameters are recalculated at run time and are different \t\t\tthan the default values for the particular entity (e.g., the volume of a compartment and the volumetric flow rate between compartments).

APAP dose is initially given in grams (APAP_Dose_gram), which is converted to moles via the APAP molecular weight (APAP_MW).\t\t\tAPAP quantities throughout the rest of the models are given in moles.

The base parameters are for a 70Kg human and a pharmacological oral dose of 1.4 gram of APAP. Metabolism is modelled as a single ODE in the liver compartment and the metabolite does not leave that compartment.

This model is loosely based on the model of Wambaugh and Shaw (PLoS Comput Biol. 2010 Apr 22;6(4):e1000756. doi: 10.1371/journal.pcbi.1000756. Pubmed ID: PMID- 20421935) with the following changes:

  1. The lung lumen compartment was omitted.
  2. A kidney compartment was added.
  3. Glomerular Filtration is from the kidney compartment.
  4. The APAP dose was changed to 1.4g.
  5. The gut adsorption rate constant (KGutabs), tissue partition coefficients and liver metabolism rate constant (CLmetabolism) were fit using the human in vivo data of Critchley (Critchley, J. A., Critchley, L. A. H., Anderson, P. J., and Tomlinson, B. 2005 Journal of clinical pharmacy and therapeutics, 30(2), 179-184).
  6. To model the extensive re-adsorption of APAP from the kidney tubules back into the blood the QGfr value is modified. This was done by \t\t\t\tchanging the scaling parameter QGFR_ref value from 0.31 to 0.039, resulting in a decrease in the QGfr value of 8 fold (from 7.2 L/hr to 0.91 L/hr).

The parameters in this file are the REFSIM parameters from our publication.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4199, + "tag": "BioModels:BIOMD0000000619" + }, + { + "id": 4200, + "tag": "Drug metabolic process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4201, + "tag": "Xenobiotic transporter activity" + } + ], + "timestamp_created": "2025-02-03 16:44:03.968199+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000619", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2920": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2920, + "name": "Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Disease Condition", + "repository_type": "biomodels", + "summary": "
Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Disease Condition
\t

This is the model with disease state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetanoet al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb].\t

\t

This model is described in the article:

Palm\u00e9r R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118.

Abstract:

Recent clinical studies suggest sustained treatment effects of interleukin-1\u03b2 (IL-1\u03b2)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. We propose that improved \u03b2-cell function, rather than mass, is likely to explain the main IL-1\u03b2-blocking effects seen in current clinical data, but that improved \u03b2-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

This model is hosted on BioModels Database and identified by: MODEL1604270002.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4202, + "tag": "BioModels:BIOMD0000000620" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4203, + "tag": "Interleukin-1 beta secretion" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3982, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-02-03 16:44:04.440768+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000620", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2921": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2921, + "name": "Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Healthy Condition", + "repository_type": "biomodels", + "summary": "
Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Healthy Condition
\t

This is the model with healthy state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetanoet al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb].\t

\t

This model is described in the article:

Palm\u00e9r R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118.

Abstract:

Recent clinical studies suggest sustained treatment effects of interleukin-1\u03b2 (IL-1\u03b2)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. We propose that improved \u03b2-cell function, rather than mass, is likely to explain the main IL-1\u03b2-blocking effects seen in current clinical data, but that improved \u03b2-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

This model is hosted on BioModels Database and identified by: MODEL1604270002.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4204, + "tag": "BioModels:BIOMD0000000621" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4203, + "tag": "Interleukin-1 beta secretion" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3982, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-02-03 16:44:04.932918+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000621", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2922": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2922, + "name": "NguyenLK2011 - Ubiquitination dynamics in Ring1B/Bmi1 system", + "repository_type": "biomodels", + "summary": "
NguyenLK2011 - Ubiquitination dynamics inRing1B-Bmi1 system
This theoretical model investigates thedynamics of Ring1B/Bmi1 ubiquitination to identify bistableswitch-like and oscillatory behaviour in thesystem.\u00a0Michaelis-Menten (MM) equations are used to formulatethe model. However, the authors show that the dynamics persist evenfor Mass-Action kinetics. This SBML file is the MM version of themodel.

This model is described in the article:

Nguyen LK, Mu\u00f1oz-Garc\u00eda J, Maccario H, Ciechanover A, Kolch W, Kholodenko BN.
PLoS Comput. Biol. 2011 Dec; 7(12): e1002317

Abstract:

In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7.

This model is hosted on BioModels Database and identified by: BIOMD0000000622.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4205, + "tag": "BioModels:BIOMD0000000622" + }, + { + "id": 4087, + "tag": "Protein catabolic process" + }, + { + "id": 4206, + "tag": "Protein ubiquitination" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:44:05.497494+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000622", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2923": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2923, + "name": "Orton2009 - Modelling cancerous mutations in the EGFR/ERK pathway - EGF Model", + "repository_type": "biomodels", + "summary": "
Orton2009 - Modelling cancerous mutations inthe EGFR/ERK pathway - EGF Model
This model studies the aberrations inERK signalling for different cancer mutations. The authors alter apreviously existing EGF model (Brown et al 2004) to include newinteractions\u00a0that\u00a0better\u00a0fit empirical data.Predictions show that the ERK signalling is a robust mechanismtaking different courses for different cancer mutations. Mostparameter values are used from the previous model and the newparameters are\u00a0estimated using experimental data performed bythe authors on PC12 cells (adrenal gland, rat).\u00a0The authorsprovide an SBML version of the model in the paper.

This model is described in the article:

Orton RJ, Adriaens ME, Gormand A, Sturm OE, Kolch W, Gilbert DR.
BMC Syst Biol 2009 Oct; 3: 100

Abstract:

The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway.We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors.Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.

This model is hosted on BioModels Database and identified by: BIOMD0000000623.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4207, + "tag": "BioModels:BIOMD0000000623" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3102, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:06.088853+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000623", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2924": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2924, + "name": "Sluka2016 - Acetaminophen metabolism", + "repository_type": "biomodels", + "summary": "
Sluka2016 - Acetaminophen metabolism
Liver metabolism of Acetaminophen: Acetaminophen (APAP) ismetabolized in the liver in both Phase I and Phase II reactions.Phase II reactions convert APAP to APAP-glucuronide andAPAP-sulfate. Phase I reactions involve Cytochrome P450 mediated(mostly Cyp450-2E1 and -1A2) conversion of APAP toN-acetyle-p-quinoneimine (NAPQI), which goes on to react withcellular nucleophiles such as glutathione (GSH). At high doses ofAPAP significant GSH depletion in hepatocyte occurs resulting incell necrosis and and in extreme cases death.

This model is described in the article:

Sluka JP, Fu X, Swat M, Belmonte JM, Cosmanescu A, Clendenon SG, Wambaugh JF, Glazier JA.
PLoS ONE 2016; 11(9): e0162428

Abstract:

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.

This model is hosted on BioModels Database and identified by: BIOMD0000000624.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4208, + "tag": "BioModels:BIOMD0000000624" + }, + { + "id": 4209, + "tag": "Endogenous drug catabolic process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:06.650795+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000624", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2925": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2925, + "name": "Leber2016 - Expanded model of Tfh-Tfr differentiation - Helicobacter pylori infection", + "repository_type": "biomodels", + "summary": "
Leber2016 - Expanded model of Tfh-Tfrdifferentiation - Helicobacter pylori infection

The parameters used in the model wereobtained from experiments conducted by the authors, previouspublications [ 1, 2, 3] andparameter optimisation carried out in the paper using particleswarm and genetic algorithms.\u00a0

This model is described in the article:

Leber A, Abedi V, Hontecillas R, Viladomiu M, Hoops S, Ciupe S, Caughman J, Andrew T, Bassaganya-Riera J.
J. Theor. Biol. 2016 Jun; 398: 74-84

Abstract:

T follicular helper (Tfh) cells are a highly plastic subset of CD4+ T cells specialized in providing B cell help and promoting inflammatory and effector responses during infectious and immune-mediate diseases. Helicobacter pylori is the dominant member of the gastric microbiota and exerts both beneficial and harmful effects on the host. Chronic inflammation in the context of H. pylori has been linked to an upregulation in T helper (Th)1 and Th17 CD4+ T cell phenotypes, controlled in part by the cytokine, interleukin-21. This study investigates the differentiation and regulation of Tfh cells, major producers of IL-21, in the immune response to H. pylori challenge. To better understand the conditions influencing the promotion and inhibition of a chronically elevated Tfh population, we used top-down and bottom-up approaches to develop computational models of Tfh and T follicular regulatory (Tfr) cell differentiation. Stability analysis was used to characterize the presence of two bi-stable steady states in the calibrated Tfh/Tfr models. Stochastic simulation was used to illustrate the ability of the parameter set to dictate two distinct behavioral patterns. Furthermore, sensitivity analysis helped identify the importance of various parameters on the establishment of Tfh and Tfr cell populations. The core network model was expanded into a more comprehensive and predictive model by including cytokine production and signaling pathways. From the expanded network, the interaction between TGFB-Induced Factor Homeobox 1 (Tgif1) and the retinoid X receptor (RXR) was displayed to exert control over the determination of the Tfh response. Model simulations predict that Tgif1 and RXR respectively induce and curtail Tfh responses. This computational hypothesis was validated experimentally by assaying Tgif1, RXR and Tfh in stomachs of mice infected with H. pylori.

The impulse of RXR as shown in the paper(figure 7C) can be implemented by creating an event in the curatedSBML file.

This model is hosted on BioModels Database and identified by: BIOMD0000000625.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4210, + "tag": "BioModels:BIOMD0000000625" + }, + { + "id": 4211, + "tag": "Duodenal ulcer" + }, + { + "id": 4212, + "tag": "Helicobacter pylori" + }, + { + "id": 4213, + "tag": "Interspecies interaction between organisms" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 4214, + "tag": "Peptic ulcer disease" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:07.216733+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000625", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2926": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2926, + "name": "Ray2013 - Meiotic initiation in S. cerevisiae", + "repository_type": "biomodels", + "summary": "
Ray2013 - Meiotic initiation in S. cerevisiae

A mathematical representation of early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signalling molecules for regulating protein activities, is described here

This model is described in the article:

Ray D, Su Y, Ye P.
BMC Syst Biol. 2013 May 1;7:37

Abstract:

BACKGROUND: Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities.

RESULTS: The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state.

CONCLUSIONS: By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000626 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4215, + "tag": "BioModels:BIOMD0000000626" + }, + { + "id": 4216, + "tag": "Meiotic cell cycle" + }, + { + "id": 4217, + "tag": "Regulation of meiotic nuclear division" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:07.758973+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000626", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2927": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2927, + "name": "Winter2017 - Brain Energy Metabolism with PPP", + "repository_type": "biomodels", + "summary": "
Winter2017 - Brain Energy Metabolism with PPP

This model is described in the article:

Felix Winter1,2, Catrin Bludszuweit-Philipp1 and Olaf Wolkenhauer2,3
Journal of Cerebral Blood Flow & Metabolism

Abstract:

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a standard clinical tool for the detection of brain activation. In Alzheimer\u2019s disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia- driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients.

This model is hosted on BioModels Database and identified by: BIOMD0000000627.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4218, + "tag": "BioModels:BIOMD0000000627" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:08.318165+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000627", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2928": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2928, + "name": "Li2012 Calcium mediated synaptic plasticity", + "repository_type": "biomodels", + "summary": "
Li2012 Calcium mediated synapticplasticity
This model is an extension of\u00a0 BIOMD0000000183.

This model is described in the article:

Li L, Stefan MI, Le Nov\u00e8re N.
PLoS ONE 2012; 7(9): e43810

Abstract:

NMDA receptor dependent long-term potentiation (LTP) and long-term depression (LTD) are two prominent forms of synaptic plasticity, both of which are triggered by post-synaptic calcium elevation. To understand how calcium selectively stimulates two opposing processes, we developed a detailed computational model and performed simulations with different calcium input frequencies, amplitudes, and durations. We show that with a total amount of calcium ions kept constant, high frequencies of calcium pulses stimulate calmodulin more efficiently. Calcium input activates both calcineurin and Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) at all frequencies, but increased frequencies shift the relative activation from calcineurin to CaMKII. Irrespective of amplitude and duration of the inputs, the total amount of calcium ions injected adjusts the sensitivity of the system to calcium input frequencies. At a given frequency, the quantity of CaMKII activated is proportional to the total amount of calcium. Thus, an input of a small amount of calcium at high frequencies can induce the same activation of CaMKII as a larger amount, at lower frequencies. Finally, the extent of activation of CaMKII signals with high calcium frequency is further controlled by other factors, including the availability of calmodulin, and by the potency of phosphatase inhibitors.

This model is hosted on BioModels Database and identified by: BIOMD0000000628.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4219, + "tag": "BioModels:BIOMD0000000628" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:08.818766+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000628", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2929": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2929, + "name": "Haffez2017 - RAR interaction with synthetic analogues", + "repository_type": "biomodels", + "summary": "
Haffez2017 - RAR interaction with syntheticanalogues

This model is described in the article:

Hesham Haffez, David R. Chisholm, Roy Valentine, Ehmke Pohl, Christopher Redfern and Andrew Whiting
MedChemComm

Abstract:

All-trans-retinoic acid (ATRA) and its synthetic analogues EC23 and EC19 direct cellular differentiation by interacting as ligands for the retinoic acid receptor (RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3) family of nuclear receptor proteins. To date, a number of crystal structures of natural and synthetic ligands complexed to their target proteins have been solved, providing molecular level snap-shots of ligand binding. However, a deeper understanding of receptor and ligand flexibility and conformational freedom is required to develop stable and effective ATRA analogues for clinical use. Therefore, we have used molecular modelling techniques to define RAR interactions with ATRA and two synthetic analogues, EC19 and EC23, and compared their predicted biochemical activities to experimental measurements of relative ligand affinity and recruitment of coactivator proteins. A comprehensive molecular docking approach that explored the conformational space of the ligands indicated that ATRA is able to bind the three RAR proteins in a number of conformations with one extended structure being favoured. In contrast the biologically-distinct isomer, 9-cis-retinoic acid (9CRA), showed significantly less conformational flexibility in the RAR binding pockets. These findings were used to inform docking studies of the synthetic retinoids EC23 and EC19, and their respective methyl esters. EC23 was found to be an excellent mimic for ATRA, and occupied similar binding modes to ATRA in all three target RAR proteins. In comparison, EC19 exhibited an alternative binding mode which reduces the strength of key polar interactions in RAR\u00ce\u00b1/\u00ce\u00b3 but is well-suited to the larger RAR\u00ce\u00b2 binding pocket. In contrast, docking of the corresponding esters revealed the loss of key polar interactions which may explain the much reduced biological activity. Our computational results were complemented using an in vitro binding assay based on FRET measurements, which showed that EC23 was a strongly binding, pan-agonist of the RARs, while EC19 exhibited specificity for RAR\u00ce\u00b2, as predicted by the docking studies. These findings can account for the distinct behaviour of EC23 and EC19 in cellular differentiation assays, and additionally, the methods described herein can be further applied to the understanding of the molecular basis for the selectivity of different retinoids to RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3.

This model is hosted on BioModels Database and identified by: BIOMD0000000629.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4220, + "tag": "BioModels:BIOMD0000000629" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:44:09.333899+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000629", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2930": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2930, + "name": "Venkatraman2011 - PLS-UPA behaviour in the presence of substrate competition_1_1_1_1", + "repository_type": "biomodels", + "summary": "
Venkatraman2011 - PLS-UPA behaviour in thepresence of substrate competition

The posibility of ultrasensitivity and bistable activation of PLS (Plasmin) and UPA (Urokinase-type plasminogen activator) in the presence of substrate competition is explained here using a mathematical model.\u00a0

This model is described in the article:

Venkatraman L, Li H, Dewey CF Jr, White JK, Bhowmick SS, Yu H, Tucker-Kellogg L.
Biophys. J. 2011 Oct; 101(8): 1825-1834

Abstract:

Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis.

The cooperativity parameter \"ci\" was missing in the original model. The parameter \"ci\" has been added to the added.

This model is hosted on BioModels Database and identified by: BIOMD0000000630.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4221, + "tag": "BioModels:BIOMD0000000630" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3130, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-02-03 16:44:09.829326+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000630", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2931": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2931, + "name": "DeCaluwe2016 - Circadian Clock", + "repository_type": "biomodels", + "summary": "
DeCaluw\u00e92016 - Circadian Clock

This model is described in the article:

De Caluw\u00e9 J, Xiao Q, Hermans C, Verbruggen N, Leloup JC, Gonze D.
Front Plant Sci 2016; 7: 74

Abstract:

The circadian clock is an endogenous timekeeper that allows organisms to anticipate and adapt to the daily variations of their environment. The plant clock is an intricate network of interlocked feedback loops, in which transcription factors regulate each other to generate oscillations with expression peaks at specific times of the day. Over the last decade, mathematical modeling approaches have been used to understand the inner workings of the clock in the model plant Arabidopsis thaliana. Those efforts have produced a number of models of ever increasing complexity. Here, we present an alternative model that combines a low number of equations and parameters, similar to the very earliest models, with the complex network structure found in more recent ones. This simple model describes the temporal evolution of the abundance of eight clock gene mRNA/protein and captures key features of the clock on a qualitative level, namely the entrained and free-running behaviors of the wild type clock, as well as the defects found in knockout mutants (such as altered free-running periods, lack of entrainment, or changes in the expression of other clock genes). Additionally, our model produces complex responses to various light cues, such as extreme photoperiods and non-24 h environmental cycles, and can describe the control of hypocotyl growth by the clock. Our model constitutes a useful tool to probe dynamical properties of the core clock as well as clock-dependent processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000631.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3192, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4222, + "tag": "BioModels:BIOMD0000000631" + }, + { + "id": 4223, + "tag": "Entrainment of circadian clock by photoperiod" + }, + { + "id": 3094, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:10.312944+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000631", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2932": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2932, + "name": "Kollarovic2016 - Cell fate decision at G1-S transition", + "repository_type": "biomodels", + "summary": "
Kollarovic2016 - Cell fate decision at G1-Stransition

This model is described in the article:

Kollarovic G, Studencka M, Ivanova L, Lauenstein C, Heinze K, Lapytsko A, Talemi SR, Figueiredo AS, Schaber J.
Aging (Albany NY) 2016 Jan;

Abstract:

Excessive DNA damage can induce an irreversible cell cycle arrest, called senescence, which is generally perceived as an important tumour-suppressor mechanism. However, it is unclear how cells decide whether to senesce or not after DNA damage. By combining experimental data with a parameterized mathematical model we elucidate this cell fate decision at the G1-S transition. Our model provides a quantitative and conceptually new understanding of how human fibroblasts decide whether DNA damage is beyond repair and senesce. Model and data imply that the G1-S transition is regulated by a bistable hysteresis switch with respect to Cdk2 activity, which in turn is controlled by the Cdk2/p21 ratio rather than cyclin abundance. We experimentally confirm the resulting predictions that to induce senescence i) in healthy cells both high initial and elevated background DNA damage are necessary and sufficient, and ii) in already damaged cells much lower additional DNA damage is sufficient. Our study provides a mechanistic explanation of a) how noise in protein abundances allows cells to overcome the G1-S arrest even with substantial DNA damage, potentially leading to neoplasia, and b) how accumulating DNA damage with age increasingly sensitizes cells for senescence.

This model is hosted on BioModels Database and identified by: BIOMD0000000632.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4224, + "tag": "BioModels:BIOMD0000000632" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:10.847015+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000632", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2933": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2933, + "name": "Bulik2016 - Regulation of hepatic glucose metabolism", + "repository_type": "biomodels", + "summary": "
Bulik2016 - Regulation of hepatic glucosemetabolism

This model is described in the article:

Bulik S, Holzh\u00fctter HG, Berndt N.
BMC Biol. 2016 Mar; 14: 15

Abstract:

Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in the activity of enzymes and transporters. Hormone-dependent reversible enzyme phosphorylation and concentration changes of reactants and allosteric effectors are the major types of rapid kinetic enzyme regulation, whereas on longer time scales changes in protein abundance may also become operative. Here, we used a comprehensive mathematical model of the hepatic glucose metabolism of rat hepatocytes to decipher the relative importance of different regulatory modes and their mutual interdependencies in the hepatic control of plasma glucose homeostasis.Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of plasma glucose in different physiological settings (starvation, ad libitum nutrient supply, diabetes). Changes in enzyme abundances adjust the metabolic output to the anticipated physiological demand but may turn into a regulatory disadvantage if sudden unexpected changes of the external conditions occur. Allosteric and hormonal control of enzyme activities allow the liver to assume a broad range of metabolic states and may even fully reverse flux changes resulting from changes of enzyme abundances alone. Metabolic control analysis reveals that control of the hepatic glucose metabolism is mainly exerted by enzymes alone, which are differently controlled by alterations in enzyme abundance, reversible phosphorylation, and allosteric effects.In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects, and reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.

This model is hosted on BioModels Database and identified by: BIOMD0000000633.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4225, + "tag": "BioModels:BIOMD0000000633" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:11.322298+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000633", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2934": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2934, + "name": "Proctor2013 - Effect of A\u03b2 immunisation in Alzheimer's disease (stochastic version)", + "repository_type": "biomodels", + "summary": "
Proctor2013 - Effect of A\u03b2 immunisationin Alzheimer's disease (stochastic version)
Extension of a previously publishedstochastic model (designed to examine some of the key pathwaysinvolved in the aggregation of amyloid-beta (A\u03b2)\u00a0andthe micro-tubular binding protein tau (BIOMD0000000286,BIOMD0000000462))to include the main processes involved in passive and activeimmunisation against A\u03b2and then to demonstrate the effects of this intervention on solubleA\u03b2.\u00a0Thisis the stochastic version of the model, the deterministic versionis BIOMD0000000488.\u00a0

This model is described in the article:

Proctor CJ, Boche D, Gray DA, Nicoll JA.
PLoS ONE 2013; 8(9): e73631

Abstract:

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000634.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3248, + "tag": "Alzheimer's disease" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4226, + "tag": "BioModels:BIOMD0000000634" + }, + { + "id": 3326, + "tag": "DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3271, + "tag": "Inclusion body assembly" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:11.832209+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000634", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2935": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2935, + "name": "Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1 Neuron)", + "repository_type": "biomodels", + "summary": "
Nair2015 - Interaction betweenneuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1Neuron)

This model is described in the article:

Nair AG, Gutierrez-Arenas O, Eriksson O, Vincent P, Hellgren Kotaleski J.
J. Neurosci. 2015 Oct; 35(41): 14017-14030

Abstract:

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000635.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4227, + "tag": "BioModels:BIOMD0000000635" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:12.323072+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000635", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2936": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2936, + "name": "Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 Neuron)", + "repository_type": "biomodels", + "summary": "
Nair2015 - Interaction betweenneuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2Neuron)

This model is described in the article:

Nair AG, Gutierrez-Arenas O, Eriksson O, Vincent P, Hellgren Kotaleski J.
J. Neurosci. 2015 Oct; 35(41): 14017-14030

Abstract:

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000636.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4228, + "tag": "BioModels:BIOMD0000000636" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:12.862246+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000636", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2937": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2937, + "name": "Bush2016 - Simplified Carrousel model of GPCR", + "repository_type": "biomodels", + "summary": "
Bush2016 - Simplified Carrousel model ofGPCR

This model is described in the article:

Bush A, Vasen G, Constantinou A, Dunayevich P, Patop IL, Blaustein M, Colman-Lerner A.
Mol. Syst. Biol. 2016 Dec; 12(12): 898

Abstract:

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.


This model is hosted on BioModels Database and identified by: BIOMD0000000637.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4229, + "tag": "BioModels:BIOMD0000000637" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:44:13.359615+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000637", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2938": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2938, + "name": "Bush2016 - Extended Carrousel model of GPCR-RGS", + "repository_type": "biomodels", + "summary": "
Bush2016 - Extended Carrousel model ofGPCR-RGS

This model is described in the article:

Bush A, Vasen G, Constantinou A, Dunayevich P, Patop IL, Blaustein M, Colman-Lerner A.
Mol. Syst. Biol. 2016 Dec; 12(12): 898

Abstract:

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.


This model is hosted on BioModels Database and identified by: BIOMD0000000638.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4230, + "tag": "BioModels:BIOMD0000000638" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:44:13.850566+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000638", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2939": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2939, + "name": "Thiaville2016 - Wild type folate pathway model with proposed PanB reaction", + "repository_type": "biomodels", + "summary": "
Thiaville2016 - Wild type folate pathwaymodel with proposed PanB reaction
This is a wild type E. coli model, andis one amongst the three models described in the paper. The othertwo models are\u00a0MODEL1602280002 (wild type with PanB overexpression) and MODEL1602280003 (wild type with PanB overexpression and THF regulation).\u00a0

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: BIOMD0000000639.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4231, + "tag": "BioModels:BIOMD0000000639" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:14.381904+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000639", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2940": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2940, + "name": "DallePezze2016 - Activation of AMPK and mTOR by amino acids", + "repository_type": "biomodels", + "summary": "
DallePezze2016 - Activation of AMPK and mTORby amino acids (Model 3)
This model is as\u00a0described in the Supplementary Software 3 of the reference publication: SBML model similar to Model S2, but including a more complex p70-S6K module.

This model is described in the article:

Dalle Pezze P, Ruf S, Sonntag AG, Langelaar-Makkinje M, Hall P, Heberle AM, Razquin Navas P, van Eunen K, T\u00f6lle RC, Schwarz JJ, Wiese H, Warscheid B, Deitersen J, Stork B, F\u00e4\u00dfler E, Sch\u00e4uble S, Hahn U, Horvatovich P, Shanley DP, Thedieck K.
Nat Commun 2016 Nov; 7: 13254

Abstract:

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase ? (CaMKK?). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000640.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4232, + "tag": "BioModels:BIOMD0000000640" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:14.849619+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000640", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2941": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2941, + "name": "Jaiswal2017 - Cell cycle arrest", + "repository_type": "biomodels", + "summary": "
Jaiswal2017 - Cell cycle arrest

This model is described in the article:

Jaiswal H, Benada J, M\u00fcllers E, Akopyan K, Burdova K, Koolmeister T, Helleday T, Medema RH, Macurek L, Lindqvist A.
EMBO J. 2017 Jul; 36(14): 2161-2176

Abstract:

After DNA damage, the cell cycle is arrested to avoid propagation of mutations. Arrest in G2 phase is initiated by ATM-/ATR-dependent signaling that inhibits mitosis-promoting kinases such as Plk1. At the same time, Plk1 can counteract ATR-dependent signaling and is required for eventual resumption of the cell cycle. However, what determines when Plk1 activity can resume remains unclear. Here, we use FRET-based reporters to show that a global spread of ATM activity on chromatin and phosphorylation of ATM targets including KAP1 control Plk1 re-activation. These phosphorylations are rapidly counteracted by the chromatin-bound phosphatase Wip1, allowing cell cycle restart despite persistent ATM activity present at DNA lesions. Combining experimental data and mathematical modeling, we propose a model for how the minimal duration of cell cycle arrest is controlled. Our model shows how cell cycle restart can occur before completion of DNA repair and suggests a mechanism for checkpoint adaptation in human cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000641.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4233, + "tag": "BioModels:BIOMD0000000641" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4234, + "tag": "Mitotic G2 DNA damage checkpoint" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:15.327330+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000641", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2942": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2942, + "name": "Mufudza2012 - Estrogen effect on the dynamics of breast cancer", + "repository_type": "biomodels", + "summary": "
Mufudza2012 - Estrogen effect on the dynamicsof breast cancer
This deterministic model shows thedynamics of breast cancer with immune response. The effects ofestrogen are incorporated to study its effects as a risk factor forthe disease.\u00a0

This model is described in the article:

Mufudza C, Sorofa W, Chiyaka ET.
Comput Math Methods Med 2012; 2012: 473572

Abstract:

Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000642.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4235, + "tag": "BioModels:BIOMD0000000642" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:15.842892+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000642", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2943": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2943, + "name": "Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions", + "repository_type": "biomodels", + "summary": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000643.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4236, + "tag": "BioModels:BIOMD0000000643" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:16.364880+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000643", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2944": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2944, + "name": "Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3", + "repository_type": "biomodels", + "summary": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions and PKA inhibitsMAST3

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000644.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4238, + "tag": "BioModels:BIOMD0000000644" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:16.845860+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000644", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2945": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2945, + "name": "Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3 and dominant negative effect", + "repository_type": "biomodels", + "summary": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3and dominant negative effect

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000645.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4239, + "tag": "BioModels:BIOMD0000000645" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:17.325246+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000645", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2946": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2946, + "name": "Barr2016 - All-or-nothing G1/S transition", + "repository_type": "biomodels", + "summary": "
Barr2016 - All-or-nothing G1/Stransition

This model is described in the article:

Barr AR, Heldt FS, Zhang T, Bakal C, Nov\u00e1k B.
Cell Syst 2016 Jan; 2(1): 27-37

Abstract:

The transition from G1 into DNA replication (S phase) is an emergent behavior resulting from dynamic and complex interactions between cyclin-dependent kinases (Cdks), Cdk inhibitors (CKIs), and the anaphase-promoting complex/cyclosome (APC/C). Understanding the cellular decision to commit to S\u00a0phase requires a quantitative description of these interactions. We apply quantitative imaging of single human cells to track the expression of G1/S regulators and use these data to parametrize a stochastic mathematical model of the G1/S transition. We show that a rapid, proteolytic, double-negative feedback loop between Cdk2:Cyclin and the Cdk inhibitor p27(Kip1) drives a switch-like entry into S phase. Furthermore, our model predicts that increasing Emi1 levels throughout S phase are critical in maintaining irreversibility of the G1/S transition, which we validate using Emi1 knockdown and live imaging of G1/S reporters. This work provides insight into the general design principles of the signaling networks governing the temporally abrupt transitions between cell-cycle phases.

This model is hosted on BioModels Database and identified by: BIOMD0000000646.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4240, + "tag": "Altered G1/S transition pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4241, + "tag": "BioModels:BIOMD0000000646" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:17.796443+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000646", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2947": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2947, + "name": "Kwang2003 - The influence of RKIP on the ERK signaling pathway", + "repository_type": "biomodels", + "summary": "
Kwang2003 - The influence of RKIP on the ERKsignaling pathway

This model is described in the article:

Kwang-Hyun Cho, Sung-Young Shin, Hyun-Woo Kim, Olaf Wolkenhauer, Brian McFerran and Walter Kolch
Computational Methods in Systems Biology: First International Workshop, CMSB 2003 Rovereto, Italy, February 24\u00e2??26, 2003 Proceedings

Abstract:

This paper investigates the influence of the Raf Kinase Inhibitor Pro- tein (RKIP) on the Extracellular signal Regulated Kinase (ERK) signaling pathway through mathematical modeling and simulation. Using nonlinear ordi- nary differential equations to represent biochemical reactions in the pathway, we suggest a technique for parameter estimation, utilizing time series data of proteins involved in the signaling pathway. The mathematical model allows the simulation the sensitivity of the ERK pathway to variations of initial RKIP and ERK-PP (phosphorylated ERK) concentrations along with time. Throughout the simulation study, we can qualitatively validate the proposed mathematical model compared with experimental results.

This model is hosted on BioModels Database and identified by: BIOMD0000000647.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4242, + "tag": "BioModels:BIOMD0000000647" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:18.278777+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000647", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2948": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2948, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (normal)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (normal)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways under normal condition.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000648.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4243, + "tag": "BioModels:BIOMD0000000648" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:18.735783+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000648", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2949": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2949, + "name": "Owen1998 - Tumour treatment model", + "repository_type": "biomodels", + "summary": "
Owen1998 - tumour treatment model

This model is described in the article:

Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun; 15(2): 165-185

Abstract:

Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.

This model is hosted on BioModels Database and identified by: MODEL1708250002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4244, + "tag": "BioModels:BIOMD0000000650" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:19.219097+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000650", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2950": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2950, + "name": "Nguyen2016 - Feedback regulation in cell signalling: Lessons for cancer therapeutics", + "repository_type": "biomodels", + "summary": "
Feedback regulation in cell signalling: Lessons for cancer therapeutics

This model is described in the article:

Nguyen LK, Kholodenko BN.
Semin. Cell Dev. Biol. 2016 Feb; 50: 85-94

Abstract:

The notion of feedback is fundamental for understanding signal transduction networks. Feedback loops attenuate or amplify signals, change the network dynamics and modify the input-output relationships between the signal and the target. Negative feedback provides robustness to noise and adaptation to perturbations, but as a double-edged sword can prevent effective pathway inhibition by a drug. Positive feedback brings about switch-like network responses and can convert analog input signals into digital outputs, triggering cell fate decisions and phenotypic changes. We show how a multitude of protein-protein interactions creates hidden feedback loops in signal transduction cascades. Drug treatments that interfere with feedback regulation can cause unexpected adverse effects. Combinatorial molecular interactions generated by pathway crosstalk and feedback loops often bypass the block caused by targeted therapies against oncogenic mutated kinases. We discuss mechanisms of drug resistance caused by network adaptations and suggest that development of effective drug combinations requires understanding of how feedback loops modulate drug responses.

This model is hosted on BioModels Database and identified by: MODEL1708250003.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4245, + "tag": "BioModels:BIOMD0000000651" + } + ], + "timestamp_created": "2025-02-03 16:44:19.864538+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000651", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2951": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2951, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (PI3K mutated)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (PI3K mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with mutated PI3K.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000652.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4246, + "tag": "BioModels:BIOMD0000000652" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:20.801027+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000652", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2952": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2952, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (bRaf mutated)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (bRaf mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with bRaf mutation

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000653.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4247, + "tag": "BioModels:BIOMD0000000653" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:21.297930+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000653", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2953": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2953, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (Ras mutated)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (Ras mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with Ras mutation.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000654.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4248, + "tag": "BioModels:BIOMD0000000654" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:21.788847+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000654", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2954": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2954, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (PTEN mutation)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (PTEN mutation)
Crosstalk model of the ERK, Wnt and AktSignalling pathways with PTEN mutation.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000655.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4249, + "tag": "BioModels:BIOMD0000000655" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:22.285465+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000655", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2955": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2955, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (EGFR overexpression)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (EGFR overexpression)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with EGFR overexpression.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000656.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4250, + "tag": "BioModels:BIOMD0000000656" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:22.848308+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000656", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2956": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 2956, + "name": "Araujo2016 - Positive feedback in Cdk1 signalling keeps mitotic duration short and constant", + "repository_type": "biomodels", + "summary": "
Araujo2016 - Positive feedback in Cdk1signalling keeps mitotic duration short and constant

This model is described in the article:

Araujo AR, Gelens L, Sheriff RS, Santos SD.
Mol. Cell 2016 Oct; 64(2): 362-375

Abstract:

Cell division is characterized by a sequence of events by which a cell gives rise to two daughter cells. Quantitative measurements of cell-cycle dynamics in single cells showed that despite variability in G1-, S-, and G2 phases, duration of mitosis is short and remarkably constant. Surprisingly, there is no correlation between cell-cycle length and mitotic duration, suggesting that mitosis is temporally insulated from variability in earlier cell-cycle phases. By combining live cell imaging and computational modeling, we showed that positive feedback is the molecular mechanism underlying the temporal insulation of mitosis. Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.

This model is hosted on BioModels Database and identified by: BIOMD0000000657.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4251, + "tag": "BioModels:BIOMD0000000657" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:23.329818+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000657", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2957": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2957, + "name": "Lee2003 - Roles of APC and Axin in Wnt Pathway (without regulatory loop)", + "repository_type": "biomodels", + "summary": "
Lee2003 - Roles of APC and Axin in WntPathway (without regulatory loop)

This model is described in the article:

Lee E, Salic A, Kr\u00fcger R, Heinrich R, Kirschner MW.
PLoS Biol. 2003 Oct; 1(1): E10

Abstract:

Wnt signaling plays an important role in both oncogenesis and development. Activation of the Wnt pathway results in stabilization of the transcriptional coactivator beta-catenin. Recent studies have demonstrated that axin, which coordinates beta-catenin degradation, is itself degraded. Although the key molecules required for transducing a Wnt signal have been identified, a quantitative understanding of this pathway has been lacking. We have developed a mathematical model for the canonical Wnt pathway that describes the interactions among the core components: Wnt, Frizzled, Dishevelled, GSK3beta, APC, axin, beta-catenin, and TCF. Using a system of differential equations, the model incorporates the kinetics of protein-protein interactions, protein synthesis/degradation, and phosphorylation/dephosphorylation. We initially defined a reference state of kinetic, thermodynamic, and flux data from experiments using Xenopus extracts. Predictions based on the analysis of the reference state were used iteratively to develop a more refined model from which we analyzed the effects of prolonged and transient Wnt stimulation on beta-catenin and axin turnover. We predict several unusual features of the Wnt pathway, some of which we tested experimentally. An insight from our model, which we confirmed experimentally, is that the two scaffold proteins axin and APC promote the formation of degradation complexes in very different ways. We can also explain the importance of axin degradation in amplifying and sharpening the Wnt signal, and we show that the dependence of axin degradation on APC is an essential part of an unappreciated regulatory loop that prevents the accumulation of beta-catenin at decreased APC concentrations. By applying control analysis to our mathematical model, we demonstrate the modular design, sensitivity, and robustness of the Wnt pathway and derive an explicit expression for tumor suppression and oncogenicity.

This model is hosted on BioModels Database and identified by: BIOMD0000000658.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4252, + "tag": "BioModels:BIOMD0000000658" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4253, + "tag": "Xenopus" + } + ], + "timestamp_created": "2025-02-03 16:44:23.820937+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000658", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2958": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2958, + "name": "Cursons2015 - Regulation of ERK-MAPK signaling in human epidermis", + "repository_type": "biomodels", + "summary": "
Cursons2015 - Regulation of ERK-MAPKsignaling in human epidermis
Model comparing the abundance ofphosphorylated MAPK signalling proteins and calcium signalling inthe epidermis.

This model is described in the article:

Cursons J, Gao J, Hurley DG, Print CG, Dunbar PR, Jacobs MD, Crampin EJ.
BMC Syst Biol 2015; 9: 41

Abstract:

The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized.We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism.These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling.Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.

This model is hosted on BioModels Database and identified by: BIOMD0000000659.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4254, + "tag": "BioModels:BIOMD0000000659" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3077, + "tag": "MAPK cascade" + }, + { + "id": 4255, + "tag": "MAPK signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:24.316028+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000659", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2959": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2959, + "name": "Barr2017 - Dynamics of p21 in hTert-RPE1 cells", + "repository_type": "biomodels", + "summary": "
Barr2017 - Dynamics of p21 in hTert-RPE1cells
This deteministic model reveals that abistable switch created by Cdt2, promotes irreversible S-phaseentry by keeping p21 levels low, prevents premature S-phase exitupon DNA damage

This model is described in the article:

Barr AR, Cooper S, Heldt FS, Butera F, Stoy H, Mansfeld J, Nov\u00e1k B, Bakal C.
Nat Commun 2017 Mar; 8: 14728

Abstract:

Following DNA damage caused by exogenous sources, such as ionizing radiation, the tumour suppressor p53 mediates cell cycle arrest via expression of the CDK inhibitor, p21. However, the role of p21 in maintaining genomic stability in the absence of exogenous DNA-damaging agents is unclear. Here, using live single-cell measurements of p21 protein in proliferating cultures, we show that naturally occurring DNA damage incurred over S-phase causes p53-dependent accumulation of p21 during mother G2- and daughter G1-phases. High p21 levels mediate G1 arrest via CDK inhibition, yet lower levels have no impact on G1 progression, and the ubiquitin ligases CRL4Cdt2 and SCFSkp2 couple to degrade p21 prior to the G1/S transition. Mathematical modelling reveals that a bistable switch, created by CRL4Cdt2, promotes irreversible S-phase entry by keeping p21 levels low, preventing premature S-phase exit upon DNA damage. Thus, we characterize how p21 regulates the proliferation-quiescence decision to maintain genomic stability.

This model is hosted on BioModels Database and identified by: BIOMD0000000660.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4256, + "tag": "BioModels:BIOMD0000000660" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:24.821924+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000660", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2960": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2960, + "name": "Webb2002 - Fas/FasL mediated tumor T-cell interaction", + "repository_type": "biomodels", + "summary": "
Webb2002 - Fas/FasL mediated tumor T-cell interaction
This deterministic model ofimmunological surveillance involving tumour cell\u2013T-lymphocyteinteraction, cell surface expression of Fas/FasL, and theirsecreted soluble forms.

This model is described in the article:

Webb SD, Sherratt JA, Fish RG.
Math Biosci 2002 Sep-Oct; 179(2): 113-129

Abstract:

One proposed mechanism of tumour escape from immune surveillance is tumour up-regulation of the cell surface ligand FasL, which can lead to apoptosis of Fas receptor (Fas) positive lymphocytes. Based upon this 'counterattack', we have developed a mathematical model involving tumour cell-lymphocyte interaction, cell surface expression of Fas/FasL, and their secreted soluble forms. The model predicts that (a) the production of soluble forms of Fas and FasL will lead to the down-regulation of the immune response; (b) matrix metalloproteinase (MMP) inactivation should lead to increased membrane FasL and result in a higher rate of Fas-mediated apoptosis for lymphocytes than for tumour cells. Recent studies on cancer patients lend support for these predictions. The clinical implications are two-fold. Firstly, the use of broad spectrum MMP inhibitors as anti-angiogenic agents may be compromised by their adverse effect on tumour FasL up-regulation. Also, Fas/FasL interactions may have an impact on the outcome of numerous ongoing immunotherapeutic trials since the final common pathway of all these approaches is the transduction of death signals within the tumour cell.

This model is hosted on BioModels Database and identified by: BIOMD0000000661.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4257, + "tag": "BioModels:BIOMD0000000661" + }, + { + "id": 4258, + "tag": "Extrinsic apoptotic pathway" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 4260, + "tag": "Regulation of Fas signaling pathway" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:25.323542+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000661", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2961": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 2961, + "name": "Moore2004 - Chronic Myeloid Leukemic cells and T-lymphocyte interaction", + "repository_type": "biomodels", + "summary": "
Moore2004 - Chronic Myeloid Leukemic cellsand T-lymphocytes interaction
A mathematical model for theinteraction of between cancer cells and immune system, involvingCML cancer cells, naive and effector T-lymphocytes.

This model is described in the article:

Moore H, Li NK.
J. Theor. Biol. 2004 Apr; 227(4): 513-523

Abstract:

In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome.

This model is hosted on BioModels Database and identified by: BIOMD0000000662.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4261, + "tag": "BioModels:BIOMD0000000662" + }, + { + "id": 4262, + "tag": "Chronic myelogenous leukemia" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:25.871501+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000662", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2962": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2962, + "name": "Wodarz2007 - HIV/CD4 T-cell interaction", + "repository_type": "biomodels", + "summary": "
Wodarz2007 - HIV/CD4 T-cell interaction
A deterministic model illustrating howCD4 T-cells can influence HIV infection.

This model is described in the article:

Wodarz D, Hamer DH.
Math Biosci 2007 Sep; 209(1): 14-29

Abstract:

Recent experimental data have shown that HIV-specific CD4 T cells provide a very important target for HIV replication. We use mathematical models to explore the effect of specific CD4 T cell infection on the dynamics of virus spread and immune responses. Infected CD4 T cells can provide antigen for their own stimulation. We show that such autocatalytic cell division can significantly enhance virus spread, and can also provide an additional reservoir for virus persistence during anti-viral drug therapy. In addition, the initial number of HIV-specific CD4 T cells is an important determinant of acute infection dynamics. A high initial number of HIV-specific CD4 T cells can lead to a sudden and fast drop of the population of HIV-specific CD4 T cells which results quickly in their extinction. On the other hand, a low initial number of HIV-specific CD4 T cells can lead to a prolonged persistence of HIV-specific CD4 T cell help at higher levels. The model suggests that boosting the population of HIV-specific CD4 T cells can increase the amount of virus-induced immune impairment, lead to less efficient anti-viral effector responses, and thus speed up disease progression, especially if effector responses such as CTL have not been sufficiently boosted at the same time.

This model is hosted on BioModels Database and identified by: BIOMD0000000663.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4263, + "tag": "BioModels:BIOMD0000000663" + }, + { + "id": 4264, + "tag": "HIV infection" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4265, + "tag": "Human immunodeficiency virus 1" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3276, + "tag": "Viral entry into host cell" + } + ], + "timestamp_created": "2025-02-03 16:44:26.344738+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000663", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2963": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2963, + "name": "Muller2008 - Simplified MAPK activation Dynamics (Model B)", + "repository_type": "biomodels", + "summary": "
Muller2008 - Simplified MAPK activation Dynamics (Model B)
Simplified mathematical model (model B)for predicting MAPK signal expression.

This model is described in the article:

Muller M, Obeyesekere M, Mills GB, Ram PT.
FASEB J. 2008 May; 22(5): 1393-1403

Abstract:

Activation of the fibroblast growth factor (FGFR) and melanocyte stimulating hormone (MC1R) receptors stimulates B-Raf and C-Raf isoforms that regulate the dynamics of MAPK1,2 signaling. Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. Further reducing a 106-node ordinary differential equations network encompassing the complete network to a 6-node network encompassing rate-limiting processes sustains the feedback loops and the bifan, providing sufficient information to predict biological responses.

This model is hosted on BioModels Database and identified by: BIOMD0000000664.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4266, + "tag": "BioModels:BIOMD0000000664" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:26.992367+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000664", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2964": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2964, + "name": "Fallon2000 - Interleukin-2 dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Computational model for effects of ligand/receptor binding properties oninterleukin-2 trafficking dynamics and T cell proliferation response.
Fallon EM, Lauffenburger DA. Biotechnol Prog 2000 Sep-Oct;16(5):905-16 11027188 ,
Abstract:
Multisubunit cytokine receptors such as the heterotrimeric receptor forinterleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance inbiotechnology and are crucial regulators of cell proliferation anddifferentiation behavior. Dynamics of cytokine/receptor endocytic traffickingcan significantly impact cell responses through effects of receptordown-regulation and ligand depletion, and in turn are governed byligand/receptor binding properties. We describe here a computational model fortrafficking dynamics of the IL-2 receptor (IL-2R) system, which is able topredict T cell proliferation responses to IL-2. This model comprises kineticequations describing binding, internalization, and postendocytic sorting of IL-2and IL-2R, including an experimentally derived dependence of cell proliferationrate on these properties. Computational results from this model predict thatIL-2 depletion can be reduced by decreasing its binding affinity for the IL-2Rbetagamma subunit relative to the alpha subunit at endosomal pH, as a result ofenhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2analogue with such altered binding properties should exhibit increased potencyfor stimulating the T cell proliferation response. These results are inagreement with our recent experimental findings for the IL-2 analogue termed 2D1[Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type ofmodel may enable prediction of beneficial cytokine/receptor binding propertiesto aid development of molecular design criteria for improvements in applicationssuch as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Fallon EM, Lauffenburger DA. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4267, + "tag": "BioModels:BIOMD0000000665" + }, + { + "id": 4268, + "tag": "Other interleukin signaling" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:27.594364+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000665", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2965": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2965, + "name": "Pappalardo2016 - PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer", + "repository_type": "biomodels", + "summary": "
Pappalardo2016 - PI3K/AKT and MAPK SignalingPathways in Melanoma Cancer

This model is described in the article:

Pappalardo F, Russo G, Candido S, Pennisi M, Cavalieri S, Motta S, McCubrey JA, Nicoletti F, Libra M.
PLoS ONE 2016; 11(3): e0152104

Abstract:

Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.

This model is hosted on BioModels Database and identified by: MODEL1609190000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4269, + "tag": "BioModels:BIOMD0000000666" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:28.074031+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000666", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2966": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2966, + "name": "Hornberg2005 - MAPKsignalling", + "repository_type": "biomodels", + "summary": "
Hornberg2005 - MAPKsignalling
Large model of the ERK signallingnetwork. Results from this model were used to generate a simplifiedversion of the network.

This model is described in the article:

Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R, Westerhoff HV.
Oncogene 2005 Aug; 24(36): 5533-5542

Abstract:

Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.

This model is hosted on BioModels Database and identified by: BIOMD0000000667.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4270, + "tag": "BioModels:BIOMD0000000667" + }, + { + "id": 4271, + "tag": "Regulation of MAPK cascade" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:28.546464+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000667", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2967": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2967, + "name": "Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - basic PD model", + "repository_type": "biomodels", + "summary": "
Zhu2015 - Combined gemcitabine and birinapantin pancreatic cancer cells - basic PD model
Mathematical model to illustrate theeffectiveness of combination chemotherapy involving gemcitabine andbirinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000668.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4272, + "tag": "BioModels:BIOMD0000000668" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:29.032556+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000668", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2968": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2968, + "name": "Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - mechanistic PD model", + "repository_type": "biomodels", + "summary": "
Zhu2015 - combined gemcitabine and birinapantin pancreatic cancer cells - mechanistic PD model
Mechanistic mathematical model toillustrate the effectiveness of combination chemotherapy involvinggemcitabine and birinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000669.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4273, + "tag": "BioModels:BIOMD0000000669" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:29.498291+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000669", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2969": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2969, + "name": "Owen1998 - tumour growth model", + "repository_type": "biomodels", + "summary": "
Owen1998 - tumour growth model
Deterministic model for the early,avascular growth of a tumour, concentrating on the inhibitoryeffect of macrophages.

This model is described in the article:

Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun; 15(2): 165-185

Abstract:

Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.

This model is hosted on BioModels Database and identified by: BIOMD0000000670.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4274, + "tag": "BioModels:BIOMD0000000670" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:30.039533+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000670", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2970": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2970, + "name": "Murphy2016 - Differences in predictions of ODE models of tumor growth", + "repository_type": "biomodels", + "summary": "
Murphy2016 - Differences in predictions ofODE models of tumor growth
Comparison of 7 ODE models for tumoursize. This models have been compared to experimental data.

This model is described in the article:

Murphy H, Jaafari H, Dobrovolny HM.
BMC Cancer 2016 Feb; 16: 163

Abstract:

While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.

This model is hosted on BioModels Database and identified by: BIOMD0000000671.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4275, + "tag": "BioModels:BIOMD0000000671" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:30.588158+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000671", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2971": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2971, + "name": "Brown1997 - Plasma Melatonin Levels", + "repository_type": "biomodels", + "summary": "
Brown1997 - Plasma Melatonin Levels
A mathematical model that incorporatesa piecewise function for NAT activity to predict melatoninconcentration.

This model is described in the article:

Brown EN, Choe Y, Shanahan TL, Czeisler CA.
Am. J. Physiol. 1997 Mar; 272(3 Pt 1): E506-16

Abstract:

Studies in animals and humans suggest that the diurnal pattern in plasma melatonin levels is due to the hormone's rates of synthesis, circulatory infusion and clearance, circadian control of synthesis onset and offset, environmental lighting conditions, and error in the melatonin immunoassay. A two-dimensional linear differential equation model of the hormone is formulated and is used to analyze plasma melatonin levels in 18 normal healthy male subjects during a constant routine. Recently developed Bayesian statistical procedures are used to incorporate correctly the magnitude of the immunoassay error into the analysis. The estimated parameters [median (range)] were clearance half-life of 23.67 (14.79-59.93) min, synthesis onset time of 2206 (1940-0029), synthesis offset time of 0621 (0246-0817), and maximum N-acetyltransferase activity of 7.17(2.34-17.93) pmol x l(-1) x min(-1). All were in good agreement with values from previous reports. The difference between synthesis offset time and the phase of the core temperature minimum was 1 h 15 min (-4 h 38 min-2 h 43 min). The correlation between synthesis onset and the dim light melatonin onset was 0.93. Our model provides a more physiologically plausible estimate of the melatonin synthesis onset time than that given by the dim light melatonin onset and the first reliable means of estimating the phase of synthesis offset. Our analysis shows that the circadian and pharmacokinetics parameters of melatonin can be reliably estimated from a single model.

This model is hosted on BioModels Database and identified by: BIOMD0000000672.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4276, + "tag": "BioModels:BIOMD0000000672" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3440, + "tag": "Melatonin metabolic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:31.167470+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000672", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2972": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2972, + "name": "Lockwood2006 - Alzheimer's Disease PBPK model", + "repository_type": "biomodels", + "summary": "
Lockwood2006 - AlzheimersDisease PBPKmodel
A mathematical model to predict theeffectiveness of CI-1017 (muscarinic agonist) for Alzheimer'sdisease by evaluating changes in ADAS-cog score.

This model is described in the article:

Lockwood P, Ewy W, Hermann D, Holford N.
Pharm. Res. 2006 Sep; 23(9): 2050-2059

Abstract:

OBJECTIVE: Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials. MATERIALS AND METHODS: A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and literature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p < 0.05) and pair-wise comparisons to placebo (p < 0.025). RESULTS: A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability. CONCLUSION: CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.

This model is hosted on BioModels Database and identified by: BIOMD0000000673.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4277, + "tag": "BioModels:BIOMD0000000673" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:31.646623+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000673", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2973": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2973, + "name": "Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version1", + "repository_type": "biomodels", + "summary": "
Reyes-Palomares2012 - a combined modelhepatic polyamine and sulfur aminoacid metabolism - version1

Mammalian polyamine metabolism consists of a bi-cycle with two required entrances, omithine and S-adenosyl methionine (SAM), and several alternative exists. The relevant regulatory roles of the short half-life enzymes ornithine decarboxylase (ODC), S-adenosyl methione decarboxylase (SAMDC) and spermindine/spermine acetyl transferase (SSAT) in polyamine metabolism are well studied, and has been modelled here.

This model is described in the article:

Reyes-Palomares A, Monta\u00f1ez R, S\u00e1nchez-Jim\u00e9nez F, Medina MA.
Amino Acids 2012 Feb; 42(2-3): 597-610

Abstract:

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

Notes by the author:

This model combines BIOMD0000000190 and BIOMD0000000268 from BioModels Database, both models include corrections respect to their originals publications.

This model is hosted on BioModels Database and identified by: BIOMD0000000674.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4278, + "tag": "BioModels:BIOMD0000000674" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:32.193892+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000674", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2974": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2974, + "name": "Chen2000_CellCycle", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Kinetic analysis of a molecular model of the budding yeast cell cycle.
Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. Mol Biol Cell 2000 Jan;11(1):369-91 10637314 ,
Abstract:
The molecular machinery of cell cycle control is known in more detail forbudding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism.In recent years, many elegant experiments on budding yeast have dissected theroles of cyclin molecules (Cln1-3 and Clb1-6) in coordinating the events of DNAsynthesis, bud emergence, spindle formation, nuclear division, and cellseparation. These experimental clues suggest a mechanism for the principalmolecular interactions controlling cyclin synthesis and degradation. Usingstandard techniques of biochemical kinetics, we convert the mechanism into a setof differential equations, which describe the time courses of three majorclasses of cyclin-dependent kinase activities. Model in hand, we examine themolecular events controlling \"Start\" (the commitment step to a new round ofchromosome replication, bud formation, and mitosis) and \"Finish\" (the transitionfrom metaphase to anaphase, when sister chromatids are pulled apart and the budseparates from the mother cell) in wild-type cells and 50 mutants. The modelaccounts for many details of the physiology, biochemistry, and genetics of cellcycle control in budding yeast.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4279, + "tag": "BioModels:BIOMD0000000675" + }, + { + "id": 3223, + "tag": "Cell cycle checkpoint" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:44:32.727905+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000675", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2975": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2975, + "name": "Chen2006 - Nitric Oxide Release from Endothelial Cells", + "repository_type": "biomodels", + "summary": "
Chen2006 - Nitric Oxide Release fromEndothelial Cells

This model is described in the article:

Chen K, Popel AS.
Free Radic. Biol. Med. 2006 Aug; 41(4): 668-680

Abstract:

Vascular endothelium expressing endothelial nitric oxide synthase (eNOS) produces nitric oxide (NO), which has a number of important physiological functions in the microvasculature. The rate of NO production by the endothelium is a critical determinant of NO distribution in the vascular wall. We have analyzed the biochemical pathways of NO synthesis and formulated a model to estimate NO production by the microvascular endothelium under physiological conditions. The model quantifies the NO produced by eNOS based on the kinetics of NO synthesis and the availability of eNOS and its intracellular substrates. The predicted NO production from microvessels was in the range of 0.005-0.1 microM/s. This range of predicted values is in agreement with some experimental values but is much lower than other rates previously measured or estimated from experimental data with the help of mathematical modeling. Paradoxical discrepancies between the model predictions and previously reported results based on experimental measurements of NO concentration in the vicinity of the arteriolar wall suggest that NO can also be released through eNOS-independent mechanisms, such as catalysis by neuronal NOS (nNOS). We also used our model to test the sensitivity of NO production to substrate availability, eNOS concentration, and potential rate-limiting factors. The results indicated that the predicted low level of NO production can be attributed primarily to a low expression of eNOS in the microvascular endothelial cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000676.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4280, + "tag": "BioModels:BIOMD0000000676" + }, + { + "id": 3279, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:33.226994+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000676", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2976": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2976, + "name": "Holmes2006 - Hill's model of muscle contraction", + "repository_type": "biomodels", + "summary": "
Holmes2006 - Hill's model of muscle contraction

This model is described in the article:

Holmes JW.
Adv Physiol Educ 2006 Jun; 30(2): 67-72

Abstract:

A. V. Hill's 1938 paper \"The heat of shortening and the dynamic constants of muscle\" is an enduring classic, presenting detailed methods, meticulous experiments, and the model of muscle contraction that now bears Hill's name. Pairing a simulation based on Hill's model with a reading of his paper allows students to follow his thought process to discover key principles of muscle physiology and gain insight into how to develop quantitative models of physiological processes. In this article, the experience of the author using this approach in a graduate biomedical engineering course is outlined, along with suggestions for adapting this approach to other audiences.

This model is hosted on BioModels Database and identified by: BIOMD0000000677.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4281, + "tag": "BioModels:BIOMD0000000677" + }, + { + "id": 4282, + "tag": "Muscle contraction" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:33.715470+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000677", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2977": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2977, + "name": "Tomida2003 - Calcium Oscillatory-induced translocation of nuclear factor of activated T cells", + "repository_type": "biomodels", + "summary": "
Tomida2003 - NFAT functions CalciumOscillation

This model is described in the article:

Tomida T, Hirose K, Takizawa A, Shibasaki F, Iino M.
EMBO J. 2003 Aug; 22(15): 3825-3832

Abstract:

Transcription by the nuclear factor of activated T cells (NFAT) is regulated by the frequency of Ca(2+) oscillation. However, why and how Ca(2+) oscillation regulates NFAT activity remain elusive. NFAT is dephosphorylated by Ca(2+)-dependent phosphatase calcineurin and translocates from the cytoplasm to the nucleus to initiate transcription. We analyzed the kinetics of dephosphorylation and translocation of NFAT. We show that Ca(2+)-dependent dephosphorylation proceeds rapidly, while the rephosphorylation and nuclear transport of NFAT proceed slowly. Therefore, after brief Ca(2+) stimulation, dephosphorylated NFAT has a lifetime of several minutes in the cytoplasm. Thus, Ca(2+) oscillation induces a build-up of dephosphorylated NFAT in the cytoplasm, allowing effective nuclear translocation, provided that the oscillation interval is shorter than the lifetime of dephosphorylated NFAT. We also show that Ca(2+) oscillation is more cost-effective in inducing the translocation of NFAT than continuous Ca(2+) signaling. Thus, the lifetime of dephosphorylated NFAT functions as a working memory of Ca(2+) signals and enables the control of NFAT nuclear translocation by the frequency of Ca(2+) oscillation at a reduced cost of Ca(2+) signaling.

This model is hosted on BioModels Database and identified by: BIOMD0000000678.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4283, + "tag": "BioModels:BIOMD0000000678" + }, + { + "id": 4284, + "tag": "Calcineurin-NFAT signaling cascade" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:34.338831+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000678", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2978": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2978, + "name": "Waugh2006 - Diabetic Wound Healing - Macrophage Dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4285, + "tag": "BioModels:BIOMD0000000679" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4286, + "tag": "Regulation of wound healing" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:34.819127+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000679", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2979": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2979, + "name": "Waugh2006 - Diabetic Wound Healing - TGF-B Dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4287, + "tag": "BioModels:BIOMD0000000680" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4286, + "tag": "Regulation of wound healing" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:35.349077+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000680", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2980": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2980, + "name": "Waugh2006 - Diabetic Wound Healing - Treated and Untreated Macrophage Dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4288, + "tag": "BioModels:BIOMD0000000681" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4286, + "tag": "Regulation of wound healing" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:35.877737+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000681", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2981": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2981, + "name": "Wierschem2004 - Electrical bursting activity in Pancreatic Islets", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Complex bursting in pancreatic islets: a potential glycolytic mechanism.
Wierschem K, Bertram R. J Theor Biol 2004 Jun 21;228(4):513-21 15178199 ,
Abstract:
The electrical activity of insulin-secreting pancreatic islets of Langerhans ischaracterized by bursts of action potentials. Most often this bursting isperiodic, but in some cases it is modulated by an underlying slower rhythm. Wesuggest that the modulatory rhythm for this complex bursting pattern is due tooscillations in glycolysis, while the bursting itself is generated by some otherslow process. To demonstrate this hypothesis, we couple a minimal model ofglycolytic oscillations to a minimal model for activity-dependent bursting inislets. We show that the combined model can reproduce several complex burstingpatterns from mouse islets published in the literature, and we illustrate howthese complex oscillations are produced through the use of a fast/slow analysis.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wierschem K, Bertram R. () - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4289, + "tag": "BioModels:BIOMD0000000682" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4290, + "tag": "Regulation of action potential" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:36.356774+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000682", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2982": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2982, + "name": "Wodarz1999 CTL memory response HIV", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Specific therapy regimes could lead to long-term immunological control of HIV.
Wodarz D, Nowak MA. Proc Natl Acad Sci U S A 1999 Dec 7;96(25):14464-9 10588728 ,
Abstract:
We use mathematical models to study the relationship between HIV and the immunesystem during the natural course of infection and in the context of differentantiviral treatment regimes. The models suggest that an efficient cytotoxic Tlymphocyte (CTL) memory response is required to control the virus. We define CTLmemory as long-term persistence of CTL precursors in the absence of antigen.Infection and depletion of CD4(+) T helper cells interfere with CTL memorygeneration, resulting in persistent viral replication and disease progression.We find that antiviral drug therapy during primary infection can enable thedevelopment of CTL memory. In chronically infected patients, specific treatmentschedules, either including deliberate drug holidays or antigenic boosts of theimmune system, can lead to a re-establishment of CTL memory. Whether suchtreatment regimes would lead to long-term immunologic control deservesinvestigation under carefully controlled conditions.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Nowak MA. (1999) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4291, + "tag": "BioModels:BIOMD0000000683" + }, + { + "id": 4264, + "tag": "HIV infection" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3276, + "tag": "Viral entry into host cell" + } + ], + "timestamp_created": "2025-02-03 16:44:36.912432+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000683", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2983": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2983, + "name": "Wodarz2003 - Immunological Memory", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Evolution of immunological memory and the regulation of competition betweenpathogens.
Wodarz D. Curr Biol 2003 Sep 16;13(18):1648-52 13678598 ,
Abstract:
Memory is a central characteristic of immune responses. It is defined as anelevated number of specific immune cells that remain after resolution ofinfection and can protect the host against reinfection. The evolution ofimmunological memory is subject to debate. The advantages of memory discussed sofar include protection from reinfection, control of chronic infection, and thetransfer of immune function to the next generation. Mathematical models are usedto identify a new force that can drive the evolution of immunological memory:the duration of memory can regulate the degree of competition between differentpathogens. While a long duration of memory provides lasting protection againstreinfection, it may also allow an inferior pathogen species to persist. This canbe detrimental for the host if the inferior pathogen is more virulent. On theother hand, a shorter duration of memory ensures that an inferior pathogenspecies is excluded. This can be beneficial for the host if the inferiorpathogen is more virulent. Thus, while in the absence of pathogen diversitymemory is always expected to evolve to a long duration, under specificcircumstances, memory can evolve toward shorter durations in the presence ofpathogen diversity.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 4061, + "tag": "Adaptive immune response" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4292, + "tag": "BioModels:BIOMD0000000684" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:37.787621+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000684", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2984": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2984, + "name": "Wodarz2003 - Cytotoxic T lymphocyte cross-priming", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A dynamical perspective of CTL cross-priming and regulation: implications forcancer immunology.
Wodarz D, Jansen VA. Immunol Lett 2003 May 1;86(3):213-27 12706524 ,
Abstract:
Cytotoxic T lymphocytes (CTL) responses are required to fight many diseases suchas viral infections and tumors. At the same time, they can cause disease wheninduced inappropriately. Which factors regulate CTL and decide whether theyshould remain silent or react is open to debate. The phenomenon calledcross-priming has received attention in this respect. That is, CTL expansionoccurs if antigen is recognized on the surface of professional antigenpresenting cells (APCs). This is in contrast to direct presentation whereantigen is seen on the surface of the target cells (e.g. infected cells or tumorcells). Here we introduce a mathematical model, which takes the phenomenon ofcross-priming into account. We propose a new mechanism of regulation which isimplicit in the dynamics of the CTL: According to the model, the ability of aCTL response to become established depends on the ratio of cross-presentation todirect presentation of the antigen. If this ratio is relatively high, CTLresponses are likely to become established. If this ratio is relatively low,tolerance is the likely outcome. The behavior of the model includes a parameterregion where the outcome depends on the initial conditions. We discuss ourresults with respect to the idea of self/non-self discrimination and the dangersignal hypothesis. We apply the model to study the role of CTL in cancerinitiation, cancer evolution/progression, and therapeutic vaccination againstcancers.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Jansen VA. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 4061, + "tag": "Adaptive immune response" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4293, + "tag": "BioModels:BIOMD0000000685" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:38.268006+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000685", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2985": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2985, + "name": "Wodarz2007 - Basic Model of Cytomegalovirus Infection", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4294, + "tag": "BioModels:BIOMD0000000686" + }, + { + "id": 4295, + "tag": "Mus" + }, + { + "id": 4296, + "tag": "Response to virus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:38.789558+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000686", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2986": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2986, + "name": "Wodarz2007 - Cytomegalovirus infection model with cytotoxic T lymphocyte response", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4297, + "tag": "BioModels:BIOMD0000000687" + }, + { + "id": 4295, + "tag": "Mus" + }, + { + "id": 4296, + "tag": "Response to virus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:39.525062+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000687", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2987": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2987, + "name": "Wodarz2007 - Cytomegalovirus infection model with cytotoxic T lymphocyte and natural killer cell response", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4298, + "tag": "BioModels:BIOMD0000000688" + }, + { + "id": 4295, + "tag": "Mus" + }, + { + "id": 4296, + "tag": "Response to virus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:40.188104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000688", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2988": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2988, + "name": "Thiaville2016 - Folate pathway model (PanB overexpression)", + "repository_type": "biomodels", + "summary": "
Henry2016 Folate pathway model with inducedPanB reaction

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: MODEL1602280002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4299, + "tag": "BioModels:BIOMD0000000689" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:40.799853+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000689", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2989": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2989, + "name": "Thiaville2016 - Folate pathway model (PanB overexpression and THF regulation)", + "repository_type": "biomodels", + "summary": "
Henry2016 Folate pathway model with inducedPanB reaction

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: MODEL1602280002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4300, + "tag": "BioModels:BIOMD0000000690" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:41.329161+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000690", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2990": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2990, + "name": "Wolf2000 - Cellular interaction on glycolytic oscillations in yeast", + "repository_type": "biomodels", + "summary": "
Wolf2000 - Cellular interaction on glycolyticoscillations in yeast
A two-cell model of glycolysis.

This model is described in the article:

Wolf J, Heinrich R.
Biochem. J. 2000 Jan; 345 Pt 2: 321-334

Abstract:

On the basis of a detailed model of yeast glycolysis, the effect of intercellular dynamics is analysed theoretically. The model includes the main steps of anaerobic glycolysis, and the production of ethanol and glycerol. Transmembrane diffusion of acetaldehyde is included, since it has been hypothesized that this substance mediates the interaction. Depending on the kinetic parameter, the single-cell model shows both stationary and oscillatory behaviour. This agrees with experimental data with respect to metabolite concentrations and phase shifts. The inclusion of intercellular coupling leads to a variety of dynamical modes, such as synchronous oscillations, and different kinds of asynchronous behavior. These oscillations can co-exist, leading to bi- and tri-rhythmicity. The corresponding parameter regions have been identified by a bifurcation analysis. The oscillatory dynamics of synchronized cell populations are investigated by calculating the phase responses to acetaldehyde pulses. Simulations are performed with respect to the synchronization of two subpopulations that are oscillating out of phase before mixing. The effect of the various process on synchronization is characterized quantitatively. While continuous exchange of acetaldehyde might synchronize the oscillations for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization. for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization.

This model is hosted on BioModels Database and identified by: BIOMD0000000691.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4301, + "tag": "BioModels:BIOMD0000000691" + }, + { + "id": 4302, + "tag": "Glycolysis" + }, + { + "id": 4303, + "tag": "Glycolysis / Gluconeogenesis" + }, + { + "id": 3149, + "tag": "Glycolytic process" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4304, + "tag": "Saccharomyces" + } + ], + "timestamp_created": "2025-02-03 16:44:41.891062+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000691", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2991": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2991, + "name": "Phillips2003 - The Mechanism of Ras GTPase Activation by Neurofibromin", + "repository_type": "biomodels", + "summary": "
Phillips2003 - The Mechanism of Ras GTPaseActivation by Neurofibromin
A mathematical model for Ras-GTPactivation by neurofibromin and the kinetic rates of the relevantreactions.

This model is described in the article:

Phillips RA, Hunter JL, Eccleston JF, Webb MR.
Biochemistry 2003 Apr; 42(13): 3956-3965

Abstract:

Individual rate constants have been determined for each step of the Ras.GTP hydrolysis mechanism, activated by neurofibromin. Fluorescence intensity and anisotropy stopped-flow measurements used the fluorescent GTP analogue, mantGTP (2'(3')-O-(N-methylanthraniloyl)GTP), to determine rate constants for binding and release of neurofibromin. Quenched flow measurements provided the kinetics of the hydrolytic cleavage step. The fluorescent phosphate sensor, MDCC-PBP was used to measure phosphate release kinetics. Phosphate-water oxygen exchange, using (18)O-substituted GTP and inorganic phosphate (P(i)), was used to determine the extent of reversal of the hydrolysis step and of P(i) binding. The data show that neurofibromin and P(i) dissociate from the NF1.Ras.GDP.P(i) complex with identical kinetics, which are 3-fold slower than the preceding cleavage step. A model is presented in which the P(i) release is associated with the change of Ras from \"GTP\" to \"GDP\" conformation. In this model, the conformation change on P(i) release causes the large change in affinity of neurofibromin, which then dissociates rapidly.

This model is hosted on BioModels Database and identified by: BIOMD0000000692.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4305, + "tag": "BioModels:BIOMD0000000692" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 4306, + "tag": "Regulation of Ras GTPase binding" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:42.376261+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000692", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2992": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2992, + "name": "Wang2008 - Mimicking the inhibitory effect of riluzole on membrane conductance in skeletal fibres", + "repository_type": "biomodels", + "summary": "
Wang2008 - Mimicking the inhibitory effect ofriluzole on membrane conductance in skeletal fibres

This model is described in the article:

Wang YJ, Lin MW, Lin AA, Wu SN.
Life Sci. 2008 Jan; 82(1-2): 11-20

Abstract:

Riluzole is known to be of therapeutic use in the management of amyotrophic lateral sclerosis. In this study, we investigated the effects of riluzole on ion currents in cultured differentiated human skeletal muscle cells (dHSkMCs). Western blotting revealed the protein expression of alpha-subunits for both large-conductance Ca2+-activated K+ (BK(Ca)) channel and Na+ channel (Na(v)1.5) in these cells. Riluzole could reduce the frequency of spontaneous beating in dHSkMCs. In whole-cell configuration, riluzole suppressed voltage-gated Na+ current (I(Na)) in a concentration-dependent manner with an IC50 value of 2.3 microM. Riluzole (10 microM) also effectively increased Ca2+-activated K+ current (I(K(Ca))) which could be reversed by iberiotoxin (200 nM) and paxilline (1 microM), but not by apamin (200 nM). In inside-out patches, when applied to the inside of the cell membrane, riluzole (10 microM) increased BK(Ca)-channel activity with a decrease in mean closed time. Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole actions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials. Taken together, inhibition of I(Na) and stimulation of BK(Ca)-channel activity caused by this drug are partly, if not entirely, responsible for its muscle relaxant actions in clinical setting.

This model is hosted on BioModels Database and identified by: BIOMD0000000693.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4307, + "tag": "Amyotrophic lateral sclerosis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4308, + "tag": "BioModels:BIOMD0000000693" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3940, + "tag": "Response to drug" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4309, + "tag": "Voltage-gated channel activity" + } + ], + "timestamp_created": "2025-02-03 16:44:42.933002+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000693", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2993": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2993, + "name": "FelixGarza2017 - Blue Light Treatment of Psoriasis (simplified)", + "repository_type": "biomodels", + "summary": "
FelixGarza2017 - Blue Light Treatment ofPsoriasis (simplified)

This model is described in the article:

F\u00e9lix Garza ZC, Liebmann J, Born M, Hilbers PA, van Riel NA.
Front Physiol 2017; 8: 28

Abstract:

Clinical investigations prove that blue light irradiation reduces the severity of psoriasis vulgaris. Nevertheless, the mechanisms involved in the management of this condition remain poorly defined. Despite the encouraging results of the clinical studies, no clear guidelines are specified in the literature for the irradiation scheme regime of blue light-based therapy for psoriasis. We investigated the underlying mechanism of blue light irradiation of psoriatic skin, and tested the hypothesis that regulation of proliferation is a key process. We implemented a mechanistic model of cellular epidermal dynamics to analyze whether a temporary decrease of keratinocytes hyper-proliferation can explain the outcome of phototherapy with blue light. Our results suggest that the main effect of blue light on keratinocytes impacts the proliferative cells. They show that the decrease in the keratinocytes proliferative capacity is sufficient to induce a transient decrease in the severity of psoriasis. To study the impact of the therapeutic regime on the efficacy of psoriasis treatment, we performed simulations for different combinations of the treatment parameters, i.e., length of treatment, fluence (also referred to as dose), and intensity. These simulations indicate that high efficacy is achieved by regimes with long duration and high fluence levels, regardless of the chosen intensity. Our modeling approach constitutes a framework for testing diverse hypotheses on the underlying mechanism of blue light-based phototherapy, and for designing effective strategies for the treatment of psoriasis.

This model is hosted on BioModels Database and identified by: BIOMD0000000695.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4310, + "tag": "BioModels:BIOMD0000000695" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:43.408268+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000695", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2994": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2994, + "name": "Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL)", + "repository_type": "biomodels", + "summary": "
Boada2016 - Incoherent type 1 feed-forwardloop (I1-FFL)
A synthetic-biology mathematicalmodelling framework that was constructed to provide guidelines forexperimental implementation and parameter optimisation resulted ina biological device demonstrating desired behaviour.

This model is described in the article:

Boada Y, Reynoso-Meza G, Pic\u00f3 J, Vignoni A.
BMC Syst Biol 2016 Mar; 10: 27

Abstract:

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.

This model is hosted on BioModels Database and identified by: BIOMD0000000696.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4311, + "tag": "BioModels:BIOMD0000000696" + }, + { + "id": 4312, + "tag": "Modelling framework" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4313, + "tag": "Theoretical Biology" + } + ], + "timestamp_created": "2025-02-03 16:44:43.892036+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000696", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2995": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2995, + "name": "Ciliberto2003 - CyclinE / Cdk2 timer in the cell cycle of Xenopus laevis embryo", + "repository_type": "biomodels", + "summary": "
Ciliberto2003 - CyclinE / Cdk2 timer in thecell cycle of Xenopus laevis embryo

This model is described in the article:

Ciliberto A, Petrus MJ, Tyson JJ, Sible JC.
Biophys. Chem. 2003 Jul; 104(3): 573-589

Abstract:

Early cell cycles of Xenopus laevis embryos are characterized by rapid oscillations in the activity of two cyclin-dependent kinases. Cdk1 activity peaks at mitosis, driven by periodic degradation of cyclins A and B. In contrast, Cdk2 activity oscillates twice per cell cycle, despite a constant level of its partner, cyclin E. Cyclin E degrades at a fixed time after fertilization, normally corresponding to the midblastula transition. Based on published data and new experiments, we constructed a mathematical model in which: (1) oscillations in Cdk2 activity depend upon changes in phosphorylation, (2) Cdk2 participates in a negative feedback loop with the inhibitory kinase Wee1; (3) cyclin E is cooperatively removed from the oscillatory system; and (4) removed cyclin E is degraded by a pathway activated by cyclin E/Cdk2 itself. The model's predictions about embryos injected with Xic1, a stoichiometric inhibitor of cyclin E/Cdk2, were experimentally validated.

This model is hosted on BioModels Database and identified by: BIOMD0000000697.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4314, + "tag": "BioModels:BIOMD0000000697" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 16:44:44.471943+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000697", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2996": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2996, + "name": "Reed2004 - Methionine Cycle", + "repository_type": "biomodels", + "summary": "
Reed2004 - Methionine Cycle

This model is described in the article:

Reed MC, Nijhout HF, Sparks R, Ulrich CM.
J. Theor. Biol. 2004 Jan; 226(1): 33-43

Abstract:

Building on the work of Martinov et al. (2000), a mathematical model is developed for the methionine cycle. A large amount of information is available about the enzymes that catalyse individual reaction steps in the cycle, from methionine to S-adenosylmethionine to S-adenosylhomocysteine to homocysteine, and the removal of mass from the cycle by the conversion of homocysteine to cystathionine. Nevertheless, the behavior of the cycle is very complicated since many substrates alter the activities of the enzymes in the reactions that produce them, and some can also alter the activities of other enzymes in the cycle. The model consists of four differential equations, based on known reaction kinetics, that can be solved to give the time course of the concentrations of the four main substrates in the cycle under various circumstances. We show that the behavior of the model in response to genetic abnormalities and dietary deficiencies is similar to the changes seen in a wide variety of experimental studies. We conduct computational \"experiments\" that give understanding of the regulatory behavior of the methionine cycle under normal conditions and the behavior in the presence of genetic variation and dietary deficiencies.

This model is hosted on BioModels Database and identified by: BIOMD0000000698.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4315, + "tag": "BioModels:BIOMD0000000698" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4316, + "tag": "S-methylmethionine cycle" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:44.987433+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000698", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2997": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2997, + "name": "Caydasi2012 - Regulation of Tem1 by the GAP complex in spindle position cell cycle checkpoint - Ubiquitous association model", + "repository_type": "biomodels", + "summary": "
Caydasi2012 - Regulation of Tem1 by the GAPcomplex in spindle position cell cycle checkpoint - Ubiquitousassociation model

This model is described in the article:

Caydasi AK, Lohel M, Gr\u00fcnert G, Dittrich P, Pereira G, Ibrahim B.
Mol. Syst. Biol. 2012; 8: 582

Abstract:

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000699.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4317, + "tag": "BioModels:BIOMD0000000699" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:44:45.468587+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000699", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2998": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2998, + "name": "Heldt2018 - Proliferation-quiescence decision in response to DNA damage", + "repository_type": "biomodels", + "summary": "
Heldt2018 - Proliferation-quiescence decisionin response to DNA damage

This model is described in the article:

Heldt FS, Barr AR, Cooper S, Bakal C, Nov\u00e1k B.
Proc. Natl. Acad. Sci. U.S.A. 2018 Feb; :

Abstract:

Human cells that suffer mild DNA damage can enter a reversible state of growth arrest known as quiescence. This decision to temporarily exit the cell cycle is essential to prevent the propagation of mutations, and most cancer cells harbor defects in the underlying control system. Here we present a mechanistic mathematical model to study the proliferation-quiescence decision in nontransformed human cells. We show that two bistable switches, the restriction point (RP) and the G1/S transition, mediate this decision by integrating DNA damage and mitogen signals. In particular, our data suggest that the cyclin-dependent kinase inhibitor p21 (Cip1/Waf1), which is expressed in response to DNA damage, promotes quiescence by blocking positive feedback loops that facilitate G1 progression downstream of serum stimulation. Intriguingly, cells exploit bistability in the RP to convert graded p21 and mitogen signals into an all-or-nothing cell-cycle response. The same mechanism creates a window of opportunity where G1 cells that have passed the RP can revert to quiescence if exposed to DNA damage. We present experimental evidence that cells gradually lose this ability to revert to quiescence as they progress through G1 and that the onset of rapid p21 degradation at the G1/S transition prevents this response altogether, insulating S phase from mild, endogenous DNA damage. Thus, two bistable switches conspire in the early cell cycle to provide both sensitivity and robustness to external stimuli.

This model is hosted on BioModels Database and identified by: MODEL1703030000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4318, + "tag": "BioModels:BIOMD0000000700" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:45.973221+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000700", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "2999": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2999, + "name": "Caydasi2012 - Inhibition of Tem1 by the GAP complex in Spindle Position Checkpoint", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A dynamical model of the spindle position checkpoint
Ayse Koca Caydasi, Maiko Lohel, Gerd Gr\u00fcnert, Peter Dittrich, Gislene Pereira, Bashar Ibrahim Molecular Systems Biology 2012; 582 doi: 10.1038/msb.2012.15
Abstract:
The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1\u2013Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1\u2013Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1\u2013Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4319, + "tag": "BioModels:BIOMD0000000701" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:44:46.440704+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000701", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3000": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3000, + "name": "Caydasi2012 - Regulation of Tem1 by the GAP complex in Spindle Position Checkpoint - Ubiquitous inactive model", + "repository_type": "biomodels", + "summary": "
Regulation of Tem1 by the GAP complex inSpindle Position Checkpoint - Ubiquitous inactive

This model is described in the article:

Caydasi AK, Lohel M, Gr\u00fcnert G, Dittrich P, Pereira G, Ibrahim B.
Mol. Syst. Biol. 2012; 8: 582

Abstract:

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000702.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4320, + "tag": "BioModels:BIOMD0000000702" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:44:46.943641+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000702", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3001": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3001, + "name": "Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response", + "repository_type": "biomodels", + "summary": "
A data-entrained computational model fortesting the regulatory logic of the vertebrate unfolded proteinresponse

This model is described in the article:

Diedrichs DR, Gomez JA, Huang CS, Rutkowski DT, Curtu R.
Mol. Biol. Cell 2018 Apr; : mbcE17090565

Abstract:

The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2? phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feed-forward circuit within the PERK/CHOP axis, and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to ER stress yet also to be rapidly deactivated by improved protein folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR.

This model is hosted on BioModels Database and identified by: MODEL1803300000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4321, + "tag": "BioModels:BIOMD0000000703" + }, + { + "id": 4322, + "tag": "Response to unfolded protein" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4323, + "tag": "Unfolded Protein Response (UPR)" + } + ], + "timestamp_created": "2025-02-03 16:44:47.656679+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000703", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3002": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3002, + "name": "Aguda1999 - G2 DNA damage checkpoint", + "repository_type": "biomodels", + "summary": "Baltazar D. Aguda. A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint system. Proceedings of the National Academy of Sciences 96, 20 (1999).

A detailed model of the G(2) DNA damage checkpoint (G2DDC) system is presented that includes complex regulatory networks of the mitotic kinase Cdc2, phosphatase Cdc25, Wee1 kinase, and damage signal transduction pathways involving Chk1 and p53. Assumptions on the kinetic equations of the G2DDC are made, and computer simulations are carried out to demonstrate how the various subsystems operate to delay or arrest cell cycle progression. The detailed model could be used to explain various experiments relevant to G2DDC reported recently, including the nuclear export of 14-3-3-bound Cdc25, the down-regulation of cyclin B1 expression by p53, the effect of Chk1 and p53 on Cdc25 levels, and Wee1 degradation. It also is shown that, under certain conditions, p53 is necessary to sustain a G(2) arrest.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4324, + "tag": "BioModels:BIOMD0000000704" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3690, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-02-03 16:44:48.208641+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000704", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3003": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3003, + "name": "Smith2010 - Response of FOXO Transcription Factors to Post-Translational Modifications Made by Ageing-Related Signalling Pathways", + "repository_type": "biomodels", + "summary": "Graham R. Smith & Daryl P. Shanley. Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways. PLoS ONE 5, 6 (2010).

FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. The use of the ancillary program to generate the SBML is necessary because the possibility that many regulatory PTMs may be added, each independently of the others, means that a large number of chemically distinct forms of the FOXO molecule must be taken into account, and the program is used to generate them. Although the model does not yet include detailed representations of events upstream and downstream of FOXO, we show how it can qualitatively, and in some cases quantitatively, reproduce the known effects of certain treatments that induce various single and multiple PTMs, and allows for a complex spatiotemporal interplay of effects due to the activation of multiple PTM-inducing treatments. Thus, it provides an important framework to integrate current knowledge about the behaviour of FOXO. The approach should be generally applicable to other proteins experiencing multiple regulations.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4325, + "tag": "BioModels:BIOMD0000000705" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:48.666309+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000705", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3004": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3004, + "name": "Smith2010 - Response of FOXO Transcription Factors to Post-Translational Modifications (with acetylation pathway)", + "repository_type": "biomodels", + "summary": "Graham R. Smith & Daryl P. Shanley. Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways. PLoS ONE 5, 6 (2010).

FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. The use of the ancillary program to generate the SBML is necessary because the possibility that many regulatory PTMs may be added, each independently of the others, means that a large number of chemically distinct forms of the FOXO molecule must be taken into account, and the program is used to generate them. Although the model does not yet include detailed representations of events upstream and downstream of FOXO, we show how it can qualitatively, and in some cases quantitatively, reproduce the known effects of certain treatments that induce various single and multiple PTMs, and allows for a complex spatiotemporal interplay of effects due to the activation of multiple PTM-inducing treatments. Thus, it provides an important framework to integrate current knowledge about the behaviour of FOXO. The approach should be generally applicable to other proteins experiencing multiple regulations.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4326, + "tag": "BioModels:BIOMD0000000706" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:49.141598+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000706", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3005": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3005, + "name": "Revilla2003 - Controlling HIV infection using recombinant viruses", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Fighting a virus with a virus: a dynamic model for HIV-1 therapy.
Revilla T, Garcia-Ramos G. Math Biosci 2003 Oct;185(2):191-203 12941536 ,
Abstract:
A mathematical model examined a potential therapy for controlling viralinfections using genetically modified viruses. The control of the infection isan indirect effect of the selective elimination by an engineered virus ofinfected cells that are the source of the pathogens. Therefore, this engineeredvirus could greatly compensate for a dysfunctional immune system compromised byAIDS. In vitro studies using engineered viruses have been shown to decrease theHIV-1 load about 1000-fold. However, the efficacy of this potential treatmentfor reducing the viral load in AIDS patients is unknown. The present modelstudied the interactions among the HIV-1 virus, its main host cell (activatedCD4+ T cells), and a therapeutic engineered virus in an in vivo context; and itexamined the conditions for controlling the pathogen. This model predicted asignificant drop in the HIV-1 load, but the treatment does not eradicate HIV. Abasic estimation using a currently engineered virus indicated an HIV-1 loadreduction of 92% and a recovery of host cells to 17% of their normal level.Greater success (98% HIV reduction, 44% host cells recovery) is expected as morecompetent engineered viruses are designed. These results suggest that therapyusing viruses could be an alternative to extend the survival of AIDS patients.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Revilla T, Garcia-Ramos G. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4327, + "tag": "BioModels:BIOMD0000000707" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4265, + "tag": "Human immunodeficiency virus 1" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:49.724851+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000707", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3006": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 3006, + "name": "Liu2017 - Dynamics of Avian Influenza with Logistic Growth", + "repository_type": "biomodels", + "summary": "Sanhong Liu, Shigui Ruan & Xinan Zhang. Nonlinear dynamics of avian influenza epidemic models. Mathematical Biosciences 283 (2017).

Avian influenza is a zoonotic disease caused by the transmission of the avian influenza A virus, such as H5N1 and H7N9, from birds to humans. The avian influenza A H5N1 virus has caused more than 500 human infections worldwide with nearly a 60% death rate since it was first reported in Hong Kong in 1997. The four outbreaks of the avian influenza A H7N9 in China from March 2013 to June 2016 have resulted in 580 human cases including 202 deaths with a death rate of nearly 35%. In this paper, we construct two avian influenza bird-to-human transmission models with different growth laws of the avian population, one with logistic growth and the other with Allee effect, and analyze their dynamical behavior. We obtain a threshold value for the prevalence of avian influenza and investigate the local or global asymptotical stability of each equilibrium of these systems by using linear analysis technique or combining Liapunov function method and LaSalle's invariance principle, respectively. Moreover, we give necessary and sufficient conditions for the occurrence of periodic solutions in the avian influenza system with Allee effect of the avian population. Numerical simulations are also presented to illustrate the theoretical results.", + "tags": [ + { + "id": 4328, + "tag": "Aves" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4329, + "tag": "BioModels:BIOMD0000000708" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:50.257283+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000708", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3007": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3007, + "name": "Liu2017 - Dynamics of Avian Influenza with Allee Growth Effect", + "repository_type": "biomodels", + "summary": "Sanhong Liu, Shigui Ruan & Xinan Zhang. Nonlinear dynamics of avian influenza epidemic models. Mathematical Biosciences 283 (2017).

Avian influenza is a zoonotic disease caused by the transmission of the avian influenza A virus, such as H5N1 and H7N9, from birds to humans. The avian influenza A H5N1 virus has caused more than 500 human infections worldwide with nearly a 60% death rate since it was first reported in Hong Kong in 1997. The four outbreaks of the avian influenza A H7N9 in China from March 2013 to June 2016 have resulted in 580 human cases including 202 deaths with a death rate of nearly 35%. In this paper, we construct two avian influenza bird-to-human transmission models with different growth laws of the avian population, one with logistic growth and the other with Allee effect, and analyze their dynamical behavior. We obtain a threshold value for the prevalence of avian influenza and investigate the local or global asymptotical stability of each equilibrium of these systems by using linear analysis technique or combining Liapunov function method and LaSalle's invariance principle, respectively. Moreover, we give necessary and sufficient conditions for the occurrence of periodic solutions in the avian influenza system with Allee effect of the avian population. Numerical simulations are also presented to illustrate the theoretical results.", + "tags": [ + { + "id": 4328, + "tag": "Aves" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4330, + "tag": "BioModels:BIOMD0000000709" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:50.871696+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000709", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3008": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3008, + "name": "Hernandez-Vargas2012 - Innate immune system dynamics to Influenza virus", + "repository_type": "biomodels", + "summary": "A. Esteban Hernandez-Vargas & Michael Meyer-Hermann. Innate Immune System Dynamics to Influenza Virus. IFAC Proceedings Volumes 45, 18 (2012).

The understanding of how influenza virus infection activates the immune system is crucial to designing prophylactic and therapeutic strategies against the infection. Nevertheless, the immune response to influenza virus infection is complex and remains largely unknown. In this paper we focus in the innate immune response to influenza virus using a mathematical model, based on interferon-induced resistance to infection of respiratory epithelial cells and the clearance of infected cells by natural killers. Simulation results show the importance of IFN-I to prevent new infections in epithelial cells and to stop the viral explosion during the first two days after infection. Nevertheless, natural killers response might be the most relevant for the first depletion in viral load due to the elimination of infected cells. Based on the reproductive number, the innate immune response is important to control the infection, although it would not be enough to clear completely the virus. The effective coordination between innate and adaptive immune response is essential for the virus eradication.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4331, + "tag": "BioModels:BIOMD0000000710" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3275, + "tag": "Influenza A virus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:51.434800+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000710", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3009": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3009, + "name": "Hancioglu2007 - Human Immune Response to Influenza A virus Infection", + "repository_type": "biomodels", + "summary": "Baris Hancioglu, David Swigon & Gilles Clermont. A dynamical model of human immune response to influenza A virus infection. Journal of Theoretical Biology 246, 1 (2007).

We present a simplified dynamical model of immune response to uncomplicated influenza A virus (IAV) infection, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells (cytotoxic T-cells and natural killer cells). Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov and Romanyukha [1994. Mathematical model of antiviral immune response. III. Influenza A virus infection. J. Theor. Biol. 167, 323-360], the model is constructed as a system of 10 ordinary differential equations with 27 parameters characterizing the rates of various processes contributing to the course of disease. The parameters are derived from published experimental data or estimated so as to reproduce available data about the time course of IAV infection in a na\u00efve host. We explore the effect of initial viral load on the severity and duration of the disease, construct a phase diagram that sheds insight into the dynamics of the disease, and perform sensitivity analysis on the model parameters to explore which ones influence the most the onset, duration and severity of infection. To account for the variability and speed of adaptation of the adaptive response to a particular virus strain, we introduce a variable that quantifies the antigenic compatibility between the virus and the antibodies currently produced by the organism. We find that for small initial viral load the disease progresses through an asymptomatic course, for intermediate value it takes a typical course with constant duration and severity of infection but variable onset, and for large initial viral load the disease becomes severe. This behavior is robust to a wide range of parameter values. The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4332, + "tag": "BioModels:BIOMD0000000711" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3275, + "tag": "Influenza A virus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:51.934664+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000711", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3010": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3010, + "name": "Manchanda2014 - Effect on Immune System by 4 different Influenza A virus strains", + "repository_type": "biomodels", + "summary": "Himanshu Manchanda, Nora Seidel, Andi Krumbholz, Andreas Sauerbrei, Michaela Schmidtke & Reinhard Guthke. Within-host influenza dynamics: a small-scale mathematical modeling approach. Biosystems 118 (2014).

The emergence of new influenza viruses like the pandemic H1N1 influenza A virus in 2009 (A(H1N1)pdm09) with unpredictable difficulties in vaccine coverage and established antiviral treatment protocols emphasizes the need of new murine models to prove the activity of novel antiviral compounds in vivo. The aim of the present study was to develop a small-scale mathematical model based on easily attainable experimental data to explain differences in influenza kinetics induced by different virus strains in mice. To develop a three-dimensional ordinary differential equation model of influenza dynamics, the following variables were included: (i) viral pathogenicity (P), (ii) antiviral immune defense (D), and (iii) inflammation due to pro-inflammatory response (I). Influenza virus-induced symptoms (clinical score S) in mice provided the basis for calculations of P and I. Both, mono- and biphasic course of mild to severe influenza induced by three clinical A(H1N1)pdm09 strains and one European swine H1N2 virus were comparatively and quantitatively studied by fitting the mathematical model to the experimental data. The model hypothesizes reasons for mild and severe influenza with mono- as well as biphasic course of disease. According to modeling results, the second peak of the biphasic course of infection is caused by inflammation. The parameters (i) maximum primary pathogenicity, (ii) viral infection rate, and (iii) rate of activation of the immune system represent most important parameters that quantitatively characterize the different pattern of virus-specific influenza kinetics.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4333, + "tag": "BioModels:BIOMD0000000712" + }, + { + "id": 4334, + "tag": "Influenza A virus (A/Jena/5258/2009(H1N1))" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:52.473992+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000712", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3011": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3011, + "name": "Aston2018 - Dynamics of Hepatitis C Infection", + "repository_type": "biomodels", + "summary": "Philip Aston. A New Model for the Dynamics of Hepatitis C Infection: Derivation, Analysis and Implications. Viruses 10, 4 (2018).

We review various existing models of hepatitis C virus (HCV) infection and show that there are inconsistencies between the models and known behaviour of the infection. A new model for HCV infection is proposed, based on various dynamical processes that occur during the infection that are described in the literature. This new model is analysed, and three steady state branches of solutions are found when there is no stem cell generation of hepatocytes. Unusually, the branch of infected solutions that connects the uninfected branch and the pure infection branch can be found analytically and always includes a limit point, subject to a few conditions on the parameters. When the action of stem cells is included, the bifurcation between the pure infection and infected branches unfolds, leaving a single branch of infected solutions. It is shown that this model can generate various viral load profiles that have been described in the literature, which is confirmed by fitting the model to four viral load datasets. Suggestions for possible changes in treatment are made based on the model.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4335, + "tag": "BioModels:BIOMD0000000713" + }, + { + "id": 4336, + "tag": "Hepacivirus C" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:52.998299+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000713", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3012": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3012, + "name": "Reynolds2006 - Reduced model of the acute inflammatory response", + "repository_type": "biomodels", + "summary": "Angela Reynolds, Jonathan Rubin, Gilles Clermont, Judy Day, Yoram Vodovotz & G. Bard Ermentrout. A reduced mathematical model of the acute inflammatory response: I. Derivation of model and analysis of anti-inflammation. Journal of Theoretical Biology 242, 1 (2006).

The acute inflammatory response, triggered by a variety of biological or physical stresses on an organism, is a delicate system of checks and balances that, although aimed at promoting healing and restoring homeostasis, can result in undesired and occasionally lethal physiological responses. In this work, we derive a reduced conceptual model for the acute inflammatory response to infection, built up from consideration of direct interactions of fundamental effectors. We harness this model to explore the importance of dynamic anti-inflammation in promoting resolution of infection and homeostasis. Further, we offer a clinical correlation between model predictions and potential therapeutic interventions based on modulation of immunity by anti-inflammatory agents.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4337, + "tag": "BioModels:BIOMD0000000714" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:53.469865+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000714", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3013": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3013, + "name": "Huo2017 - SEIS epidemic model with the impact of media", + "repository_type": "biomodels", + "summary": "Hai-Feng Huo, Peng Yang & Hong Xiang. Stability and bifurcation for an SEIS epidemic model with the impact of media. Physica A: Statistical Mechanics and its Applications 490 (2018).

A novel SEIS epidemic model with the impact of media is introduced. By analyzing the characteristic equation of equilibrium, the basic reproduction number is obtained and the stability of the steady states is proved. The occurrence of a forward, backward and Hopf bifurcation is derived. Numerical simulations and sensitivity analysis are performed. Our results manifest that media can regard as a good indicator in controlling the emergence and spread of the epidemic disease.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4338, + "tag": "BioModels:BIOMD0000000715" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:53.938580+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000715", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3014": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3014, + "name": "Lee2018 - Avian human bilinear incidence (BI) model", + "repository_type": "biomodels", + "summary": "Hanl Lee & Angelyn Lao. Transmission dynamics and control strategies assessment of avian influenza A (H5N6) in the Philippines. Infectious Disease Modelling 3 (2018).

Due to the outbreaks of Highly Pathogenic Avian Influenza A (HPAI) H5N6 in the Philippines (particularly in Pampanga and Nueva Ecija) in August 2017, there has been an increase in the need to cull the domestic birds to control the spread of the infection. However, this control method poses a negative impact on the poultry industry. In addition, the pathogenicity and transmissibility of the H5N6 in both the birds and the humans remain largely unknown which call for the necessity to develop more strategic control methods for the virus. In this study, we constructed a mathematical model for the bilinear and half-saturated incidence to compare their corresponding effect on transmission dynamics of H5N6. The simulations of half-saturated incidence model were similar to what occurred during the H5N6 outbreak (2017) in the Philippines. Instead of culling the birds, we implemented other control strategies such as non-medicinal (personal protection and poultry isolation) and medicinal (poultry vaccination) ways to prevent, reduce, and control the rate of the H5N6 virus transmission. Among the proposed control strategies, we have shown that the poultry isolation strategy is still the most effective in reducing the infected birds.", + "tags": [ + { + "id": 4328, + "tag": "Aves" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4339, + "tag": "BioModels:BIOMD0000000716" + }, + { + "id": 4340, + "tag": "H5N6 subtype" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:54.409593+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000716", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3015": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3015, + "name": "Lee2018 - Avian human half-saturated incidence (HSI) model", + "repository_type": "biomodels", + "summary": "Hanl Lee & Angelyn Lao. Transmission dynamics and control strategies assessment of avian influenza A (H5N6) in the Philippines. Infectious Disease Modelling 3 (2018).

Due to the outbreaks of Highly Pathogenic Avian Influenza A (HPAI) H5N6 in the Philippines (particularly in Pampanga and Nueva Ecija) in August 2017, there has been an increase in the need to cull the domestic birds to control the spread of the infection. However, this control method poses a negative impact on the poultry industry. In addition, the pathogenicity and transmissibility of the H5N6 in both the birds and the humans remain largely unknown which call for the necessity to develop more strategic control methods for the virus. In this study, we constructed a mathematical model for the bilinear and half-saturated incidence to compare their corresponding effect on transmission dynamics of H5N6. The simulations of half-saturated incidence model were similar to what occurred during the H5N6 outbreak (2017) in the Philippines. Instead of culling the birds, we implemented other control strategies such as non-medicinal (personal protection and poultry isolation) and medicinal (poultry vaccination) ways to prevent, reduce, and control the rate of the H5N6 virus transmission. Among the proposed control strategies, we have shown that the poultry isolation strategy is still the most effective in reducing the infected birds.", + "tags": [ + { + "id": 4328, + "tag": "Aves" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4341, + "tag": "BioModels:BIOMD0000000717" + }, + { + "id": 4340, + "tag": "H5N6 subtype" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:54.927634+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000717", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3016": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3016, + "name": "Li2008 - Caulobacter Cell Cycle", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells.
Shenghua Li, Paul Brazhnik, Bruno Sobral, John J. Tyson PLoS Comput Biol 2008 Jan 25:4(1): e9 18225942 ,
Abstract:
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genomereplication and partitioning to daughter cells. From published experimental evidence, we propose a molecularmechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on thesynthesis and degradation of three \u2018\u2018master regulator\u2019\u2019 proteins (CtrA, GcrA, and DnaA), is converted into a quantitativemodel, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts forimportant details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. Itreproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, andpredicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating thecell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may beapplicable to other species of importance in agriculture and medicine.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4342, + "tag": "BioModels:BIOMD0000000718" + }, + { + "id": 4343, + "tag": "Caulobacter vibrioides" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:55.400025+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000718", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3017": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3017, + "name": "Tsai2014 - Cell cycle duration control by oscillatory Dynamics in Early Xenopus laevis Embryos", + "repository_type": "biomodels", + "summary": "

During the early development of Xenopus laevis embryos, the first mitotic cell cycle is long (\u223c85 min) and the subsequent 11 cycles are short (\u223c30 min) and clock-like. Here we address the question of how the Cdk1 cell cycle oscillator changes between these two modes of operation. We found that the change can be attributed to an alteration in the balance between Wee1/Myt1 and Cdc25. The change in balance converts a circuit that acts like a positive-plus-negative feedback oscillator, with spikes of Cdk1 activation, to one that acts like a negative-feedback-only oscillator, with a shorter period and smoothly varying Cdk1 activity. Shortening the first cycle, by treating embryos with the Wee1A/Myt1 inhibitor PD0166285, resulted in a dramatic reduction in embryo viability, and restoring the length of the first cycle in inhibitor-treated embryos with low doses of cycloheximide partially rescued viability. Computations with an experimentally parameterized mathematical model show that modest changes in the Wee1/Cdc25 ratio can account for the observed qualitative changes in the cell cycle. The high ratio in the first cycle allows the period to be long and tunable, and decreasing the ratio in the subsequent cycles allows the oscillator to run at a maximal speed. Thus, the embryo rewires its feedback regulation to meet two different developmental requirements during early development.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4344, + "tag": "BioModels:BIOMD0000000719" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 16:44:55.879761+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000719", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3018": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3018, + "name": "Yan2012 - Rb-E2F pathway dynamics with miR449", + "repository_type": "biomodels", + "summary": "MiRNAs, which are a family of small non-coding RNAs, regulate a broad array of physiological and developmental processes. However, their regulatory roles have remained largely mysterious. E2F is a positive regulator of cell cycle progression and also a potent inducer of apoptosis. Positive feedback loops in the regulation of Rb-E2F pathway are predicted and shown experimentally. Recently, it has been discovered that E2F induce a cluster of miRNAs called miR449. In turn, E2F is inhibited by miR449 through regulating different transcripts, thus forming negative feedback loops in the interaction network. Here, based on the integration of experimental evidence and quantitative data, we studied Rb-E2F pathway coupling the positive feedback loops and negative feedback loops mediated by miR449. Therefore, a mathematical model is constructed based in part on the model proposed in Yao-Lee et al. (2008) and nonlinear dynamical behaviors including the stability and bifurcations of the model are discussed.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4345, + "tag": "BioModels:BIOMD0000000720" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:56.351358+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000720", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3019": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3019, + "name": "Graham2013 - Role of osteocytes in targeted bone remodeling", + "repository_type": "biomodels", + "summary": "Jason M. Graham, Bruce P. Ayati, Sarah A. Holstein & James A. Martin. The role of osteocytes in targeted bone remodeling: a mathematical model. PLoS ONE 8, 5 (2013).

Until recently many studies of bone remodeling at the cellular level have focused on the behavior of mature osteoblasts and osteoclasts, and their respective precursor cells, with the role of osteocytes and bone lining cells left largely unexplored. This is particularly true with respect to the mathematical modeling of bone remodeling. However, there is increasing evidence that osteocytes play important roles in the cycle of targeted bone remodeling, in serving as a significant source of RANKL to support osteoclastogenesis, and in secreting the bone formation inhibitor sclerostin. Moreover, there is also increasing interest in sclerostin, an osteocyte-secreted bone formation inhibitor, and its role in regulating local response to changes in the bone microenvironment. Here we develop a cell population model of bone remodeling that includes the role of osteocytes, sclerostin, and allows for the possibility of RANKL expression by osteocyte cell populations. We have aimed to give a simple, yet still tractable, model that remains faithful to the underlying system based on the known literature. This model extends and complements many of the existing mathematical models for bone remodeling, but can be used to explore aspects of the process of bone remodeling that were previously beyond the scope of prior modeling work. Through numerical simulations we demonstrate that our model can be used to explore theoretically many of the qualitative features of the role of osteocytes in bone biology as presented in recent literature.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4346, + "tag": "BioModels:BIOMD0000000721" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:56.809532+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000721", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3020": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3020, + "name": "Bianchi2015 -Model for lymphangiogenesis in normal and diabetic wounds", + "repository_type": "biomodels", + "summary": "Arianna Bianchi, Kevin J. Painter & Jonathan A. Sherratt. A mathematical model for lymphangiogenesis in normal and diabetic wounds. Journal of Theoretical Biology 383 (2015).

Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis) very few have been proposed for the regeneration of the lymphatic network. Moreover, lymphangiogenesis is markedly distinct from angiogenesis, occurring at different times and in a different manner. Here a model of five ordinary differential equations is presented to describe the formation of lymphatic capillaries following a skin wound. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from experimental and clinical data. The system is then solved numerically and the results are compared with the available biological literature. Finally, a parameter sensitivity analysis of the model is taken as a starting point for suggesting new therapeutic approaches targeting the enhancement of lymphangiogenesis in diabetic wounds. The work provides a deeper understanding of the phenomenon in question, clarifying the main factors involved. In particular, the balance between TGF-\u03b2 and VEGF levels, rather than their absolute values, is identified as crucial to effective lymphangiogenesis. In addition, the results indicate lowering the macrophage-mediated activation of TGF-\u03b2 and increasing the basal lymphatic endothelial cell growth rate, inter alia, as potential treatments. It is hoped the findings of this paper may be considered in the development of future experiments investigating novel lymphangiogenic therapies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4347, + "tag": "BioModels:BIOMD0000000722" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:57.269641+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000722", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3021": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3021, + "name": "Weis2014 - Data driven Mammalian Cell Cycle Model", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation.
Michael C. Weis, Jayant Avva, James W. Jacobberger, Sree N. Sreenath PLoS ONE 2014 May 13: 9(5): e97130 24824602 ,
Abstract:
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genomereplication and partitioning to daughter cells. From published experimental evidence, we propose a molecularmechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on thesynthesis and degradation of three \u2018\u2018master regulator\u2019\u2019 proteins (CtrA, GcrA, and DnaA), is converted into a quantitativemodel, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts forimportant details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. Itreproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, andpredicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating thecell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may beapplicable to other species of importance in agriculture and medicine.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4348, + "tag": "BioModels:BIOMD0000000723" + }, + { + "id": 4349, + "tag": "Cell Cycle Pathway" + }, + { + "id": 3073, + "tag": "Cell cycle" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:57.738768+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000723", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3022": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3022, + "name": "Theinmozhi2018 - Mechanism of PD1 inhibiting TCR signaling in Tumor immune regulation", + "repository_type": "biomodels", + "summary": "
Its a Deterministic ODE model showcasing mechanism of PDL1 induced TCR and CD38 signalling inhibition. The model also contains the LCK activation and inactivation phenomenon dependent on the particular phosphorylation site. This model is relevant in immunotherapy. 
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4350, + "tag": "BioModels:BIOMD0000000724" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:58.220478+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000724", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3023": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3023, + "name": "Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation", + "repository_type": "biomodels", + "summary": "
Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation

This model is described in the article:

Enrique Salcedo-Sora J, Mc Auley MT.
Mol Biosyst. 2016 Jan 21.

Abstract:

The metabolic biochemistry of folate biosynthesis and utilisation has evolved into a complex network of reactions. Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole.

This model is hosted on BioModels Database and identified by: MODEL1511020000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4351, + "tag": "BioModels:BIOMD0000000725" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:58.802617+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000725", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3024": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3024, + "name": "Ruan2017 - Transmission dynamics and control of rabies in China", + "repository_type": "biomodels", + "summary": "Shigui Ruan. Modeling the transmission dynamics and control of rabies in China. Mathematical Biosciences 286 (2017).

Human rabies was first recorded in ancient China in about 556 BC and is still one of the major public-health problems in China. From 1950 to 2015, 130,494 human rabies cases were reported in Mainland China with an average of 1977 cases per year. It is estimated that 95% of these human rabies cases are due to dog bites. The purpose of this article is to provide a review about the models, results, and simulations that we have obtained recently on studying the transmission of rabies in China. We first construct a basic susceptible, exposed, infectious, and recovered (SEIR) type model for the spread of rabies virus among dogs and from dogs to humans and use the model to simulate the human rabies data in China from 1996 to 2010. Then we modify the basic model by including both domestic and stray dogs and apply the model to simulate the human rabies data from Guangdong Province, China. To study the seasonality of rabies, in Section\u00a04 we further propose a SEIR model with periodic transmission rates and employ the model to simulate the monthly data of human rabies cases reported by the Chinese Ministry of Health from January 2004 to December 2010. To understand the spatial spread of rabies, in Section\u00a05 we add diffusion to the dog population in the basic SEIR model to obtain a reaction-diffusion equation model and determine the minimum wave speed connecting the disease-free equilibrium to the endemic equilibrium. Finally, in order to investigate how the movement of dogs affects the geographically inter-provincial spread of rabies in Mainland China, in Section\u00a06 we propose a multi-patch model to describe the transmission dynamics of rabies between dogs and humans and use the two-patch submodel to investigate the rabies virus clades lineages and to simulate the human rabies data from Guizhou and Guangxi, Hebei and Fujian, and Sichuan and Shaanxi, respectively. Some discussions are provided in Section\u00a07.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4352, + "tag": "BioModels:BIOMD0000000726" + }, + { + "id": 4040, + "tag": "Canis lupus familiaris" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4353, + "tag": "Rabies lyssavirus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:59.272455+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000726", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3025": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3025, + "name": "Li2009- Assymetric Caulobacter cell cycle", + "repository_type": "biomodels", + "summary": "
The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4354, + "tag": "BioModels:BIOMD0000000727" + }, + { + "id": 4343, + "tag": "Caulobacter vibrioides" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:44:59.752762+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000727", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3026": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3026, + "name": "Norel1990 - MPF and Cyclin Oscillations", + "repository_type": "biomodels", + "summary": "
A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the interaction of the maturation promotion factor (MPF) and cyclin. The model retrieves the dynamics observed in early embryos and explains how multiple cycles of MPF activity can be produced and how the internal clock that determines durations and number of cycles can be adjusted by modulating the rate of change in MPF or cyclin concentrations. Experiments are suggested for verifying the role of MPF activity in determining the length of the somatic cell cycle.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4355, + "tag": "BioModels:BIOMD0000000728" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:00.236987+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000728", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3027": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3027, + "name": "Goldbeter1996 - Cyclin Cdc2 kinase Oscillations", + "repository_type": "biomodels", + "summary": "

We consider a minimal cascade model previously proposed for the mitotic oscillator driving the embryonic cell division cycle. The model is based on a bicyclic phosphorylation-dephosphorylation cascade involving cyclin and cdc2 kinase. By constructing stability diagrams showing domains of periodic behavior as a function of the maximum rates of the kinases and phosphatases involved in the two cycles of the cascade, we investigate the role of these converter enzymes in the oscillatory mechanism. Oscillations occur when the balance of kinase and phosphatase rates in each cycle is in a range bounded by two critical values. The results suggest ways to arrest the mitotic oscillator by altering the maximum rates of the converter enzymes. These results bear on the control of cell proliferation.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4356, + "tag": "BioModels:BIOMD0000000729" + }, + { + "id": 3361, + "tag": "Eukaryota" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:00.767031+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000729", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3028": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3028, + "name": "Gerard2009 - An Integrated Mammalian Cell Cycle Model", + "repository_type": "biomodels", + "summary": "

We propose an integrated computational model for the network of cyclin-dependent kinases (Cdks) that controls the dynamics of the mammalian cell cycle. The model contains four Cdk modules regulated by reversible phosphorylation, Cdk inhibitors, and protein synthesis or degradation. Growth factors (GFs) trigger the transition from a quiescent, stable steady state to self-sustained oscillations in the Cdk network. These oscillations correspond to the repetitive, transient activation of cyclin D/Cdk4-6 in G(1), cyclin E/Cdk2 at the G(1)/S transition, cyclin A/Cdk2 in S and at the S/G(2) transition, and cyclin B/Cdk1 at the G(2)/M transition. The model accounts for the following major properties of the mammalian cell cycle: (i) repetitive cell cycling in the presence of suprathreshold amounts of GF; (ii) control of cell-cycle progression by the balance between antagonistic effects of the tumor suppressor retinoblastoma protein (pRB) and the transcription factor E2F; and (iii) existence of a restriction point in G(1), beyond which completion of the cell cycle becomes independent of GF. The model also accounts for endoreplication. Incorporating the DNA replication checkpoint mediated by kinases ATR and Chk1 slows down the dynamics of the cell cycle without altering its oscillatory nature and leads to better separation of the S and M phases. The model for the mammalian cell cycle shows how the regulatory structure of the Cdk network results in its temporal self-organization, leading to the repetitive, sequential activation of the four Cdk modules that brings about the orderly progression along cell-cycle phases.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4357, + "tag": "BioModels:BIOMD0000000730" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:01.250176+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000730", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3029": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3029, + "name": "Robertson-Tessi M 2012 A model of tumor Immune interaction", + "repository_type": "biomodels", + "summary": "

Its a mathematical model presenting the interaction between a growing tumor and immune system. Model involves tumor cells, dendritic cell, helper Tcells, regulatory Tcells, effector cells and certain cytokines (e.g. TGFbeta, IL10, IL2 ) produced by these cells. It represent a dynamic regulation of tumor production/killing by different immune cells and cytokines.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4358, + "tag": "BioModels:BIOMD0000000731" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:01.717904+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000731", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3030": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3030, + "name": "Kirschner1998_Immunotherapy_Tumour", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling immunotherapy of the tumor-immune interaction.
Kirschner D, Panetta JC. J Math Biol 1998 Sep;37(3):235-52 9785481 ,
Abstract:
A number of lines of evidence suggest that immunotherapy with the cytokineinterleukin-2 (IL-2) may boost the immune system to fight tumors. CD4+ T cells,the cells that orchestrate the immune response, use these cytokines as signalingmechanisms for immune-response stimulation as well as lymphocyte stimulation,growth, and differentiation. Because tumor cells begin as 'self', the immunesystem may not respond in an effective way to eradicate them. Adoptive cellularimmunotherapy can potentially restore or enhance these effects. We illustratethrough mathematical modeling the dynamics between tumor cells, immune-effectorcells, and IL-2. These efforts are able to explain both short tumor oscillationsin tumor sizes as well as long-term tumor relapse. We then explore the effectsof adoptive cellular immunotherapy on the model and describe under whatcircumstances the tumor can be eliminated.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Kirschner D, Panetta JC. (1998) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4359, + "tag": "BioModels:BIOMD0000000732" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4360, + "tag": "Response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:02.221721+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000732", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3031": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3031, + "name": "Moore_2004_Mathematical model for CML and T cell interaction", + "repository_type": "biomodels", + "summary": "Its a mathematical model depicting CML (chronic myelogenous leukemia) interaction with T cells and impact of T cell activations on CML progression over time.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4361, + "tag": "BioModels:BIOMD0000000733" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:02.797411+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000733", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3032": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3032, + "name": "Mouse Iron Distribution - Rich and Deficient iron diets (tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model attempts to fit the radioiron tracer data from Lopes et al. 2010 for mice fed iron deficient and rich diets by adjusting the rate of iron intake (vDiet) and the hepcidin synthesis rate (vhepcidin) independently for each experiment. All other parameters are those that provide the best fit for the adequate diet.

This model includes the radioiron tracer species.


Differences in parameter values between deficient, rich, and adequate diets:

Diet vDiet vhepcidin
Adequate 0.00377422 1.7393e-08
Deficient 0 8.54927e-09
Rich 0.00415624 2.30942e-08
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4362, + "tag": "BioModels:BIOMD0000000734" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:03.270169+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000734", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3033": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3033, + "name": "Mouse Iron Distribution - Adequate iron diet (tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes normal iron and radioactive labelled tracer iron species and was used for parameter estimation given the data from Lopes et al. 2010 for mice fed an adequate iron diet.


", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4363, + "tag": "BioModels:BIOMD0000000735" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:03.738883+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000735", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3034": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3034, + "name": "Mouse Iron Distribution - Adequate iron diet (No Tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed an adequate iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4364, + "tag": "BioModels:BIOMD0000000736" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:04.202946+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000736", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3035": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3035, + "name": "Mouse Iron Distribution - Deficient iron diet (No Tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed a deficient iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4365, + "tag": "BioModels:BIOMD0000000737" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:04.697040+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000737", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3036": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3036, + "name": "Mouse Iron Distribution - Rich iron diet (No Tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed a rich iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4366, + "tag": "BioModels:BIOMD0000000738" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:05.167955+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000738", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3037": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3037, + "name": "Bravo2012 - Modelling blood coagulation factor Va inactivation by APC", + "repository_type": "biomodels", + "summary": "Mathematical model of blood coagulation factor Va and Va fragment inactivation by APC, reactions with Xa and prothrombinase-prothrombin complex formation.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4367, + "tag": "BioModels:BIOMD0000000739" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:05.713096+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000739", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3038": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3038, + "name": "Panteleev2010 - Blood Coagulation: Full Model", + "repository_type": "biomodels", + "summary": "Full and reduced mathematical model of blood coagulation focusing on fibrin formation and the response to varied TF and V.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4368, + "tag": "BioModels:BIOMD0000000740" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:06.213078+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000740", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3039": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3039, + "name": "Eftimie2018 - Cancer and Immune biomarkers", + "repository_type": "biomodels", + "summary": "
The paper describes a model on the detection of cancer based on cancer and immune biomarkers. Created by COPASI 4.25 (Build 207) This model is described in the article: Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach Raluca Eftimie and and Esraa Hassanein J Transl Med (2018) 16:73 Abstract: Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific. Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times This model is hosted on BioModels Database and identified by: MODEL1907050002. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4369, + "tag": "BioModels:BIOMD0000000741" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:06.712967+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000741", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3040": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "13", + "id": 3040, + "name": "Garcia2018basic - cancer and immune cell count basic model", + "repository_type": "biomodels", + "summary": "
The paper describes a basic model of immune-tumor cell interactions. Created by COPASI 4.25 (Build 207) This model is described in the article: Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination Victor Garcia, Sebastian Bonhoeffer and Feng Fu bioRxiv, 2018 Abstract: Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can reproduce biologically plausible behavior. This behavior is consistent with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible, consistent with the notion of an immunological barrier. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into cancer-free state. In combination with the administration of immune effector cells, modifications in cancer cell killing may also be harnessed for immunotherapy without resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers. This model is hosted on BioModels Database and identified by: MODEL1907050005. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4370, + "tag": "BioModels:BIOMD0000000742" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:07.185376+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000742", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3041": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3041, + "name": "Gallaher2018 - Tumor\u2013Immune dynamics in multiple myeloma", + "repository_type": "biomodels", + "summary": "

The paper describes a model on the key components for tumor\u2013immune dynamics in multiple myeloma.Created by COPASI 4.25 (Build 207)This model is described in the article:Methods for determining key components in a mathematical model for tumor\u2013immune dynamics in multiple myelomaJill Gallaher, Kamila Larripa, Marissa Renardy, Blerta Shtylla, Nessy Tania, Diana White, Karen Wood, Li Zhu, Chaitali Passey, Michael Robbins, Natalie Bezman, Suresh Shelat, Hearn Jay Choo, Helen MooreJournal of Theoretical Biology 458 (2018) 31\u201346Abstract:In this work, we analyze a mathematical model we introduced previously for the dynamics of multiple myeloma and the immune system. We focus on four main aspects: (1) obtaining and justifying ranges and values for all parameters in the model; (2) determining a subset of parameters to which the model is most sensitive; (3) determining which parameters in this subset can be uniquely estimated given cer- tain types of data; and (4) exploring the model numerically. Using global sensitivity analysis techniques, we found that the model is most sensitive to certain growth, loss, and efficacy parameters. This anal- ysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.This model is hosted on BioModels Database and identified by: MODEL1907050001To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4371, + "tag": "BioModels:BIOMD0000000743" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:07.683888+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000743", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3042": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3042, + "name": "Hu2019 - Pancreatic cancer dynamics", + "repository_type": "biomodels", + "summary": "
The paper describes a model on the size of pancreatic tumour. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling Pancreatic Cancer Dynamics with Immunotherapy Xiaochuan Hu, Guoyi Ke and Sophia R.-J. Jang Bulletin of Mathematical Biology (2019) 81:1885\u20131915 Abstract: We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor- suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sen- sitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive trans- fers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunother- apies between adoptive ex vivo expanded immune cells and TGF-\u03b2 inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-\u03b2 and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-\u03b2 inhibition first followed by adoptive immune cell transfers can yield better survival outcomes. This model is hosted on BioModels Database and identified by: MODEL1907050003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4372, + "tag": "BioModels:BIOMD0000000744" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:08.259379+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000744", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3043": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3043, + "name": "Jarrett2018 - trastuzumab-induced immune response in murine HER2+ breast cancer model", + "repository_type": "biomodels", + "summary": "
The paper describes a model on the trastuzumab-induced immune response in murine(mouse) HER2+ breast cancer.Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer Angela M. Jarrett, Meghan J. Bloom, Wesley Godfrey, Anum K. Syed, David A. Ekrut, Lauren I. Ehrlich, Thomas E. Yankeelov, Anna G. Sorace Mathematical Medicine and Biology: A Journal of the IMA (2018) 00, 1\u201330 Abstract: The goal of this study is to develop an integrated, mathematical\u2013experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls. We posit that the proposed mathematical\u2013experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth\u2014conclusions revealed by the mathematical model that were not deducible from the experimental data alone. This model is hosted on BioModels Database and identified by: MODEL1907050004. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4373, + "tag": "BioModels:BIOMD0000000745" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:08.755581+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000745", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3044": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3044, + "name": "Saad2017 - immune checkpoint and BCG in superficial bladder cancer", + "repository_type": "biomodels", + "summary": "The paper describes a model on the Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of Superficial Bladder Cancer. Created by COPASI 4.25 (Build 207) This model is described in the article: Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of Superficial Bladder CancerFarouk Tijjani Saad, Evren Hincal, and Bilgen KaymakamzadeComputational and Mathematical Methods in Medicine, vol. 2017, no. 3573082Abstract: This paper aims to study the dynamics of immune suppressors/checkpoints, immune system, and BCG in the treatment of superficial bladder cancer. Programmed cell death protein-1 (PD-1), cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), and transforming growth factor-beta (TGF-b) are some of the examples of immune suppressors/checkpoints. They are responsible for deactivating the immune system and enhancing immunological tolerance. Moreover, they categorically downregulate and suppress the immune system by preventing and blocking the activation of T-cells, which in turn decreases autoimmunity and enhances self- tolerance. In cancer immunotherapy, the immune checkpoints/suppressors prevent and block the immune cells from attacking, spreading, and killing the cancer cells, which leads to cancer growth and development. We formulate a mathematical model that studies three possible dynamics of the treatment and establish the effects of the immune checkpoints on the immune system and the treatment at large. Although the effect cannot be seen explicitly in the analysis of the model, we show it by numerical simulations.This model is hosted on BioModels Database and identified by: MODEL1907100001.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4374, + "tag": "BioModels:BIOMD0000000746" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:09.358661+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000746", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3045": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3045, + "name": "Nagashima2002 - Simulating blood coagulation inhibitory effects", + "repository_type": "biomodels", + "summary": "Mathematical model of blood coagulation and the effects of inhibitors of Xa, Va:Xa and IIa.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4375, + "tag": "BioModels:BIOMD0000000747" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:09.819333+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000747", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3046": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3046, + "name": "Phan2017 - innate immune in oncolytic virotherapy", + "repository_type": "biomodels", + "summary": "The paper describes a model on the key components for tumor\u2013immune dynamics in multiple myeloma. Created by COPASI 4.25 (Build 207) This model is described in the article: The Role of the Innate Immune System in Oncolytic Virotherapy Tuan Anh Phan and Jianjun Paul TianComputational and Mathematical Methods in Medicine (2017) 6587258Abstract: The complexity of the immune responses is a major challenge in current virotherapy. This study incorporates the innate immune response into our basic model for virotherapy and investigates how the innate immunity affects the outcome of virotherapy. The viral therapeutic dynamics is largely determined by the viral burst size, relative innate immune killing rate, and relative innate immunity decay rate. The innate immunity may complicate virotherapy in the way of creating more equilibria when the viral burst size is not too big, while the dynamics is similar to the system without innate immunity when the viral burst size is big.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4376, + "tag": "BioModels:BIOMD0000000748" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4377, + "tag": "Oncolytic Virus Therapy" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:10.276919+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000748", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3047": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3047, + "name": "Reppas2015 - tumor control via alternating immunostimulating and immunosuppressive phases", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor control via alternating immunostimulating and immunosuppressive phases. Created by COPASI 4.25 (Build 207) This model is described in the article: In silico tumor control induced via alternating immunostimulating and immunosuppressive phasesAI Reppas, JCL Alfonso, and H HatzikirouVirulence 7:2, 174--186Abstract: Despite recent advances in the field of Oncoimmunology, the success potential of immunomodulatory therapies against cancer remains to be elucidated. One of the reasons is the lack of understanding on the complex interplay between tumor growth dynamics and the associated immune system responses. Toward this goal, we consider a mathematical model of vascularized tumor growth and the corresponding effector cell recruitment dynamics. Bifurcation analysis allows for the exploration of model\u2019s dynamic behavior and the determination of these parameter regimes that result in immune-mediated tumor control. In this work, we focus on a particular tumor evasion regime that involves tumor and effector cell concentration oscillations of slowly increasing and decreasing amplitude, respectively. Considering a temporal multiscale analysis, we derive an analytically tractable mapping of model solutions onto a weakly negatively damped harmonic oscillator. Based on our analysis, we propose a theory-driven intervention strategy involving immunostimulating and immunosuppressive phases to induce long-term tumor control.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4378, + "tag": "BioModels:BIOMD0000000749" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:10.774053+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000749", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3048": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3048, + "name": "Lolas2016 - tumour-induced neoneurogenesis and perineural tumour growth", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumour-induced neoneurogenesis and perineural tumour growth. Created by COPASI 4.25 (Build 207) This model is described in the article: Tumour-induced neoneurogenesis and perineural tumour growth: a mathematical approachGeorgios Lolas, Arianna Bianchi and Konstantinos N. SyrigosScientific Reports 6:20684Abstract:It is well-known that tumours induce the formation of a lymphatic and a blood vasculature around themselves. A similar but far less studied process occurs in relation to the nervous system and is referred to as neoneurogenesis. The relationship between tumour progression and the nervous system is still poorly understood and is likely to involve a multitude of factors. It is therefore relevant to study tumour-nerve interactions through mathematical modelling: this may reveal the most significant factors of the plethora of interacting elements regulating neoneurogenesis. The present work is a first attempt to model the neurobiological aspect of cancer development through a system of differential equations. The model confirms the experimental observations that a tumour is able to promote nerve formation/elongation around itself, and that high levels of nerve growth factor and axon guidance molecules are recorded in the presence of a tumour. Our results also reflect the observation that high stress levels (represented by higher norepinephrine release by sympathetic nerves) contribute to tumour development and spread, indicating a mutually beneficial relationship between tumour cells and neurons. The model predictions suggest novel therapeutic strategies, aimed at blocking the stress effects on tumour growth and dissemination.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4379, + "tag": "BioModels:BIOMD0000000750" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4380, + "tag": "Host-Tumor Interaction" + }, + { + "id": 1769, + "tag": "Neurogenesis" + }, + { + "id": 4381, + "tag": "Perineural Invasion" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:11.244906+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000750", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3049": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3049, + "name": "Wilkie2013b - immune-induced cancer dormancy and immune evasion-basic", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of immune-induced cancer dormancy and immune evasion. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical models of immune-induced cancer dormancy and the emergence of immune evasionKathleen P. Wilkie and Philip HahnfeldtInterface Focus 3: 20130010Abstract: Cancer dormancy, a state in which cancer cells persist in a host without sig- nificant growth, is a natural forestallment of progression to manifest disease and is thus of great clinical interest. Experimental work in mice suggests that in immune-induced dormancy, the longer a cancer remains dormant in a host, the more resistant the cancer cells become to cytotoxic T-cell-mediated killing. In this work, mathematical models are used to analyse the possible causative mechanisms of cancer escape from immune-induced dormancy. Using a data-driven approach, both decaying efficacy in immune predation and immune recruitment are analysed with results suggesting that decline in recruitment is a stronger determinant of escape than increased resistance to predation. Using a mechanistic approach, the existence of an immune- resistant cancer cell subpopulation is considered, and the effects on cancer dormancy and potential immunoediting mechanisms of cancer escape are analysed and discussed. The immunoediting mechanism assumes that the immune system selectively prunes the cancer of immune-sensitive cells, which is shown to cause an initially heterogeneous population to become a more homogeneous, and more resistant, population. The fact that this selec- tion may result in the appearance of decreasing efficacy in T-cell cytotoxic effect with time in dormancy is also demonstrated. This work suggests that through actions that temporarily delay cancer growth through the targeted removal of immune-sensitive subpopulations, the immune response may actually progress the cancer to a more aggressive state.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4382, + "tag": "BioModels:BIOMD0000000751" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:11.732305+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000751", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3050": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3050, + "name": "Wilkie2013r - immune-induced cancer dormancy and immune evasion-resistance", + "repository_type": "biomodels", + "summary": "The paper describes a model of immune-induced cancer dormancy and immune evasion with resistance. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical models of immune-induced cancer dormancy and the emergence of immune evasionKathleen P. Wilkie and Philip HahnfeldtInterface Focus 3: 20130010Abstract: Cancer dormancy, a state in which cancer cells persist in a host without sig- nificant growth, is a natural forestallment of progression to manifest disease and is thus of great clinical interest. Experimental work in mice suggests that in immune-induced dormancy, the longer a cancer remains dormant in a host, the more resistant the cancer cells become to cytotoxic T-cell-mediated killing. In this work, mathematical models are used to analyse the possible causative mechanisms of cancer escape from immune-induced dormancy. Using a data-driven approach, both decaying efficacy in immune predation and immune recruitment are analysed with results suggesting that decline in recruitment is a stronger determinant of escape than increased resistance to predation. Using a mechanistic approach, the existence of an immune- resistant cancer cell subpopulation is considered, and the effects on cancer dormancy and potential immunoediting mechanisms of cancer escape are analysed and discussed. The immunoediting mechanism assumes that the immune system selectively prunes the cancer of immune-sensitive cells, which is shown to cause an initially heterogeneous population to become a more homogeneous, and more resistant, population. The fact that this selec- tion may result in the appearance of decreasing efficacy in T-cell cytotoxic effect with time in dormancy is also demonstrated. This work suggests that through actions that temporarily delay cancer growth through the targeted removal of immune-sensitive subpopulations, the immune response may actually progress the cancer to a more aggressive state.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4383, + "tag": "BioModels:BIOMD0000000752" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:12.247104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000752", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3051": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3051, + "name": "Figueredo2013/1 - immunointeraction base model", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of immune-itumor interaction.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4384, + "tag": "BioModels:BIOMD0000000753" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:12.954495+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000753", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3052": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3052, + "name": "Figueredo2013/2 - immunointeraction model with IL2", + "repository_type": "biomodels", + "summary": "The paper describes a model of immune-itumor interaction with IL2.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4385, + "tag": "BioModels:BIOMD0000000754" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:13.441049+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000754", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3053": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3053, + "name": "Hansen2019 - Nine species reduced model of blood coagulation", + "repository_type": "biomodels", + "summary": "
its a nine species reduced model of Hockin 2002. Model uses different level of reduction (5,7,9,11) and testing the best alignment with Hockin model results
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4386, + "tag": "BioModels:BIOMD0000000755" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:13.938077+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000755", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3054": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3054, + "name": "Figueredo2013/3 - immunointeraction full model", + "repository_type": "biomodels", + "summary": "The paper describes a full model of immune-itumor interaction.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4387, + "tag": "BioModels:BIOMD0000000756" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4259, + "tag": "Immune response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:14.467454+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000756", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3055": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3055, + "name": "Abernathy2016 - glioblastoma treatment", + "repository_type": "biomodels", + "summary": "The paper describes a model of glioblastoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling the Treatment of Glioblastoma Multiforme and Cancer Stem Cells with Ordinary Differential EquationsKristen Abernathy and Jeremy Burke BMC Computational and Mathematical Methods in Medicine Volume 2016, Article ID 1239861, 11 pagesAbstract: Despite improvements in cancer therapy and treatments, tumor recurrence is a common event in cancer patients. One explanation of recurrence is that cancer therapy focuses on treatment of tumor cells and does not eradicate cancer stem cells (CSCs). CSCs are postulated to behave similar to normal stem cells in that their role is to maintain homeostasis. That is, when the population of tumor cells is reduced or depleted by treatment, CSCs will repopulate the tumor, causing recurrence. In this paper, we study the application of the CSC Hypothesis to the treatment of glioblastoma multiforme by immunotherapy. We extend the work of Kogan et al. (2008) to incorporate the dynamics of CSCs, prove the existence of a recurrence state, and provide an analysis of possible cancerous states and their dependence on treatment levels.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4388, + "tag": "BioModels:BIOMD0000000757" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:14.957337+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000757", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3056": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3056, + "name": "Babbs2012 - immunotherapy", + "repository_type": "biomodels", + "summary": "The paper describes a simple model of tumor immunotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model Charles F BabbsAm J Cancer Res 2012;2(2):204-213Abstract: The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, \u03bb; and immune cell half-life decay, \u03bc. The predator-prey equations as functions of time, t, for nor- malized tumor cell numbers, y, (the prey) and immunocyte numbers, x, (the predators) are: dy/dt = gy \u2013 kx and dx/dt = \u03bbxy \u2013 \u03bcx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell num- bers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parame- ters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4389, + "tag": "BioModels:BIOMD0000000758" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:15.440851+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000758", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3057": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3057, + "name": "Cappuccio2006 - Cancer immunotherapy by interleukin-21", + "repository_type": "biomodels", + "summary": "This model describes the effects of Il-21 on tumor eradication via natural killer cell-mediated and CD8+ T-cell-mediated lysis of tumor cells. The model demonstrates changes in growth dynamics in nonimmunogenic B16 melanoma and the immunogenic MethA and MCA205 fibrosarcomas, showing a strong dependence of the NK-cell/CD8+ T-cell balance on tumor immunogenicity.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4390, + "tag": "BioModels:BIOMD0000000761" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:15.956332+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000761", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3058": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3058, + "name": "Kuznetsov1994 - Nonlinear dynamics of immunogenic tumors", + "repository_type": "biomodels", + "summary": "
This mathematical model describes the response of cytotoxic T lymphocytes to the growth of an immunogenic tumor, with the inclusion on a number of in vivo phenomena, such as the immunostimulation of tumor growth, evasion of immune function by the tumor, and the formation of a tumor \"dormant state\". Specifically, this model is used to describe the kinetics of growth and regression of the B-cell lymphoma BCL1 in the spleen of mice.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4391, + "tag": "BioModels:BIOMD0000000762" + }, + { + "id": 4392, + "tag": "Ordinary differential equation model" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4393, + "tag": "T cell mediated immune response to tumor cell" + } + ], + "timestamp_created": "2025-02-03 16:45:16.420529+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000762", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3059": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3059, + "name": "Dritschel2018 - A mathematical model of cytotoxic and helper T cell interactions in a tumour microenvironment", + "repository_type": "biomodels", + "summary": "
This model examines the role of helper and cytotoxic T cells in an anti-tumour response, with implicit inclusions of immunosuppressive effects. The model demonstrates the dependence of immunoediting on infilftration by helper and cytotoxic T cells, as well as the importance of these cells in mediating tumour elimination.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4394, + "tag": "BioModels:BIOMD0000000763" + }, + { + "id": 4392, + "tag": "Ordinary differential equation model" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4393, + "tag": "T cell mediated immune response to tumor cell" + } + ], + "timestamp_created": "2025-02-03 16:45:17.191290+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000763", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3060": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3060, + "name": "Malinzi2019 - chemovirotherapy", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virothherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical Analysis of a Mathematical Model of Chemovirotherapy: Effect of Drug Infusion Method Joseph Malinzi Computational and Mathematical Methods in Medicine Volume 2019, Article ID 7576591, 16 pages Abstract: IA mathematical model for the treatment of cancer using chemovirotherapy is developed with the aim of determining the efficacy of three drug infusion methods: constant, single bolus, and periodic treatments. The model is in the form of ODEs and is further extended into DDEs to account for delays as a result of the infection of tumor cells by the virus and chemotherapeutic drug responses. Analysis of the model is carried out for each of the three drug infusion methods. Analytic solutions are determined where possible and stability analysis of both steady state solutions for the ODEs and DDEs is presented. The results indicate that constant and periodic drug infusion methods are more efficient compared to a single bolus injection. Numerical simulations show that with a large virus burst size, irrespective of the drug infusion method, chemovirotherapy is highly effective compared to either treatments. The simulations further show that both delays increase the period within which a tumor can be cleared from body tissue.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4395, + "tag": "BioModels:BIOMD0000000764" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:17.661285+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000764", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3061": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3061, + "name": "Macnamara2015/1 - virotherapy full model", + "repository_type": "biomodels", + "summary": "The paper describes a full model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Memory versus effector immune responses in oncolytic virotherapiesCicely Macnamara, Raluca Eftimie Abstract: The main priority when designing cancer immuno-therapies has been to seek viable biological mechanisms that lead to permanent cancer eradica- tion or cancer control. Understanding the delicate balance between the role of effector and memory cells on eliminating cancer cells remains an elusive problem in immunology. Here we make an initial investigation into this problem with the help of a mathematical model for oncolytic virotherapy; although the model can in fact be made general enough to be applied also to other immunological problems. Our results show that long-term cancer con- trol is associated with a large number of persistent effector cells (irrespective of the initial peak in effector cell numbers). However, this large number of persistent effector cells is sustained by a relatively large number of memory cells. Moreover, we show that cancer control from a dormant state cannot be predicted by the size of the memory population.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4396, + "tag": "BioModels:BIOMD0000000766" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:18.118436+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000766", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3062": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3062, + "name": "Macnamara2015/2 - virotherapy virus-free submodel", + "repository_type": "biomodels", + "summary": "The paper describes a submodel of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Memory versus effector immune responses in oncolytic virotherapiesCicely Macnamara, Raluca Eftimie Abstract: The main priority when designing cancer immuno-therapies has been to seek viable biological mechanisms that lead to permanent cancer eradica- tion or cancer control. Understanding the delicate balance between the role of effector and memory cells on eliminating cancer cells remains an elusive problem in immunology. Here we make an initial investigation into this problem with the help of a mathematical model for oncolytic virotherapy; although the model can in fact be made general enough to be applied also to other immunological problems. Our results show that long-term cancer con- trol is associated with a large number of persistent effector cells (irrespective of the initial peak in effector cell numbers). However, this large number of persistent effector cells is sustained by a relatively large number of memory cells. Moreover, we show that cancer control from a dormant state cannot be predicted by the size of the memory population.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4397, + "tag": "BioModels:BIOMD0000000767" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:18.584505+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000767", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3063": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3063, + "name": "Eftimie2010 - immunity to melanoma", + "repository_type": "biomodels", + "summary": "The paper describes a model of immunity to melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling anti-tumor Th1 and Th2 immunity in the rejection of melanoma Raluca Eftimie, Jonathan L. Bramson, David J.D. Earn Journal of Theoretical Biology 265 (2010) 467\u2013480Abstract: Recent experiments indicate that CD4+ Th2 cells can reject skin tumors in mice, while CD4+ Th1 cells cannot (Mattes et al., 2003; Zhang et al., 2009). These results are surprising because CD4+ Th1 cells are typically considered to be capable of tumor rejection. We used mathematical models to investigate this unexpected outcome. We found that neither CD4+ Th1 nor CD4+ Th2 cells could eliminate the cancer cells when acting alone, but that tumor elimination could be induced by recruitment of eosinophils by the Th2 cells. These recruited eosinophils had unexpected indirect effects on the decay rate of type 2 cytokines and the rate at which Th2 cells are inactivated through interactions with cancer cells. Strikingly, the presence of eosinophils impacted tumor growth more significantly than the release of tumor-suppressing cytokines such as IFN-g and TNF-a. Our simulations suggest that novel strategies to enhance eosinophil recruitment into skin tumors may improve cancer immunotherapies. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4398, + "tag": "BioModels:BIOMD0000000768" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:19.532854+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000768", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3064": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3064, + "name": "Eftimie2017/2 - interaction of Th and macrophage in melanoma", + "repository_type": "biomodels", + "summary": "The paper describes a model of interaction of Th cells and macrophage in melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling and investigation of the CD4 T cells \u2013 Macrophages paradox in melanoma immunotherapiesRaluca Eftimie, Haneen HamamJournal of Theoretical Biology 420 (2017) 82\u2013104 Abstract: It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10\u201315 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. In some studies, elimination of B16 melanoma was associated with a Th1 immune response (i.e., elimination occurred in the presence of cytokines produced by Th1 cells), while in other studies melanoma elimination was associated with a Th2 immune response (i.e., elimination occurred in the presence of cytokines produced by Th2 cells). Moreover, macrophages have been shown to be present inside the tumours, during both Th1 and Th2 immune responses. To investigate the possible biological mechanisms behind these apparently contradictory results, we develop a class of mathematical models for the dynamics of Th1 and Th2 cells, and M1 and M2 macrophages in the presence/absence of tumour cells. Using this mathematical model, we show that depending on the re- polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type- II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4399, + "tag": "BioModels:BIOMD0000000769" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:20.216418+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000769", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3065": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3065, + "name": "Eftimie2017/1 - interaction of Th and macrophage", + "repository_type": "biomodels", + "summary": "The paper describes a model of interaction of Th cells and macrophage in melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling and investigation of the CD4 T cells \u2013 Macrophages paradox in melanoma immunotherapiesRaluca Eftimie, Haneen HamamJournal of Theoretical Biology 420 (2017) 82\u2013104 Abstract: It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10\u201315 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. In some studies, elimination of B16 melanoma was associated with a Th1 immune response (i.e., elimination occurred in the presence of cytokines produced by Th1 cells), while in other studies melanoma elimination was associated with a Th2 immune response (i.e., elimination occurred in the presence of cytokines produced by Th2 cells). Moreover, macrophages have been shown to be present inside the tumours, during both Th1 and Th2 immune responses. To investigate the possible biological mechanisms behind these apparently contradictory results, we develop a class of mathematical models for the dynamics of Th1 and Th2 cells, and M1 and M2 macrophages in the presence/absence of tumour cells. Using this mathematical model, we show that depending on the re- polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type- II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4400, + "tag": "BioModels:BIOMD0000000770" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:20.933997+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000770", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3066": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3066, + "name": "Bajzer2008 - Modeling of cancer virotherapy with recombinant measles viruses", + "repository_type": "biomodels", + "summary": "
This model describes the interactions between tumor cells and virus particles, with particular reference to virus-induced syncytium formation and ultimately death of tumor cells. Dynamics of infected cells, and production of new virus particules by infected cells, is also included.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4401, + "tag": "BioModels:BIOMD0000000771" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:21.481413+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000771", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3067": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3067, + "name": "Wang2019 - A mathematical model of oncolytic virotherapy with time delay", + "repository_type": "biomodels", + "summary": "
A mathematical model describing oncolytic virotherapy with incorporation the viral lytic cycle and the virus-specific CTL response. The thresholds for viral treatment and virus-specific CTl response are also obtained.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4402, + "tag": "BioModels:BIOMD0000000772" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:21.982624+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000772", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3068": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3068, + "name": "Wodarz2018/2 - model with transit amplifying cells", + "repository_type": "biomodels", + "summary": "The paper describes a model of effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells. Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulationDominik Wodarz J Theor Biol. 2018 July 07; 448: 86\u201393Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4403, + "tag": "BioModels:BIOMD0000000773" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:22.482589+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000773", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3069": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3069, + "name": "Wodarz2018/1 - simple model", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells. Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulationDominik Wodarz J Theor Biol. 2018 July 07; 448: 86\u201393Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4404, + "tag": "BioModels:BIOMD0000000774" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:23.004960+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000774", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3070": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3070, + "name": "Iarosz2015 - brain tumor", + "repository_type": "biomodels", + "summary": "The paper describes a model of brain tumor. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical model of brain tumour with glia-neuron interactions and chemotherapy treatmentKelly C. Iarosz, Fernando S. Borges, Antonio M. Batista, Murilo S. Baptista, Regiane A. N. Siqueira, Ricardo L. Viana, Sergio R. LopesAbstract: In recent years, it became clear that a better understanding of the interactions among the main elements involved in the cancer network is necessary for the treatment of cancer and the suppression of cancer growth. In this work we propose a system of coupled differential equations that model brain tumour under treatment by chemotherapy, which considers interactions among the glial cells, the glioma, the neurons, and the chemotherapeutic agents. We study the conditions for the glioma growth to be eliminated, and identify values of the parameters for which the inhibition of the glioma growth is obtained with a minimal loss of healthy cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4405, + "tag": "BioModels:BIOMD0000000775" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:23.508656+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000775", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3071": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3071, + "name": "Monro2008 - chemotherapy resistance", + "repository_type": "biomodels", + "summary": "The paper describes a model of resistance of cancer to chemotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling chemotherapy resistance in palliation and failed cure Helen C. Monro, Eamonn A. Gaffney J Theor Biol. 2009, 257 (2), pp.292 Abstract: The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4406, + "tag": "BioModels:BIOMD0000000776" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:24.005364+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000776", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3072": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3072, + "name": "Chakrabarty2010 - A control theory approach to cancer remission aided by an optimal therapy", + "repository_type": "biomodels", + "summary": "
This is a reinvestigation of a previous model depicting cancer remission. It involves application of mathematical tools from control theory to assess the optimal approach during the use of Adaptive Cellular Immunotherapy and interleukin-2 treatment.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4407, + "tag": "BioModels:BIOMD0000000777" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:24.479118+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000777", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3073": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3073, + "name": "Wei2017 - tumor, T cell and cytokine interaction", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor-immune interaction. Created by COPASI 4.25 (Build 207) This model is described in the article: Periodically Pulsed Immunotherapy in a Mathematical Model of Tumor, CD4+ T Cells, and Antitumor Cytokine InteractionsHsiu-Chuan Wei, Jui-Ling Yu, Chia-Yu Hsu Computational and Mathematical Methods in Medicine Volume 2017, Article ID 2906282, 12 pages Abstract: Immunotherapy is one of the most recent approaches for controlling and curing malignant tumors. In this paper, we consider a mathematical model of periodically pulsed immunotherapy using CD4+ T cells and an antitumor cytokine. Mathematical analyses are performed to determine the threshold of a successful treatment. The interindividual variability is explored by one-, two-, and three-parameter bifurcation diagrams for a nontreatment case. Numerical simulation conducted in this paper shows that (i) the tumor can be regulated by administering CD4+ T cells alone in a patient with a strong immune system or who has been diagnosed at an early stage, (ii) immunotherapy with a large amount of an antitumor cytokine can boost the immune system to remit or even to suppress tumor cells completely, and (iii) through polytherapy the tumor can be kept at a smaller size with reduced dosages.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4408, + "tag": "BioModels:BIOMD0000000778" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:24.948283+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000778", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3074": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3074, + "name": "dePillis2009 - Mathematical model creation for cancer chemo-immunotherapy", + "repository_type": "biomodels", + "summary": "
This is an updated version of a previous model that described the dynamics of cancer treatment, with descriptions of tumour cell numbers, specific and non-specific immune cells (NK, CD8+ T cells and other lymphocytes) with inclusion of chemo- and immunotherapy. This model incorporates new data to provide an improved and more comprehensive model, with specific emphasis on better descriptions of IL-2 dynamics.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4409, + "tag": "BioModels:BIOMD0000000779" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:25.409712+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000779", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3075": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3075, + "name": "Wang2016/1 - oncolytic efficacy of M1 virus-SNTM model", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4410, + "tag": "BioModels:BIOMD0000000780" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:25.920566+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000780", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3076": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3076, + "name": "Wang2016/2 - oncolytic efficacy of M1 virus-SNT model", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4411, + "tag": "BioModels:BIOMD0000000781" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:26.373295+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000781", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3077": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3077, + "name": "Wang2016/3 - oncolytic efficacy of M1 virus-SN model", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4412, + "tag": "BioModels:BIOMD0000000782" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:26.870913+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000782", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3078": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3078, + "name": "Dong2014 - Mathematical modeling on helper t cells in a tumor immune system", + "repository_type": "biomodels", + "summary": "
This model gives a mathematical description of the interactions between tumor cells, cytotoxic T lymphocytes and helper T cells (HTCs) within the tumor microenvironment, with emphasis on the role played by HTCs. The effects and dynamics of adoptive cell immunotherapy and HTC recruitment are also specifically discussed.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4413, + "tag": "BioModels:BIOMD0000000783" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:27.329901+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000783", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3079": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3079, + "name": "Lopez2014 - A Validated Mathematical Model of Tumor Growth Including Tumor-Host Interaction and Cell-Mediated Immune Response", + "repository_type": "biomodels", + "summary": "
This is a dynamical model of cancer growth that includes three interacting cell populations of tumor cells, healthy host cells and immune effector cells. The tumor-immune and the tumor-host interactions are characterized to reproduce experimental results.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4414, + "tag": "BioModels:BIOMD0000000784" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:27.784943+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000784", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3080": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3080, + "name": "Sotolongo-Costa2003 - Behavior of tumors under nonstationary therapy", + "repository_type": "biomodels", + "summary": "
This model describes the interaction dynamics of a lymphocyte-tumor cell population.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4415, + "tag": "BioModels:BIOMD0000000785" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:28.290923+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000785", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3081": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3081, + "name": "Lipniacki2004 - Mathematical model of NFKB regulatory module", + "repository_type": "biomodels", + "summary": "
its a mathematical model studying impact of TNF on NFKB nuclear dynamics. This model is derived from Hoffmann2002 (PMID:12424381).
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4416, + "tag": "BioModels:BIOMD0000000786" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:28.778662+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000786", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3082": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3082, + "name": "Frascoli2014 - A dynamical model of tumour immunotherapy", + "repository_type": "biomodels", + "summary": "
This is a coupled ordinary differential equation model of tumour-immune dynamics, accounting for biological and clinical factors which regulate the interaction rates of cytotoxic T lymphocytes on the surface of the tumour mass.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4417, + "tag": "BioModels:BIOMD0000000787" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:29.347209+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000787", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3083": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3083, + "name": "Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies", + "repository_type": "biomodels", + "summary": "
This model presents a general target-mediated drug disposition (TMDD) model for bispecific antibodies (BsAbs), which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters is also present.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4418, + "tag": "BioModels:BIOMD0000000788" + }, + { + "id": 4419, + "tag": "Bispecific Antibody" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:29.846356+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000788", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3084": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3084, + "name": "Jenner2018 - treatment of oncolytic virus", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical Modelling of the Interaction Between Cancer Cells and an Oncolytic Virus: Insights into the Effects of Treatment ProtocolsAdrianne L. Jenner, Chae-Ok Yun, Peter S. Kim, Adelle C. F. CosterBull Math Biol (2018) 80:1615\u20131629Abstract: Oncolyticvirotherapyisanexperimentalcancertreatmentthatusesgenet- ically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity Kim et al. (Biomaterials 32(9):2314\u20132326, 2011) modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. Whilst PEG mod- ification appeared to improve plasma retention and initial infectivity, it also increased the virus particle arrival time. We derive a mathematical model that describes the inter- action between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations, we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy are explored.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4420, + "tag": "BioModels:BIOMD0000000789" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:30.371079+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000789", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3085": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3085, + "name": "Alvarez2019 - A nonlinear mathematical model of cell-mediated immune response for tumor phenotypic heterogeneity", + "repository_type": "biomodels", + "summary": "
This is a non-linear mathematical model of cancer immunosurveillance that takes into account intratumoral phenotypic heterogeneity, such as differential expression of cell surface receptors and growth factors, according to cell-mediated immune responses. The model describes phenomena that have also been observed in vivo, such as tumor dormancy, cancer immunoediting, and a strong sensitivity to initial conditions.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4421, + "tag": "BioModels:BIOMD0000000790" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:31.004784+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000790", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3086": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3086, + "name": "Wilson2012 - tumor vaccine efficacy", + "repository_type": "biomodels", + "summary": "The paper describes a model of antitumor vaccine therapy.Created by COPASI 4.25 (Build 207) This model is described in the article: A Mathematical Model of the Enhancement of Tumor Vaccine Efficacy by ImmunotherapyShelby Wilson and Doron LevyBull Math Biol. 2012 July ; 74(7)Abstract: TGF-\u03b2 is an immunoregulatory protein that contributes to inadequate antitumor immune responses in cancer patients. Recent experimental data suggests that TGF-\u03b2 inhibition alone, provides few clinical benefits, yet it can significantly amplify the anti-tumor immune response when combined with a tumor vaccine. We develop a mathematical model in order to gain insight into the cooperative interaction between anti-TGF-\u03b2 and vaccine treatments. The mathematical model follows the dynamics of the tumor size, TGF-\u03b2 concentration, activated cytotoxic effector cells, and regulatory T cells. Using numerical simulations and stability analysis, we study the following scenarios: a control case of no treatment, anti-TGF-\u03b2 treatment, vaccine treatment, and combined anti-TGF-\u03b2 vaccine treatments. We show that our model is capable of capturing the observed experimental results, and hence can be potentially used in designing future experiments involving this approach to immunotherapy.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4422, + "tag": "BioModels:BIOMD0000000791" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:31.465057+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000791", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3087": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3087, + "name": "Hu2019 - Modeling Pancreatic Cancer Dynamics with Immunotherapy", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of pancreatic cancer that includes descriptions of pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor-suppressing cytokines to investigate the effects of immunotherapies on patient survival.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4423, + "tag": "BioModels:BIOMD0000000792" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:32.003533+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000792", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3088": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3088, + "name": "Chen2011/1 - bone marrow invasion absolute model", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is described in the article: Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principlesKun-Wan Chen, Kenneth J Pienta Theoretical Biology and Medical Modelling 2011, 8:36 Abstract: Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4424, + "tag": "BioModels:BIOMD0000000793" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:32.599285+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000793", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3089": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3089, + "name": "Benary2019 - Controlling NFKB dynamics by B-TrCP", + "repository_type": "biomodels", + "summary": "
its a mathematical model studying impact of b_TrCP on NFKB nuclear dynamics. This model is derived from Lipniacki2004 (PMID:15094015).
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4425, + "tag": "BioModels:BIOMD0000000794" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:33.104325+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000794", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3090": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3090, + "name": "Chen2011/2 - bone marrow invasion relative model", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is described in the article: Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principlesKun-Wan Chen, Kenneth J Pienta Theoretical Biology and Medical Modelling 2011, 8:36 Abstract: Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4426, + "tag": "BioModels:BIOMD0000000795" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:33.570960+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000795", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3091": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3091, + "name": "Yang2012 - cancer growth with angiogenesis", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor growth with angiogenesis. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical modeling of solid cancer growth with angiogenesisHyun M Yang Theoretical Biology and Medical Modelling 2012, 9:2 Abstract: Background: Cancer arises when within a single cell multiple malfunctions of control systems occur, which are, broadly, the system that promote cell growth and the system that protect against erratic growth. Additional systems within the cell must be corrupted so that a cancer cell, to form a mass of any real size, produces substances that promote the growth of new blood vessels. Multiple mutations are required before a normal cell can become a cancer cell by corruption of multiple growth-promoting systems.Methods: We develop a simple mathematical model to describe the solid cancer growth dynamics inducing angiogenesis in the absence of cancer controlling mechanisms.Results: The initial conditions supplied to the dynamical system consist of a perturbation in form of pulse: The origin of cancer cells from normal cells of an organ of human body. Thresholds of interacting parameters were obtained from the steady states analysis. The existence of two equilibrium points determine the strong dependency of dynamical trajectories on the initial conditions. The thresholds can be used to control cancer.Conclusions: Cancer can be settled in an organ if the following combination matches: better fitness of cancer cells, decrease in the efficiency of the repairing systems, increase in the capacity of sprouting from existing vascularization, and higher capacity of mounting up new vascularization. However, we show that cancer is rarely induced in organs (or tissues) displaying an efficient (numerically and functionally) reparative or regenerative mechanism.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4427, + "tag": "BioModels:BIOMD0000000796" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:34.042785+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000796", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3092": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3092, + "name": "Hu2018 - Dynamics of tumor-CD4+-cytokine-host cells interactions with treatments", + "repository_type": "biomodels", + "summary": "
This is a proposed mathematical model describing interactions between tumor cells, CD4+ T cells, cytokines, and host cells within the context of CD4+ T cells inducing tumor regression. A platform is provided to assess the effectiveness of single or combination therapy with CD4+ T cells and/or cytokines.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4428, + "tag": "BioModels:BIOMD0000000797" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:34.512430+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000797", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3093": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3093, + "name": "Sharp2019 - AML", + "repository_type": "biomodels", + "summary": "The paper describes a model of acute myeloid leukaemia. Created by COPASI 4.26 (Build 213) This model is described in the article: Optimal control of acute myeloid leukaemia Jesse A. Sharp, Alexander P Browning, Tarunendu Mapder, Kevin Burrage, Matthew J SimpsonJournal of Theoretical Biology 470 (2019) 30\u201342 Abstract: Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corre- sponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin\u2019s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4429, + "tag": "BioModels:BIOMD0000000798" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:34.961422+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000798", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3094": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3094, + "name": "Cucuianu2010 - A hypothetical-mathematical model of acute myeloid leukaemia pathogenesis", + "repository_type": "biomodels", + "summary": "
This is a simple mathematical model describing the growth and removal of normal and leukemic haematopoietic stem cell populations and the role of these cellular processes in generating monoclonal leukemic patterns.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4430, + "tag": "BioModels:BIOMD0000000799" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:35.430865+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000799", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3095": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3095, + "name": "Precup2012 - Mathematical modeling of cell dynamics after allogeneic bone marrow transplantation", + "repository_type": "biomodels", + "summary": "
This is a basic mathematical model describing the dynamics of three cell lines (normal host cells, leukemic host cells and donor cells) after allogeneic stem cell transplantation.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4431, + "tag": "BioModels:BIOMD0000000800" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:35.905918+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000800", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3096": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3096, + "name": "Sturrock2015 - glioma growth", + "repository_type": "biomodels", + "summary": "The paper describes a model of glioma. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of pre-diagnostic glioma growth Marc Sturrock, Wenrui Hao, Judith Schwartzbaum, Grzegorz A. Rempala J Theor Biol. 2015 September 7; 380: 299\u2013308 Abstract: Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4432, + "tag": "BioModels:BIOMD0000000801" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:36.361784+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000801", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3097": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3097, + "name": "Hoffman2018- ADCC against cancer", + "repository_type": "biomodels", + "summary": "The paper describes a model of ADCC. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of antibody-dependent cellular cytotoxicity (ADCC) F. Hoffman, D. Gavaghan, J. Osborne, I.P. Barrett, T. You, H. Ghadially, R. Sainson, R.W. Wilkinson, H.M. ByrneJournal of Theoretical Biology 436 (2018) 39\u201350 Abstract: Immunotherapies exploit the immune system to target and kill cancer cells, while sparing healthy tis- sue. Antibody therapies, an important class of immunotherapies, involve the binding to specific antigens on the surface of the tumour cells of antibodies that activate natural killer (NK) cells to kill the tu- mour cells. Preclinical assessment of molecules that may cause antibody-dependent cellular cytotoxicity (ADCC) involves co-culturing cancer cells, NK cells and antibody in vitro for several hours and measuring subsequent levels of tumour cell lysis. Here we develop a mathematical model of such an in vitro ADCC assay, formulated as a system of time-dependent ordinary differential equations and in which NK cells kill cancer cells at a rate which depends on the amount of antibody bound to each cancer cell. Numerical simulations generated using experimentally-based parameter estimates reveal that the system evolves on two timescales: a fast timescale on which antibodies bind to receptors on the surface of the tumour cells, and NK cells form complexes with the cancer cells, and a longer time-scale on which the NK cells kill the cancer cells. We construct approximate model solutions on each timescale, and show that they are in good agreement with numerical simulations of the full system. Our results show how the processes involved in ADCC change as the initial concentration of antibody and NK-cancer cell ratio are varied. We use these results to explain what information about the tumour cell kill rate can be extracted from the cytotoxicity assays.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4433, + "tag": "BioModels:BIOMD0000000802" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:36.828274+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000802", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3098": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3098, + "name": "Park2019 - IL7 receptor signaling in T cells", + "repository_type": "biomodels", + "summary": "
This model is an attempt to provide a mathematical description of IL-7 dependent T cell homeostasis at the molecular and cellular level, with inclusion of gamma-chain and ligand binding in the context of receptors for IL-7 and IL-15 receptors.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4434, + "tag": "BioModels:BIOMD0000000803" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:37.289683+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000803", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3099": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3099, + "name": "Koenders2015 - multiple myeloma", + "repository_type": "biomodels", + "summary": "The paper describes a model of multiple myeloma. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of cell equilibrium and joint cell formation in multiple myelomaM.A. Koenders, R. Saso Journal of Theoretical Biology 390 (2016) 73\u201379Abstract: In Multiple Myeloma Bone Disease healthy bone remodelling is affected by tumour cells by means of paracrine cytokinetic signalling in such a way that osteoclast formation is enhanced and the growth of osteoblast cells inhibited. The participating cytokines are described in the literature. Osteoclast-induced myeloma cell growth is also reported. Based on existing mathematical models for healthy bone remo- delling a three-way equilibrium model is presented for osteoclasts, osteoblasts and myeloma cell populations to describe the progress of the illness in a scenario in which there is a secular increase in the cytokinetic interactive effectiveness of paracrine processes. The equilibrium state for the system is obtained. The paracrine interactive effectiveness is explored by parameter variation and the stable region in the parameter space is identified. Then recently-discovered joint myeloma\u2013osteoclast cells are added to the model to describe the populations inside lytic lesions. It transpires that their presence expands the available parameter space for stable equilibrium, thus permitting a detrimental, larger population of osteoclasts and myeloma cells. A possible relapse mechanism for the illness is explored by letting joint cells dissociate. The mathematics then permits the evaluation of the evolution of the cell populations as a function of time during relapse.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4435, + "tag": "BioModels:BIOMD0000000804" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:37.748291+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000804", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3100": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3100, + "name": "Al-Husari2013 - pH and lactate in tumor", + "repository_type": "biomodels", + "summary": "The paper describes a model of pH control in tumor. Created by COPASI 4.26 (Build 213) This model is described in the article: Regulation of tumour intracellular pH: A mathematical model examining the interplay between H and lactateMaymona Al-Husari, Steven D. WebbJournal of Theoretical Biology 322 (2013) 58\u201371 Abstract:Non-invasive measurements of pH have shown that both tumour and normal cells have intracellular pH (pHi) that lies on the alkaline side of neutrality (7.1\u20137.2). However, extracellular pH (pHe) is reported to be more acidic in some tumours compared to normal tissues. Many cellular processes and therapeutic agents are known to be tightly pH dependent which makes the study of intracellular pH regulation of paramount importance. We develop a mathematical model that examines the role of various membrane-based ion transporters in tumour pH regulation, in particular, with a focus on the interplay between lactate and H ions and whether the lactate/H symporter activity is sufficient to give rise to the observed reversed pH gradient that is seen is some tumours. Using linear stability analysis and H ions. We extend this analysis using perturbation techniques to specifically examine a rapid change in H-ion concentrations relative to variations in lactate. We then perform a parameter sensitivity analysis to explore solution robustness to parameter variations. An important result from our study is that a reversed pH gradient is possible in our system but for unrealistic parameter estimates\u2014pointing to the possible involvement of other mechanisms in cellular pH gradient reversal, for example acidic vesicles, lysosomes, golgi and endosomes.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4436, + "tag": "BioModels:BIOMD0000000805" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:38.221168+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000805", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3101": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3101, + "name": "Eftimie2019-Macrophages Plasticity", + "repository_type": "biomodels", + "summary": "This paper describes the complex interactions between two extreme types of macrophages (M1 and M2 cells), effector T cells and an oncolytic Vesicular Stomatitis Virus (VSV), on the growth/elimination of B16F10 melanoma. The mathematics model descirbes, in terms of VSV and macrophages levels, two different types of immune responses which could ensure tumour control and eventual elimination. It shows that both innate and adaptive anti-tumour immune responses, as well as the oncolytic virus, could be very important in delaying tumour relapse and eventually eliminating the tumour. Overall this study supports the use mathematical modelling to increase our understanding of the complex immune interaction following oncolytic virotherapies. However, the complexity of the model combined with a lack of sufficient data for model parametrisation has an impact on the possibility of making quantitative predictions. The Model was created using COPASI version 4.24 (Build 197)", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4437, + "tag": "BioModels:BIOMD0000000806" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4438, + "tag": "Skin cancer" + }, + { + "id": 4439, + "tag": "Vesicular stomatitis virus" + } + ], + "timestamp_created": "2025-02-03 16:45:39.420893+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000806", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3102": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3102, + "name": "Fassoni2019 - Oncogenesis encompassing mutations and genetic instability", + "repository_type": "biomodels", + "summary": "This model describes the multistep process that transform a normal cell and its descendants into a malignant tumour by considering three populations: normal, premalignant and cancer cells. Created by COPASI 4.24(Build 197)Abstract:Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: self-sufficiency on growth signals, insensibility to anti-growth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. We find that the rates of apoptosis and mutations have a large influence on the pace of tumour progression and on the time it takes to become clinically detectable.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4440, + "tag": "BioModels:BIOMD0000000807" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:40.325020+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000807", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3103": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3103, + "name": "Kronik2008 - Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics", + "repository_type": "biomodels", + "summary": "
This mathematical model describes interactions between glioma tumors and the immune system that may occur following direct intra-tumoral administration of ex-vivo activated alloreactive cytotoxic-T-lymphocytes (aCTLs) as part of adoptive immunotherapy. The model includes descriptions of aCTL, neoplastic cells, MHC class I and II molecules, TGF-beta and IFN-gamma.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4441, + "tag": "BioModels:BIOMD0000000808" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:40.923084+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000808", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3104": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3104, + "name": "Malinzi2018 - tumour-immune interaction model", + "repository_type": "biomodels", + "summary": "The paper describes a spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Created by COPASI 4.24 (Build 197)Abstract:A spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Analysis of the model is carried out for both temporal and spatio-temporal cases. Stability analysis and numerical simulations of the temporal model replicate experimental observations of immune-induced tumour dormancy. Travelling wave solutions of the spatio-temporal model are determined using the hyperbolic tangent method and minimum wave speeds of invasion are calculated. Travelling wave analysis depicts that cell invasion dynamics are mainly driven by their motion and growth rates. A stability analysis of the spatio-temporal model shows a possibility of dynamical stabilization of the tumour-free steady state. Simulation results reveal that the tumour swells to a dormant level.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4442, + "tag": "BioModels:BIOMD0000000809" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:41.453777+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000809", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3105": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3105, + "name": "Ganguli2018-immuno regulatory mechanisms in tumor microenvironment", + "repository_type": "biomodels", + "summary": "This model describes the concept of Cancer Stem Cells(CSC) differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Created by COPASI 4.24(Build197)Abstract:The tumor microenvironment comprising of the immune cells and cytokines acts as the 'soil' that nourishes a developing tumor. Lack of a comprehensive study of the interactions of this tumor microenvironment with the heterogeneous sub-population of tumor cells that arise from the differentiation of Cancer Stem Cells (CSC), i.e. the 'seed', has limited our understanding of the development of drug resistance and treatment failures in Cancer. Based on this seed and soil hypothesis, for the very first time, we have captured the concept of CSC differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Using this model we report that as the CSC differentiation shifts from symmetric to asymmetric pattern, resistant cancer cells start accumulating in the tumor that makes it refractory to therapeutic interventions. Model analyses unveiled the presence of feedback loops that establish the dual role of M2 macrophages in regulating tumor proliferation. The study further revealed oscillations in the tumor sub-populations in the presence of TH1 derived IFN-\u03b3 that eliminates CSC; and the role of IL10 feedback in the regulation of TH1/TH2 ratio. These analyses expose important observations that are indicative of Cancer prognosis. Further, the model has been used for testing known treatment protocols to explore the reasons of failure of conventional treatment strategies and propose an improvised protocol that shows promising results in suppressing the proliferation of all the cellular sub-populations of the tumor and restoring a healthy TH1/TH2 ratio that assures better Cancer remission.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4443, + "tag": "BioModels:BIOMD0000000810" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4444, + "tag": "breast cancer" + } + ], + "timestamp_created": "2025-02-03 16:45:41.979007+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000810", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3106": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3106, + "name": "He2017 - A mathematical model of pancreatic cancer with two kinds of treatments", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of pancreatic cancer which includes descriptions of regulatory T cell activity and inhibition therapy. Descriptions of cytokine induced killer immunotherapy are also included.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4445, + "tag": "BioModels:BIOMD0000000811" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:42.461442+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000811", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3107": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3107, + "name": "Galante2012 - B7-H1 and a Mathematical Model for Cytotoxic T Cell and Tumor Cell Interaction", + "repository_type": "biomodels", + "summary": "
This is a mathematical model that describes the interactions between cytotoxic T cells and tumor cells as influenced by B7-H1 (PD-L1) activity, with a focus on how B7-H1 affects cancer cells apoptosis.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4446, + "tag": "BioModels:BIOMD0000000812" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:42.956088+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000812", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3108": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3108, + "name": "Anderson2015 - Qualitative behavior of systems of tumor-CD4+-cytokine interactions with treatments", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing tumor-CD4+-cytokine interactions, with specific emphasis on the role that CD4+ T lymphocytes play in tumor regression.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4447, + "tag": "BioModels:BIOMD0000000813" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:43.409594+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000813", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3109": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3109, + "name": "Perez-Garcia19 - Computational design of improved standardized chemotherapy protocols for grade 2 oligodendrogliomas", + "repository_type": "biomodels", + "summary": "This is a model built by COPASI4.24(Build 197)This a model from the article: Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomasV\u00edctor M. P\u00e9rez-Garc\u00eda, Luis E. Ayala-Hern\u00e1ndez, Juan Belmonte-Beitia, Philippe Schucht, Michael Murek, Andreas Raabe, Juan Sep\u00falveda. PLoS Comput Biol. 2019 Jul; 15(7): e1006778.Abstract: Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model describes the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutic strategies were tried computationally on the \u2018in-silico twins\u2019 of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large trial of 2000 virtual patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances. On the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. We used 220 sets of experiments in-silico to show that a clinical trial incorporating 100 patients per arm (standard intensive treatment versus 5 + 12 scheme) could demonstrate the superiority of the novel scheme after a follow-up period of 10 years. Thus, the proposed treatment plan could be the basis for a standardized TMZ treatment for LGO patients with survival benefits.This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4448, + "tag": "BioModels:BIOMD0000000814" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:43.883407+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000814", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3110": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3110, + "name": "Chrobak2011 - A mathematical model of induced cancer-adaptive immune system competition", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing competition between an artificially induced tumor and the adaptive immune system based on the use of an autonomous system of ordinary differential equations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4449, + "tag": "BioModels:BIOMD0000000815" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:44.458250+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000815", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3111": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3111, + "name": "Gevertz2018 - Cancer Treatment with Oncolytic Viruses and Dendritic Cell injections original model", + "repository_type": "biomodels", + "summary": "The model is based on 'Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections', PMID:30510594. Author:Jana L.Gevertz and Joanna R.Wares. This model describes the original mathematical model described in section 2.1. Built by COPASI 4.24( Build 197)Abstract:Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4450, + "tag": "BioModels:BIOMD0000000816" + }, + { + "id": 4451, + "tag": "Melanoma" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:44.957275+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000816", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3112": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3112, + "name": "Gevertz2018 - cancer treatment with oncolytic viruses and dendritic cell injections minimal model", + "repository_type": "biomodels", + "summary": "The model is based on 'Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections', PMID:30510594. Author:Jana L.Gevertz and Joanna R.Wares. This model describes the minimal model described in section 2.1. Built by COPASI 4.24( Build 197)Abstract:Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4452, + "tag": "BioModels:BIOMD0000000817" + }, + { + "id": 4451, + "tag": "Melanoma" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:45.435680+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000817", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3113": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3113, + "name": "Lee2008 - ERK and PI3K signal integration by Myc", + "repository_type": "biomodels", + "summary": "
Mechanisitc model of PI3K and ERK signal integration by Myc. ERK and PI3K regulated Myc satbility by phosphorylating the same. (PMID:18463697)
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4453, + "tag": "BioModels:BIOMD0000000818" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:45.918154+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000818", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3114": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3114, + "name": "Nazari2018 - IL6 mediated stem cell driven tumor growth and targeted treatment", + "repository_type": "biomodels", + "summary": "This a model from the article: A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatmentFereshteh Nazari, Alexander T. Pearson, Jacques Eduardo Nor, Trachette L. Jackson. PloS Computational Biology, 2018 Jan.Abstract:Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth.Author Summary:A small population of cancer stem cells that share many of the biological characteristics of normal adult stem cells are believed to initiate and sustain tumor growth for a wide variety of malignancies. Growth and survival of these cancer stem cells is highly influenced by tumor micro-environmental factors and molecular signaling initiated by cytokines and growth factors. This work focuses on quantifying the influence of IL-6, a pleiotropic cytokine secreted by a variety of cell types, on cancer stem cell self-renewal and survival. We present a mathematical model for IL-6 mediated, cancer stem cell driven tumor growth that operates at the following levels: (1) the molecular level\u2014capturing cell surface dynamics of receptor-ligand binding and receptor activation that lead to intra-cellular signal transduction cascades; and (2) the cellular level\u2014describing tumor growth, cellular composition, and response to treatments targeted against IL-6.This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4454, + "tag": "BioModels:BIOMD0000000819" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:46.418399+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000819", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3115": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3115, + "name": "West2019 - Cellular interactions constrain tumor growth", + "repository_type": "biomodels", + "summary": "These selections of models are described in the paper:Cellular interactions constrain tumor growthby Jeffrey West and Paul K. NewtonSignificance:A mathematical model relating tumor heterogeneity at the cellular level to tumor growth at the macroscopic level is described based on a statistical mechanics framework. The model takes into account the number of accessible states available to each cell as well as their long-range coupling (population cooperation) to other cells. We show that the degree to which cell populations cooperate determines the number of independent cell states, which in turn dictates the macroscopic (volumetric) growth law. It follows that targeting cell-to-cell interactions or functional coupling among cell populations could be a way of mitigating and controlling tumor growth.Abstract:A tumor is made up of a heterogeneous collection of cell types, all competing on a fitness landscape mediated by microenvironmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling, cellular cooperation, and the functional constraints that affect cellular behavior, the specifics of how these constraints (and the range over which they act) affect the macroscopic tumor growth laws that govern total volume, mass, and carrying capacity remain poorly understood. We develop a statistical mechanics approach that focuses on the total number of possible states each cell can occupy and show how different assumptions on correlations of these states give rise to the many different macroscopic tumor growth laws used in the literature. Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of states at the cellular level is what determines macroscopic growth characteristics.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4455, + "tag": "BioModels:BIOMD0000000820" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:46.880214+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000820", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3116": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3116, + "name": "Yazdjer2019 - reinforcement learning-based control of tumor growth under anti-angiogenic therapy", + "repository_type": "biomodels", + "summary": "This model is based on:Reinforcement learning-based control of tumor growth under anti-angiogenic therapyAuthors: Parisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi, Ala-Eddin Al Moustafa, Levente KovacsAbstract:Background and objectives: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration.Methods: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient.Results: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to \u202f\u00b1\u202f20% of tumor growth parameters changes.Conclusion: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4456, + "tag": "BioModels:BIOMD0000000821" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:47.353733+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000821", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3117": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3117, + "name": "Dorvash2019 - Dynamic modeling of signal transduction by mTOR complexes in cancer", + "repository_type": "biomodels", + "summary": "This model is based on:Dynamic modeling of signal transduction by mTOR complexes in cancerAuthor:Mohammadreza Dorvash, Mohammad Farahmandnia, Pouria Mosaddeghi, Mitra Farahmandnejad, Hosein Saber, Mohammadhossein Khorraminejad-Shirazi, Amir Azadi, Iman TavassolyAbstract:Signal integration has a crucial role in the cell fate decision and dysregulation of the cellular signaling pathways is a primary characteristic of cancer. As a signal integrator, mTOR shows a complex dynamical behavior which determines the cell fate at different cellular processes levels, including cell cycle progression, cell survival, cell death, metabolic reprogramming, and aging. The dynamics of the complex responses to rapamycin in cancer cells have been attributed to its differential time-dependent inhibitory effects on mTORC1 and mTORC2, the two main complexes of mTOR. Two explanations were previously provided for this phenomenon: 1-Rapamycin does not inhibit mTORC2 directly, whereas it prevents mTORC2 formation by sequestering free mTOR protein (Le Chatelier\u2019s principle). 2-Components like Phosphatidic Acid (PA) further stabilize mTORC2 compared with mTORC1. To understand the mechanism by which rapamycin differentially inhibits the mTOR complexes in the cancer cells, we present a mathematical model of rapamycin mode of action based on the first explanation, i.e., Le Chatelier\u2019s principle. Translating the interactions among components of mTORC1 and mTORC2 into a mathematical model revealed the dynamics of rapamycin action in different doses and time-intervals of rapamycin treatment. This model shows that rapamycin has stronger effects on mTORC1 compared with mTORC2, simply due to its direct interaction with free mTOR and mTORC1, but not mTORC2, without the need to consider other components that might further stabilize mTORC2. Based on our results, even when mTORC2 is less stable compared with mTORC1, it can be less inhibited by rapamycin.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4457, + "tag": "BioModels:BIOMD0000000822" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:47.854864+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000822", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3118": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3118, + "name": "Varusai2018 - Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR", + "repository_type": "biomodels", + "summary": "
This is a mathematical describing the effect that DEP domain-containing mTOR-interacting protein (DEPTOR) has on the mammalian target of rapamycin (mTOR) signalling network.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4458, + "tag": "BioModels:BIOMD0000000823" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:48.462870+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000823", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3119": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3119, + "name": "Lewkiewics2019 - effects of aging on naive T cell populations and diversity", + "repository_type": "biomodels", + "summary": "This model is built by COPASI 4.24(Build197), based on paper:A mathematical model of the effects of aging on naive T-cell population and diversityAuthors:Stephanie Lewkiewicz, Yao-li Chuang, Tom ChouAbstract:The human adaptive immune response is known to weaken in advanced age, resulting in increased severity of pathogen-born illness, poor vaccine efficacy, and a higher prevalence of cancer in the elderly. Age-related erosion of the T cell compartment has been implicated as a likely cause, but the underlying mechanisms driving this immunosenescence have not been quantitatively modeled and systematically analyzed. T cell receptor diversity, or the extent of pathogen-derived antigen responsiveness of the T cell pool, is known to diminish with age, but inherent experimental difficulties preclude accurate analysis on the full organismal level. In this paper, we formulate a mechanistic mathematical model of T cell population dynamics on the immunoclonal subpopulation level, which provides quantitative estimates of diversity. We define different estimates for diversity that depend on the individual number of cells in a specific immunoclone. We show that diversity decreases with age primarily due to diminished thymic output of new T cells and the resulting overall loss of small immunoclones.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4459, + "tag": "BioModels:BIOMD0000000824" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4460, + "tag": "Thymus" + } + ], + "timestamp_created": "2025-02-03 16:45:48.927134+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000824", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3120": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3120, + "name": "Greene2019 - Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment", + "repository_type": "biomodels", + "summary": "This model is built by COPASI 4.24(Build 197), based on paper:Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment.Author:James M. Greene, Jana L. Gevertz, Eduardo D. sontagAbstract:PURPOSE:Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols. MATERIALS AND METHODS:To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance. RESULTS:Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment's propensity to induce resistance.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4461, + "tag": "BioModels:BIOMD0000000825" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:49.488258+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000825", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3121": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3121, + "name": "Shin_2018_EGFR-PYK2-c-Met interaction network_model", + "repository_type": "biomodels", + "summary": "
Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4462, + "tag": "BioModels:BIOMD0000000826" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:49.962286+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000826", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3122": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3122, + "name": "Ito2019 - gefitnib resistance of lung adenocarcinoma caused by MET amplification", + "repository_type": "biomodels", + "summary": "The model is based on publication:Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplificationAbstract:Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5\u201322% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. Limited contribution of ErbB3 in gefitinib resistance by MET amplification is also demonstrated using HCC827-GR cells in culture experiments. Our mathematical model provides a quantitative understanding of the molecular reactions underlying drug resistance.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4463, + "tag": "BioModels:BIOMD0000000827" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:50.430332+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000827", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3123": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3123, + "name": "Jung2019 - Regulating glioblastoma signaling pathways and anti-invasion therapy - core control model", + "repository_type": "biomodels", + "summary": "This model is based on paper:Strategies in regulating glioblastoma signaling pathways and anti-invasion therapyAbstract:Glioblastoma multiforme is one of the most invasive type of glial tumors, which rapidly grows and commonly spreads into nearby brain tissue. It is a devastating brain cancer that often results in death within approximately 12 to 15 months after diagnosis. In this work, optimal control theory was applied to regulate intracellular signaling pathways of miR-451\u2013AMPK\u2013mTOR\u2013cell cycle dynamics via glucose and drug intravenous administration infusions. Glucose level is controlled to activate miR-451 in the up-stream pathway of the model. A potential drug blocking the inhibitory pathway of mTOR by AMPK complex is incorporated to explore regulation of the down-stream pathway to the cell cycle. Both miR-451 and mTOR levels are up-regulated inducing cell proliferation and reducing invasion in the neighboring tissues. Concomitant and alternating glucose and drug infusions are explored under various circumstances to predict best clinical outcomes with least administration costs.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4464, + "tag": "BioModels:BIOMD0000000828" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:50.929018+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000828", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3124": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3124, + "name": "Jung2019 - egulating glioblastoma signaling pathways and anti-invasion therapy cell cycle dynamics model", + "repository_type": "biomodels", + "summary": "This model is based on paper, based on its cell cycle dynamics model:Strategies in regulating glioblastoma signaling pathways and anti-invasion therapyAbstract:Glioblastoma multiforme is one of the most invasive type of glial tumors, which rapidly grows and commonly spreads into nearby brain tissue. It is a devastating brain cancer that often results in death within approximately 12 to 15 months after diagnosis. In this work, optimal control theory was applied to regulate intracellular signaling pathways of miR-451\u2013AMPK\u2013mTOR\u2013cell cycle dynamics via glucose and drug intravenous administration infusions. Glucose level is controlled to activate miR-451 in the up-stream pathway of the model. A potential drug blocking the inhibitory pathway of mTOR by AMPK complex is incorporated to explore regulation of the down-stream pathway to the cell cycle. Both miR-451 and mTOR levels are up-regulated inducing cell proliferation and reducing invasion in the neighboring tissues. Concomitant and alternating glucose and drug infusions are explored under various circumstances to predict best clinical outcomes with least administration costs.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4465, + "tag": "BioModels:BIOMD0000000829" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:51.471567+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000829", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3125": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3125, + "name": "GiantsosAdams2013 - Growth of glycocalyx under static conditions", + "repository_type": "biomodels", + "summary": "
Giantsos-Adams2013 - Growth of glycocalyxunder static conditions

This model is described in the article:

Giantsos-Adams KM, Koo AJ, Song S, Sakai J, Sankaran J, Shin JH, Garcia-Cardena G, Dewey CF.
Cell Mol Bioeng 2013 Jun; 6(2): 160-174

Abstract:

The local hemodynamic shear stress waveforms present in an artery dictate the endothelial cell phenotype. The observed decrease of the apical glycocalyx layer on the endothelium in atheroprone regions of the circulation suggests that the glycocalyx may have a central role in determining atherosclerotic plaque formation. However, the kinetics for the cells' ability to adapt its glycocalyx to the environment have not been quantitatively resolved. Here we report that the heparan sulfate component of the glycocalyx of HUVECs increases by 1.4-fold following the onset of high shear stress, compared to static cultured cells, with a time constant of 19\u00a0h. Cell morphology experiments show that 12\u00a0h are required for the cells to elongate, but only after 36\u00a0h have the cells reached maximal alignment to the flow vector. Our findings demonstrate that following enzymatic degradation, heparan sulfate is restored to the cell surface within 12\u00a0h under flow whereas the time required is 20\u00a0h under static conditions. We also propose a model describing the contribution of endocytosis and exocytosis to apical heparan sulfate expression. The change in HS regrowth kinetics from static to high-shear EC phenotype implies a differential in the rate of endocytic and exocytic membrane turnover.

This model is hosted on BioModels Database and identified by: MODEL1609100001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4466, + "tag": "BioModels:BIOMD0000000830" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:52.026535+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000830", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3126": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3126, + "name": "Smith1980 - Hypothalamic Regulation", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Hypothalamic regulation of pituitary secretion of luteinizing hormone.II. Feedback control of gonadotropin secretion.
Smith WR Bull Math Biol. (1980) 42(1): 57-78 6986927 ,
Abstract:
No Abstract Available

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: smith,1980,version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4467, + "tag": "BioModels:BIOMD0000000831" + }, + { + "id": 3309, + "tag": "Bos taurus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:52.527214+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000831", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3127": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3127, + "name": "Shin2016 - Unveiling Hidden Dynamics of Hippo Signalling", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing Hippo signalling pathway activity. It includes descriptions of crosstalk with the Akt and ERK MAPK pathways; crosstalk activity is described using complex regulatory mechanisms comprised of competitive protein-protein interactions and phosphorylation mediated feedback loops.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4468, + "tag": "BioModels:BIOMD0000000832" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:53.029738+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000832", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3128": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3128, + "name": "DiCamillo2016 - Insulin signalling pathway - Rule-based model", + "repository_type": "biomodels", + "summary": "Barbara Di Camillo, Azzurra Carlon, Federica Eduati & Gianna Maria Toffolo. A rule-based model of insulin signalling pathway. BMC Systems Biology 10, 1 (2016).

The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as \"combinatorial complexity\", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4469, + "tag": "BioModels:BIOMD0000000833" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:53.502952+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000833", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3129": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3129, + "name": "Verma2016 - Ca(2+) Signal Propagation Along Hepatocyte Cords", + "repository_type": "biomodels", + "summary": "
Verma2016 - Ca(2+) Signal Propagation Along Hepatocyte Cords

This model is described in the article:

Verma A, Makadia H, Hoek JB, Ogunnaike BA, Vadigepalli R.
IEEE Trans Biomed Eng 2016 Oct; 63(10): 2047-2055

Abstract:

The purpose of this study is to model the dynamics of lobular Ca(2+) wave propagation induced by an extracellular stimulus, and to analyze the effect of spatially systematic variations in cell-intrinsic signaling parameters on sinusoidal Ca(2+) response.We developed a computational model of lobular scale Ca(2+) signaling that accounts for receptor- mediated initiation of cell-intrinsic Ca(2+) signal in hepatocytes and its propagation to neighboring hepatocytes through gap junction-mediated molecular exchange.Analysis of the simulations showed that a pericentral-to-periportal spatial gradient in hormone sensitivity and/or rates of IP3 synthesis underlies the Ca(2+) wave propagation. We simulated specific cases corresponding to localized disruptions in the graded pattern of these parameters along a hepatic sinusoid. Simulations incorporating locally altered parameters exhibited Ca(2+) waves that do not propagate throughout the hepatic plate. Increased gap junction coupling restored normal Ca(2+) wave propagation when hepatocytes with low Ca(2+) signaling ability were localized in the midlobular or the pericentral region.Multiple spatial patterns in intracellular signaling parameters can lead to Ca(2+) wave propagation that is consistent with the experimentally observed spatial patterns of Ca(2+) dynamics. Based on simulations and analysis, we predict that increased gap junction-mediated intercellular coupling can induce robust Ca(2+) signals in otherwise poorly responsive hepatocytes, at least partly restoring the sinusoidally oriented Ca (2+) waves.Our bottom-up model of agonist-evoked spatial Ca(2+) patterns can be integrated with detailed descriptions of liver histology to study Ca(2+) regulation at the tissue level.

This model is hosted on BioModels Database and identified by: MODEL1603110003.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4470, + "tag": "BioModels:BIOMD0000000834" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:53.974997+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000834", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3130": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3130, + "name": "Rao2014 - Fatty acid beta-oxidation (reduced model)", + "repository_type": "biomodels", + "summary": "
This represents the reduced version of the \"time course model\" of Van Eunen et al (2013): Biochemical competition makes fatty-acid beta-oxidation vulnerable to substrate overload. The SBML was created from that of the original model and produces identical results when a time-course of 25 mins is run in COPASI
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4471, + "tag": "BioModels:BIOMD0000000835" + }, + { + "id": 4472, + "tag": "Fatty acid beta-oxidation" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:54.434033+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000835", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3131": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3131, + "name": "Radosavljevic2009_BioterroristAttack_PanicProtection_1", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Epidemics of panic during a bioterrorist attack--a mathematical model.
Radosavljevic V, Radunovic D, Belojevic G. Med Hypotheses 2009 Sep;73(3):342-6 19423234 ,
Abstract:
A bioterrorist attacks usually cause epidemics of panic in a targeted population. We have presented epidemiologic aspect of this phenomenon as a three-component model--host, information on an attack and social network. We have proposed a mathematical model of panic and counter-measures as the function of time in a population exposed to a bioterrorist attack. The model comprises ordinary differential equations and graphically presented combinations of the equations parameters. Clinically, we have presented a model through a sequence of psychic conditions and disorders initiated by an act of bioterrorism. This model might be helpful for an attacked community to timely and properly apply counter-measures and to minimize human mental suffering during a bioterrorist attack.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4473, + "tag": "BioModels:BIOMD0000000836" + }, + { + "id": 4474, + "tag": "Fear response" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4475, + "tag": "Panic disorder" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:54.916525+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000836", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3132": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3132, + "name": "Hanson2016 - Toxicity Management in CAR T cell therapy for B-ALL", + "repository_type": "biomodels", + "summary": "
This model provides an in silico mathematical platform to explore the interactions between chimeric antigen receptor-modified T cells, inflammatory toxicitiy, and the tumour burdens of individual patients.
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This is a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, used to predict the response of melanoma patients to immune checkpoint inhibitors.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4477, + "tag": "BioModels:BIOMD0000000838" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:55.883550+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000838", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3134": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3134, + "name": "Almeida2019 - Transcription-based circadian mechanism controls the duration of molecular clock states in response to signaling inputs", + "repository_type": "biomodels", + "summary": "
This is a transcriptional-based mathematical model centered on linear combinations of the clock controlled elements (CCEs): E-box, R-box and D-box, used to identify the essential interactions needed to generate phase opposition between the activating CLOCK:BMAL1 and the repressing PER:CRY complexes.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4478, + "tag": "BioModels:BIOMD0000000839" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:56.356372+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000839", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3135": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3135, + "name": "Caldwell2019 - The Vicodin abuse problem", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of Vicodin use and abuse used to investigate methods of combating Vicodin abuse in a population of patients who have obtained the drug through prescription. Mathematical descriptions of transitions through acute, chronic, abusive, and in-treatment populations are included.
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This is a delay differential equation model showing how non-coding RNA, acting as microRNA (miRNA) sponges in a conserved RNA-transcription factor feedback motif, can five rise to oscillatory behaviour.
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This model is from the article:
Competing G protein-coupled receptor kinases balance G protein and \u03b2-arrestin signaling
Heitzler D, Durand G, Gallay N, Rizk A, Ahn S, Kim J, Violin JD, Dupuy L, Gauthier C, Piketty V, Cr\u00e9pieux P, Poupon A, Cl\u00e9ment F, Fages F, Lefkowitz RJ, Reiter E. Mol Syst Biol. 2012; 8: 590. 22735336 ,
Abstract:
Seven-transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through \u03b2-arrestins, whose recruitment to the activated receptor is regulated by G protein-coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal-regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT(1A)R) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)-based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well-established function in the desensitization of G-protein activation, GRK2 exerts a strong negative effect on \u03b2-arrestin-dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2-dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT(1A)R, and HEK293 cells expressing other 7TMRs.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4481, + "tag": "BioModels:BIOMD0000000842" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:57.776543+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000842", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3138": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3138, + "name": "Dudziuk2019 - Biologically sound formal model of Hsp70 heat induction", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of Hsp70 induction. To model heat shock effects, the model incorporates temperature dependencies in transcirption to Hsp70 mRNA and in dissociation of transcriptional complexes, in addition to a formal expression relating temperature to protein denaturation.
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This is a mathematical conductance-based model of the bursting activity in external tufted (ET) cells of the olfactory bulb. The model includes ion-current based descriptions of the mechanisms underlying bursting in ET cells, with facilitation of blocking various currents to characterise bursting behaviour.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4483, + "tag": "BioModels:BIOMD0000000844" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:58.736088+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000844", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3140": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3140, + "name": "Gulbudak2019.1 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Lytic)", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing describing the population dynamics of microbes infected by lytic viruses.
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This is a mathematical model describing describing the population dynamics of microbes infected by chronically infecting viruses.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4485, + "tag": "BioModels:BIOMD0000000846" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:45:59.672029+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000846", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3142": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3142, + "name": "Adams2019 - The regulatory role of shikimate in plant phenylalanine metabolism", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of phenylalanine metabolism in plants as influenced by shikimate, with specific evidence of how shikimate dynamics influence phenylalanine metabolism as a function of phenylalanine availability.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4486, + "tag": "BioModels:BIOMD0000000847" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:00.128170+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000847", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3143": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3143, + "name": "FatehiChenar2018 - Mathematical model of immune response to hepatitis B", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the dynamics of the immune response to hepatitis B, which takes into account contributions form innate and adaptive immune responses, as well as cytokines.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4487, + "tag": "BioModels:BIOMD0000000848" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:00.618971+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000848", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3144": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3144, + "name": "Potassium balance in lactating and non-lactating dairy cows", + "repository_type": "biomodels", + "summary": "M. Berg, J. Pl\u00f6ntzke, S. Leonhard-Marek, K.E. M\u00fcller & S. R\u00f6blitz. A dynamic model to simulate potassium balance in dairy cows. Journal of Dairy Science 100, 12 (2017).

High-performing dairy cows require a particular composition of nutritional ingredients, adapted to their individual requirements and depending on their production status. The optimal dimensioning of minerals in the diet, one being potassium, is indispensable for the prevention of imbalances. Potassium balance in cows is the result of potassium intake, distribution in the organism, and excretion, and it is closely related to glucose and electrolyte metabolism. In this paper, we present a dynamical model for potassium balance in lactating and nonlactating dairy cows based on ordinary differential equations. Parameter values were obtained from clinical trial data and from the literature. To verify the consistency of the model, we present simulation outcomes for 3 different scenarios: potassium balance in (1) nonlactating cows with varying feed intake, (2) nonlactating cows with varying potassium fraction in the diet, and (3) lactating cows with varying milk production levels. The results give insights into the short- and long-term potassium metabolism, providing an important step toward the understanding of the potassium network, the design of prophylactic feed additives, and possible treatment strategies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4488, + "tag": "BioModels:BIOMD0000000849" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:01.130192+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000849", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3145": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3145, + "name": "Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law", + "repository_type": "biomodels", + "summary": "
This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4489, + "tag": "BioModels:BIOMD0000000850" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:01.585190+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000850", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3146": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3146, + "name": "Ho2019 - Mathematical models of transmission dynamics and vaccine strategies in Hong Kong during the 2017-2018 winter influenza season (Simple)", + "repository_type": "biomodels", + "summary": "
This is the simple version of the two mathematical models presented by Ho et al. It is a model comprised of simple ordinary differential equations describing the overall epidemic dynamics of influenza infection in the 2017-2018 winter influenza season in Hong Kong.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4490, + "tag": "BioModels:BIOMD0000000851" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:02.092035+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000851", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3147": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3147, + "name": "Andersen2017 - Mathematical modelling as a proof of concept for MPNs as a human inflammation model for cancer development", + "repository_type": "biomodels", + "summary": "
This is a mathematical model investigating the role of chronic inflammation in the development and progression of myeloproliferative neoplasms (MPNs). The model describes the proliferation from stem cells to mature cells, including mutations of healthy stem cells to become malignant stem cells. The model also features a simple inflammatory coupling coping with cell death and affecting the basic model beneath.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4491, + "tag": "BioModels:BIOMD0000000852" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:02.657286+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000852", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3148": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3148, + "name": "Smolen2018 - Paradoxical LTP maintenance with inhibition of protein synthesis and the proteasome", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the formation of long-term potentiation (LTP) at the Schaffer collateral of CA1 pyramidal cell synapse. The model consists of nine ordinary differential equations that denote synaptic states associated with different degrees of LTP, as well as the levels of regulatory proteins involved in the formation and maintenance of LTP.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4492, + "tag": "BioModels:BIOMD0000000853" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:03.540870+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000853", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3149": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3149, + "name": "Gray2016 - The Akt switch model", + "repository_type": "biomodels", + "summary": "
This is a simple, linear, four-compartment ordinary differential equation (ODE) model Akt activation that tracks both the phosphorylation state and the physical location (cytosol, plasma membrane) of Akt.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4493, + "tag": "BioModels:BIOMD0000000854" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:04.066983+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000854", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3150": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3150, + "name": "Cooper2015 - Modeling the effects of systemic mediators on the inflammatory phase of wound healing", + "repository_type": "biomodels", + "summary": "
This is an ordinary differential equation-based mathematical model describing the inflammatory phase of the wound healing response. The model describes the interactions in the wound between wound debris, pathogens, neutrophils and macrophages, as well as the modulation of these interactions by estrogen and cortisol.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4494, + "tag": "BioModels:BIOMD0000000855" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:04.520703+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000855", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3151": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3151, + "name": "Budding yeast size control by titration of nuclear sites", + "repository_type": "biomodels", + "summary": "
This model is decribed in the article:Dilution and titration of cell-cycle regulators may control cell size in budding yeastFrank S. Heldt, Reece Lunstone, John J. Tyson, Bela NovakPLoS Comput Biol, October 2018, 14(10), e1006548, doi: 10.1371/journal.pcbi.1006548Abstract:The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the START transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cell\u2019s overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect \u2018sizer\u2019 behaviour in G1 and a \u2018timer\u2019 in S/G2/M, which combine to yield an \u2018adder\u2019 over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.
", + "tags": [ + { + "id": 4495, + "tag": "\nSaccharomyces cerevisiae" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4496, + "tag": "BioModels:BIOMD0000000856" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:05.214632+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000856", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3152": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3152, + "name": "Larbat2016.1 - Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light or dark conditions (Base Model)", + "repository_type": "biomodels", + "summary": "
This is a global mathematical model describing metabolic partitioning of carbon resources in plants between growth and defense, as a function of nitrate fertilization. The model hinges on the dynamics of sucrose inflow/outflow properties.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4497, + "tag": "BioModels:BIOMD0000000857" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:05.772638+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000857", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3153": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3153, + "name": "Larbat2016.2 - Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light or dark conditions (Light Dark Cycles)", + "repository_type": "biomodels", + "summary": "
This is a global mathematical model describing metabolic partitioning of carbon resources in plants between growth and defense, as a function of nitrate fertilization. The model hinges on the dynamics of sucrose inflow/outflow properties. The model also accounts for variations in photosynthetic activity as dictated by a daily light/dark cycle.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4498, + "tag": "BioModels:BIOMD0000000858" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:06.244730+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000858", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3154": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3154, + "name": "Larbat2016.3 - Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light or dark conditions (Light Dark Cycles with Minimum Starch Adaption)", + "repository_type": "biomodels", + "summary": "
This is a global mathematical model describing metabolic partitioning of carbon resources in plants between growth and defense, as a function of nitrate fertilization. The model hinges on the dynamics of sucrose inflow/outflow properties. The model also accounts for variations in photosynthetic activity as dictated by a daily light/dark cycle. Furthermore, the adaptive changes in starch metabolism that occur with changing light durations (short days vs long days) are also investigated.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4499, + "tag": "BioModels:BIOMD0000000859" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:06.714736+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000859", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3155": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3155, + "name": "Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedforward Incoherent By MicroRNA)_1", + "repository_type": "biomodels", + "summary": "
Proctor2017- Role of microRNAs inosteoarthritis (Positive Feedforward Incoherent By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100004.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4500, + "tag": "BioModels:BIOMD0000000860" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:07.180652+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000860", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3156": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3156, + "name": "Bachmann2011 - Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range", + "repository_type": "biomodels", + "summary": "
This is a dynamic pathway model examining the roles of of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK/STAT5 signaling, within the context of primary erythroid progenitor cells.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4501, + "tag": "BioModels:BIOMD0000000861" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:07.642443+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000861", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3157": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3157, + "name": "Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedback By Micro RNA)", + "repository_type": "biomodels", + "summary": "
Proctor2017- Role of microRNAs inosteoarthritis (Positive Feedback By Micro RNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4502, + "tag": "BioModels:BIOMD0000000862" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:08.112636+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000862", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3158": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3158, + "name": "Kosinsky2018 - Radiation and PD-(L)1 treatment combinations", + "repository_type": "biomodels", + "summary": "This is a quantitative systems pharmacology (QSP) model that describes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features. The model describes the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation under immunotherapeutic and radiological treatments.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4503, + "tag": "BioModels:BIOMD0000000863" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:08.582297+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000863", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3159": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3159, + "name": "Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA)", + "repository_type": "biomodels", + "summary": "
Proctor2017- Role of microRNAs inosteoarthritis (Negative Feedback By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100001.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4504, + "tag": "BioModels:BIOMD0000000864" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:09.057632+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000864", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3160": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3160, + "name": "Nikolaev2019 - Immunobiochemical reconstruction of influenza lung infection-melanoma skin cancer interactions", + "repository_type": "biomodels", + "summary": "
This is a mathematical mechanistic immunobiochemical model that incorporates T cell pathways that control programmed cell death protein 1 (PD-1) expression. A core component of the model is a kinetic motif, termed a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), which reflects known interactions between IRF4, Blimp-1, and Bcl-6.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4505, + "tag": "BioModels:BIOMD0000000865" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:09.556190+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000865", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3161": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3161, + "name": "Simon2019 - NIK-dependent p100 processing into p52, Michaelis-Menten, SBML 2v4", + "repository_type": "biomodels", + "summary": "

This model represents NIK-dependent p100 processing into p52 with Michaelis-Menten kinetics. While this model shows identical dose-response to the mass action representation, when IkBd degradation is included the dose-response is no-longer monotonic in mass action models due to substrate complex competition.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4506, + "tag": "BioModels:BIOMD0000000866" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:10.138032+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000866", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3162": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3162, + "name": "Coulibaly2019 - Interleukin-15 Signaling in HIF-1a Regulation in Natural Killer Cells", + "repository_type": "biomodels", + "summary": "
This is a mathematical model comprised of non-linear ordinary differential equations describing the dynamic relationship between hypoxia-inducible factor-1 alpha (HIF-1a) mRNA, HIF-1a protein, and interleukin-15-mediated upstream signalling events in natural killer cells from human blood. Regulatory expressions are also included for mammalian target of rapamycin (mTOR), nuclear factor-kappa beta, and signal transducer and activator of transcription 3 (STAT3).
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4507, + "tag": "BioModels:BIOMD0000000867" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:10.749766+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000867", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3163": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3163, + "name": "Simon2019 - NIK-dependent p100 processing into p52, Mass Action, SBML 2v4", + "repository_type": "biomodels", + "summary": "

This model represents NIK-dependent p100 processing into p52 with mass action kinetics. While this model shows identical dose-response to the Michaelis-Menten representation, when IkBd degradation is included the dose-response is no longer monotonic in mass action models due to substrate complex competition.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4508, + "tag": "BioModels:BIOMD0000000868" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:11.236399+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000868", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3164": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3164, + "name": "Simon2019 - NIK-dependent p100 processing into p52 and IkBd degradation, Michaelis-Menten, SBML 2v4", + "repository_type": "biomodels", + "summary": "

This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with Michaelis-Menten kinetics. Compare this Michaelis-Menten representation to the mass action model in which the dose-response to increasing p100 mRNA is no longer monotonic due to substrate complex competition.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4509, + "tag": "BioModels:BIOMD0000000869" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:11.698873+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000869", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3165": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3165, + "name": "Simon2019 - NIK-dependent p100 processing into p52 and IkBd degradation, mass action, SBML 2v4", + "repository_type": "biomodels", + "summary": "

This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with mass action kinetics. Compare this mass action representation to the Michaelis-Menten model. In this model the dose-response to increasing p100 mRNA is not monotonic due to substrate complex competition.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4510, + "tag": "BioModels:BIOMD0000000870" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:12.188497+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000870", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3166": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3166, + "name": "NIK-dependent p100 processing into p52 with RelB binding and IkBd degradation, mass action, SBML 2v4", + "repository_type": "biomodels", + "summary": "

This model represents NIK-dependent p100 processing into p52 followed by binding to RelB and NIK-dependent IkBd degradation. This model assesses the impact of substrate complex competition on RelB-p52.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4511, + "tag": "BioModels:BIOMD0000000871" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:12.733114+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000871", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3167": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3167, + "name": "Verma2016 - HIV and HPV co-infection, T-cell response", + "repository_type": "biomodels", + "summary": "
This is a COPASI version of the HIV/HPV coinfection model submitted to PLoS One.Title: Modeling the mechanisms by which HIV-associated immunosuppression influences HPV persistence at the oral mucosaAuthors: Meghna Verma*, Samantha Erwin*, Vida Abedi, Raque Hontecillas, Stefan Hoops, Andrew Leber, Josep Bassaganya-Riera, Stanca Ciupe* Contributed equally to the workCorresponding Authors: Josep Bassaganya-Riera and Stanca Ciupe
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4512, + "tag": "BioModels:BIOMD0000000872" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:13.294933+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000872", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3168": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3168, + "name": "Soni2018 - IL6 induced M2 Phenotype in Leishmania major infected macrophage", + "repository_type": "biomodels", + "summary": "

IL-6 has been proposed to favor the development of Th2 responses and play an important role in the communication between cells of multicellular organisms. They are involved in the regulation of complex cellular processes such as proliferation, differentiation and act as key player during inflammation and immune response. Th2 cytokines play an immunoregulatory role in early infection. Literature says in mice infected with L. major, IL-6 may promote the development of both Th1 and Th2 responses. IL-4 is also considered to be the signature cytokine of Th-2 response. IL6 was initially characterized as a Th1 cytokine but later on it was proved to be a pleiotropic cytokine, secreted from different cell types including the macrophages. A major challenge is to understand how these complex non-linear processes are connected and regulated. Systems biology approaches may be used to tackle this challenge in an iterative process of quantitative mathematical analysis. In this study, we created an in silico model of IL6 mediated macrophage activation which suffers from an excessive impact of the negative feedback loop involving SOCS1. The strategy adopted in this framework may help to reduce the complexity of the leishmanial IL6 model analysis and also laydown various physiological or pathological conditions of IL6 signaling in future.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4513, + "tag": "BioModels:BIOMD0000000873" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:13.774936+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000873", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3169": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3169, + "name": "Perelson1993 - HIVinfection_CD4Tcells_ModelA", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci 1993 Mar;114(1):81-125 8096155 ,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considersfour populations: uninfected T cells, latently infected T cells, activelyinfected T cells, and free virus. Using this model we show that many of thepuzzling quantitative features of HIV infection can be explained simply. We alsoconsider effects of AZT on viral growth and T-cell population dynamics. Themodel exhibits two steady states, an uninfected state in which no virus ispresent and an endemically infected state, in which virus and infected T cellsare present. We show that if N, the number of infectious virions produced peractively infected T cell, is less a critical value, Ncrit, then the uninfectedstate is the only steady state in the nonnegative orthant, and this state isstable. For N > Ncrit, the uninfected state is unstable, and the endemicallyinfected state can be either stable, or unstable and surrounded by a stablelimit cycle. Using numerical bifurcation techniques we map out the parameterregimes of these various behaviors. oscillatory behavior seems to lie outsidethe region of biologically realistic parameter values. When the endemicallyinfected state is stable, it is characterized by a reduced number of T cellscompared with the uninfected state. Thus T-cell depletion occurs through theestablishment of a new steady state. The dynamics of the establishment of thisnew steady state are examined both numerically and via the quasi-steady-stateapproximation. We develop approximations for the dynamics at early times inwhich the free virus rapidly binds to T cells, during an intermediate time scalein which the virus grows exponentially, and a third time scale on which viralgrowth slows and the endemically infected steady state is approached. Using thequasi-steady-state approximation the model can be simplified to two ordinarydifferential equations the summarize much of the dynamical behavior. We computethe level of T cells in the endemically infected state and show how that levelvaries with the parameters in the model. The model predicts that different viralstrains, characterized by generating differing numbers of infective virionswithin infected T cells, can cause different amounts of T-cell depletion andgenerate depletion at different rates. Two versions of the model are studied. Inone the source of T cells from precursors is constant, whereas in the other thesource of T cells decreases with viral load, mimicking the infection and killingof T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4514, + "tag": "BioModels:BIOMD0000000874" + }, + { + "id": 4264, + "tag": "HIV infection" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3276, + "tag": "Viral entry into host cell" + } + ], + "timestamp_created": "2025-02-03 16:46:14.278628+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000874", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3170": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3170, + "name": "Nelson2000- HIV-1 general model 1", + "repository_type": "biomodels", + "summary": "

This is the general model without delay described by the equation system (1) in: A model of HIV-1 pathogenesis that includes an intracellular delay.
Nelson PW, Murray JD, Perelson AS; Math Biosci. 2000 Feb;163(2):201-15. PMID: 10701304 ; doi: 10.1016/S0025-5564(99)00055-3
Abstract:
Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection.
Author Keywords: HIV; Delay; Viral life cycle; T-cells

As there are no results given for this model in the article it cannot be checked for MIRIAM compliance. The SBML file should be equivalent to the described ODE file though.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4515, + "tag": "BioModels:BIOMD0000000875" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4113, + "tag": "Human immunodeficiency virus infectious disease" + }, + { + "id": 3940, + "tag": "Response to drug" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:14.760351+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000875", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3171": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3171, + "name": "Aavani2019 - The role of CD4 T cells in immune system activation and viral reproduction in a simple model for HIV infection", + "repository_type": "biomodels", + "summary": "
This is a mathematical model comprised of a simple system of four ordinary differential equations that account for the two opposing roles of infected and healthy cytotoxic T lymphocytes within the context of HIV infection, with each type responsible for the production of virus and the activation and stimulation of immune responses, respectively.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4516, + "tag": "BioModels:BIOMD0000000876" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:15.252153+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000876", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3172": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3172, + "name": "Ontah2019 - Dynamic analysis of a tumor treatment model using oncolytic virus and chemotherapy with saturated infection rate", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the treatment of tumors using oncolytic virus and chemotherapy. The model is comprised of nonlinear ordinary differential equations describing the interactions between uninfected tumor cells, infected tumor cells, an oncolytic virus, and chemotherapy.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4517, + "tag": "BioModels:BIOMD0000000877" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:15.764875+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000877", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3173": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3173, + "name": "Lenbury2001_InsulinKineticsModel_A", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling insulin kinetics: responses to a single oral glucose administration or ambulatory-fed conditions.
Lenbury Y, Ruktamatakul S, Amornsamarnkul S. Biosystems. 2001 Jan;59(1):15-25. 11226623 ,
Abstract:
This paper presents a nonlinear mathematical model of the glucose-insulin feedback system, which has been extended to incorporate the beta-cells' function on maintaining and regulating plasma insulin level in man. Initially, a gastrointestinal absorption term for glucose is utilized to effect the glucose absorption by the intestine and the subsequent release of glucose into the bloodstream, taking place at a given initial rate and falling off exponentially with time. An analysis of the model is carried out by the singular perturbation technique in order to derive boundary conditions on the system parameters which identify, in particular, the existence of limit cycles in our model system consistent with the oscillatory patterns often observed in clinical data. We then utilize a sinusoidal term to incorporate the temporal absorption of glucose in order to study the responses in the patients under ambulatory-fed conditions. A numerical investigation is carried out in this case to construct a bifurcation diagram to identify the ranges of parametric values for which chaotic behavior can be expected, leading to interesting biological interpretations.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: lenbury_ruktamatakul_amornsamarnkul_2001_A
The original CellML model was created by:
Catherine Lloyd
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4518, + "tag": "BioModels:BIOMD0000000878" + }, + { + "id": 3247, + "tag": "Diabetes mellitus" + }, + { + "id": 4519, + "tag": "Glucose homeostasis" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:16.317302+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000878", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3174": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3174, + "name": "Rodrigues2019 - A mathematical model for chemoimmunotherapy of chronic lymphocytic leukemia", + "repository_type": "biomodels", + "summary": "
THis is a simple ordinary differential equation model describing chemoimmunotherapy of chronic lymphocytic leukemia, including descriptions of the combinatorial effects of chemotherapy and adoptive cellular immunotherapy.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4520, + "tag": "BioModels:BIOMD0000000879" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:16.805335+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000879", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3175": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3175, + "name": "Trisilowati2018 - Optimal control of tumor-immune system interaction with treatment", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of a growing tumor and its interaction with the immune system. The model consists of four populations: tumor cells, dendritic cells (representing the innate immune system), cytotoxic T cells, and helper T cells (as the specific immune system). The model is comprised of a system of ordinary differential equations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4521, + "tag": "BioModels:BIOMD0000000880" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:17.268188+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000880", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3176": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3176, + "name": "Kogan2013 - A mathematical model for the immunotherapeutic control of the TH1 TH2 imbalance in melanoma", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the imbalance between T helper (Th1/Th2) cell types in melanome patients, together with its regulation via IL-12 treatment. The model focuses on the interactions between the two T helper cell types as mediated by their respective key cytokines, interferon gamma and IL-10.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4522, + "tag": "BioModels:BIOMD0000000881" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:17.723702+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000881", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3177": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3177, + "name": "Munz2009 - Zombie SIZRC", + "repository_type": "biomodels", + "summary": "
Munz2009 - Zombie SIZRC This is the model with an latent infection and cure for zombies described in the article. This model was originally created by libAntimony v1.4 (using libSBML 3.4.1). This model is described in the article: When zombies attack!: Mathematical modelling of an outbreak of zombie infection P. Munz, I. Hudea, J. Imad and R.J. Smith? Infectious Disease Modelling Research Progress 2009, chapter 4, pp 133-150. Editors: Jean Michel Tchuenche and C. Chiyaka; Nova Science Publishers, Inc., NY, USA. Abstract: Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. This model is hosted on BioModels Database and identified by: MODEL1008060001 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4523, + "tag": "BioModels:BIOMD0000000882" + }, + { + "id": 4524, + "tag": "Disease by infectious agent" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:18.197921+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000882", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3178": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3178, + "name": "Giani2019 - Computational modeling to predict MAP3K8 effects as mediator of resistance to vemurafenib in thyroid cancer stem cells", + "repository_type": "biomodels", + "summary": "
Computational modeling to predict MAP3K8 effects as mediator of resistance to vemurafenib in thyroid cancer stem cells
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4525, + "tag": "BioModels:BIOMD0000000883" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:18.672477+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000883", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3179": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3179, + "name": "Cortes2019 - Optimality of the spontaneous prophage induction rate.", + "repository_type": "biomodels", + "summary": "
Optimality of the spontaneous prophage induction rate.Cortes MG1, Krog J2, Bal\u00e1zsi G3.1 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.2 The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.3 Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: gabor.balazsi@stonybrook.edu.AbstractLysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage \u03bb and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.Copyright \u00a9 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4526, + "tag": "BioModels:BIOMD0000000884" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:19.134706+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000884", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3180": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3180, + "name": "Sumana2018 - Mathematical modeling of cancer-immune system, considering the role of antibodies.", + "repository_type": "biomodels", + "summary": "
Mathematical modeling of cancer-immune system, considering the role of antibodies.Ghosh S1, Banerjee S2.Author information1    Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal, 247667, India.2    Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal, 247667, India. sandofma@iitr.ac.in.AbstractA mathematical model for the quantitative analysis of cancer-immune interaction, considering the role of antibodies has been proposed in this paper. The model is based on the clinical evidence, which states that antibodies can directly kill cancerous cells (Ivano et al. in J Clin Investig 119(8):2143-2159, 2009). The existence of transcritical bifurcation, which has been proved using Sotomayor theorem, provides strong biological implications. Through numerical simulations, it has been illustrated that under certain therapy (like monoclonal antibody therapy), which is capable of altering the parameters of the system, cancer-free state can be obtained.KEYWORDS:Antibodies; B cells; Cancer cells; Global stability; Plasma cells; Transcritical bifurcation
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4527, + "tag": "BioModels:BIOMD0000000885" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:19.644733+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000885", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3181": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3181, + "name": "Dubey2008 - Modeling the interaction between avascular cancerous cells and acquired immune response", + "repository_type": "biomodels", + "summary": "
MODELING THE INTERACTION BETWEEN AVASCULAR CANCEROUS CELLS AND ACQUIRED IMMUNE RESPONSEB. DUBEY, UMA S. DUBEY and SANDIP BANERJEEAbstractThis paper deals with the interaction between dispersed cancer cells and the major populations of the immune system, namely, the T helper cells, T Cytotoxic cells, B cells, and antibodies produced. The system is described by a set of five ordinary differential equations. Both local and global stability of the system has been investigated. It has been observed that under appropriate conditions this interaction is capable of controlling the growth of these cancer cells. The analytical findings are supported by numerical and computational analytical methods.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4528, + "tag": "BioModels:BIOMD0000000886" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:20.313550+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000886", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3182": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3182, + "name": "Lim2014 - HTLV-I infection A dynamic struggle between viral persistence and host immunity", + "repository_type": "biomodels", + "summary": "
This is a four-dimensional, non-linear system of ordinary differential equations that describes the dynamic interactions among viral expression, infected target cell activation, and the HTLV-I-specific CTL response.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4529, + "tag": "BioModels:BIOMD0000000887" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:20.937958+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000887", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3183": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3183, + "name": "Unni2019 - Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug Interventions", + "repository_type": "biomodels", + "summary": "
Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug InterventionsPranav Unni 1 and Padmanabhan SeshaiyerAbstractOver the last few decades, there have been significant developments in theoretical, experimental, and clinical approaches to understand the dynamics of cancer cells and their interactions with the immune system. These have led to the development of important methods for cancer therapy including virotherapy, immunotherapy, chemotherapy, targeted drug therapy, and many others. Along with this, there have also been some developments on analytical and computational models to help provide insights into clinical observations. This work develops a new mathematical model that combines important interactions between tumor cells and cells in the immune systems including natural killer cells, dendritic cells, and cytotoxic CD8+ T cells combined with drug delivery to these cell sites. These interactions are described via a system of ordinary differential equations that are solved numerically. A stability analysis of this model is also performed to determine conditions for tumor-free equilibrium to be stable. We also study the influence of proliferation rates and drug interventions in the dynamics of all the cells involved. Another contribution is the development of a novel parameter estimation methodology to determine optimal parameters in the model that can reproduce a given dataset. Our results seem to suggest that the model employed is a robust candidate for studying the dynamics of tumor cells and it helps to provide the dynamic interactions between the tumor cells, immune system, and drug-response systems.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4530, + "tag": "BioModels:BIOMD0000000888" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:21.400293+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000888", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3184": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3184, + "name": "Fribourg2014 - Model of influenza A virus infection dynamics of viral antagonism and innate immune response.", + "repository_type": "biomodels", + "summary": "
This is an ordinary differential equation mathematical model investigating the early responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4531, + "tag": "BioModels:BIOMD0000000889" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:21.882845+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000889", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3185": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3185, + "name": "Bhattacharya2014 - A mathematical model of the sterol regulatory element binding protein 2 cholesterol biosynthesis pathway", + "repository_type": "biomodels", + "summary": "
This is a deterministic nonlinear ordinary differential equation mathematical model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in a hepatocyte.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4532, + "tag": "BioModels:BIOMD0000000890" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:22.359347+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000890", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3186": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3186, + "name": "Khajanchi2019 - Stability Analysis of a Mathematical Model forGlioma-Immune Interaction under OptimalTherapy", + "repository_type": "biomodels", + "summary": "
Stability Analysis of a Mathematical Model for Glioma-Immune Interaction under Optimal TherapySubhas KhajanchiAbstractWe investigate a mathematical model using a system of coupled ordinary differential equations, which describes the interplay of malignant glioma cells, macrophages, glioma specific CD8+T cells and the immunotherapeutic drug Adoptive Cellular Immunotherapy (ACI). To better understand under what circumstances the glioma cells can be eliminated, we employ the theory of optimal control. We investigate the dynamics of the system by observing biologically feasible equilibrium points and their stability analysis before administration of the external therapy ACI. We solve an optimal control problem with an objective functional which minimizes the glioma cell burden as well as the side effects of the treatment. We characterize our optimal control in terms of the solutions to the optimality system, in which the state system coupled with the adjoint system. Our model simulation demonstrates that the strength of treatment u1(t) plays an important role to eliminate the glioma cells. Finally, we derive an optimal treatment strategy and then solve it numerically.Keywords: malignant gliomas; stability analysis; optimal control; adoptive cellular immunotherapy
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4533, + "tag": "BioModels:BIOMD0000000891" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:22.935837+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000891", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3187": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3187, + "name": "Sandip2013 - Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.", + "repository_type": "biomodels", + "summary": "
Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.Banerjee S1, Keval R, Gakkhar S.Author information1    Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee 247667, Uttaranchal, India. Electronic address: sandofma@iitr.ernet.in.AbstractA mathematical modeling of hepatitis C virus (HCV) dynamics and antiviral therapy has been presented in this paper. The proposed model, which involves four coupled ordinary differential equations, describes the interaction of target cells (hepatocytes), infected cells, infectious virions and non-infectious virions. The model takes into consideration the addition of ribavirin to interferon therapy and explains the dynamics regarding a biphasic and triphasic decline of viral load in the model. A critical drug efficacy parameter has been defined and it is shown that for an efficacy above this critical value, HCV is eradicated whereas for efficacy lower this critical value, a new steady state for infectious virions is reached, which is lower than the previous steady state value.Copyright \u00a9 2013 Elsevier Inc. All rights reserved.KEYWORDS:Hepatitis C virus (HCV); Infected cells; Infectious virions; Interferon; Noninfectious virions; Ribavirin; Target cells
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4534, + "tag": "BioModels:BIOMD0000000892" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:23.471262+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000892", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3188": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3188, + "name": "GonzalezMiranda2013 - The effect of circadian oscillations on biochemical cell signaling by NF-\u03baB", + "repository_type": "biomodels", + "summary": "
This is a mathematical model for NF-\u03baB oscillations, described by a set of ordinary nonlinear differential equations, when perturbed by a circadian oscillation.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4535, + "tag": "BioModels:BIOMD0000000893" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:24.065409+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000893", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3189": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3189, + "name": "Bose2011 - Noise-assisted interactions of tumor and immune cells", + "repository_type": "biomodels", + "summary": "
Noise-assisted interactions of tumor and immune cells.Bose T1, Trimper S.Author information1    Institute of Physics, Martin-Luther-University, D-06099 Halle, Germany. thomas.bose@physik.uni-halle.deAbstractWe consider a three-state model comprising tumor cells, effector cells, and tumor-detecting cells under the influence of noises. It is demonstrated that inevitable stochastic forces existing in all three cell species are able to suppress tumor cell growth completely. Whereas the deterministic model does not reveal a stable tumor-free state, the auto-correlated noise combined with cross-correlation functions can either lead to tumor-dormant states, tumor progression, as well as to an elimination of tumor cells. The auto-correlation function exhibits a finite correlation time \u03c4, while the cross-correlation functions shows a white-noise behavior. The evolution of each of the three kinds of cells leads to a multiplicative noise coupling. The model is investigated by means of a multivariate Fokker-Planck equation for small \u03c4. The different behavior of the system is, above all, determined by the variation of the correlation time and the strength of the cross-correlation between tumor and tumor-detecting cells. The theoretical model is based on a biological background discussed in detail, and the results are tested using realistic parameters from experimental observations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4536, + "tag": "BioModels:BIOMD0000000894" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:24.525100+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000894", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3190": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3190, + "name": "Schokker2013 - A mathematical model representing cellular immune development and response to Salmonella of chicken intestinal tissue", + "repository_type": "biomodels", + "summary": "
This is a dynamic mathematical model describing the development of the cellular branch of the intestinal immune system of poultry during the first 42 days of life, and of its response towards an oral infection with Salmonella enterica serovar Enteritidis.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4537, + "tag": "BioModels:BIOMD0000000895" + }, + { + "id": 4538, + "tag": "Gallus gallus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:25.003167+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000895", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3191": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3191, + "name": "Szymanska2009 - Mathematical modeling of heat shock protein synthesis in response to temperature change", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of heat shock protein synthesis induced by an external temperature stimulus. The model consists of a system of nine nonlinear ordinary differential equations describing the temporal evolution of key variables involved in the regulation of HSP synthesis.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4539, + "tag": "BioModels:BIOMD0000000896" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:25.469630+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000896", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3192": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3192, + "name": "Khajanchi2015 - The combined effects of optimal control in cancer remission", + "repository_type": "biomodels", + "summary": "
The combined effects of optimal control in cancer remissionSubhasKhajanchiDibakarGhoshAbstractWe investigate a mathematical model depicting the nonlinear dynamics of immunogenic tumors as envisioned by Kuznetsov et al. [1]. To understand the dynamics under what circumstances the cancer cells can be eliminated, we implement the theory of optimal control. We design two types of external treatment strategies, one is Adoptive Cellular Immunotherapy and another is interleukin-2. Our aim is to establish the treatment regimens that maximize the effector cell count and minimize the tumor cell burden and the deleterious effects of the total amount of drugs. We derive the existence of an optimal control by using the boundedness of solutions. We characterize the optimality system, in which the state system is coupled with co-states. The uniqueness of an optimal control of our problem is also analyzed. Finally, we demonstrate the numerical illustrations that the optimal regimens reduce the tumor burden under different scenarios.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4540, + "tag": "BioModels:BIOMD0000000897" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:25.942134+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000897", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3193": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3193, + "name": "Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the hematopoietic lineages with leukemia lineages, as controlled by end-product negative feedback inhibition. Variables include hematopoietic stem cells, progenitor cells, terminally differentiated HSCs, leukemia stem cells, and terminally differentiated leukemia stem cells.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4541, + "tag": "BioModels:BIOMD0000000898" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:26.409949+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000898", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3194": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3194, + "name": "Ota2015 - Positive regulation of Rho GTPase activity by RhoGDIs as a result of their direct interaction with GAPs (GDI integrated)", + "repository_type": "biomodels", + "summary": "
This is a ordinary differential equation mathematical model describing the Rho GTPase cycle in which Rho GDP-dissociation inhibitors (RhoGDIs) inhibit the regulatory activities of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) by interacting with them directly as well as by sequestering the Rho GTPases. The model was constructed with the intent of analyzing the role of RhoGDIs in Rho GTPase signaling.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4542, + "tag": "BioModels:BIOMD0000000899" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:26.879076+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000899", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3195": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3195, + "name": "Bianca2013 - Persistence analysis in a Kolmogorov-type model for cancer-immune system competition", + "repository_type": "biomodels", + "summary": "
Persistence analysis in a Kolmogorov-type model for cancer-immune system competitionAIP Conference Proceedings 1558, 1797 (2013); https://doi.org/10.1063/1.4825874C. BiancaDipartimento di Scienze Matematiche, Politecnico di Torino, Torino, ItalyF. PappalardoDipartimento di Scienza del Farmaco, Universit\u00e0 degli Studi di Catania, Catania, ItalyM. Pennisi and M. A. RagusaDipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Catania, Catania, Italy
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4543, + "tag": "BioModels:BIOMD0000000900" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:27.337336+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000900", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3196": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3196, + "name": "ChowHall2008 Dynamics of Human Weight Change_ODE_1", + "repository_type": "biomodels", + "summary": "This ODE model is a representation of the two compartment macronutrient partition model that Chow and Hall outlined in their 2008 publication. It is one of three models that they outlined in that publication. The model was constructed by a team of five as part of a Workshop on Modelling conducted at Indian Institute of Technology Madras by the EBI.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4544, + "tag": "BioModels:BIOMD0000000901" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:27.801539+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000901", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3197": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3197, + "name": "Wang2019 - A mathematical model of oncolytic virotherapy with time delay", + "repository_type": "biomodels", + "summary": "
A mathematical model describing oncolytic virotherapy with incorporation the viral lytic cycle and the virus-specific CTL response. The thresholds for viral treatment and virus-specific CTl response are also obtained.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4545, + "tag": "BioModels:BIOMD0000000902" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:28.256981+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000902", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3198": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3198, + "name": "Solis-perez2019 - A fractional mathematical model of breast cancer competition model", + "repository_type": "biomodels", + "summary": "
A fractional mathematical model of breast cancer competition modelAuthor links open overlay panelJ.E.Sol\u00eds-P\u00e9rezaJ.F.G\u00f3mez-AguilarbA.Atanganaca    Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xicob    CONACyT-Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xicoc    Institute for Groundwater Studies, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9300, South AfricaAbstractIn this paper, a mathematical model which considers population dynamics among cancer stem cells, tumor cells, healthy cells, the effects of excess estrogen and the body\u2019s natural immune response on the cell populations was considered. Fractional derivatives with power law and exponential decay law in Liouville\u2013Caputo sense were considered. Special solutions using an iterative scheme via Laplace transform were obtained. Furthermore, numerical simulations of the model considering both derivatives were obtained using the Atangana\u2013Toufik numerical method. Also, random model described by a system of random differential equations was presented. The use of fractional derivatives provides more useful information about the complexity of the dynamics of the breast cancer competition model.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4546, + "tag": "BioModels:BIOMD0000000903" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:28.711507+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000903", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3199": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3199, + "name": "Admon2017 - Modelling tumor growth with immune response and drug using ordinary differential equations", + "repository_type": "biomodels", + "summary": "
Modelling tumor growth with immune response and drug using ordinary differential equationsMohd Rashid Admon, Normah MaanThis is a mathematical study about tumor growth from a different perspective, with the aim of predicting and/or controlling the disease. The focus is on the effect and interaction of tumor cell with immune and drug. This paper presents a mathematical model of immune response and a cycle phase specific drug using a system of ordinary differential equations. Stability analysis is used to produce stability regions for various values of certain parameters during mitosis. The stability region of the graph shows that the curve splits the tumor decay and growth regions in the absence of immune response. However, when immune response is present, the tumor growth region is decreased. When drugs are considered in the system, the stability region remains unchanged as the system with the presence of immune response but the population of tumor cells at interphase and metaphase is reduced with percentage differences of 1.27 and 1.53 respectively. The combination of immunity and drug to fight cancer provides a better method to reduce tumor population compared to immunity alone.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4547, + "tag": "BioModels:BIOMD0000000904" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:29.182706+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000904", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3200": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3200, + "name": "Dubey2007 - A mathematical model for the effect of toxicant on the immune system (with toxicant effect) Model2", + "repository_type": "biomodels", + "summary": "
In this paper, a nonlinear mathematical model is proposed and analyzed to study theeffect of environmental toxicant on the immune response of the body. Criteria for localstability, instability and global stability are obtained. It is shown that the immuneresponse of the body decreases as the concentration of environmental toxicant increases,and certain criteria are obtained under which it settles down at its equilibrium level.In the absence of toxicant, an oscillatory behavior of immune system and pathogenicgrowth is observed. However, in the presence of toxicant, oscillatory behavior is notobserved. These studies show that the toxicant may have a grave effect on our body\u2019sdefense mechanism.Keywords: Pathogen; Immune Response; Toxicant; Stability.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4548, + "tag": "BioModels:BIOMD0000000905" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:29.666886+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000905", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3201": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3201, + "name": "Dubey2007 - A mathematical model for the effect of toxicant on the immune system (without toxicant effect) Model1", + "repository_type": "biomodels", + "summary": "
In this paper, a nonlinear mathematical model is proposed and analyzed to study theeffect of environmental toxicant on the immune response of the body. Criteria for localstability, instability and global stability are obtained. It is shown that the immuneresponse of the body decreases as the concentration of environmental toxicant increases,and certain criteria are obtained under which it settles down at its equilibrium level.In the absence of toxicant, an oscillatory behavior of immune system and pathogenicgrowth is observed. However, in the presence of toxicant, oscillatory behavior is notobserved. These studies show that the toxicant may have a grave effect on our body\u2019sdefense mechanism.Keywords: Pathogen; Immune Response; Toxicant; Stability.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4549, + "tag": "BioModels:BIOMD0000000906" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:30.379178+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000906", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3202": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3202, + "name": "HeberleRazquinNavas2019 - The PI3K and MAPK/p38 pathways control stress granuleassembly in a hierarchical manner model 3", + "repository_type": "biomodels", + "summary": "
All cells and organisms exhibit stress-coping mechanisms toensure survival. Cytoplasmic protein-RNA assemblies termedstress granules are increasingly recognized to promote cellularsurvival under stress. Thus, they might represent tumor vul-nerabilities that are currently poorly explored. The translation-inhibitory eIF2\u03b1kinases are established as main drivers ofstress granule assembly. Using a systems approach, we identifythe translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regu-lator mammalian target of rapamycin complex 1 (mTORC1) topromote stress granule assembly. When highly active, PI3K is themain driver of stress granules; however, the impact of p38becomes apparent as PI3K activity declines. PI3K and p38 thusact in a hierarchical manner to drive mTORC1 activity and stressgranule assembly. Of note, this signaling hierarchy is also presentin human breast cancer tissue. Importantly, only the recognition ofthe PI3K-p38 hierarchy under stress enabled the discovery of p38\u2019srole in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as theyhierarchically promote stress granule formation
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4550, + "tag": "BioModels:BIOMD0000000907" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:30.941523+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000907", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3203": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3203, + "name": "dePillis2013 - Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment", + "repository_type": "biomodels", + "summary": "
Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatmentLisette dePillis 1, , Trevor Caldwell 2, , Elizabeth Sarapata 2, and  Heather Williams 2,1. \tDepartment of Mathematics, Harvey Mudd College, Claremont, CA 917112. \tHarvey Mudd College, Claremont, CA 91711, United States, United States, United States    AbstractWe present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not.    Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response.Keywords: Renal cell carcinoma, mathematical modeling., sunitinib, immune system, regulatory T cells.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4551, + "tag": "BioModels:BIOMD0000000908" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:31.513981+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000908", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3204": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3204, + "name": "dePillis2003 - The dynamics of an optimally controlled tumor model: A case study", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <pre>The dynamics of an optimally controlled tumor model: A case studyL.GDe PillisabARadunskayaahttps://doi.org/10.1016/S0895-7177(03)00133-XAbstractWe present a phase-space analysis of a mathematical model of tumor growth with an immune response and chemotherapy. We prove that all orbits are bounded and must converge to one of several possible equilibrium points. Therefore, the long-term behavior of an orbit is classified according to the basin of attraction in which it starts. The addition of a drug term to the system can move the solution trajectory into a desirable basin of attraction. We show that the solutions of the model with a time-varying drug term approach the solutions of the system without the drug once traatment has stopped. We present numerical experiments in which optimal control therapy is able to drive the system into a desirable basin of attraction, whereas traditional pulsed chemotherapy is not.</pre> </body> </notes>", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4552, + "tag": "BioModels:BIOMD0000000909" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:32.015630+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000909", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3205": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3205, + "name": "Isaeva2008 - Modelling of Anti-Tumour Immune Response Immunocorrective Effect of Weak Centimetre Electromagnetic Waves", + "repository_type": "biomodels", + "summary": "
Modelling of anti-tumour immune response: Immunocorrective effectof weak centimetre electromagnetic wavesO.G. Isaeva* and V.A. OsipovBogoliubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna,Moscow Region, RussiaWe formulate the dynamical model for the anti-tumour immune response based onintercellular cytokine-mediated interactions with the interleukin-2 (IL-2) taken intoaccount. The analysis shows that the expression level of tumour antigens on antigenpresenting cells has a distinct influence on the tumour dynamics. At low antigenpresentation, a progressive tumour growth takes place to the highest possible value.At high antigen presentation, there is a decrease in tumour size to some value when thedynamical equilibrium between the tumour and the immune system is reached. In thecase of the medium antigen presentation, both these regimes can be realized dependingon the initial tumour size and the condition of the immune system. A pronouncedimmunomodulating effect (the suppression of tumour growth and the normalization ofIL-2 concentration) is established by considering the influence of low-intensityelectromagnetic microwaves as a parametric perturbation of the dynamical system. Thisfinding is in qualitative agreement with the recent experimental results onimmunocorrective effects of centimetre electromagnetic waves in tumour-bearing mice.Keywords: carcinogenesis; interleukin-2; modelling; anti-tumour immunity;electromagnetic waves
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4553, + "tag": "BioModels:BIOMD0000000910" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:32.486452+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000910", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3206": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3206, + "name": "Merola2008 - An insight into tumor dormancy equilibrium via the analysis of its domain of attraction", + "repository_type": "biomodels", + "summary": "
An insight into tumor dormancy equilibrium via the analysis of its domain ofattractionA. Merola, C. Cosentino *, F. AmatoSchool of Computer and Biomedical Engineering, Universita` degli Studi Magna Gr\u00e6cia di Catanzaro, Campus \u2018\u2018Salvatore Venuta\u2019\u2019, 88100 Catanzaro, ItalyA B S T R A C TThe trajectories of the dynamic system which regulates the competition between the populations ofmalignant cells and immune cells may tend to an asymptotically stable equilibrium in which the sizes ofthese populations do not vary, which is called tumor dormancy. Especially for lower steady-state sizes ofthe population of malignant cells, this equilibrium represents a desirable clinical condition since thetumor growth is blocked. In this context, it is of mandatory importance to analyze the robustness of thisclinical favorable state of health in the face of perturbations. To this end, the paper presents anoptimization technique to determine whether an assigned rectangular region, which surrounds anasymptotically stable equilibrium point of a quadratic systems, is included into the domain of attractionof the equilibrium itself. The biological relevance of the application of this technique to the analysis oftumor growth dynamics is shown on the basis of a recent quadraticmodel of the tumor\u2013immune systemcompetition dynamics. Indeed the application of the proposedmethodology allows to ensure that a givensafety region, determined on the basis of clinical considerations, belongs to the domain of attraction ofthe tumor blocked equilibrium; therefore for the set of perturbed initial conditions which belong to suchregion, the convergence to the healthy steady state is guaranteed. The proposed methodology can alsoprovide an optimal strategy for cancer treatment.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4554, + "tag": "BioModels:BIOMD0000000911" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:32.997483+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000911", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3207": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3207, + "name": "Caravagna2010 - Tumour suppression by immune system", + "repository_type": "biomodels", + "summary": "
Tumour suppression by immune system through stochastic oscillationsGiulioCaravagnaa Albertod\u2019Onofriob PaoloMilazzoaRobertoBarbutiahttps://doi.org/10.1016/j.jtbi.2010.05.013AbstractThe well-known Kirschner\u2013Panetta model for tumour\u2013immune System interplay [Kirschner, D., Panetta, J.C., 1998. Modelling immunotherapy of the tumour\u2013immune interaction. J. Math. Biol. 37 (3), 235\u2013252] reproduces a number of features of this essential interaction, but it excludes the possibility of tumour suppression by the immune system in the absence of therapy. Here we present a hybrid\u2013stochastic version of that model. In this new framework, we show that in reality the model is also able to reproduce the suppression, through stochastic extinction after the first spike of an oscillation.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4555, + "tag": "BioModels:BIOMD0000000912" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:33.461623+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000912", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3208": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3208, + "name": "dePillis2008 - Optimal control of mixed immunotherapy and chemotherapy of tumors", + "repository_type": "biomodels", + "summary": "&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level2/version4&quot;&gt; &lt;body xmlns=&quot;http://www.w3.org/1999/xhtml&quot;&gt; &lt;pre&gt;Optimal control of mixed immunotherapy and chemotherapy of tumorsLisette Depillis, K. R. Fister , W. Gu, Tiffany Head, Kenny Maples, Todd Neal, Anand Murugan and Kenji KozaiAbstractWe investigate a mathematical population model of tumor-immune interactions. Thepopulations involved are tumor cells, specific and non-specific immune cells, and con-centrations of therapeutic treatments. We establish the existence of an optimal con-trol for this model and provide necessary conditions for the optimal control triple forsimultaneous application of chemotherapy, tumor infiltrating lymphocyte (TIL) ther-apy, and interleukin-2 (IL-2) treatment. We discuss numerical results for the combina-tion of the chemo-immunotherapy regimens. We find that the qualitative nature of ourresults indicates that chemotherapy is the dominant intervention with TIL interactingin a complementary fashion with the chemotherapy. However, within the optimal con-trol context, the interleukin-2 treatment does not become activated for the estimatedparameter ranges.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4556, + "tag": "BioModels:BIOMD0000000913" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:33.952724+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000913", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3209": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3209, + "name": "Parra_Guillen2013 - Mathematical model approach to describe tumour response in mice after vaccine administration_model1", + "repository_type": "biomodels", + "summary": "
Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies.Parra-Guillen ZP1, Berraondo P, Grenier E, Ribba B, Troconiz IF.Author informationAbstractImmunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4557, + "tag": "BioModels:BIOMD0000000914" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:34.478424+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000914", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3210": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3210, + "name": "Sun2018 - Instantaneous mutation rate in cancer initiation and progression", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <p>BackgroundCancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes.ResultsTo accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time.ConclusionsThe proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.</p> </body> </notes>", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4558, + "tag": "BioModels:BIOMD0000000915" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:34.950137+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000915", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3211": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3211, + "name": "Kraan199_Kinetics of Cortisol Metabolism and Excretion.", + "repository_type": "biomodels", + "summary": "
A new model is proposed to study the kinetics of [3H]cortisol metabolism by using urinary data only. The model consists of 5 pools, in which changes of the fractions of dose are given by a system of 5 ordinary differential equations. After i.v. administration of [3H]cortisol to 8 multiple pituitary deficient (MPD) patients (group I) the urines from each patient were collected in 9-15 portions during the following 3 days. From the urinary data the rate constants of cortisol metabolism were calculated. A published set of urinary data from patients with a normal cortisol metabolism (group II) was used for comparison. The overall half-life of the label in the circulation was 30 min for both groups; the half-life of the label excretion by both groups was 6 h and the time of maximal activity in the main metabolizing pool was 1.8 h in group I and 1.5 h in group II. The 20% of normal cortisol production rate (CPR) in the 8 MPD patients amounted to 7.2 + 1.9/zmol/(m2*d). Therefore, the low CPR but normal rate constants, i.e. a normal metabolic clearance rate of cortisol, in the MPD patients suggest a sensitive adjustment of the cortisol response in the target organs.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4559, + "tag": "BioModels:BIOMD0000000916" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:35.425332+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000916", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3212": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3212, + "name": "Phillips2007_AscendingArousalSystem_SleepWakeDynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A quantitative model of sleep-wake dynamics based on the physiology of thebrainstem ascending arousal system.
Phillips AJ, Robinson PA. J Biol Rhythms 2007 Apr;22(2):167-79 17440218 ,
Abstract:
A quantitative, physiology-based model of the ascending arousal system isdeveloped, using continuum neuronal population modeling, which involvesaveraging properties such as firing rates across neurons in each population. Themodel includes the ventrolateral preoptic area (VLPO), where circadian andhomeostatic drives enter the system, the monoaminergic and cholinergic nuclei ofthe ascending arousal system, and their interconnections. The human sleep-wakecycle is governed by the activities of these nuclei, which modulate thebehavioral state of the brain via diffuse neuromodulatory projections. The modelparameters are not free since they correspond to physiological observables.Approximate parameter bounds are obtained by requiring consistency withphysiological and behavioral measures, and the model replicates the humansleep-wake cycle, with physiologically reasonable voltages and firing rates.Mutual inhibition between the wake-promoting monoaminergic group andsleep-promoting VLPO causes ;;flip-flop'' behavior, with most time spent in 2stable steady states corresponding to wake and sleep, with transitions betweenthem on a timescale of a few minutes. The model predicts hysteresis in thesleep-wake cycle, with a region of bistability of the wake and sleep states.Reducing the monoaminergic-VLPO mutual inhibition results in a smallerhysteresis loop. This makes the model more prone to wake-sleep transitions inboth directions and makes the states less distinguishable, as in narcolepsy. Themodel behavior is robust across the constrained parameter ranges, but withsufficient flexibility to describe a wide range of observed phenomena.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Phillips AJ, Robinson PA. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4560, + "tag": "BioModels:BIOMD0000000917" + }, + { + "id": 4561, + "tag": "Circadian sleep/wake cycle process" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:35.911879+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000917", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3213": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3213, + "name": "Schwarz2018-Cdk Activity Threshold Determines Passage through the Restriction Point", + "repository_type": "biomodels", + "summary": "
At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is passed when growth factor signaling initiates a positive feedback loop of Cdk activity. However, recent studies have cast doubt on this model by claiming R occurs prior to positive feedback activation in G1 or even before completion of the previous cell cycle. Here we reconcile these results and show that whereas many commonly used cell lines do not exhibit a G1 R, primary fibroblasts have a G1 R that is defined by a precise Cdk activity threshold and the activation of cell-cycle-dependent transcription. A simple threshold model, based solely on Cdk activity, predicted with more than 95% accuracy whether individual cells had passed R. That a single measurement accurately predicted cell fate shows that the state of complex regulatory networks can be assessed using a few critical protein activities.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4562, + "tag": "BioModels:BIOMD0000000918" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:36.380911+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000918", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3214": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3214, + "name": "Ledzewicz2013 - On optimal chemotherapy with a strongly targeted agent for a model of tumor immune system interactions with generalized logistic growth", + "repository_type": "biomodels", + "summary": "
On optimal chemotherapy with a strongly targeted agent for a model of tumor-immune system interactions with generalized logistic growth.Ledzewicz U1, Olumoye O, Sch\u00e4ttler H.1Dept. of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois 62026-1653, USA. uledzew@siue.eduAbstractIn this paper, a mathematical model for chemotherapy that takes tumor immune-system interactions into account is considered for a strongly targeted agent. We use a classical model originally formulated by Stepanova, but replace exponential tumor growth with a generalised logistic growth model function depending on a parameter v. This growth function interpolates between a Gompertzian model (in the limit v \u2192 0) and an exponential model (in the limit v \u2192 \u221e). The dynamics is multi-stable and equilibria and their stability will be investigated depending on the parameter v. Except for small values of v, the system has both an asymptotically stable microscopic (benign) equilibrium point and an asymptotically stable macroscopic (malignant) equilibrium point. The corresponding regions of attraction are separated by the stable manifold of a saddle. The optimal control problem of moving an initial condition that lies in the malignant region into the benign region is formulated and the structure of optimal singular controls is determined
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4563, + "tag": "BioModels:BIOMD0000000919" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:36.837875+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000919", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3215": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3215, + "name": "Jarrett2015 - Modelling the interaction between immune response, bacterial dynamics and inflammatory damage", + "repository_type": "biomodels", + "summary": "Mathematical model of pro- and anti-inflammatory response, inflammation/damage and infection dynamics in BALB/c mouse with Staphylococcal aureus infection.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4564, + "tag": "BioModels:BIOMD0000000920" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 4069, + "tag": "Regulation of inflammatory response" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:37.309332+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000920", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3216": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3216, + "name": "Khajanchi2017 - Uniform Persistence and Global Stability for a Brain Tumor and Immune System Interaction", + "repository_type": "biomodels", + "summary": "This paper describes the synergistic interaction between the growth of malignant gliomas and the immune system interactions using a system of coupled ordinary di\u00aeerential equations (ODEs). The proposed mathematical model comprises the interaction of glioma cells, macrophages, activated Cytotoxic T-Lymphocytes (CTLs), the immunosuppressive factor TGF- and the immuno-stimulatory factor IFN-. The dynamical behavior of the proposed system both analytically and numerically is investigated from the point of view of stability. By constructing Lyapunov functions, the global behavior of the glioma-free and the interior equilibrium point have been analyzed under some assumptions. Finally, we perform numerical simulations in order to illustrate our analytical \u00afndings by varying the system parameters.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4565, + "tag": "BioModels:BIOMD0000000921" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:37.774160+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000921", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3217": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3217, + "name": "Turner2015-Human/Mosquito ELP Model", + "repository_type": "biomodels", + "summary": "
the growth of the mosquito population is directly related to the spread of malaria, the Four Stage Life Cycle is incorporated to model the effects of climate change and interspecies competition within the mosquito life cycle stages of Egg, Larvae, and Pupae.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4566, + "tag": "BioModels:BIOMD0000000922" + }, + { + "id": 4567, + "tag": "Lutzia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:38.244989+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000922", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3218": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3218, + "name": "Li\u00f22012_Modelling osteomyelitis_Control Model", + "repository_type": "biomodels", + "summary": "
BackgroundThis work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure.ResultsSince both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way.ConclusionsWe present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformaticsModel is encoded by Ruby and submitted and curated to BioModels by Ahmad Zyoud
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4568, + "tag": "BioModels:BIOMD0000000923" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:38.774185+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000923", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3219": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3219, + "name": "Smith2011 - Three Stage Innate Immune Response to a Pneumococcal Lung Infection", + "repository_type": "biomodels", + "summary": "
Pneumococcal pneumonia is a leading cause of death and a major source of human morbidity. The initial immune response plays a central role in determining the course and outcome of pneumococcal disease. We combine bacterial titer measurements from mice infected with Streptococcus pneumoniae with mathematical modeling to investigate the coordination of immune responses and the effects of initial inoculum on outcome. To evaluate the contributions of individual components, we systematically build a mathematical model from three subsystems that describe the succession of defensive cells in the lung: resident alveolar macrophages, neutrophils and monocyte-derived macrophages. The alveolar macrophage response, which can be modeled by a single differential equation, can by itself rapidly clear small initial numbers of pneumococci. Extending the model to include the neutrophil response required additional equations for recruitment cytokines and host cell status and damage. With these dynamics, two outcomes can be predicted: bacterial clearance or sustained bacterial growth. Finally, a model including monocyte-derived macrophage recruitment by neutrophils suggests that sustained bacterial growth is possible even in their presence. Our model quantifies the contributions of cytotoxicity and immune-mediated damage in pneumococcal pathogenesis.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4569, + "tag": "BioModels:BIOMD0000000924" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:39.561351+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000924", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3220": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3220, + "name": "Dunster2016 - Nondimensional Coagulation Model", + "repository_type": "biomodels", + "summary": "
We undertake a mathematical investigation of a model for the generation of thrombin, an enzyme central to haemostatic blood coagulation, as well as to thrombotic disorders, that is the end product of a complicated protein cascade with multiple feedbacks that ensures its production in the right place at the right time. In a laboratory setting, its central role is reflected in thrombin evolution over time being used as a measure of the ability of a patient's blood to clot. Here, we present a model for the generation of thrombin (based on earlier work) and analyse it using the method of matched asymptotic expansions to derive a sequence of simplified models that characterize the roles of distinct interactions over various timescales. In particular, we are able through the asymptotic analysis to provide simplified models that are an excellent substitute for the full model (capturing the explosive growth and decay of thrombin) and approximations for the key experimental measurements used to describe thrombin's characteristic evolution over time. The asymptotic results are validated against numerical simulations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4570, + "tag": "BioModels:BIOMD0000000925" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:40.374408+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000925", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3221": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3221, + "name": "Rhodes2019 - Immune-Mediated theory of Metastasis", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <p>Abstract:Accumulating experimental and clinical evidence suggest that the immune response to cancer is not exclusively anti-tumor. Indeed, the pro-tumor roles of the immune system - as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example - have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed "immune-mediated" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor "education" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. Our results suggest that further work is warranted to fully elucidate the pro-tumor effects of the immune system in metastatic cancer.</p> </body> </notes>", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4571, + "tag": "BioModels:BIOMD0000000926" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:40.922372+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000926", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3222": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3222, + "name": "Grigolon2018-Responses to auxin signals", + "repository_type": "biomodels", + "summary": "
Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4572, + "tag": "BioModels:BIOMD0000000927" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4573, + "tag": "unidentified plant" + } + ], + "timestamp_created": "2025-02-03 16:46:41.384721+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000927", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3223": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3223, + "name": "Baker2017 - The role of cytokines, MMPs and fibronectin fragments osteoarthritis", + "repository_type": "biomodels", + "summary": "
Baker2017 - The role of cytokines, MMPs andfibronectin fragments osteoarthritis

This model is described in the article:

Baker M, Brook BS, Owen MR.
J Math Biol 2017 Feb; :

Abstract:

Osteoarthritis (OA) is a degenerative disease which causes pain and stiffness in joints. OA progresses through excessive degradation of joint cartilage, eventually leading to significant joint degeneration and loss of function. Cytokines, a group of cell signalling proteins, present in raised concentrations in OA joints, can be classified into pro-inflammatory and anti-inflammatory groups. They mediate cartilage degradation through several mechanisms, primarily the up-regulation of matrix metalloproteinases (MMPs), a group of collagen-degrading enzymes. In this paper we show that the interactions of cytokines within cartilage have a crucial role to play in OA progression and treatment. We develop a four-variable ordinary differential equation model for the interactions between pro- and anti-inflammatory cytokines, MMPs and fibronectin fragments (Fn-fs), a by-product of cartilage degradation and up-regulator of cytokines. We show that the model has four classes of dynamic behaviour: homoeostasis, bistable inflammation, tristable inflammation and persistent inflammation. We show that positive and negative feedbacks controlling cytokine production rates can determine either a pre-disposition to OA or initiation of OA. Further, we show that manipulation of cytokine, MMP and Fn-fs levels can be used to treat OA, but we suggest that multiple treatment targets may be essential to halt or slow disease progression.

This model is hosted on BioModels Database and identified by: MODEL1704120000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4574, + "tag": "BioModels:BIOMD0000000928" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:41.855328+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000928", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3224": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3224, + "name": "Li2016 - Model for pancreatic cancer patients receiving immunotherapy", + "repository_type": "biomodels", + "summary": "
immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4575, + "tag": "BioModels:BIOMD0000000929" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4138, + "tag": "Pancreatic cancer" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:42.335594+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000929", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3225": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3225, + "name": "Liu2017 - chemotherapy targeted model of tumor immune system", + "repository_type": "biomodels", + "summary": "
Its a mathematical model reflecting chemotherapy response in tumor immune interaction system. Model encoded by Sarubini Kananathan and annotated+submitted to Biomodels by Krishna Tiwari
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4576, + "tag": "BioModels:BIOMD0000000930" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:42.919400+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000930", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3226": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3226, + "name": "Voliotis2019-GnRH Pulse Generation", + "repository_type": "biomodels", + "summary": "
Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised of hypothalamic neurons coexpressing kisspeptin, neurokoinin-B and dynorphin. Here, using mathematical modeling and in vivo optogenetics we reveal for the first time how this neural construct initiates and sustains the appropriate ultradian frequency essential for reproduction. Prompted by mathematical modeling, we show experimentally using female estrous mice that robust pulsatile release of luteinizing hormone, a proxy for GnRH, emerges abruptly as we increase the basal activity of the neuronal network using continuous low-frequency optogenetic stimulation. Further increase in basal activity markedly increases pulse frequency and eventually leads to pulse termination. Additional model predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically. Our results shed light on the long-elusive GnRH pulse generator offering new horizons for reproductive health and wellbeing.SIGNIFICANCE STATEMENT The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulationModel is encoded by Johannes and submitted to BioModels by Ahmad Zyoud.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4577, + "tag": "BioModels:BIOMD0000000931" + }, + { + "id": 4295, + "tag": "Mus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:43.400248+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000931", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3227": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3227, + "name": "Garde2020-Minimal model describing metabolic oscillations in Bacillus subtilis biofilms", + "repository_type": "biomodels", + "summary": "
Biofilms offer an excellent example of ecological interaction among bacteria. Temporal and spatial oscillations in biofilms are an emerging topic. In this paper, we describe the metabolic oscillations in Bacillus subtilis biofilms by applying the smallest theoretical chemical reaction system showing Hopf bifurcation proposed by Wilhelm and Heinrich in 1995. The system involves three differential equations and a single bilinear term. We specifically select parameters that are suitable for the biological scenario of biofilm oscillations. We perform computer simulations and a detailed analysis of the system including bifurcation analysis and quasi-steady-state approximation. We also discuss the feedback structure of the system and the correspondence of the simulations to biological observations. Our theoretical work suggests potential scenarios about the oscillatory behaviour of biofilms and also serves as an application of a previously described chemical oscillator to a biological system.
", + "tags": [ + { + "id": 3265, + "tag": "Bacillus subtilis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4578, + "tag": "BioModels:BIOMD0000000932" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:43.864021+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000932", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3228": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3228, + "name": "Kosiuk2015-Geometric analysis of the Goldbeter minimal model for the embryonic cell cycle", + "repository_type": "biomodels", + "summary": "
A minimal model describing the embryonic cell division cycle at the molecular level in eukaryotes is analyzed mathematically. It is known from numerical simulations that the corresponding three-dimensional system of ODEs has periodic solutions in certain parameter regimes. We prove the existence of a stable limit cycle and provide a detailed description on how the limit cycle is generated. The limit cycle corresponds to a relaxation oscillation of an auxiliary system, which is singularly perturbed and has the same orbits as the original model. The singular perturbation character of the auxiliary problem is caused by the occurrence of small Michaelis constants in the model. Essential pieces of the limit cycle of the auxiliary problem consist of segments of slow motion close to several branches of a two dimensional critical manifold which are connected by fast jumps. In addition, a new phenomenon of exchange of stability occurs at lines, where the branches of the two-dimensional critical manifold intersect. This novel type of relaxation oscillations is studied by combining standard results from geometric singular perturbation with several suitable blow-up transformations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4579, + "tag": "BioModels:BIOMD0000000933" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4580, + "tag": "unclassified eukaryotes" + } + ], + "timestamp_created": "2025-02-03 16:46:44.431304+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000933", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3229": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3229, + "name": "Linke2017 - Synchronization of Cyclins' expression by the Fkh2 transcription factor in the budding yeast cell cycle", + "repository_type": "biomodels", + "summary": "

Modeling of the expression of cyclin B2 and B3 in the budding yeast cell cycle

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4581, + "tag": "BioModels:BIOMD0000000934" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3127, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-02-03 16:46:45.012922+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000934", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3230": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3230, + "name": "Ferrel2011 - Cdk1 and APC regulation in cell cycle in Xenopus laevis", + "repository_type": "biomodels", + "summary": "Mathematical model of the regulation of Cdk1 and APC in cell cycle in Xenopus Laevis", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4582, + "tag": "BioModels:BIOMD0000000935" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 16:46:45.474114+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000935", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3231": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3231, + "name": "ferrel2011 - autonomous biochemical oscillator in cell cycle in Xenopus laevis v2", + "repository_type": "biomodels", + "summary": "
Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. The simplest examples of the eukaryotic cell cycle operate like autonomous oscillators. Here, we present the basic theory of oscillatory biochemical circuits in the context of the Xenopus embryonic cell cycle. We examine Boolean models, delay differential equation models, and especially ordinary differential equation (ODE) models. For ODE models, we explore what it takes to get oscillations out of two simple types of circuits (negative feedback loops and coupled positive and negative feedback loops). Finally, we review the procedures of linear stability analysis, which allow one to determine whether a given ODE model and a particular set of kinetic parameters will produce oscillations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4583, + "tag": "BioModels:BIOMD0000000936" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 16:46:45.934405+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000936", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3232": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3232, + "name": "Ferrel2011 - Autonomous biochemical oscillator in regulation of CDK1, Plk1, and APC in Xenopus Laevis cell cycle", + "repository_type": "biomodels", + "summary": "
Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. The simplest examples of the eukaryotic cell cycle operate like autonomous oscillators. Here, we present the basic theory of oscillatory biochemical circuits in the context of the Xenopus embryonic cell cycle. We examine Boolean models, delay differential equation models, and especially ordinary differential equation (ODE) models. For ODE models, we explore what it takes to get oscillations out of two simple types of circuits (negative feedback loops and coupled positive and negative feedback loops). Finally, we review the procedures of linear stability analysis, which allow one to determine whether a given ODE model and a particular set of kinetic parameters will produce oscillations.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4584, + "tag": "BioModels:BIOMD0000000937" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 16:46:46.420304+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000937", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3233": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3233, + "name": "Gerard2013 - Model 3 - Embryonic-type eukaryotic Cell Cycle regulation based on negative feedback between Cdk/cyclin and APC and competitive inhibition between Cdk/cyclin and securin for polyubiquitylation_1", + "repository_type": "biomodels", + "summary": "
The eukaryotic cell cycle is characterized by alternating oscillations in the activities of cyclin-dependent kinase (Cdk) and the anaphase-promoting complex (APC). Successful completion of the cell cycle is dependent on the precise, temporally ordered appearance of these activities. A modest level of Cdk activity is sufficient to initiate DNA replication, but mitosis and APC activation require an elevated Cdk activity. In present-day eukaryotes, this temporal order is provided by a complex network of regulatory proteins that control both Cdk and APC activities via sharp thresholds, bistability, and time delays. Using simple computational models, we show here that these dynamical features of cell-cycle organization could emerge in a control system driven by a single Cdk/cyclin complex and APC wired in a negative-feedback loop. We show that ordered phosphorylation of cellular proteins could be explained by multisite phosphorylation/dephosphorylation and competition of substrates for interconverting kinase (Cdk) and phosphatase. In addition, the competition of APC substrates for ubiquitylation can create and maintain sustained oscillations in cyclin levels. We propose a sequence of models that gets closer and closer to a realistic model of cell-cycle control in yeast. Since these models lack the elaborate control mechanisms characteristic of modern eukaryotes, they suggest that bistability and time delay
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4585, + "tag": "BioModels:BIOMD0000000938" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:46.874955+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000938", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3234": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3234, + "name": "Iwamoto2010 - Cell cycle reponse to DNA damage", + "repository_type": "biomodels", + "summary": "
After DNA damage, cells activate p53, a tumor suppressor gene, and select a cell fate (e.g., DNA repair, cell cycle arrest, or apoptosis). Recently, a p53 oscillatory behavior was observed following DNA damage. However, the relationship between this p53 oscillation and cell-fate selection is unclear. Here, we present a novel model of the DNA damage signaling pathway that includes p53 and whole cell cycle regulation and explore the relationship between p53 oscillation and cell fate selection. The simulation run without DNA damage qualitatively realized experimentally observed data from several cell cycle regulators, indicating that our model was biologically appropriate. Moreover, the comprehensive sensitivity analysis for the proposed model was implemented by changing the values of all kinetic parameters, which revealed that the cell cycle regulation system based on the proposed model has robustness on a fluctuation of reaction rate in each process. Simulations run with four different intensities of DNA damage, i.e. Low-damage, Medium-damage, High-damage, and Excess-damage, realized cell cycle arrest in all cases. Low-damage, Medium-damage, High-damage, and Excess-damage corresponded to the DNA damage caused by 100, 200, 400, and 800 J/m(2) doses of UV-irradiation, respectively, based on expression of p21, which plays a crucial role in cell cycle arrest. In simulations run with High-damage and Excess-damage, the length of the cell cycle arrest was shortened despite the severe DNA damage, and p53 began to oscillate. Cells initiated apoptosis and were killed at 400 and 800 J/m(2) doses of UV-irradiation, corresponding to High-damage and Excess-damage, respectively. Therefore, our model indicated that the oscillatory mode of p53 profoundly affects cell fate selection.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4586, + "tag": "BioModels:BIOMD0000000939" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:47.368104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000939", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3235": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3235, + "name": "Tang2019 - Pharmacology modelling of AURKB and ZAK interaction in TNBC", + "repository_type": "biomodels", + "summary": "

Aurora Kinase B and ZAK interaction model

Equivalent of the stochastic model used in \"Network pharmacology model predicts combined Aurora B and ZAK inhibition in MDA-MB-231 breast cancer cells\" by Tang et. al. 2018.The only difference is cell division and partitioning of the components, which are available in the original model for SGNS2.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4587, + "tag": "BioModels:BIOMD0000000940" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:47.829162+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000940", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3236": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3236, + "name": "Gerard2010 - Progression of mammalian cell cycle by successive activation of various cyclin cdk complexes", + "repository_type": "biomodels", + "summary": "
We previously proposed a detailed, 39-variable model for the network of cyclin-dependent kinases (Cdks) that controls progression along the successive phases of the mammalian cell cycle. Here, we propose a skeleton, 5-variable model for the Cdk network that can be seen as the backbone of the more detailed model for the mammalian cell cycle. In the presence of sufficient amounts of growth factor, the skeleton model also passes from a stable steady state to sustained oscillations of the various cyclin/Cdk complexes. This transition corresponds to the switch from quiescence to cell proliferation. Sequential activation of the cyclin/Cdk complexes allows the ordered progression along the G1, S, G2 and M phases of the cell cycle. The 5-variable model can also account for the existence of a restriction point in G1, and for endoreplication. Like the detailed model, it contains multiple oscillatory circuits and can display complex oscillatory behaviour such as quasi-periodic oscillations and chaos. We compare the dynamical properties of the skeleton model with those of the more detailed model for the mammalian cell cycle.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4588, + "tag": "BioModels:BIOMD0000000941" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:48.294146+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000941", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3237": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3237, + "name": "Sible2007 - Mitotic cell cycle mecanism in Xenopus Laevis", + "repository_type": "biomodels", + "summary": "
Although not a traditional experimental \"method,\" mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4589, + "tag": "BioModels:BIOMD0000000942" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3078, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-02-03 16:46:48.747014+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000942", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3238": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3238, + "name": "Hat2016 - Reponse of p53 System to irradiation in cell fate decision making", + "repository_type": "biomodels", + "summary": "
The p53 transcription factor is a regulator of key cellular processes including DNA repair, cell cycle arrest, and apoptosis. In this theoretical study, we investigate how the complex circuitry of the p53 network allows for stochastic yet unambiguous cell fate decision-making. The proposed Markov chain model consists of the regulatory core and two subordinated bistable modules responsible for cell cycle arrest and apoptosis. The regulatory core is controlled by two negative feedback loops (regulated by Mdm2 and Wip1) responsible for oscillations, and two antagonistic positive feedback loops (regulated by phosphatases Wip1 and PTEN) responsible for bistability. By means of bifurcation analysis of the deterministic approximation we capture the recurrent solutions (i.e., steady states and limit cycles) that delineate temporal responses of the stochastic system. Direct switching from the limit-cycle oscillations to the \"apoptotic\" steady state is enabled by the existence of a subcritical Neimark-Sacker bifurcation in which the limit cycle loses its stability by merging with an unstable invariant torus. Our analysis provides an explanation why cancer cell lines known to have vastly diverse expression levels of Wip1 and PTEN exhibit a broad spectrum of responses to DNA damage: from a fast transition to a high level of p53 killer (a p53 phosphoform which promotes commitment to apoptosis) in cells characterized by high PTEN and low Wip1 levels to long-lasting p53 level oscillations in cells having PTEN promoter methylated (as in, e.g., MCF-7 cell line).
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4590, + "tag": "BioModels:BIOMD0000000943" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:49.246225+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000943", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3239": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3239, + "name": "Goldbeter2013-Oscillatory activity of cyclin-dependent kinases in the cell cycle", + "repository_type": "biomodels", + "summary": "
A model for oscillations of Cdc2 kinase in embryonic cell cycles based on Michaelis\u2013Menten phosphorylation\u2013dephosphorylation kinetics shows that the occurrence and amplitude of the oscillations strongly depend on the ultrasensitivity of the enzymatic cascade that controls the activity of the cyclin-dependent kinase.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4591, + "tag": "BioModels:BIOMD0000000944" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:49.711184+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000944", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3240": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3240, + "name": "Evans2004 - Cell based mathematical model of topotecan", + "repository_type": "biomodels", + "summary": "

A two compartment mathematical model of the antineoplastic compound topotecan

", + "tags": [ + { + "id": 4592, + "tag": "Antineoplastic drug pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4593, + "tag": "BioModels:BIOMD0000000945" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:50.182054+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000945", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3241": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3241, + "name": "Evans2005 - Compartmental model for antineoplastic drug topotecan in breast cancer cells", + "repository_type": "biomodels", + "summary": "

Compartment model for the antineoplastic drug topotecan. Modelling drug in its active lactone and inactive hydroxy acid forms in the medium, extracellular, cytoplasm and nucleus.

", + "tags": [ + { + "id": 4592, + "tag": "Antineoplastic drug pathway" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4594, + "tag": "BioModels:BIOMD0000000946" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:50.656838+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000946", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3242": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3242, + "name": "Lee2017 - Paracetamol first-pass metabolism PK model", + "repository_type": "biomodels", + "summary": "Authors developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. The study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4595, + "tag": "BioModels:BIOMD0000000947" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4596, + "tag": "Pharmacokinetic Study" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:51.226539+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000947", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3243": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3243, + "name": "Landberg2009 - Alkylresorcinol Dose Response", + "repository_type": "biomodels", + "summary": "Pharmacokinetic model of alkylresorcinols. Both plasma AR concentrations and urinary metabolites in 24-h samples showed a dose-response relation to increased AR intake, which strongly supports the hypothesis that ARs and their metabolites may be useful as biomarkers of whole-grain wheat and rye intakes.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4597, + "tag": "BioModels:BIOMD0000000948" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:51.695381+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000948", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3244": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3244, + "name": "Chitnis2008 - Mathematical model of malaria transmission", + "repository_type": "biomodels", + "summary": "Mathematical model of malaria transmission for low and high transmission rates.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4598, + "tag": "BioModels:BIOMD0000000949" + }, + { + "id": 4599, + "tag": "Disease Transmission" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:52.171258+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000949", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3245": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3245, + "name": "Chitnis2012 - Model Rift Valley Fever transmission between cattle and mosquitoes (Model 1)", + "repository_type": "biomodels", + "summary": "Mathematical model for Rift Valley Fever transmission between cattle and mosquitoes without infectious eggs.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4600, + "tag": "BioModels:BIOMD0000000950" + }, + { + "id": 4599, + "tag": "Disease Transmission" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:52.660800+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000950", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3246": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3246, + "name": "Mitrophanov2015 - Simulating extended Hockin Blood Coagulation Model under varied pH", + "repository_type": "biomodels", + "summary": "Mathematical model of the blood coagulation cascade with new kinetic rates to simulate acidosis. Extended Hockin2002 model. Reused Mitrophanov2011 model with new parameters k: 5, 6, 7, 10, 15, 16, 17, 22, 26, 31, 32, 43 and 44.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4601, + "tag": "BioModels:BIOMD0000000951" + }, + { + "id": 3806, + "tag": "Blood coagulation" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:53.145400+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000951", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3247": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3247, + "name": "Rodenfels2019 - Heat Oscillations Driven by the Embryonic Cell Cycle Reveal the Energetic Costs of Signaling", + "repository_type": "biomodels", + "summary": "
All living systems function out of equilibrium and exchange energy in the form of heat with their environment. Thus, heat flow can inform on the energetic costs of cellular processes, which are largely unknown. Here, we have repurposed an isothermal calorimeter to measure heat flow between developing zebrafish embryos and the surrounding medium. Heat flow increased over time with cell number. Unexpectedly, a prominent oscillatory component of the heat flow, with periods matching the synchronous early reductive cleavage divisions, persisted even when DNA synthesis and mitosis were blocked by inhibitors. Instead, the heat flow oscillations were driven by the phosphorylation and dephosphorylation reactions catalyzed by the cell-cycle oscillator, the biochemical network controlling mitotic entry and exit. We propose that the high energetic cost of cell-cycle signaling reflects the significant thermodynamic burden of imposing accurate and robust timing on cell proliferation during development.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4602, + "tag": "BioModels:BIOMD0000000952" + }, + { + "id": 4603, + "tag": "Danio rerio" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:53.620517+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000952", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3248": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3248, + "name": "Queralt2006 - Initiation of mitotic exit by downregulation of PP2A in budding yeast", + "repository_type": "biomodels", + "summary": "

Mathematical model of mitotic exit in budding yeast.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4604, + "tag": "BioModels:BIOMD0000000953" + }, + { + "id": 4605, + "tag": "Negative regulation of mitotic nuclear division" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3292, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-02-03 16:46:54.090245+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000953", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3249": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3249, + "name": "Pandey2018-reversible transition between quiescence and proliferation", + "repository_type": "biomodels", + "summary": "Cells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the restriction point (R-point), cells become irreversibly committed to the completion of the cell cycle independent of mitogen. The mechanism involving hyper-phosphorylation of retinoblastoma (Rb) and activation of transcription factor E2F is linked to the R-point passage. However, stress stimuli trigger exit from the cell cycle back to the mitogen-sensitive quiescent state after Rb hyper-phosphorylation but only until APC/CCdh1 inactivation. In this study, we developed a mathematical model to investigate the reversible transition between quiescence and proliferation in mammalian cells with respect to mitogen and stress signals. The model integrates the current mechanistic knowledge and accounts for the recent experimental observations with cells exiting quiescence and proliferating cells. We show that Cyclin E:Cdk2 couples Rb-E2F and APC/CCdh1 bistable switches and temporally segregates the R-point and the G1/S transition. A redox-dependent mutual antagonism between APC/CCdh1 and its inhibitor Emi1 makes the inactivation of APC/CCdh1 bistable. We show that the levels of Cdk inhibitor (CKI) and mitogen control the reversible transition between quiescence and proliferation. Further, we propose that shifting of the mitogen-induced transcriptional program to G2-phase in proliferating cells might result in an intermediate Cdk2 activity at the mitotic exit and in the immediate inactivation of APC/CCdh1. Our study builds a coherent framework and generates hypotheses that can be further explored by experiments.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4606, + "tag": "BioModels:BIOMD0000000954" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:54.554737+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000954", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3250": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3250, + "name": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy", + "repository_type": "biomodels", + "summary": "In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4607, + "tag": "BioModels:BIOMD0000000955" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:55.035726+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000955", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3251": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3251, + "name": "Bertozzi2020 - SIR model of scenarios of COVID-19 spread in CA and NY", + "repository_type": "biomodels", + "summary": "The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4609, + "tag": "BioModels:BIOMD0000000956" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:55.850147+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000956", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3252": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3252, + "name": "Roda2020 - SIR model of COVID-19 spread in Wuhan", + "repository_type": "biomodels", + "summary": "=Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4610, + "tag": "BioModels:BIOMD0000000957" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:56.361641+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000957", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3253": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3253, + "name": "Ndairou2020 - early-stage transmission dynamics of COVID-19 in Wuhan", + "repository_type": "biomodels", + "summary": "We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic reproduction number, and we investigate the sensitivity of the model with respect to the variation of each one of its parameters. Numerical simulations show the suitability of the proposed COVID-19 model for the outbreak that occurred in Wuhan, China", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4611, + "tag": "BioModels:BIOMD0000000958" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:56.833504+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000958", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3254": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3254, + "name": "Kok2020 - IFNalpha-induced signaling in Huh7.5 cells", + "repository_type": "biomodels", + "summary": "
The proposed ODE model describes dynamics of IFNalpha-induced signaling in Huh7.5 cells for a time scale up to 32 hours after stimulation with IFNalpha. The model consists of an IFN receptor model, formation/degradation and cytoplasmic/nuclear shuttling of STAT1-homodimers, STAT1-STAT2-heterodimers and STAT1-STAT2-IRF9 (ISGF3) complexes. On top, formation of feedback proteins STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3 and IRF2 and corresponding influences on IFNalpha signaling dynamics was incorporated. The model was calibrated by dose response and time course measurements over 32 hours as well as time courses for USP18 inhibition and overexpression experiments. As a special focus, the model is able to describe dose-dependent sensitization and desensitization of IFNalpha signaling in form of double treatment experiments at 0h and 24h.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4612, + "tag": "BioModels:BIOMD0000000959" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:57.344979+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000959", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3255": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3255, + "name": "Paiva2020 - SEIAHRD model of transmission dynamics of COVID-19", + "repository_type": "biomodels", + "summary": "This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4613, + "tag": "BioModels:BIOMD0000000960" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:57.918840+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000960", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3256": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 3256, + "name": "McDougal2017 - Metabolism in ischemic cardiomyocytes", + "repository_type": "biomodels", + "summary": "Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, our model enables prediction of the cell\u2019s condition up to the moment of reperfusion. The simulations revealed a distinct transition between energetically sustainable and unsustainable ATP concentrations for various energetic demands. Our model illustrates how even low oxygen concentrations allow the cell to perform essential functions. We found that the oxygen level required for a sustainable level of ATP increases roughly linearly with the ATP consumption rate. An extracellular O2 concentration of ~0.007 mM could supply basic energy needs in non-beating cardiomyocytes, suggesting that increased collateral circulation may provide an important source of oxygen to sustain the cardiomyocyte during extended ischemia. Our model provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4614, + "tag": "BioModels:BIOMD0000000961" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:46:58.384878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000961", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3257": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3257, + "name": "Zhao2020 - SUQC model of COVID-19 transmission dynamics in Wuhan, Hubei, and China", + "repository_type": "biomodels", + "summary": "Background - The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures. Methods - We develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models. Results - The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R &amp;gt; 1, and after January 30, all regions had a reproductive number R lesser than 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty. Conclusions - We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4615, + "tag": "BioModels:BIOMD0000000962" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:58.849093+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000962", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3258": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3258, + "name": "Weitz2020 - SIR model of COVID-19 transmission with shielding", + "repository_type": "biomodels", + "summary": "The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 2020. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, developing what we term \u2018shield immunity\u2019 at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence and in the potential development of plasma-based therapies.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4616, + "tag": "BioModels:BIOMD0000000963" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:59.308914+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000963", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3259": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3259, + "name": "Mwalili2020 - SEIR model of COVID-19 transmission and environmental pathogen prevalence", + "repository_type": "biomodels", + "summary": "Objective: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number R0. The model equations are solved numerically using fourth and ffth order Runge\u2013Kutta methods. Results: We found an R0 of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4617, + "tag": "BioModels:BIOMD0000000964" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:46:59.960408+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000964", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3260": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3260, + "name": "LeBeau1999 - IP3-dependent intracellular calcium oscillations due to agonist stimulation from Cholecytokinin", + "repository_type": "biomodels", + "summary": "The properties of inositol 1,4,5-trisphosphate (IP3)-dependent intracellular calcium oscillations in pancreatic acinar cells depend crucially on the agonist used to stimulate them. Acetylcholine or carbachol (CCh) cause high-frequency (10\u201312-s period) calcium oscillations that are superimposed on a raised baseline, while cholecystokinin (CCK) causes long-period (.100-s period) baseline spiking. We show that physiological concentrations of CCK induce rapid phosphorylation of the IP3 receptor, which is not true of physiological concentrations of CCh. Based on this and other experimental data, we construct a mathematical model of agonist-specific intracellular calcium oscillations in pancreatic acinar cells. Model simulations agree with previous experimental work on the rates of activation and inactivation of the IP3 receptor by calcium (DuFour, J.-F., I.M. Arias, and T.J. Turner. 1997. J. Biol. Chem. 272:2675\u20132681), and reproduce both short-period, raised baseline oscillations, and long-period baseline spiking. The steady state open probability curve of the model IP3 receptor is an increasing function of calcium concentration, as found for type-III IP3 receptors by Hagar et al. (Hagar, R.E., A.D. Burgstahler, M.H. Nathanson, and B.E. Ehrlich. 1998. Nature. 396:81\u201384). We use the model to predict the effect of the removal of external calcium, and this prediction is confirmed experimentally. We also predict that, for type-III IP3 receptors, the steady state open probability curve will shift to lower calcium concentrations as the background IP3 concentration increases. We conclude that the differences between CCh- and CCK-induced calcium oscillations in pancreatic acinar cells can be explained by two principal mechanisms: (a) CCK causes more phosphorylation of the IP3 receptor than does CCh, and the phosphorylated receptor cannot pass calcium current; and (b) the rate of calcium ATPase pumping and the rate of calcium influx from the outside the cell are greater in the presence of CCh than in the presence of CCK.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4618, + "tag": "BioModels:BIOMD0000000965" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:00.442702+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000965", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3261": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3261, + "name": "Cui2008 - in vitro transcriptional response of zinc homeostasis system in Escherichia coli", + "repository_type": "biomodels", + "summary": "BACKGROUND: The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc homeostasis systems. Its underlying regulatory machine consists of repression on zinc influx through ZnuABC by Zur (Zn2+ uptake regulator) and activation on zinc efflux via ZntA by ZntR (a zinc-responsive regulator). Although these transcriptional regulations seem to be well characterized, and there is an abundance of detailed in vitro experimental data available, as yet there is no mathematical model to help interpret these data. To our knowledge, the work described here is the first attempt to use a mathematical model to simulate these regulatory relations and to help explain the in vitro experimental data. RESULTS: We develop a unified mathematical model consisting of 14 reactions to simulate the in vitro transcriptional response of the zinc homeostasis system in E. coli. Firstly, we simulate the in vitro Zur-DNA interaction by using two of these reactions, which are expressed as 4 ordinary differential equations (ODEs). By imposing the conservation restraints and solving the relevant steady state equations, we find that the simulated sigmoidal curve matches the corresponding experimental data. Secondly, by numerically solving the ODEs for simulating the Zur and ZntR run-off transcription experiments, and depicting the simulated concentrations of zntA and znuC transcripts as a function of free zinc concentration, we find that the simulated curves fit the corresponding in vitro experimental data. Moreover, we also perform simulations, after taking into consideration the competitive effects of ZntR with the zinc buffer, and depict the simulated concentration of zntA transcripts as a function of the total ZntR concentration, both in the presence and absence of Zn(II). The obtained simulation results are in general agreement with the corresponding experimental data. CONCLUSION: Simulation results show that our model can quantitatively reproduce the results of several of the in vitro experiments conducted by Outten CE and her colleagues. Our model provides a detailed insight into the dynamics of the regulatory system and also provides a general framework for simulating in vitro metal-binding and transcription experiments and interpreting the relevant experimental data.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4619, + "tag": "BioModels:BIOMD0000000966" + }, + { + "id": 3082, + "tag": "Escherichia coli" + }, + { + "id": 4392, + "tag": "Ordinary differential equation model" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4620, + "tag": "Zinc ion homeostasis" + } + ], + "timestamp_created": "2025-02-03 16:47:00.934859+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000966", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3262": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3262, + "name": "McLean1991 - Behaviour of HIV in the presence of zidovudine", + "repository_type": "biomodels", + "summary": "A new mechanism is proposed for the apparent breakthrough of HIV that occurs approximately 6 months after the commencement of therapy with zidovudine (AZT). Using a simple mathematical model of the interacting population dynamics of HIV and its major host cell in the circulation (the CD4+ lymphocyte), predicted patterns of HIV plasma viraemia in the weeks following treatment with zidovudine are generated. These are in close agreement with observed patterns despite the fact that the model contains no mechanisms for the development of drug-resistant strains of virus. It is suggested that the patterns of viral abundance observed during the first 6 months after treatment may be the result of non-linearities in the interactions between HIV and CD4+ cells, and that it is only after the first post-treatment burst of viral production that drug resistance plays an important role.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4621, + "tag": "BioModels:BIOMD0000000967" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4622, + "tag": "Human immunodeficiency virus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:01.398587+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000967", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3263": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3263, + "name": "Palmer2008 - Negative Feedback in IL-7 mediated Jak-Stat signaling", + "repository_type": "biomodels", + "summary": "Interleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Binding of IL-7 to its cognate receptor, the IL-7 receptor (IL-7R), activates multiple pathways that regulate lymphocyte survival, glucose uptake, proliferation and differentiation. There has been much interest in understanding how IL-7 receptor signaling is modulated at multiple interconnected network levels. This review examines how the strength of the signal through the IL-7 receptor is modulated in T and B cells, including the use of shared receptor components, signaling crosstalk, shared interaction domains, feedback loops, integrated generegulation, multimerization and ligand competition. We discuss how these network control mechanisms could integrate to govern the properties of IL-7R signaling in lymphocytes in health and disease. Analysis of IL-7 receptor signaling at a network level in a systematic manner will allow for a comprehensive approach to understanding the impact of multiple signaling pathways on lymphocyte biology.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4623, + "tag": "BioModels:BIOMD0000000968" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:01.985453+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000968", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3264": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3264, + "name": "Cuadros2020 - SIHRD spatiotemporal model of COVID-19 transmission in Ohio", + "repository_type": "biomodels", + "summary": "The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4624, + "tag": "BioModels:BIOMD0000000969" + }, + { + "id": 2807, + "tag": "COVID-19" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:02.462054+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000969", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3265": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3265, + "name": "Hou2020 - SEIR model of COVID-19 transmission in Wuhan", + "repository_type": "biomodels", + "summary": "A novel coronavirus pneumonia, first identified in Wuhan City and referred to as COVID-19 by the World Health Organization, has been quickly spreading to other cities and countries. To control the epidemic, the Chinese government mandated a quarantine of the Wuhan city on January 23, 2020. To explore the effectiveness of the quarantine of the Wuhan city against this epidemic, transmission dynamics of COVID-19 have been estimated. A well-mixed "susceptible exposed infectious recovered" (SEIR) compartmental model was employed to describe the dynamics of the COVID-19 epidemic based on epidemiological characteristics of individuals, clinical progression of COVID-19, and quarantine intervention measures of the authority. Considering infected individuals as contagious during the latency period, the well-mixed SEIR model fitting results based on the assumed contact rate of latent individuals are within 6-18, which represented the possible impact of quarantine and isolation interventions on disease infections, whereas other parameter were suppose as unchanged under the current intervention. The present study shows that, by reducing the contact rate of latent individuals, interventions such as quarantine and isolation can effectively reduce the potential peak number of COVID-19 infections and delay the time of peak infection.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4625, + "tag": "BioModels:BIOMD0000000970" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:03.006669+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000970", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3266": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3266, + "name": "Tang2020 - Estimation of transmission risk of COVID-19 and impact of public health interventions", + "repository_type": "biomodels", + "summary": "Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71\u20137.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4626, + "tag": "BioModels:BIOMD0000000971" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:03.483610+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000971", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3267": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3267, + "name": "Tang2020 - Estimation of transmission risk of COVID-19 and impact of public health interventions - update", + "repository_type": "biomodels", + "summary": "The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a na\u00efve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th, 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4627, + "tag": "BioModels:BIOMD0000000972" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:03.966684+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000972", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3268": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3268, + "name": "Dasgupta2020 - Reduced model of receptor clusturing and aggregation", + "repository_type": "biomodels", + "summary": "a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering inresponse to activation both in 2D and in 3D", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4628, + "tag": "BioModels:BIOMD0000000973" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:04.424956+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000973", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3269": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3269, + "name": "Carcione2020 - Deterministic SEIR simulation of a COVID-19 outbreak", + "repository_type": "biomodels", + "summary": "An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4629, + "tag": "BioModels:BIOMD0000000974" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:04.918033+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000974", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3270": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3270, + "name": "Leloup2004 - Mammalian Circadian Rhythm models for 23.8 and 24.2 hours timeperiod", + "repository_type": "biomodels", + "summary": "We extend the study of a computational model recently proposed for the mammalian circadian clock (Proc. Natl Acad. Sci. USA 100 (2003) 7051). The model, based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, and Clock genes, can give rise to sustained circadian oscillations in conditions of continuous darkness. These limit cycle oscillations correspond to circadian rhythms autonomously generated by suprachiasmatic nuclei and by some peripheral tissues. By using different sets of parameter values producing circadian oscillations, we compare the effect of the various parameters and show that both the occurrence and the period of the oscillations are generally most sensitive to parameters related to synthesis or degradation of Bmal1 mRNA and BMAL1 protein. The mechanism of circadian oscillations relies on the formation of an inactive complex between PER and CRY and the activators CLOCK and BMAL1 that enhance Per and Cry expression. Bifurcation diagrams and computer simulations nevertheless indicate the possible existence of a second source of oscillatory behavior. Thus, sustained oscillations might arise from the sole negative autoregulation of Bmal1 expression. This second oscillatory mechanism may not be functional in physiological conditions, and its period need not necessarily be circadian. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark (LD) cycles. Long-term suppression of circadian oscillations by a single light pulse can occur in the model when a stable steady state coexists with a stable limit cycle. The phase of the oscillations upon entrainment in LD critically depends on the parameters that govern the level of CRY protein. Small changes in the parameters governing CRY levels can shift the peak in Per mRNA from the L to the D phase, or can prevent entrainment. The results are discussed in relation to physiological disorders of the sleep-wake cycle linked to perturbations of the human circadian clock, such as the familial advanced sleep phase syndrome or the non-24h sleep-wake syndrome.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4630, + "tag": "BioModels:BIOMD0000000975" + }, + { + "id": 4223, + "tag": "Entrainment of circadian clock by photoperiod" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:05.401880+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000975", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3271": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3271, + "name": "Ghanbari2020 - forecasting the second wave of COVID-19 in Iran", + "repository_type": "biomodels", + "summary": "One of the common misconceptions about COVID-19 disease is to assume that we will not see a recurrence after the first wave of the disease has subsided. This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. From the results, improving the recovery rate of people with weak immune systems via appropriate medical incentives is resulted as one of the most effective prescriptions to prevent the widespread unbridled outbreak of the second wave of COVID-19.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4631, + "tag": "BioModels:BIOMD0000000976" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:05.995192+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000976", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3272": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3272, + "name": "Sarkar2020 - SAIR model of COVID-19 transmission with quarantine measures in India", + "repository_type": "biomodels", + "summary": "In India, 100,340 confirmed cases and 3155 confirmed deaths due to COVID-19 were reported as of May 18, 2020. Due to absence of specific vaccine or therapy, non-pharmacological interventions including so- cial distancing, contact tracing are essential to end the worldwide COVID-19. We propose a mathematical model that predicts the dynamics of COVID-19 in 17 provinces of India and the overall India. A complete scenario is given to demonstrate the estimated pandemic life cycle along with the real data or history to date, which in turn divulges the predicted inflection point and ending phase of SARS-CoV-2. The proposed model monitors the dynamics of six compartments, namely susceptible (S), asymptomatic (A), recovered (R), infected (I), isolated infected ( I q ) and quarantined susceptible ( S q ), collectively expressed SARII q S q . A sensitivity analysis is conducted to determine the robustness of model predictions to parameter values and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in India. Our re- sults reveal that achieving a reduction in the contact rate between uninfected and infected individuals by quarantined the susceptible individuals, can effectively reduce the basic reproduction number. Our model simulations demonstrate that the elimination of ongoing SARS-CoV-2 pandemic is possible by combining the restrictive social distancing and contact tracing. Our predictions are based on real data with reason- able assumptions, whereas the accurate course of epidemic heavily depends on how and when quaran- tine, isolation and precautionary measures are enforced.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4632, + "tag": "BioModels:BIOMD0000000977" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:06.475602+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000977", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3273": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3273, + "name": "Mukandavire2020 - SEIR model of early COVID-19 transmission in South Africa", + "repository_type": "biomodels", + "summary": "The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. Africa is still early in this epidemic, therefore the extent of disease severity is not yet clear. We used a mathematical model to fit to the observed cases of COVID-19 in South Africa to estimate the basic reproductive number and critical vaccination coverage to control the disease for different hypothetical vaccine efficacy scenarios. We also estimated the percentage reduction in effective contacts due to the social distancing measures implemented. Early model estimates show that COVID-19 outbreak in South Africa had a basic reproductive number of 2.95 (95% credible interval [CrI] 2.83\u20133.33). A vaccine with 70% efficacy had the capacity to contain COVID-19 outbreak but at very higher vaccination coverage 94.44% (95% Crl 92.44\u201399.92%) with a vaccine of 100% efficacy requiring 66.10% (95% Crl 64.72\u201369.95%) coverage. Social distancing measures put in place have so far reduced the number of social contacts by 80.31% (95% Crl 79.76\u201380.85%). These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. Therefore, the current social distancing measures to reduce contacts will remain key in controlling the infection in the absence of vaccines and other therapeutics.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4633, + "tag": "BioModels:BIOMD0000000978" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:06.952064+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000978", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3274": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3274, + "name": "Malkov2020 - SEIRS model of COVID-19 transmission with reinfection", + "repository_type": "biomodels", + "summary": "Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully protected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious-Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are indistinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4634, + "tag": "BioModels:BIOMD0000000979" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:07.425622+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000979", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3275": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3275, + "name": "Malkov2020 - SEIRS model of COVID-19 transmission with time-varying R values and reinfection", + "repository_type": "biomodels", + "summary": "Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully pro- tected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious- Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are in- distinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4635, + "tag": "BioModels:BIOMD0000000980" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:07.920866+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000980", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3276": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3276, + "name": "Wan2020 - risk estimation and prediction of the transmission of COVID-19 in maninland China excluding Hubei province", + "repository_type": "biomodels", + "summary": "Background: In December 2019, an outbreak of coronavirus disease (later named as COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.Methods: A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of the mainland of China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number (Rc), as well as the effective daily reproduction ratio- Re(t), of the disease transmission in the mainland of China excluding Hubei province.Results: The estimation outcomes indicate that Rc is 3.36 (95% CI: 3.20-3.64) and Re(t) has dropped below 1 since 31 January 2020, which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected, and may even cause a second wave of epidemic or outbreaks. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020.Conclusions: To ensure the pandemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, keeping social distance, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4636, + "tag": "BioModels:BIOMD0000000981" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:08.395138+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000981", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3277": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3277, + "name": "Law2020 - SIR model of COVID-19 transmission in Malyasia with time-varying parameters", + "repository_type": "biomodels", + "summary": "The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4637, + "tag": "BioModels:BIOMD0000000982" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:08.859921+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000982", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3278": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3278, + "name": "Zongo2020 - model of COVID-19 transmission dynamics under containment measures in France", + "repository_type": "biomodels", + "summary": "The main objective of this paper is to address the following question: are the containment measures imposed by most of the world governments effective and sufficient to stop the epidemic of COVID-19 beyond the lock-down period? In this paper, we propose a mathematical model which allows us to investigate and analyse this problem. We show by means of the reproductive number, R0 that the containment measures appear to have slowed the growth of the outbreak. Nevertheless, these measures remain only effective as long as a very large fraction of population, p, greater than the critical value 1 \u2212 1/R0 remains confined. Using French current data, we give some simulation experiments with five scenarios including: (i) the validation of model with p estimated to 93%, (ii) the study of the effectiveness of containment measures, (iii) the study of the effectiveness of the large-scale testing, (iv) the study of the social distancing and wearing masks measures and (v) the study taking into account the combination of the large-scale test of detection of infected individuals and the social distancing with linear progressive easing of restrictions. The latter scenario was shown to be effective at overcoming the outbreak if the transmission rate decreases to 75% and the number of tests of detection is multiplied by three. We also noticed that if the measures studied in our five scenarios are taken separately then the second wave might occur at least as far as the parameter values remain unchanged.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4638, + "tag": "BioModels:BIOMD0000000983" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:09.337103+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000983", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3279": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3279, + "name": "Fang2020 - SEIR model of COVID-19 transmission considering government interventions in Wuhan", + "repository_type": "biomodels", + "summary": "Using the parameterized susceptible\u2010exposed\u2010infectious\u2010recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID\u201019) outbreak and impact of different control measures, conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the datafitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS\u2010CoV\u20102) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. Evidence from the fourphase stringent measures showed that it was significant to ensure early detection, early isolation, early treatment, adequate medical supplies, patients\u2019 being admitted to designated hospitals, and comprehensive therapeutic strategy. Collaborative efforts are required to combat the novel coronavirus, focusing on both persistent strict domestic interventions and vigilance against exogenous imported cases.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4639, + "tag": "BioModels:BIOMD0000000984" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:09.820034+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000984", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3280": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3280, + "name": "Gex-Fabry1984 - model of receptor-mediated endocytosis of EGF in BALB/c 3T3 cells", + "repository_type": "biomodels", + "summary": "We present a mathematical model for analyzing, simulating, and quantitating the dynamic and steady-state characteristics of receptor-mediated endocytosis. The basic processes considered by the model are ligand-receptor binding, diffusion of receptors and ligand-receptor complexes in the plane of the membrane toward and away from coated pits, binding of ligand-receptor complexes to coated pit proteins, endocytosis of coated pit contents, degradation of ligand, and recycling of undegraded receptors. The model accounts quantitatively for a wide variety of kinetic data and makes new predictions about steady-state characteristics. We show that for homogeneous receptors the slope of the Scatchard plot is not necessarily constant but can have a positive or negative derivative, depending on the concentration of coated pit proteins and their reactivity. This finding suggests that binding data, which show linear and concave curves, might be explainable be a simple coated pit-related mechanism. Similarly the relationship between the x-intercept and the number of receptors is also affected by kinetic parameters controlling endocytosis. We briefly discuss these results in terms of possible mechanisms for the action of tumor promoters, the large variations in receptor number and affinity in the literature, and methods for quantitative characterization of parameters.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4640, + "tag": "BioModels:BIOMD0000000985" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:10.287233+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000985", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3281": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3281, + "name": "Aubry1995 - Multi-compartment model of fluid-phase endocytosis kinetics in Dictyostelium discoideum", + "repository_type": "biomodels", + "summary": "Fluid-phase endoeytosis (pinocytosis) kinetics were studied in Dictyostelium discoideum amoebae from the axenic strain Ax-2 that exhibits high rates of fluid-phase endoeytosis when cultured in liquid nutrient media. Fluorescein-labelled dextran (FITC-dextran) was used as a marker in continuous uptake- and in pulse-chase exocytosis experiments. In the latter case, efflux of the marker was monitored on cells loaded for short periods of time and resuspended in marker-free medium. A multicompartmental model was developed which describes satisfactorily fluid-phase endocytosis kinetics. In particular, it accounts correctly for the extended latency period before exocytosis in pulse-chase experiments and it suggests the existence of some sorts of maturation stages in the pathway.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4641, + "tag": "BioModels:BIOMD0000000986" + }, + { + "id": 4642, + "tag": "Dictyostelium discoideum AX2" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:10.798824+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000986", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3282": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3282, + "name": "Aubry1995 - Nine-compartment model of fluid-phase endocytosis kinetics in Dictyostelium discoideum", + "repository_type": "biomodels", + "summary": "Fluid-phase endoeytosis (pinocytosis) kinetics were studied in Dictyostelium discoideum amoebae from the axenic strain Ax-2 that exhibits high rates of fluid-phase endoeytosis when cultured in liquid nutrient media. Fluorescein-labelled dextran (FITC-dextran) was used as a marker in continuous uptake- and in pulse-chase exocytosis experiments. In the latter case, efflux of the marker was monitored on cells loaded for short periods of time and resuspended in marker-free medium. A multicompartmental model was developed which describes satisfactorily fluid-phase endocytosis kinetics. In particular, it accounts correctly for the extended latency period before exocytosis in pulse-chase experiments and it suggests the existence of some sorts of maturation stages in the pathway.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4643, + "tag": "BioModels:BIOMD0000000987" + }, + { + "id": 4642, + "tag": "Dictyostelium discoideum AX2" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:11.362866+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000987", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3283": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3283, + "name": "Westerhoff2020 - systems biology model of the coronavirus pandemic 2020", + "repository_type": "biomodels", + "summary": "

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4644, + "tag": "BioModels:BIOMD0000000988" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:11.962399+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000988", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3284": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 3284, + "name": "Strasen2018 - TGFb SMAD Signalling - Dose dependent dynamics upon TGFb stimulation", + "repository_type": "biomodels", + "summary": "
This model simulates TGFb dose dependent kinetics of The SMADs.  TGFb ligand dose applied are  1pM, 2.5pM, 5pM, 25pM, and 100pM as explained in the manuscript.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4645, + "tag": "BioModels:BIOMD0000000989" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:12.450606+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000989", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3285": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "13", + "id": 3285, + "name": "Strasen2018 - TGFb SMAD Signalling - Degradation of 25pM ligand (TGFb)", + "repository_type": "biomodels", + "summary": "
MOdel simulates 25pM ligand degradation kinetics as shown in Figure 4D
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4646, + "tag": "BioModels:BIOMD0000000990" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:13.499951+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000990", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3286": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3286, + "name": "Okuonghae2020 - SEAIR model of COVID-19 transmission in Lagos, Nigeria", + "repository_type": "biomodels", + "summary": "This work examines the impact of various non-pharmaceutical control measures (government and personal) on the population dynamics of the novel coronavirus disease 2019 (COVID-19) in Lagos, Nigeria, using an appropriately formulated mathematical model. Using the available data, since its first reported case on 16 March 2020, we seek to develop a predicative tool for the cumulative number of reported cases and the number of active cases in Lagos; we also estimate the basic reproduction number of the disease outbreak in the aforementioned State in Nigeria. Using numerical simulations, we show the effect of control measures, specifically the common social distancing, use of face mask and case detection (via contact tracing and subsequent testings) on the dynamics of COVID-19. We also provide forecasts for the cumulative number of reported cases and active cases for different levels of the control measures being implemented. Numerical simulations of the model show that if at least 55% of the population comply with the social distancing regulation with about 55% of the population effectively making use of face masks while in public, the disease will eventually die out in the population and that, if we can step up the case detection rate for symptomatic individuals to about 0.8 per day, with about 55% of the population complying with the social distancing regulations, it will lead to a great decrease in the incidence (and prevalence) of COVID-19.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4647, + "tag": "BioModels:BIOMD0000000991" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-02-03 16:47:13.987438+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000991", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3287": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "15", + "id": 3287, + "name": "Strasen2018 - TGFb SMAD Signalling - Restimulation with 5pM TGFb at 3hr", + "repository_type": "biomodels", + "summary": "
 Restimulation with 5pM TGF\u03b2 at 3hr - Figure 4E
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4648, + "tag": "BioModels:BIOMD0000000994" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:14.517989+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000994", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3288": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3288, + "name": "Strasen2018 - TGFb SMAD Signalling - Restimulation with 5pM TGFb at 8hr", + "repository_type": "biomodels", + "summary": "
Restimulation with 5pM TGF\u03b2 at 8 hrs, shown in Figure 4F  
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4649, + "tag": "BioModels:BIOMD0000000995" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:14.985386+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000995", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3289": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3289, + "name": "Strasen2018 - TGFb SMAD Signalling - Restimulation with 100pM TGFb at 6hr", + "repository_type": "biomodels", + "summary": "
Restimulation with 100pM TGF\u03b2 at 6hr- figure 4G
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4650, + "tag": "BioModels:BIOMD0000000996" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:15.442688+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000996", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3290": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3290, + "name": "Strasen2018 - TGFb SMAD Signalling - DRB treatment", + "repository_type": "biomodels", + "summary": "
 Simulates figure 4H. DRB = 1 simulates the wild type condition and DRB = 0.2 simulates the DRB treated condition.  
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4651, + "tag": "BioModels:BIOMD0000000997" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:15.933011+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000997", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3291": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3291, + "name": "Strasen2018 - TGFb SMAD Signalling Class 1", + "repository_type": "biomodels", + "summary": "Model depicting response Class 1 defined by a minimal response to stimulation which is considered as non\u2010responders.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4652, + "tag": "BioModels:BIOMD0000000998" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:16.492241+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000998", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3292": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3292, + "name": "Strasen2018 - TGFb SMAD Signalling Class 2", + "repository_type": "biomodels", + "summary": "Model depicting class 2 signaling class observed upon stimulation with 100 pM TGF\u03b21.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4653, + "tag": "BioModels:BIOMD0000000999" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:16.994501+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000999", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3293": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3293, + "name": "Strasen2018 - TGFb SMAD Signalling Class 3", + "repository_type": "biomodels", + "summary": "
Simulates signaling class 3
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4654, + "tag": "BioModels:BIOMD0000001000" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:17.451642+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001000", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3294": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3294, + "name": "Strasen2018 - TGFb SMAD Signalling Class 4", + "repository_type": "biomodels", + "summary": "Simulating Class 4 signalling", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4655, + "tag": "BioModels:BIOMD0000001001" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:17.963764+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001001", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3295": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3295, + "name": "Strasen2018 - TGFb SMAD Signalling Class 5", + "repository_type": "biomodels", + "summary": "simulating class 5 signalling", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4656, + "tag": "BioModels:BIOMD0000001002" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:18.424394+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001002", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3296": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3296, + "name": "Strasen2018 - TGFb SMAD Signalling Class 6", + "repository_type": "biomodels", + "summary": "simulating class 6 signalling", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4657, + "tag": "BioModels:BIOMD0000001003" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:18.918910+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001003", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3297": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3297, + "name": "Intosalmi2015 - Th17 core network model", + "repository_type": "biomodels", + "summary": "
a dynamic description for the core molecular mechanisms steering Th17 cell differentiation and use mathematical modeling to quantitatively predict the resulting molecular dynamics
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4658, + "tag": "BioModels:BIOMD0000001004" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:19.401875+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001004", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3298": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3298, + "name": "Bae2017 - Mathematical analysis of circadian disruption and metabolic re-entrainment of hepatic gluconeogenesis", + "repository_type": "biomodels", + "summary": "The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host's health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4659, + "tag": "BioModels:BIOMD0000001005" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 3161, + "tag": "Rattus norvegicus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:20.040855+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001005", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3299": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3299, + "name": "Ciliberto2005 - Steady states and oscillations in the p53/Mdm2 network", + "repository_type": "biomodels", + "summary": "
Its a mathematial model studying steady state and oscialltions in p53-MDM2 network triggered by IR induced DNA Damage.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4660, + "tag": "BioModels:BIOMD0000001006" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:47:20.621681+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001006", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3300": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3300, + "name": "Zhang2007 - Mechanism of DNA damage response (Model1)", + "repository_type": "biomodels", + "summary": "
Its a mechanistic model explaining the impact of p53 om apoptosis decision. This model represents schema 1 of manuscript.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4661, + "tag": "BioModels:BIOMD0000001007" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:48.525888+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001007", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3301": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3301, + "name": "Scaramellini1997 - Two-receptor:One-transducer (2R1T) model for analysis of interactions between agonists", + "repository_type": "biomodels", + "summary": "The two-receptor:one-transducerm odel (Leff, 1987) is here extended to analyze interactions between agonistsd isplaying E/[A] curves of different shapes, by incorporating slope factors into the separate and common parts of the transduction pathway. Interactions were modelled as the effect of one agonist, at fixed concentration, on the curve to the other. A variety of patterns of position and slope changes are predicted. These do not depend on the shape of the control curve, rather, they depend on the slope factors in the separate and common pathways. The following specific predictions are made: (1) when the common pathway is steep, curves undergo potentiation and flattening; (2) when the common pathway is flat, curves undergo right-shift and steepening; (3) when the common pathway is hyperbolic, curves undergo right-shift, with no slope change; (4) when the slope depends on the separate pathways, curves only undergo right-shift with no change in slope. The model provides a sound basis for classifying agonist interactions and for detecting additional, synergistic or antagonistic properties. This analysisin dicatest hat methodsb asedo n dose-additivity or independencea re less reliable for these purposes. The model provides a practical test, based on slope changes, to detect and quantify additional properties", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4662, + "tag": "BioModels:BIOMD0000001008" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:49.032222+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001008", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3302": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3302, + "name": "Zhang2007 - Mechanism of DNA damage response (Model2)", + "repository_type": "biomodels", + "summary": "
Its a mechanistic model explaining the impact of p53 on apoptosis decision. This model represents schema 2 of manuscript.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4663, + "tag": "BioModels:BIOMD0000001009" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:49.642845+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001009", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3303": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3303, + "name": "Zhang2007 - Mechanism of DNA damage response (Model3)", + "repository_type": "biomodels", + "summary": "It's a mechanistic model explaining the impact of p53 on apoptosis decision. This model represents schema 3 of manuscript.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4664, + "tag": "BioModels:BIOMD0000001010" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:50.128216+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001010", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3304": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3304, + "name": "Leon-Triana2020 - CAR T-cell therapy in B-cell acute lymphoblastic leukaemia", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models\".Odelaisy Le\u00f3n-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Mar\u00eda Rosa, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ramirez-Orellana, V\u00edctor M. P\u00e9rez-Garc\u00edadoi: 10.1016/j.cnsns.2020.105570 Comment:This model is based on equations (4a)-(4c).Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4665, + "tag": "BioModels:BIOMD0000001011" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4748, + "tag": "Lymphoid leukemia" + } + ], + "timestamp_created": "2025-02-03 16:55:50.628012+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001011", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3305": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3305, + "name": "Leon-Triana2020 - CAR T-cell therapy in B-cell acute lymphoblastic leukaemia with contribution from immature B cells", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models\".Odelaisy Le\u00f3n-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Mar\u00eda Rosa, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ramirez-Orellana, V\u00edctor M. P\u00e9rez-Garc\u00edadoi: 10.1016/j.cnsns.2020.105570 Comment:This model is based on equations (3a)-(3c) from the paper.Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4666, + "tag": "BioModels:BIOMD0000001012" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4748, + "tag": "Lymphoid leukemia" + } + ], + "timestamp_created": "2025-02-03 16:55:51.126266+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001012", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3306": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3306, + "name": "Leon-Triana2021 - Competition between tumour cells and single-target CAR T-cells", + "repository_type": "biomodels", + "summary": "
This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article:\"Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept\"Odelaisy Le\u00f3n-Triana, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ram\u00edrez-Orellana and V\u00edctor M. P\u00e9rez-Garc\u00edaCancers 2021, 13, 703.; doi: 10.3390/cancers13040703Comment:This is the first mathematical model, derived from equations 1 and 2, used in the paper.Reproduction of Fig. 5a was achieved by setting alpha_1 = 0.04, different to the value quoted in the article caption for Fig. 5.Abstract:Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4667, + "tag": "BioModels:BIOMD0000001013" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:51.601311+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001013", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3307": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3307, + "name": "Leon-Triana2021 - Competition between tumour cells and dual-target CAR T-cells", + "repository_type": "biomodels", + "summary": "
This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article:\"Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept\"Odelaisy Le\u00f3n-Triana, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ram\u00edrez-Orellana and V\u00edctor M. P\u00e9rez-Garc\u00edaCancers 2021, 13, 703.; doi: 10.3390/cancers13040703Comment:This is the second mathematical model, derived from equations 3 to 6, used in the paper.Reproduction of Figure 5b was achieved by setting alpha_1 = 0.183, in substitution for alpha_1 = 0.2 as quoted in the article.Abstract:Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4668, + "tag": "BioModels:BIOMD0000001014" + }, + { + "id": 4669, + "tag": "Glioblastoma multiforme" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:52.105087+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001014", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3308": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3308, + "name": "Jarrah2014 - mathematical model of the immune response in muscle degeneration and subsequent regeneration in Duchenne muscular dystrophy in mdx mice", + "repository_type": "biomodels", + "summary": "Duchenne muscular dystrophy (DMD) is a genetic disease that results in the death of affected boys by early adulthood.The genetic defect responsible for DMD has been known for over 25 years, yet at present there is neither cure nor effective treatment for DMD. During early disease onset, the mdx mouse has been validated as an animal model for DMD and use of this model has led to valuable but incomplete insights into the disease process. For example, immune cells are thought to be responsible for a significant portion of muscle cell death in the mdx mouse; however, the role and time course of the immune response in the dystrophic process have not been well described. In this paper we constructed a simple mathematical model to investigate the role of the immune response in muscle degeneration and subsequent regeneration in the mdx mouse model of Duchenne muscular dystrophy. Our model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes. Furthermore, the analysis of the model predicts that the immune system response oscillates throughout the life of the mice, and the damaged fibers are never completely cleared.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4670, + "tag": "BioModels:BIOMD0000001015" + }, + { + "id": 3178, + "tag": "Mus musculus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:52.615645+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001015", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3309": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3309, + "name": "Bakshi2020 - Truncated minimal model of alternative pathway of complement system", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"Mathematical Modelling of Alternative Pathway of Complement System\".Suruchi Bakshi, Fraser Cunningham, Eva-Maria Nichols, Marta Biedzka-Sarek, Jessica Neisen, Sebastien Petit-Frere, Christina Bessant, Loveleena Bansal, Lambertus A Peletier, Stefano Zamuner, Piet H van der GraafDOI: 10.1007/s11538-020-00708-zComment:This model is based on the truncated minimal model equations (Eq. B.1) from the manuscript, which simulate depletion of factor H.Abstract:The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4671, + "tag": "BioModels:BIOMD0000001016" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4749, + "tag": "Complement deficiency" + } + ], + "timestamp_created": "2025-02-03 16:55:53.134096+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001016", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3310": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3310, + "name": "Bakshi2020 - Minimal model of alternative pathway of complement system", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"Mathematical Modelling of Alternative Pathway of Complement System\".Suruchi Bakshi, Fraser Cunningham, Eva-Maria Nichols, Marta Biedzka-Sarek, Jessica Neisen, Sebastien Petit-Frere, Christina Bessant, Loveleena Bansal, Lambertus A Peletier, Stefano Zamuner, Piet H van der GraafDOI: 10.1007/s11538-020-00708-zComment:This model is based on the truncated minimal model equations (Eq. B.1) from the manuscript, which simulate depletion of factor H.Abstract:The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4672, + "tag": "BioModels:BIOMD0000001017" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:53.815871+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001017", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3311": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3311, + "name": "Bakshi2020 - Properdin model of alternative pathway of complement system", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"Mathematical Modelling of Alternative Pathway of Complement System\".Suruchi Bakshi, Fraser Cunningham, Eva-Maria Nichols, Marta Biedzka-Sarek, Jessica Neisen, Sebastien Petit-Frere, Christina Bessant, Loveleena Bansal, Lambertus A Peletier, Stefano Zamuner, Piet H van der GraafDOI: 10.1007/s11538-020-00708-zComment:Correction to the original manuscript is accessible hereModel schematics in Figure 3 and parameter d4 were corrected.Abstract:The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation -of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4673, + "tag": "BioModels:BIOMD0000001018" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:54.324267+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001018", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3312": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3312, + "name": "Barros2021 - CARTmath, Mathematical Model of CAR-T Immunotherapy in HDLM-2 cell line", + "repository_type": "biomodels", + "summary": "A mathematical model (CART-math) studying the impact of CAR-T cells therapy on haematological cancer cell line which in this case is HDLM-2.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4674, + "tag": "BioModels:BIOMD0000001019" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:54.857592+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001019", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3313": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3313, + "name": "Barros2021 - CARTmath, Mathematical Model of CAR-T Immunotherapy in Raji Cell Line", + "repository_type": "biomodels", + "summary": "A mathematical model (CART-math) studying the impact of CAR-T cells therapy on haematological cancer cell lines which in this case is RAJI.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4675, + "tag": "BioModels:BIOMD0000001020" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:55.345578+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001020", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3314": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3314, + "name": "Lavigne2021 - Non-spatial model of viral infection dynamics and interferon response of well-mixed viral infection", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model is described in the following article:\"Autocrine and paracrine interferon signalling as \u2018ring vaccination\u2019 and \u2018contact tracing\u2019 strategies to suppress virus infection in a host\"G. Michael Lavigne, Hayley Russell, Barbara Sherry and Ruian KeDOI: 10.1098/rspb.2020.3002Comment:This model is based on the ordinary differential equations of the non-spatial model of well-mixed viral infection stated in the manuscript (Eq. 2.1 in the article).Abstract:The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as \u2018contact tracing\u2019 or \u2018ring vaccination\u2019. Thus, our results here may have implications for the outbreak control at the population scale more broadly.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4676, + "tag": "BioModels:BIOMD0000001021" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:55.875088+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001021", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3315": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3315, + "name": "Creemers2021 - Tumor-immune dynamics and implications on immunotherapy responses", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication:Creemers JHA, Lesterhuis WJ, Mehra N, et al.\"A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.\"Journal for ImmunoTherapy of Cancer 2021;9:e002032.doi:10.1136/jitc-2020-002032Comment:Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used:Reproduction of Fig. 1(C), xi = 0.0005Reproduction of Fig. 1(D), xi = 0.00025Abstract:Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.Results: Our model shows that a tipping point\u2014a sharp state transition between immune control and immune evasion\u2014induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient\u2019s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4677, + "tag": "BioModels:BIOMD0000001022" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:56.420965+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001022", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3316": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3316, + "name": "Alharbi2020 - An ODE-based model of the dynamics of tumor cell progression and its effects on normal cell growth and immune system functionality", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of tumor cell growth, called the normal-tumor-immune-unhealthy diet model (NTIUNHDM), is described by the publication:Alharbi, S.A.; Rambely, A.S.\"A New ODE-Based Model for Tumor Cells and Immune System Competition\"Mathematics 2020, 8, 1285.doi:10.3390/math8081285Abstract:Changes in diet are heavily associated with high mortality rates in several types of cancer. In this paper, a new mathematical model of tumor cells growth is established to dynamically demonstrate the effects of abnormal cell progression on the cells affected by the tumor in terms of the immune system\u2019s functionality and normal cells\u2019 dynamic growth. This model is called the normal-tumor-immune-unhealthy diet model (NTIUNHDM) and governed by a system of ordinary differential equations. In the NTIUNHDM, there are three main populations normal cells, tumor cell and immune cells. The model is discussed analytically and numerically by utilizing a fourth-order Runge\u2013Kutta method. The dynamic behavior of the NTIUNHDM is discussed by analyzing the stability of the system at various equilibrium points and the Mathematica software is used to simulate the model. From analysis and simulation of the NTIUNHDM, it can be deduced that instability of the response stage, due to a weak immune system, is classified as one of the main reasons for the coexistence of abnormal cells and normal cells. Additionally, it is obvious that the NTIUNHDM has only one stable case when abnormal cells begin progressing into early stages of tumor cells such that the immune cells are generated once. Thus, early boosting of the immune system might contribute to reducing the risk of cancer.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4678, + "tag": "BioModels:BIOMD0000001023" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:56.900695+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001023", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3317": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3317, + "name": "Chaudhury2020 - Lotka-Volterra mathematical model of CAR-T cell and tumour kinetics", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the cellular kinetics and pharmacodynamics of CAR-T cell therapy is described in the publication:Chaudhury, A., Zhu, X., Chu, L., Goliaei, A., June, C., Kearns, J. and Stein, A., 2020. Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic\u2010Pharmacodynamic Modeling Approaches. The Journal of Clinical Pharmacology, 60(S1).DOI: 10.1002/jcph.1691Comment:This model is based on equations 4-5 from the manuscript.Abstract:Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4679, + "tag": "BioModels:BIOMD0000001024" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4750, + "tag": "Leukemia" + }, + { + "id": 4751, + "tag": "Lymphoma" + } + ], + "timestamp_created": "2025-02-03 16:55:57.376216+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001024", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3318": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3318, + "name": "Chaudhury2020 - EC50 expansion and killing mathematical model of CAR-T cell and tumour kinetics", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the cellular kinetics and pharmacodynamics of CAR-T cell therapy is described in the publication:Chaudhury, A., Zhu, X., Chu, L., Goliaei, A., June, C., Kearns, J. and Stein, A., 2020. Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic\u2010Pharmacodynamic Modeling Approaches. The Journal of Clinical Pharmacology, 60(S1).DOI: 10.1002/jcph.1691Comment:This model is based on equations 7-9 from the manuscript.Abstract:Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4680, + "tag": "BioModels:BIOMD0000001025" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4750, + "tag": "Leukemia" + }, + { + "id": 4751, + "tag": "Lymphoma" + } + ], + "timestamp_created": "2025-02-03 16:55:57.874060+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001025", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3319": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3319, + "name": "Kurlovics2021 - Metformin partitioning between plasma and RBC with independent Kin and Kout coefficients", + "repository_type": "biomodels", + "summary": "
This model is a supplementary material of a manuscript\"Diffusion driven metformin exchange transport rates between plasma and red blood cells\"by Janis Kurlovics, Darta Maija Zake, Linda Zaharenko, Kristaps Berzins, Janis Klovins, Egils StalidzansThe setting of the model correspond to Fig.2 for the case with a single coefficient for experimental values of average concentration curve. Parameter estimation can be executed using experimental data file \"average_exp_data.txt\".A=B if metformin concentration is 0 at t=0.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4681, + "tag": "BioModels:BIOMD0000001026" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:58.341956+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001026", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3320": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3320, + "name": "Zake2021 - PBPK model of metformin in mice: single dose peroral", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, Janis Kurlovics, Vitalijs Komasilovs, Janis Klovins and Egils Stalidzans.The model is pre-set for simulation of a single peroral dose.This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments. The model consists of 20 compartments (\u201cCompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 20 species (\u201cSpecies\u201d in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 33 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 52 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing metformin dose \u2013 either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg). 2.Parameter describing mice physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots).", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4682, + "tag": "BioModels:BIOMD0000001027" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:58.805340+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001027", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3321": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3321, + "name": "Zake2021 - PBPK model of metformin in humans, single PO dose", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments. The model consists of 21 compartments (\u201ccompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 21 species (\u201cspecies\u201d in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 35 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 62 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).2.Parameter describing human physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Parameters involved in the scale-up of the model\u2022Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.\u2022Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions \u201c13.4. KidneyPlasma -> KidneyTissue\u201d and \u201c13.5. KidneyTissue -> KidneyTubular\u201d. This parameter was determined using parameter estimation. \u2022Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine. 4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). The time-course also includes multiple \"Sliders\" that allow to easily change 3 parameters - \"Body Weight\", \"Cardiac Output\", \"Metformin Dose in Lumen in mg\".", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4683, + "tag": "BioModels:BIOMD0000001028" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:59.300784+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001028", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3322": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3322, + "name": "Zake2021 - PBPK model of metformin in humans, eight PO administrations with 12h interval", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments. The model consists of 21 compartments (\u201ccompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 21 species (\u201cspecies\u201d in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 35 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 62 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).2.Parameter describing human physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Parameters involved in the scale-up of the model\u2022Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.\u2022Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions \u201c13.4. KidneyPlasma -> KidneyTissue\u201d and \u201c13.5. KidneyTissue -> KidneyTubular\u201d. This parameter was determined using parameter estimation. \u2022Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine. 4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). The time-course also includes multiple \"Sliders\" that allow to easily change 3 parameters - \"Body Weight\", \"Cardiac Output\", \"Metformin Dose in Lumen in mg\".", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4684, + "tag": "BioModels:BIOMD0000001029" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:55:59.948920+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001029", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3323": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3323, + "name": "Sontag2017 - Dynamic model of immune responses to antigen presentation by tumor or pathogen", + "repository_type": "biomodels", + "summary": "
This model of the immune system response to antigen presentation is based on the publication:Eduardo D.Sontag (2017) 'A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination', Cell Systems, 4(2)DOI: 10.1016/j.cels.2016.12.003Comment:This model is based on the \"toy model\" as described by Equations 1A-1C from the manuscript and the system represented by Equation 2, which exhibits a type of incoherent feedforward loop (IFFL).Abstract:The immune system must discriminate between agents of disease and an organism\u2019s healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system\u2019s response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed \u201ctwo-zone tolerance.\u201d Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T cells.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4685, + "tag": "BioModels:BIOMD0000001030" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 3690, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-02-03 16:56:00.493853+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001030", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3324": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3324, + "name": "Al-Tuwairqi2020 - Dynamics of cancer virotherapy - Phase I treatment", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of cancer virotherapy dynamics is described in the publication:Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,\"Modeling dynamics of cancer radiovirotherapy\",Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,DOI: 10.1016/j.jtbi.2020.110405.Comment:This model is represented by the equations in system (2) of the publication manuscript and describes the cancer-virus interactions for the Phase I virotherapy treatment.Abstract:Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4686, + "tag": "BioModels:BIOMD0000001031" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:01.012930+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001031", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3325": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3325, + "name": "Al-Tuwairqi2020 - Dynamics of cancer radiovirotherapy - Phase II treatment", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of cancer radiovirotherapy dynamics is described in the publication:Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,\"Modeling dynamics of cancer radiovirotherapy\",Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,DOI: 10.1016/j.jtbi.2020.110405.Comment:This model is represented by the equations in system (14) of the publication manuscript and describes the cancer-virus interactions for the Phase II radiovirotherapy treatment.Abstract:Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4687, + "tag": "BioModels:BIOMD0000001032" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:01.628016+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001032", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3326": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3326, + "name": "Almuallem2020 - Virus-macrophage-tumour interactions in oncolytic viral therapies", + "repository_type": "biomodels", + "summary": "
This mathematical model of the role of the innate immune responses generated by macrophages in the context of anti-tumour oncolytic viral therapies is described in the publication:Nada Almuallem, Dumitru Trucu, Raluca Eftimie. \"Oncolytic viral therapies and the delicate balance between virus-macrophage-tumour interactions: A mathematical approach\". Mathematical Biosciences and Engineering, 2021, 18(1): 764-799.doi: 10.3934/mbe.2021041Comment:This model is represented by Equations 2.1a-f of the publication manuscript.Abstract:The success of oncolytic virotherapies depends on the tumour microenvironment, which contains a large number of infiltrating immune cells. In this theoretical study, we derive an ODE model to investigate the interactions between breast cancer tumour cells, an oncolytic virus (Vesicular Stomatitis Virus), and tumour-infiltrating macrophages with different phenotypes which can impact the dynamics of oncolytic viruses. The complexity of the model requires a combined analytical-numerical approach to understand the transient and asymptotic dynamics of this model. We use this model to propose new biological hypotheses regarding the impact on tumour elimination/relapse/persistence of: (i) different macrophage polarisation/re-polarisation rates; (ii) different infection rates of macrophages and tumour cells with the oncolytic virus; (iii) different viral burst sizes for macrophages and tumour cells. We show that increasing the rate at which the oncolytic virus infects the tumour cells can delay tumour relapse and even eliminate tumour. Increasing the rate at which the oncolytic virus particles infect the macrophages can trigger transitions between steady-state dynamics and oscillatory dynamics, but it does not lead to tumour elimination unless the tumour infection rate is also very large. Moreover, we confirm numerically that a large tumour-induced M1\u2192M2 polarisation leads to fast tumour growth and fast relapse (if the tumour was reduced before by a strong anti-tumour immune and viral response). The increase in viral-induced M2\u2192M1 re-polarisation reduces temporarily the tumour size, but does not lead to tumour elimination. Finally, we show numerically that the tumour size is more sensitive to the production of viruses by the infected macrophages.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4688, + "tag": "BioModels:BIOMD0000001033" + }, + { + "id": 3245, + "tag": "Breast cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:02.179714+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001033", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3327": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3327, + "name": "Bunimovich-Mendrazitsky2007 - Mathematical model of BCG immunotherapy", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model, simulating the tumor-immune interactions involved in BCG immunotherapy to treat superficial bladder cancer, is described by the publication:Bunimovich-Mendrazitsky, S., Shochat, E., Stone, L. \"Mathematical Model of BCG Immunotherapy in Superficial Bladder Cancer\". Bull. Math. Biol. 69, 1847\u20131870 (2007). DOI: 10.1007/s11538-007-9195-zComment:This model is based on the system of ODEs given in Equation 4 of the publication manuscript.Reproduction of Figure 4 was achieved by setting p4 = 0.085.Abstract:Immunotherapy with Bacillus Calmette-Gu\u00e9rin (BCG)-an attenuated strain of Mycobacterium bovis (M. bovis) used for anti tuberculosis immunization-is a clinically established procedure for the treatment of superficial bladder cancer. However, the mode of action has not yet been fully elucidated, despite much extensive biological experience. The purpose of this paper is to develop a first mathematical model that describes tumor-immune interactions in the bladder as a result of BCG therapy. A mathematical analysis of the ODE model identifies multiple equilibrium points, their stability properties, and bifurcation points. Intriguing regimes of bistability are identified in which treatment has potential to result in a tumor-free equilibrium or a full-blown tumor depending only on initial conditions. Attention is given to estimating parameters and validating the model using published data taken from in vitro, mouse and human studies. The model makes clear that intensity of immunotherapy must be kept in limited bounds. While small treatment levels may fail to clear the tumor, a treatment that is too large can lead to an over-stimulated immune system having dangerous side effects for the patient.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4689, + "tag": "BioModels:BIOMD0000001034" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4690, + "tag": "Mycobacterium tuberculosis variant bovis BCG" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4752, + "tag": "Urinary bladder cancer" + } + ], + "timestamp_created": "2025-02-03 16:56:02.772099+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001034", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3328": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3328, + "name": "Al-Tuwairqi2020 - Dynamics of cancer virotherapy with immune response", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the role of the immune response in cancer virotherapy dynamics is described in the publication:Al-Tuwairqi, S.M., Al-Johani, N.O.,  Simbawa, E.A. \"Modeling dynamics of cancer virotherapy with immune response.\" Adv Differ Equ 2020, 438 (2020).DOI: 10.1186/s13662-020-02893-6Comment:This model is represented by the system described in Equation 2 of the publication manuscript.Abstract:Virotherapy is a therapeutic treatment for cancer. It uses genetically engineered viruses to selectively infect, replicate in, and destroy cancer cells without damaging normal cells. In this paper, we present a modified model to include, within the dynamics of virotherapy, the interaction between uninfected tumor cells and immune response. The model is analyzed qualitatively to produce five equilibrium points. One of these equilibriums demonstrates the effect observed in virotherapy, where the immune system demolishes infected cells as well as viruses. Moreover, the existence and stability of the equilibrium points are established under certain criteria. Numerical simulations are performed to display the agreement with the analytical results. Finally, parameter analysis is carried out to illustrate which parameters in the model affect the outcome of virotherapy.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4691, + "tag": "BioModels:BIOMD0000001035" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:03.258236+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001035", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3329": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3329, + "name": "Cappuccio2007 - Tumor-immune system interactions and determination of the optimal therapeutic protocol in immunotherapy", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model is the Panetta-Kirschner model of tumor-immune interactions is described in the publication:Cappuccio A, Castiglione F, Piccoli B.\" Determination of the optimal therapeutic protocols in cancer immunotherapy.\" Math Biosci. 2007 Sep;209(1):1-13.doi: 10.1016/j.mbs.2007.02.009. Comment:The model represented in Equations 1-3 is the Panetta-Kirschner model and was used to reproduce Fig. 1.N.B.: Labelling of Fig. 1 graphs in manuscript mismatches with simulation results - CTL and tumor cell plots swapped.Abstract:Cancer immunotherapy aims at eliciting an immune system response against the tumor. However, it is often characterized by toxic side-effects. Limiting the tumor growth and, concurrently, avoiding the toxicity of a drug, is the problem of protocol design. We formulate this question as an optimization problem and derive an algorithm for its solution. Unlike the standard optimal control approach, the algorithm simulates impulse-like drug administrations. It relies on an exact computation of the gradient of the cost function with respect to any protocol by means of the variational equations, that can be solved in parallel with the system. In comparison with previous versions of this method [F. Castiglione, B. Piccoli, Optimal control in a model of dendritic cell transfection cancer immunotherapy, Bull. Math. Biol. 68 (2006) 255-274; B. Piccoli, F. Castiglione, Optimal vaccine scheduling in cancer immunotherapy, Physica A. 370 (2) (2007) 672-680], we optimize both the timing and the dosage of each administration and introduce a penalty term to avoid clustering of subsequent injections, a requirement consistent with the clinical practice. In addition, we implement the optimization scheme to simulate the case of multi-therapies. The procedure works for any ODE system describing the pharmacokinetics and pharmacodynamics of an arbitrary number of therapeutic agents. In this work, it was tested for a well known model of the tumor-immune system interaction [D. Kirschner, J.C. Panetta, Modeling immunotherapy of tumor-immune interaction, J. Math. Biol. 37 (1998) 235-252]. Exploring three immunotherapeutic scenarios (CTL therapy, IL-2 therapy and combined therapy), we display the stability and efficacy of the optimization method, obtaining protocols that are successful compromises between various clinical requirements.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4692, + "tag": "BioModels:BIOMD0000001036" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:03.729475+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001036", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3330": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3330, + "name": "Alharbi2019 - Tumor-normal model (TNM) of the development of tumor cells and their impact on normal cell dynamics", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the interactions between tumor and normal cells is based on the publication:S. A. Alharbi and A. S. Rambely, \"Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells,\" in IEEE Access, vol. 7, pp. 128816-128827, 2019doi: 10.1109/ACCESS.2019.2940060.Comment:This TNM model is described by the system of equations presented in (2), in the publication manuscript.Abstract:The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4693, + "tag": "BioModels:BIOMD0000001037" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:04.267987+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001037", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3331": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3331, + "name": "Alharbi2019 - Tumor-normal-vitamins model (TNVM) of the effects of vitamins on delaying the growth of tumor cells", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the interactions between tumor and normal cells, in the presence of a regular rate of vitamins, is based on the publication:S. A. Alharbi and A. S. Rambely, \"Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells,\" in IEEE Access, vol. 7, pp. 128816-128827, 2019doi: 10.1109/ACCESS.2019.2940060.Comment:This TNVM model is described by the system of equations presented in (10), in the publication manuscript.Reproduction of Fig. 11 was achieved by swapping the values of gamma and beta2 stated in the manuscript, i.e. gamma = 0.9817 and beta2 = 0.2291.Abstract:The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4694, + "tag": "BioModels:BIOMD0000001038" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 4360, + "tag": "Response to tumor cell" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:04.746988+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001038", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3332": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3332, + "name": "Zake2021 - PBPK model of metformin in mice: single dose intavenous", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments. The model consists of 20 compartments (\u201cCompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 20 species (\u201cSpecies\u201d in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 33 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 52 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing metformin dose \u2013 either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg). 2.Parameter describing mice physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). Also plotting the species result after 0.5 hours will reproduce the literature results.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4695, + "tag": "BioModels:BIOMD0000001039" + }, + { + "id": 4237, + "tag": "Mus sp." + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:05.259186+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001039", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3333": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3333, + "name": "Kurlovics2021 - Metformin partitioning from plasma to RBC, single coefficient", + "repository_type": "biomodels", + "summary": "
This model is a supplementary material of a manuscript\"Diffusion driven metformin exchange transport rates between plasma and red blood cells\"by Janis Kurlovics, Darta Maija Zake, Linda Zaharenko, Kristaps Berzins, Janis Klovins, Egils StalidzansThe setting of the model correspond to Fig.2 for the case with a single coefficient for experimental values of average concentration curve. Parameter estimation can be executed using experimental data file \"average_exp_data.txt\".A=B if metformin concentration is 0 at t=0.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4696, + "tag": "BioModels:BIOMD0000001040" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:05.733524+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001040", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3334": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3334, + "name": "Kimmel2021 - T cell competition and stochastic extinction events in CAR T cell therapy", + "repository_type": "biomodels", + "summary": "
This mathematical model of T cell-tumour interactions considering the roles of T cell competition and stochastic extinction events in CAR T cell therapy is described by the publication:Kimmel GJ, Locke FL, Altrock PM. \"The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy.\" Proc Biol Sci. 2021 Mar 31;288(1947):20210229.doi: 10.1098/rspb.2021.0229Comment:Reproduction of Fig. 2(a) and (b) was simulated by using the fitted model parameter set given in Table 1 of the manuscript's Supplementary Material, however substituting the values of r_N and rho_C for those stated in Table 1 of the publication manuscript, i.e. r_N = 0.17 and rho_C = 0.0251.Abstract:Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this expansion and subsequent tumour eradication remain unknown. We develop a mathematical model of T cell\u2013tumour cell interactions and demonstrate that expansion can be explained by immune reconstitution dynamics after LD and competition among T cells. CAR T cells rapidly grow and engage tumour cells but experience an emerging growth rate disadvantage compared to normal T cells. Since tumour eradication is deterministically unstable in our model, we define cure as a stochastic event, which, even when likely, can occur at variable times. However, we show that variability in timing is largely determined by patient variability. While cure events impacted by these fluctuations occur early and are narrowly distributed, progression events occur late and are more widely distributed in time. We parameterized our model using population-level CAR T cell and tumour data over time and compare our predictions with progression-free survival rates. We find that therapy could be improved by optimizing the tumour-killing rate and the CAR T cells' ability to adapt, as quantified by their carrying capacity. Our tumour extinction model can be leveraged to examine why therapy works in some patients but not others, and to better understand the interplay of deterministic and stochastic effects on outcomes. For example, our model implies that LD before a second CAR T injection is necessary.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4697, + "tag": "BioModels:BIOMD0000001041" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4753, + "tag": "Non-Hodgkin lymphoma" + } + ], + "timestamp_created": "2025-02-03 16:56:06.226888+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001041", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3335": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3335, + "name": "Makhlouf2020 - No treatment model of the role of CD4 T cells in tumor-immune interactions", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model simulating the interactions between tumor and immune cells is detailed in the publication:Ahmed M. Makhlouf, Lamiaa El-Shennawy, Hesham A. Elkaranshawy, \"Mathematical Modelling for the Role of CD4+T Cells in Tumor-Immune Interactions\", Comput Math Methods Med. 2020 Feb 19;2020:7187602.doi: 10.1155/2020/7187602Comment:This no treatment model is described by equations 1-7 of the publication manuscript. Abstract:Mathematical modelling has been used to study tumor-immune cell interaction. Some models were proposed to examine the effect of circulating lymphocytes, natural killer cells, and CD8+T cells, but they neglected the role of CD4+T cells. Other models were constructed to study the role of CD4+T cells but did not consider the role of other immune cells. In this study, we propose a mathematical model, in the form of a system of nonlinear ordinary differential equations, that predicts the interaction between tumor cells and natural killer cells, CD4+T cells, CD8+T cells, and circulating lymphocytes with or without immunotherapy and/or chemotherapy. This system is stiff, and the Runge\u2013Kutta method failed to solve it. Consequently, the \u201cAdams predictor-corrector\u201d method is used. The results reveal that the patient\u2019s immune system can overcome small tumors; however, if the tumor is large, adoptive therapy with CD4+T cells can be an alternative to both CD8+T cell therapy and cytokines in some cases. Moreover, CD4+T cell therapy could replace chemotherapy depending upon tumor size. Even if a combination of chemotherapy and immunotherapy is necessary, using CD4+T cell therapy can better reduce the dose of the associated chemotherapy compared to using combined CD8+T cells and cytokine therapy. Stability analysis is performed for the studied patients. It has been found that all equilibrium points are unstable, and a condition for preventing tumor recurrence after treatment has been deduced. Finally, a bifurcation analysis is performed to study the effect of varying system parameters on the stability, and bifurcation points are specified. New equilibrium points are created or demolished at some bifurcation points, and stability is changed at some others. Hence, for systems turning to be stable, tumors can be eradicated without the possibility of recurrence. The proposed mathematical model provides a valuable tool for designing patients\u2019 treatment intervention strategies.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4698, + "tag": "BioModels:BIOMD0000001042" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:06.721450+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001042", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3336": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3336, + "name": "Wodarz2001 - Viruses as antitumor weapons", + "repository_type": "biomodels", + "summary": "
This mathematical model of the dynamics between tumor, virus and virus-specific CTL populations is described by the publication:Wodarz D. \"Viruses as antitumor weapons: defining conditions for tumor remission\". Cancer Res. 2001 Apr 15;61(8):3501-7.PMID: 11309314Comment:Reproduction of Fig. 3A was achieved by using the initial conditions infected_tumor_cells = 0.01, uninfected_tumor_cells = 0.0001, virus-specific_CTLs = 0, and with modified parameter sets.For Fig. 3A non-cytotoxic virus, k = 17 and beta= 0.5.For Fig. 3A cytotoxic virus, beta = 0.5.These simulation conditions yield plots similar to Fig. 3A in the manuscript.Abstract:Recent research has indicated that viruses specifically infecting tumor cells could be used as an alternative therapeutic approach in cancer patients. A particular example is the adenovirus ONYX-015, which has entered clinical trials in the context of head and neck cancer. Successful therapy crucially requires an understanding about how viral and host parameters influence tumor load. The interactions between the growing tumor, the replicating virus, and possible immune responses are multifactorial and nonlinear. Hence, a complete understanding of how virus and host characteristics influence the outcome of therapy requires mathematical models. In this study, such mathematical models are presented and analyzed. The study investigates three possible scenarios that could be relevant for therapy: (a) viral cytotoxicity alone kills tumor cells; (b) a virus-specific lytic CTL response contributes to killing of infected tumor cells; (c) the virus elicits immunostimulatory signals within the tumor that promote the development of tumor-specific CTL. The models precisely define conditions required for successful therapy. They identify the parameters that need to be measured and modulated to evaluate and refine the existing therapy regimes.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4699, + "tag": "BioModels:BIOMD0000001043" + }, + { + "id": 3671, + "tag": "Cancer" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:07.205889+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001043", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3337": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3337, + "name": "Csikasz-Nagy2006 - Mammalian Cell Cycle model", + "repository_type": "biomodels", + "summary": "

This model originates from the Cell Cycle Database . It is described in:
Analysis of a generic model of eukaryotic cell-cycle regulation. Csik\u00e1sz-Nagy A , Battogtokh D , Chen KC , Nov\u00e1k B , Tyson JJ Biophys. J. [2006 Jun],90(12 ):4361-79
PMID: 16581849
Abstract:
We propose a protein interaction network for the regulation of DNA synthesis and mitosis that emphasizes the universality of the regulatory system among eukaryotic cells. The idiosyncrasies of cell cycle regulation in particular organisms can be attributed, we claim, to specific settings of rate constants in the dynamic network of chemical reactions. The values of these rate constants are determined ultimately by the genetic makeup of an organism. To support these claims, we convert the reaction mechanism into a set of governing kinetic equations and provide parameter values (specific to budding yeast, fission yeast, frog eggs, and mammalian cells) that account for many curious features of cell cycle regulation in these organisms. Using one-parameter bifurcation diagrams, we show how overall cell growth drives progression through the cell cycle, how cell-size homeostasis can be achieved by two different strategies, and how mutations remodel bifurcation diagrams and create unusual cell-division phenotypes. The relation between gene dosage and phenotype can be summarized compactly in two-parameter bifurcation diagrams. Our approach provides a theoretical framework in which to understand both the universality and particularity of cell cycle regulation, and to construct, in modular fashion, increasingly complex models of the networks controlling cell growth and division.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4700, + "tag": "BioModels:BIOMD0000001044" + }, + { + "id": 4701, + "tag": "Cell Cycle" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:07.705508+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001044", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3338": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3338, + "name": "Smith&Moore2004 - The SIR model for the spread of HongKong Flu", + "repository_type": "biomodels", + "summary": "This is the SIR model for disease spread of the Hong Kong flu in New York City in the late 1960's.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4702, + "tag": "BioModels:BIOMD0000001045" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 3274, + "tag": "Influenza" + }, + { + "id": 4703, + "tag": "Influenza A virus (strain A/Hong Kong/1/1968 H3N2)" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:08.193770+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001045", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3339": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3339, + "name": "Raman2005 - Mycolic acid pathway of M. tuberculosis", + "repository_type": "biomodels", + "summary": "The mycobacterial cell wall is a distinctive thick layer that protects the tubercle bacillus from general antibiotics and the host\u2019s immune system. Mycolic acids, which are long-chain \u03b1-alkyl-\u03b2-hydroxy fatty acids, are the major constituents of this protective layer, and their synthesis has been shown to be critical for the survival of M. tuberculosis. This model captures the mycolic acid pathway in M. tuberculosis with 197 metabolites participating in 219 reactions catalysed by 28 proteins. The model helps in the rational identification of potential anti-tubercular drug targets.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4704, + "tag": "BioModels:BIOMD0000001046" + }, + { + "id": 3576, + "tag": "Mycobacterium tuberculosis" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:08.818510+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001046", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3340": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3340, + "name": "Collier1996 - Delta Notch intercellular signalling and lateral inhibition", + "repository_type": "biomodels", + "summary": "
ODE model describing how slight variations in Notch and Delta cellular concentrations, through lateral inhibition, lead to cells with different states of differentiation. Lateral inhibition is a process whereby a given cell adopting a given fate prevents its immediate neighbouring cells from doing likewise. Notch and Delta are interacting transmembrane proteins and according to this model, lateral inhibition is due to a process where the inhibited cells (where notch has been activated by Delta) loose their ability to inhibit other cells (by synthesizing Delta). This process creates a feedback loop where cells with more delta proteins on their surface inhibit their immediate neighbours and adopt a different cell fate than those neighbours.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4705, + "tag": "BioModels:BIOMD0000001047" + }, + { + "id": 229, + "tag": "Drosophila" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:09.314433+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001047", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3341": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3341, + "name": "Siddhartha2002 - Kinetic modelling of cancer therapies", + "repository_type": "biomodels", + "summary": "Siddhartha Jain. Kinetic model for designing a cancer therapy. Cancer Cell International 2, 1 (2002).

A kinetic model has been developed to study cancer growth. Cancer growth has been considered as interaction between various independent but interacting compartments. The model considers cell growth and metastasis resulting in the formation of new tumor masses. Using certain representative parameter values, cell growth has been modeled in the absence and the presence of various cancer therapies. Based on this analysis, the critical parameters involved in cancer development have been identified. This model may thus be useful in studying and designing a cancer therapy using the data obtained from specific in vitro experiments.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4706, + "tag": "BioModels:BIOMD0000001048" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:09.803756+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001048", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3342": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3342, + "name": "Alharbi2020 - Tumor and immune system competition", + "repository_type": "biomodels", + "summary": "Sana Abdulkream Alharbi & Azmin Sham Rambely. A New ODE-Based Model for Tumor Cells and Immune System Competition. Mathematics 8, 8 (2020).

Changes in diet are heavily associated with high mortality rates in several types of cancer. In this paper, a new mathematical model of tumor cells growth is established to dynamically demonstrate the effects of abnormal cell progression on the cells affected by the tumor in terms of the immune system\u2019s functionality and normal cells\u2019 dynamic growth. This model is called the normal-tumor-immune-unhealthy diet model (NTIUNHDM) and governed by a system of ordinary differential equations. In the NTIUNHDM, there are three main populations normal cells, tumor cell and immune cells. The model is discussed analytically and numerically by utilizing a fourth-order Runge\u2013Kutta method. The dynamic behavior of the NTIUNHDM is discussed by analyzing the stability of the system at various equilibrium points and the Mathematica software is used to simulate the model. From analysis and simulation of the NTIUNHDM, it can be deduced that instability of the response stage, due to a weak immune system, is classified as one of the main reasons for the coexistence of abnormal cells and normal cells. Additionally, it is obvious that the NTIUNHDM has only one stable case when abnormal cells begin progressing into early stages of tumor cells such that the immune cells are generated once. Thus, early boosting of the immune system might contribute to reducing the risk of cancer.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4707, + "tag": "BioModels:BIOMD0000001052" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:10.288194+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001052", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3343": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3343, + "name": "Garde2020 - metabolic oscillations in Bacillus subtilis biofilms", + "repository_type": "biomodels", + "summary": "Ravindra Garde, Bashar Ibrahim & Stefan Schuster. Extending the minimal model of metabolic oscillations in Bacillus subtilis biofilms. Scientific Reports 10, 1 (2020).

Biofilms are composed of microorganisms attached to a solid surface or floating on top of a liquid surface. They pose challenges in the field of medicine but can also have useful applications in industry. Regulation of biofilm growth is complex and still largely elusive. Oscillations are thought to be advantageous for biofilms to cope with nutrient starvation and chemical attacks. Recently, a minimal mathematical model has been employed to describe the oscillations in Bacillus subtilis biofilms. In this paper, we investigate four different modifications to that minimal model in order to better understand the oscillations in biofilms. Our first modification is towards making a gradient of metabolites from the center of the biofilm to the periphery. We find that it does not improve the model and is therefore, unnecessary. We then use realistic Michaelis-Menten kinetics to replace the highly simple mass-action kinetics for one of the reactions. Further, we use reversible reactions to mimic the diffusion in biofilms. As the final modification, we check the combined effect of using Michaelis-Menten kinetics and reversible reactions on the model behavior. We find that these two modifications alone or in combination improve the description of the biological scenario.", + "tags": [ + { + "id": 3265, + "tag": "Bacillus subtilis" + }, + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4708, + "tag": "BioModels:BIOMD0000001053" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:10.777585+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001053", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3344": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3344, + "name": "Pearce2021 - Fibrin Polymerization", + "repository_type": "biomodels", + "summary": "Here is an ode model for in vitro fibrin matrix polymerization reproducing interactions among fibrinogen, fibrin and other proteins involved in the homeostatic phase of wound healing.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4709, + "tag": "BioModels:BIOMD0000001054" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:11.263776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001054", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3345": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3345, + "name": "Jeon2018 - Enzyme clustering in Glucose metabolism", + "repository_type": "biomodels", + "summary": "Miji Jeon, Hye-Won Kang & Songon An. A Mathematical Model for Enzyme Clustering in Glucose Metabolism. Scientific Reports 8, 1 (2018).

We have recently demonstrated that the rate-limiting enzymes in human glucose metabolism organize into cytoplasmic clusters to form a multienzyme complex, the glucosome, in at least three different sizes. Quantitative high-content imaging data support a hypothesis that the glucosome clusters regulate the direction of glucose flux between energy metabolism and building block biosynthesis in a cluster size-dependent manner. However, direct measurement of their functional contributions to cellular metabolism at subcellular levels has remained challenging. In this work, we develop a mathematical model using a system of ordinary differential equations, in which the association of the rate-limiting enzymes into multienzyme complexes is included as an essential element. We then demonstrate that our mathematical model provides a quantitative principle to simulate glucose flux at both subcellular and population levels in human cancer cells. Lastly, we use the model to simulate 2-deoxyglucose-mediated alteration of glucose flux in a population level based on subcellular high-content imaging data. Collectively, we introduce a new mathematical model for human glucose metabolism, which promotes our understanding of functional roles of differently sized multienzyme complexes in both single-cell and population levels.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4710, + "tag": "BioModels:BIOMD0000001055" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:11.751828+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001055", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3346": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3346, + "name": "Chulian2021 - feedback signalling in B lymphopoeisis", + "repository_type": "biomodels", + "summary": "Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Anna Marciniak-Czochra, Thomas Stiehl, Cristina Bl\u00e1zquez Go\u00f1i, Juan Francisco Rodr\u00edguez Guti\u00e9rrez, Manuel Ram\u00edrez Orellana, Ana Castillo Robleda, V\u00edctor M. P\u00e9rez-Garc\u00eda & Mar\u00eda Rosa. Dynamical properties of feedback signalling in B lymphopoiesis: A mathematical modelling approach. Journal of Theoretical Biology 522 (2021).

Haematopoiesis is the process of generation of blood cells. Lymphopoiesis generates lymphocytes, the cells in charge of the adaptive immune response. Disruptions of this process are associated with diseases like leukaemia, which is especially incident in children. The characteristics of self-regulation of this process make them suitable for a mathematical study.

In this paper we develop mathematical models of lymphopoiesis using currently available data. We do this by drawing inspiration from existing structured models of cell lineage development and integrating them with paediatric bone marrow data, with special focus on regulatory mechanisms. A formal analysis of the models is carried out, giving steady states and their stability conditions. We use this analysis to obtain biologically relevant regions of the parameter space and to understand the dynamical behaviour of B-cell renovation. Finally, we use numerical simulations to obtain further insight into the influence of proliferation and maturation rates on the reconstitution of the cells in the B line. We conclude that a model including feedback regulation of cell proliferation represents a biologically plausible depiction for B-cell reconstitution in bone marrow. Research into haematological disorders could benefit from a precise dynamical description of B lymphopoiesis.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4711, + "tag": "BioModels:BIOMD0000001056" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:12.265493+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001056", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3347": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3347, + "name": "Nikolov2020 - p53-miR34 model", + "repository_type": "biomodels", + "summary": "Svetoslav Nikolov, Olaf Wolkenhauer, Julio Vera & Momchil Nenov. The role of cooperativity in a p53-miR34 dynamical mathematical model. Journal of Theoretical Biology 495 (2020).

The objective of this study is to evaluate the role of cooperativity, captured by the Hill coefficient, in a minimal mathematical model describing the interactions between p53 and miR-34a. The model equations are analyzed for negative, none and normal cooperativity using a specific version of bifurcation theory and they are solved numerically. Special attention is paid to the sign of so-called first Lyapunov value. Interpretations of the results are given, both according to dynamic theory and in biological terms. In terms of cell signaling, we propose the hypothesis that when the outgoing signal of a system spends a physiologically significant amount of time outside of its equilibrium state, then the value of that signal can be sampled at any point along the trajectory towards that equilibrium and indeed, at multiple points. Coupled with non-linear behavior, such as that caused by cooperativity, this feature can account for a complex and varied response, which p53 is known for. From dynamical point of view, we found that when cooperativity is negative, the system has only one stable equilibrium point. In the absence of cooperativity, there is a single unstable equilibrium point with a critical boundary of stability. In the case with normal cooperativity, the system can have one, two, or three steady states with both, bi-stability and bi-instability occurring.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4712, + "tag": "BioModels:BIOMD0000001057" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4754, + "tag": "Signal transduction by p53 class mediator" + } + ], + "timestamp_created": "2025-02-03 16:56:12.780610+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001057", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3348": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3348, + "name": "Novak2022 - Mitotic kinase oscillation", + "repository_type": "biomodels", + "summary": "Bela Novak & John J. Tyson. Mitotic kinase oscillation governs the latching of cell cycle switches. Current Biology 32, 12 (2022).

In 1996, Kim Nasmyth1 proposed that the eukaryotic cell cycle is an alternating sequence of transitions from G1 to S-G2-M and back again. These two phases correlate to high activity of cyclin-dependent kinases (CDKs) that trigger S-G2-M events and CDK antagonists that stabilize G1 phase. We associated these \u201calternative phases\u201d with the coexistence of two stable steady states of the biochemical reactions among CDKs and their antagonists. Transitions between these steady states (G1-to-S and M-to-G1) are driven by \u201chelper\u201d proteins. The fact that the transitions are irreversible is guaranteed by a \u201clatching\u201d property of the molecular switches, as we have argued in previous publications. Here, we show that if the latch is broken, then the biochemical reactions can swing back-and-forth across the transitions; either G1-S-G1-S \u2026 (periodic DNA replication without mitosis or cell division) or M-(G1)-M-(G1) \u2026 (periodic Cdc14 release, without fully exiting mitosis). Using mathematical modeling of the molecular control circuit in budding yeast, we provide a fresh account of aberrant cell cycles in mutant strains: endoreplication in the clb1-5\u0394 strain and periodic release and resequestration of Cdc14 (an \u201cexit\u201d phosphatase) in the CLB2kd\u0394 strain.7,8 In our opinion, these \u201cendocycles\u201d are not autonomous oscillatory modules that must be entrained by the CDK oscillator but rather inadvertent and deleterious oscillations that are normally suppressed by the CDK latching-gate mechanism.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4713, + "tag": "BioModels:BIOMD0000001058" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:13.255782+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001058", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3349": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3349, + "name": "Stucki2005 - caspase-3 metabolism", + "repository_type": "biomodels", + "summary": "J\u00f6rg W. Stucki & Hans-Uwe Simon. Mathematical modeling of the regulation of caspase-3 activation and degradation. Journal of Theoretical Biology 234, 1 (2005).

Caspases are thought to be important players in the execution process of apoptosis. Inhibitors of apoptosis (IAPs) are able to block caspases and therefore apoptosis. The fact that a subgroup of the IAP family inhibits active caspases implies that not each caspase activation necessarily leads to apoptosis. In such a scenario, however, processed and enzymically active caspases should somehow be removed. Indeed, IAP\u2013caspase complexes covalently bind ubiquitin, resulting in degradation by the 26S proteasome. Following release from mitochondria, IAP antagonists (e.g. second mitochondrial activator of caspases (Smac)) inactivate IAPs. Moreover, although pro-apoptotic factors such as irradiation or anti-cancer drugs may release Smac from mitochondria in tumor cells, high cytoplasmic survivin and ML-IAP levels might be able to neutralize it and, consequently, IAPs would further be able to bind activated caspases. Here, we propose a simple mathematical model, describing the molecular interactions between Smac deactivators, Smac, IAPs, and caspase-3, including the requirements for both induction and prevention of apoptosis, respectively. In addition, we predict a novel mechanism of caspase-3 degradation that might be particularly relevant in long-living cells.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4714, + "tag": "BioModels:BIOMD0000001059" + }, + { + "id": 4295, + "tag": "Mus" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:13.724240+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001059", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3350": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3350, + "name": "Frank2021 - Macrophage polarization", + "repository_type": "biomodels", + "summary": "The model describes the mechanisms by which macrophages differentiate into a given phenotype. The model shows that both extracellular and intracellular signalling are both important for that process. More specifically, STAT1 activity favors macrophages polarization towards M1 phenotype and STAT6 activity favors macrophage polarization towards M2 phenotype. However, these polarizations are can be reversed by molecular signalling.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4715, + "tag": "BioModels:BIOMD0000001060" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:14.505024+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001060", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3351": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3351, + "name": "Canto-Encalada2022-FBA of simultaneous degradation of ammonia and pollutants", + "repository_type": "biomodels", + "summary": "The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants enable it to thrive under various harsh environmental conditions. This model of N. europaea (iGC535) is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. It is the first upload of the model.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4716, + "tag": "BioModels:BIOMD0000001061" + }, + { + "id": 4717, + "tag": "Nitrosomonas europaea" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:15.017863+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001061", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3352": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3352, + "name": "Kim2021 - Development of a Genome-Scale Metabolic Model and Phenome Analysis of the Probiotic Escherichia coli Strain Nissle 1917", + "repository_type": "biomodels", + "summary": "Escherichia coli Nissle 1917 (EcN) is an intestinal probiotic that is effective for the treatment of intestinal disorders, such as inflammatory bowel disease and ulcerative colitis. EcN is a representative Gram-negative probiotic in biomedical research and is an intensively studied probiotic. However, to date, its genome-wide metabolic network model has not been developed. Here, we developed a comprehensive and highly curated EcN metabolic model, referred to as iDK1463, based on genome comparison and phenome analysis. The model was improved and validated by comparing the simulation results with experimental results from phenotype microarray tests. iDK1463 comprises 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions. Phenome data of EcN were compared with those of Escherichia coli intestinal commensal K-12 MG1655. iDK1463 was simulated to identify the genetic determinants responsible for the observed phenotypic differences between EcN and K-12. Further, the model was simulated for gene essentiality analysis and utilization of nutrient sources under anaerobic growth conditions. These analyses provided insights into the metabolic mechanisms by which EcN colonizes and persists in the gut. iDK1463 will contribute to the system-level understanding of the functional capacity of gut microbes and their interactions with microbiota and human hosts, as well as the development of live microbial therapeutics.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4718, + "tag": "BioModels:BIOMD0000001062" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:15.498041+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001062", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3353": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3353, + "name": "Lu2019 - Genome scale metabolic model for Saccharomyces cerevisiae - yeastGEM8.5.0", + "repository_type": "biomodels", + "summary": "yeast-GEM: The consensus genome-scale metabolic model of Saccharomyces cerevisiae. Further curations of this model will be tracked in the GitHub repository:https://github.com/SysBioChalmers/yeast-GEM For you use yeast-GEM, please cite the yeast8 paper: Lu, H. et al. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications 10, 3586 (2019). https://doi.org/10.1038/s41467-019-11581-3. The FROG analysis was performed with the yeastGEM_rich_medium.mat, which is a rich medium setup.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4719, + "tag": "BioModels:BIOMD0000001063" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:15.988353+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001063", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3354": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3354, + "name": "Kim2011 - the genome scale reconstruction of the Vibrio vulnificus metabolic network - VvuMBEL943", + "repository_type": "biomodels", + "summary": "

This is a model of the genome scale reconstruction of the Vibrio vulnificus metabolic network, VvuMBEL943, described in the article:
Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery
Hyun Uk Kim, Soo Young Kim, Haeyoung Jeong, Tae Yong Kim, Jae Jong Kim, Hyon E Choy, Kyu Yang Yi, Joon Haeng Rhee, and Sang Yup Lee. Molecular Systems Biology 7:460 Jan 2011 doi: 10.1038/msb.2010.115

Abstract:
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.

This metabolic network model has been thoroughly validated by the authors. VvuMBEL943 is a stoichiometric model that contains the metabolic information of the microbial pathogen, Vibrio vulnificus CMCP6, at genome-scale. The SBML version was generated by Hyun Uk Kim using MetaFluxNet.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4720, + "tag": "BioModels:BIOMD0000001064" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4721, + "tag": "Vibrio vulnificus" + } + ], + "timestamp_created": "2025-02-03 16:56:16.447706+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001064", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3355": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3355, + "name": "vonDassow2000 - Segment Polarity Network model on 1x4 grid of cells", + "repository_type": "biomodels", + "summary": "This is the segment polarity network model described by von Dassow et al. (2000). It represents a toroidal hexagonal array of cells (1x4), where each cell can express various genes (winglessengrailed, hedgehog, cubitus interruptus, and patched) and where their protein products interact within a cell, and across neighboring cells.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4722, + "tag": "BioModels:BIOMD0000001065" + }, + { + "id": 4723, + "tag": "Rhodococcus ruber" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:16.925385+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001065", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3356": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3356, + "name": "Chowell2022 - Random Forest model to predict efficacy of immune checkpoint blockade across multiple cancer patient cohorts", + "repository_type": "biomodels", + "summary": "This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be aware that the column names in the GitHub code and the downloaded dataset from the publication may vary. Users are advised to make minor adjustments to either the code or the dataset to ensure compatibility. The curated version of the model has modified the column names in the training code to align with the dataset.GitHub repository: https://github.com/CCF-ChanLab/MSK-IMPACT-IO", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4724, + "tag": "BioModels:BIOMD0000001066" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:17.396386+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001066", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3357": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3357, + "name": "Deshpande2019 - Random Forest model to predict long non-coding RNAs from coding RNAs in Zea Mays plant transcriptomic data", + "repository_type": "biomodels", + "summary": "This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. The model assigns 1 for coding sequences and 2 for long non-coding sequences. The prediction is performed using a combination of Open Reading Frame (ORF) based, Sequence-based and Codon-bias features. Users need to download the curated ONNX model and also need to convert the sequences into feature matrix as mentioned in PLIT paper (Deshpande et al. 2019) to make predictions on sequences from Zea Mays sequence data.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4726, + "tag": "BioModels:BIOMD0000001067" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:17.860179+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001067", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3358": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3358, + "name": "Nassar2022 - Metagenomics Classification Task for Scientific Literature Text", + "repository_type": "biomodels", + "summary": "This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image. This conversion ensures interoperability and open access. The ONNX format utility can perform the following essential tasks: model conversion, inference, inspection, and optimization. Reference: 1) https://github.com/elixir-europe/biohackathon-projects-2022/tree/main/9 2) https://www.ebi.ac.uk/biomodels/search?query=Maaly+Nassar&domain=biomodels 3) https://gitlab.com/maaly7/emerald_metagenomics_annotations 4) This model is built upon the model of the following publication: Maaly Nassar, Alexander B Rogers, Francesco Talo', Santiago Sanchez, Zunaira Shafique, Robert D Finn, Johanna McEntyre, A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications, GigaScience, Volume 11, 2022, giac077, https://doi.org/10.1093/gigascience/giac077", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4727, + "tag": "BioModels:BIOMD0000001068" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:18.301406+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001068", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3359": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3359, + "name": "Alam2019 - Machine learning approach of automatic identification and counting of blood cells", + "repository_type": "biomodels", + "summary": "This model is used for automatic identification and counting of three types of blood cells: Red Blood Cells (RBC), White Blood Cells (WBC) and Platelet (Platelets) using the \u2018you only look once\u2019 (YOLO) object detection and classification algorithm with some additions to remove overannotation. The YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Postprocessing with k-nearest neighbor (KNN) and intersection over union (IOU) approach reduces issues with multiple annotation of platelets. The original code was extended to save the trained YoloV2 network state into the protobuf format. This is then used to generate the ONNX model, containing the weigths. Additional code was added to implement the inference step for image annotation based on the ONNX model, as well as the post-processing logic as used on the original model output. Dependencies have been documented explicitly using a conda environment.yml file to simplify reproducibility.Original GitHub repository: https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-CellsGitHub repository: https://github.com/nilshoffmann/Automatic-Identification-and-Counting-of-Blood-Cells", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4728, + "tag": "BioModels:BIOMD0000001069" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:18.752315+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001069", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3360": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3360, + "name": "Kong2022 - Conditional Antibody Design as 3D Equivariant Graph Translation", + "repository_type": "biomodels", + "summary": "Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4729, + "tag": "BioModels:BIOMD0000001070" + }, + { + "id": 4730, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-02-03 16:56:19.263623+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001070", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3361": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3361, + "name": "Wang2022 - Scaffolding protein functional sites using deep learning", + "repository_type": "biomodels", + "summary": "Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, \u201cconstrained hallucination,\u201d optimizes sequences such that their predicted structures contain the desired functional site. The second approach, \u201cinpainting,\u201d starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4731, + "tag": "BioModels:BIOMD0000001071" + }, + { + "id": 4730, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-02-03 16:56:19.878210+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001071", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3362": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3362, + "name": "Phillips2013 - physiologically based modeling explaining Mammalian rest/activity patterns", + "repository_type": "biomodels", + "summary": "The model provides a framework for understanding rest/activity patterns effected by the circadian rhythm and relating them to underlying diverse phenotypes.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4732, + "tag": "BioModels:BIOMD0000001072" + }, + { + "id": 3202, + "tag": "Mammalia" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:20.437198+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001072", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3363": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3363, + "name": "Patterson2022 - Tumour mutation data driven Random Forest model to predict immune checkpoint inhibitor therapy benefit in metastatic melanoma", + "repository_type": "biomodels", + "summary": "A Random Forest model is developed to incorporate tumor mutation data within the context of the biological process known as leukocyte proliferation regulation. This model aims to predict a patient's response to anti-PD1 treatment.The authors conducted experiments using four different types of classifiers: Random Forest, Gradient Boosting, Feed Forward Neural Network, and Long Short-Term Memory (LSTM) recurrent neural network. Among these classifiers, the Random Forest algorithm yielded the best predictive performance when modeling gene mutation data associated with the 'leukocyte proliferation regulation' biological process. Hence, this curated version of the model focuses on the Random Forest model trained specifically on the 'Leukocyte Proliferation Regulation' process.In this model, a value of '0' is assigned to NonResponders, while a value of '1' is assigned to Responders. Please note that to obtain predictions, users should provide mutation data containing only the genes corresponding to the 'GO_REGULATION_OF_LEUKOCYTE_PROLIFERATION' process keyword, as specified in the 'GO_test_genes_dict_intersection' dictionary.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4733, + "tag": "BioModels:BIOMD0000001073" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:20.948666+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001073", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3364": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3364, + "name": "Liu2023 - Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data", + "repository_type": "biomodels", + "summary": "Immunotherapy has improved the prognosis of patients with advanced non-small cell lungcancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4734, + "tag": "BioModels:BIOMD0000001074" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:21.427168+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001074", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3365": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3365, + "name": "Sammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy", + "repository_type": "biomodels", + "summary": "In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection. Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-MLFor more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4735, + "tag": "BioModels:BIOMD0000001075" + }, + { + "id": 4730, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-02-03 16:56:21.959143+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001075", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3366": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3366, + "name": "L\u00f3pez-Cort\u00e9s2020 - Prediction of Breast Cancer (BC) proteins involved in cancer immunotherapy using molecular descriptors and Multi Layer Perceptron (MLP) neural network", + "repository_type": "biomodels", + "summary": "This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4736, + "tag": "BioModels:BIOMD0000001076" + }, + { + "id": 4725, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-02-03 16:56:22.489762+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001076", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3367": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3367, + "name": "Adlung2021 - Cell-to-cell variability in JAK2/STAT5 pathway", + "repository_type": "biomodels", + "summary": "

A mathematical model for cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4737, + "tag": "BioModels:BIOMD0000001077" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:22.999562+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001077", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3368": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3368, + "name": "Hammaren-Geissen2022_PPToP_Model12", + "repository_type": "biomodels", + "summary": "The model encodes the general biological process of protein synthesis and post-translational modification (PTM, such as protein phosphorylation), viewed through the eyes of a specific, widely-used experimental method. Specifically, we model measurements of pulsed stable isotope labelling of amino acids in cell culture (pSILAC), which is a method often used to quantify protein turnover (i.e. the degradation of old and replacement with new) of proteins in a cell.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4738, + "tag": "BioModels:BIOMD0000001078" + }, + { + "id": 3090, + "tag": "Homo sapiens" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:23.472579+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001078", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3369": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3369, + "name": "DeBoeck2021 - Modular approach to modeling the cell cycle, simple cell cycle model", + "repository_type": "biomodels", + "summary": "

Models the production and degradation of cyclin B that drives the early embryonic cell cycle.Cyclin B is degraded by APC/C. The activity of APC/C is modeled not through biochemical interactions, but through a 'functional response curve'. This can be ultrasensitive (with the parameter alpha=0). in this case the system does not oscillate. Importantly the response can be made bistable and the form of the bistability can be easily prescribed. With a bistable response, the system oscillates.The uploaded file corresponds to the model used for Figs.3H, I in the publication.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4739, + "tag": "BioModels:BIOMD0000001079" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:23.948138+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001079", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3370": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3370, + "name": "DeBoeck2021 - Modular approach to modeling the cell cycle, 5 ODE model with 3 bistable switches", + "repository_type": "biomodels", + "summary": "

Model of the mammalian cell cycle as a chain of bistable switches. There are three bistable responses: response of E2F to Cyclin D, of Cdk1 to Cyclin B and of APC/C to Cdk1 activity. The model for the given parameters admits a complex limit cycle characterized by transitions through the bistable switches. The bistable responses are modeled directly using a functional motif, not through biochemical interactions. This modular approach allows to easily modify the properties of the bistable response curves. This version of the model correspond to Fig. 7 in the publication. We illustrated how, using this model, the system can be coupled to the circadian clock, by periodically modifying thresholds of one of the switches. We also illustrated how to implement the restriction point checkpoint using this model (those applications are not coded in the associated sbml file and can be seen in Fig. 8 of the publication). A related, simpler model that illustrates the bistable motif is MODEL2212060001

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4740, + "tag": "BioModels:BIOMD0000001080" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-02-03 16:56:24.457722+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001080", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3371": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 3371, + "name": "test", + "repository_type": "dandi", + "summary": "", + "tags": [ + { + "id": 11, + "tag": "DANDI" + } + ], + "timestamp_created": "2025-02-14 09:31:25.807615+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/001273", + "user": { + "email": "darshanmandge@gmail.com", + "first_name": "Darshan", + "id": "d5696ba1-996d-4a1d-9f8f-c981b8cf631e", + "last_name": "Mandge", + "username": "darshanmandge" + }, + "user_id": "d5696ba1-996d-4a1d-9f8f-c981b8cf631e" + }, + "3372": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 3372, + "name": "opensourcebrain-tutorial", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 5, + "tag": "Tutorial" + } + ], + "timestamp_created": "2025-02-27 15:36:24.876037+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/SaeedTaghavi/opensourcebrain-tutorial", + "user": { + "email": "saeed.taghavi.v@gmail.com", + "first_name": "Saeed", + "id": "0494704e-b31c-41b4-9f84-7a67d2f9c1c0", + "last_name": "Taghavi", + "username": "saeedtaghavi" + }, + "user_id": "0494704e-b31c-41b4-9f84-7a67d2f9c1c0" + }, + "3373": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 3373, + "name": "M1 ", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2025-03-22 09:55:47.378227+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023", + "user": { + "email": "aggarwalmehul26@gmail.com", + "first_name": "Mehul", + "id": "68fe3ba1-153e-4b3d-92ba-3e71df901aee", + "last_name": "Aggarwal", + "username": "acehunterr" + }, + "user_id": "68fe3ba1-153e-4b3d-92ba-3e71df901aee" + }, + "3374": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 3374, + "name": "Worm2D", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 79, + "tag": "C. elegans" + }, + { + "id": 3023, + "tag": "Cplusplus" + }, + { + "id": 659, + "tag": "Nervous system" + }, + { + "id": 4745, + "tag": "Neurons" + }, + { + "id": 316, + "tag": "OSBv1" + }, + { + "id": 38, + "tag": "OpenWorm" + } + ], + "timestamp_created": "2025-04-14 15:25:26.805723+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/openworm/CE_locomotion", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3375": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3375, + "name": "Mager2005 - Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition", + "repository_type": "biomodels", + "summary": "
This model was developed with the aim of constructing an equilibrium model of the pharmacokinetic behaviour of a drug exhibiting target-mediated drug disposition (TMDD). TMDD involves the inclusion of drug-target interactions within a pharmacokinetic description, something which is usually considered negligible and subsequently excluded. Two approaches were used, one of which involved a quasi-equilibrium method to describe the kinetics of TMDD.
", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4746, + "tag": "BioModels:BIOMD0000000765" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4747, + "tag": "Unknown" + } + ], + "timestamp_created": "2025-05-07 14:37:01.150254+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000765", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3376": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3376, + "name": "Naizabekov2020 - Methylosinus trichosporium OB3b - iMsOB3b", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports compiled for the metabolic model, iMsOB3b. The model can be found in the Supplementary Section of the Naizabekov et al, 2020 paper cited here.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4755, + "tag": "BioModels:BIOMD0000001081" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:03.381032+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001081", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3377": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3377, + "name": "Charusanti2011 - Y. pestis (iPC815)", + "repository_type": "biomodels", + "summary": "

FROG and miniFROG Reports for the organism Yersinia pestis.
These models originate from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4757, + "tag": "BioModels:BIOMD0000001082" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:04.258720+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001082", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3378": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3378, + "name": "Broddrick2016 - Synechococcus elongatus PCC 7942 (iJB785)", + "repository_type": "biomodels", + "summary": "

FROG and miniFROG Reports for the organism Synechococcus elongatus PCC 7942.
These models originate from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4758, + "tag": "BioModels:BIOMD0000001083" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:05.542086+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001083", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3379": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3379, + "name": "Mol2021 - P. thermoglucosidasius NCIMB 11955 (p_thermo)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports are provided for p_thermo genome-scale metabolic model. p_thermo is the model for Parageobacillus thermoglucosidasius NCIMB 11955. An analysis of the metabolism and internal fluxes of P. thermoglucosidasius is done in this study and the model can be found in the Supplementary data of Mol et al, 2021.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4759, + "tag": "BioModels:BIOMD0000001084" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:06.511688+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001084", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3380": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3380, + "name": "Norsigian2018 - A. baumannii (iCN718)", + "repository_type": "biomodels", + "summary": "

FROG and miniFROG Reports for the organism A. baumannii.
The model (iCN718) originates from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4760, + "tag": "BioModels:BIOMD0000001085" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:07.794712+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001085", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3381": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3381, + "name": "Jensen2020 - Streptococcus oralis (iCJ415)", + "repository_type": "biomodels", + "summary": "We have presented FROG and miniFROG reports for the first genome-scale model, iCJ415, for Streptococcus oralis SK141. The model can be found in the Supplementary Material of the publication by Jensen et al, 2020 cited here.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4761, + "tag": "BioModels:BIOMD0000001086" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:09.656248+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001086", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3382": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3382, + "name": "Kumelj2019 - Streptomyces coelicolor (iKS1317)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports compiled for iKS1317, a reconstructed genome-scale metabolic model (GEM) for Streptomyces coelicolor. The model reconstruction is available in the Supporting Information of Kumelj et al, 2019 cited here.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4762, + "tag": "BioModels:BIOMD0000001087" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:10.865809+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001087", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3383": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3383, + "name": "Nouri2020 - Zymomonas mobilis (iHN446)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports are compiled for the reconstructed genome-scale metabolic model (iHN446) for Z. mobilis. The model can be found in the article Nouri et al, 2020 cited here.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4763, + "tag": "BioModels:BIOMD0000001088" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:12.861936+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001088", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3384": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3384, + "name": "\u00d6zcan2019 - Leuconostoc mesenteroides subsp. cremoris (iLM.c559)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports have been compiled for iLM.c559, which is a GSM of the bacterium Leuconostoc mesenteroides subsp. cremoris. The model can be found in the \u00d6zcan et al, 2019 paper cited here.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4764, + "tag": "BioModels:BIOMD0000001089" + }, + { + "id": 4756, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:41:13.686552+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001089", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3385": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3385, + "name": "Dias2019 - GSM model of S. pneumoniae R6", + "repository_type": "biomodels", + "summary": "Phenotypically Reconciled Genome-scale Metabolic Model for the Metabolism of Streptococcus pneumoniae Strain R6.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4765, + "tag": "BioModels:BIOMD0000001090" + }, + { + "id": 92, + "tag": "SBML" + }, + { + "id": 4766, + "tag": "Streptococcus pneumoniae (strain ATCC BAA-255 / R6)" + } + ], + "timestamp_created": "2025-05-07 14:41:14.650489+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001090", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3386": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3386, + "name": "Kulyashov2020 - Genome-Scale Metabolic Model for Geobacillus icigianus", + "repository_type": "biomodels", + "summary": "Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4767, + "tag": "BioModels:BIOMD0000001091" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:15.755687+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001091", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3387": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3387, + "name": "Gautam2020-Metabolic network of Thermotoga sp. Strain RQ7", + "repository_type": "biomodels", + "summary": "As in DOI: 10.1007/s12010-020-03470-z", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4768, + "tag": "BioModels:BIOMD0000001092" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:16.499162+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001092", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3388": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3388, + "name": "Nogales2020 - Genome-scale metabolic network of Pseudomonas putida (iJN1462)", + "repository_type": "biomodels", + "summary": "iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4769, + "tag": "BioModels:BIOMD0000001093" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:17.347339+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001093", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3389": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3389, + "name": "Liao2011 - Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228)", + "repository_type": "biomodels", + "summary": "
Liao2011 - Genome-scale metabolicreconstruction of Klebsiella pneumoniae (iYL1228)

This model is described in the article:

Liao YC, Huang TW, Chen FC, Charusanti P, Hong JS, Chang HY, Tsai SF, Palsson BO, Hsiung CA.
J. Bacteriol. 2011 Apr; 193(7): 1710-1717

Abstract:

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.

This model is hosted on BioModels Database and identified by: MODEL1507180054.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4770, + "tag": "BioModels:BIOMD0000001094" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:18.278271+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001094", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3390": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3390, + "name": "Jamshidi2007 - Genome-scale metabolic network of Mycobacterium tuberculosis (iNJ661)", + "repository_type": "biomodels", + "summary": "
Jamshidi2007 - Genome-scale metabolic networkof Mycobacterium tuberculosis (iNJ661)

This model is described in the article:

Jamshidi N, Palsson B\u00d8.
BMC Syst Biol 2007; 1: 26

Abstract:

BACKGROUND: Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. RESULTS: We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints. CONCLUSION: Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.

This model is hosted on BioModels Database and identified by: MODEL1507180001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4771, + "tag": "BioModels:BIOMD0000001095" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:19.162541+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001095", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3391": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3391, + "name": "Irani2015 - Genome-scale metabolic model of P.pastoris N-glycosylation", + "repository_type": "biomodels", + "summary": "
Irani2015 - Genome-scale metabolic model ofP.pastoris N-glycosylation

This model is described in the article:

Irani ZA, Kerkhoven E, Shojaosadati SA, Nielsen J.
Biotechnol. Bioeng. 2015 Oct;

Abstract:

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better predictions of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. This article is protected by copyright. All rights reserved.

This model is hosted on BioModels Database and identified by: MODEL1510220000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4772, + "tag": "BioModels:BIOMD0000001096" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:20.331675+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001096", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3392": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3392, + "name": "Benedict2011 - Genome-scale metoblic network of Methanosarcina acetivorans (iMB745)", + "repository_type": "biomodels", + "summary": "
Benedict2011 - Genome-scale metoblic networkof Methanosarcina acetivorans (iMB745)

This model is described in the article:

Benedict MN, Gonnerman MC, Metcalf WW, Price ND.
J. Bacteriol. 2012 Feb; 194(4): 855-865

Abstract:

Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis.

This model is hosted on BioModels Database and identified by: MODEL1507180040.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4773, + "tag": "BioModels:BIOMD0000001097" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:21.694708+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001097", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3393": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3393, + "name": "Feist2006_methanogenesis_OptiMethanol", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4774, + "tag": "BioModels:BIOMD0000001098" + }, + { + "id": 4775, + "tag": "Methanogenesis" + }, + { + "id": 4776, + "tag": "Methanosarcina barkeri" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:22.467689+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001098", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3394": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3394, + "name": "Richards2016 - Genome-scale metabolic reconstruction of Methanococcus maripaludis (iMR539)", + "repository_type": "biomodels", + "summary": "
Richards2016 - Genome-scale metabolicreconstruction of Methanococcus maripaludis (iMR539)

This model is described in the article:

Richards MA, Lie TJ, Zhang J, Ragsdale SW, Leigh JA, Price ND.
J. Bacteriol. 2016 Dec; 198(24): 3379-3390

Abstract:

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

This model is hosted on BioModels Database and identified by: MODEL1607200000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4777, + "tag": "BioModels:BIOMD0000001099" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:23.311851+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001099", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3395": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3395, + "name": "Mohr2022 - Rabbit Purkinje Cardiac Electrophysiology", + "repository_type": "biomodels", + "summary": "we performed a comparative benchmark and investigated a variety of mathematical cardiac AP models, including a newly developed minimalistic model specifically tailored to the AP of rabbit Purkinje cells, for their ability to substitute experiments. The simulated changes in AP duration (dAPD90) at increasing drug concentrations were compared to experimental results from 588 internal Purkinje fiber studies covering 555 different drugs with diverse modes of action. Using our minimalistic model, 80% of the Purkinje experiments could be quantitatively reproduced.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4778, + "tag": "BioModels:BIOMD0000001100" + }, + { + "id": 4730, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-05-07 14:41:24.227668+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001100", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3396": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3396, + "name": "Zhou2024 - Post Infarction Myocyte Electromechanics", + "repository_type": "biomodels", + "summary": "A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets. Abnormalities caused by scar and border zone ionic remodeling are introduced in varying degrees as reported in experimental data obtained in acute and chronic infarction. Simulations enabled reproducing and explaining clinical phenotypes post-MI, from ionic remodelling to ECGs and pressure-volume loops.", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4779, + "tag": "BioModels:BIOMD0000001101" + }, + { + "id": 39, + "tag": "matlab" + } + ], + "timestamp_created": "2025-05-07 14:41:25.135704+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001101", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3397": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3397, + "name": "Burbano2023 - HGFsignaling_in_FattyLiverDisease", + "repository_type": "biomodels", + "summary": "

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, non-alcoholic fatty liver disease (NAFLD) is gaining importance. As the disease progresses it can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in NAFLD development, but the molecular mechanisms responsible for disease development remained unresolved. Here, we uncover a strong downregulation of the PI3K-AKT pathway and an upregulation of the MAPK pathway in primary hepatocytes from a preclinical model fed with a Western diet (WD). Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveals a patient-specific variability in basal MET phosphorylation, which correlates with the outcome of patients after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

", + "tags": [ + { + "id": 3056, + "tag": "BioModels" + }, + { + "id": 4780, + "tag": "BioModels:BIOMD0000001102" + }, + { + "id": 92, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:41:26.024186+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001102", + "user": { + "email": "p.glee.s.on@gmail.com", + "first_name": "OSB", + "id": "7aafb661-2f39-4683-8f35-528de0752dd7", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "7aafb661-2f39-4683-8f35-528de0752dd7" + }, + "3398": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3398, + "name": "Palaniappan2021 - Cell free modelling of second generation Toehold switches", + "repository_type": "biomodels", + "summary": "
Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Poor awareness and access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative approach for a new kind of diagnosis has been developed, comprising identification of optimal biomarkers, design of the sensor, and simulation of the diagnostic circuit. Using miRNA expression data in the public domain, we identified circulating miRNA biomarkers specific to cervical cancer using multi-tier screening. Synthetic riboregulators called toehold switches specific for the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we developed a generic grammar of these switches, and built a multivariate linear regression model using thermodynamic features derived from RNA secondary structure and interaction. The model yielded predictions of toehold efficacy with an adjusted R2 = 0.59. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10nM and 100nM before saturation. Our study demonstrates an end-to-end workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic signatures that would be increasingly important in today\u2019s world. The approach has the potential to direct experimental efforts and minimise costs. All resources are provided open-source (https://github.com/igem2019) under GNU GPLv3 licence.
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"thatusernameistakenari09@gmail.com", + "first_name": "Arabella", + "id": "e8499e41-e31b-4d37-a99f-88e783f1fee6", + "last_name": "T", + "username": "re5275" + }, + "user_id": "e8499e41-e31b-4d37-a99f-88e783f1fee6" + }, + "3400": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 3400, + "name": "delete", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 3, + "tag": "netpyne" + } + ], + "timestamp_created": "2025-06-14 21:02:18.540937+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/cmranieri/MarmosetModel", + "user": { + "email": "thatusernameistakenari09@gmail.com", + "first_name": "Arabella", + "id": "e8499e41-e31b-4d37-a99f-88e783f1fee6", + "last_name": "T", + "username": "re5275" + }, + "user_id": "e8499e41-e31b-4d37-a99f-88e783f1fee6" + }, + "3401": { + "auto_sync": true, + "content_types": "modeling,experimental", + "content_types_list": [ + "modeling", + 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"uri": "https://github.com/cmranieri/MarmosetModel", + "user": { + "email": "thatusernameistakenari09@gmail.com", + "first_name": "Arabella", + "id": "e8499e41-e31b-4d37-a99f-88e783f1fee6", + "last_name": "T", + "username": "re5275" + }, + "user_id": "e8499e41-e31b-4d37-a99f-88e783f1fee6" + }, + "3403": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 3403, + "name": "neuromorphic_model", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2025-07-07 14:24:19.401734+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/kishonsd/neuromorphic_agent", + "user": { + "email": "kishondaniels@gmail.com", + "first_name": "KISHON", + "id": "e3490250-bfe0-4d41-8b09-48ec7060af11", + "last_name": "DANIELS", + "username": "kishon" + }, + "user_id": "e3490250-bfe0-4d41-8b09-48ec7060af11" + }, + "3404": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "main", + "id": 3404, + "name": "Data Skills for Neuroscientists", + "repository_type": "github", + "summary": "This material accompanies the powerpoint presentation for the UCL workshop on Data Skills for Neuroscientists.", + "tags": [ + { + "id": 5, + "tag": "Tutorial" + } + ], + "timestamp_created": "2025-07-09 11:16:21.468049+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenNeuroAI/DataSkillsforNeuroscientists", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "0103eaaf-6a34-4509-a025-14367a52aa2b", + "last_name": "Gleeson", + "username": "padraig" + }, + "user_id": "0103eaaf-6a34-4509-a025-14367a52aa2b" } } } \ No newline at end of file diff --git a/libraries/client/cached_info/repos_v2dev.json b/libraries/client/cached_info/repos_v2dev.json index e932caed3..4e4f582f9 100644 --- a/libraries/client/cached_info/repos_v2dev.json +++ b/libraries/client/cached_info/repos_v2dev.json @@ -103878,7 +103878,7 @@ ], "default_context": "main", "id": 1836, - "name": "Role of synchrony in sensation and the basis for paresthesia-free spinal cord stimulation", + "name": "Role of synchrony in sensation and the basis for paresthesia-free spinal cord stimulation (Sagalajev et al., 2024)", "repository_type": "github", "summary": "We showed that DC axons are activated synchronously during c-SCS (50 Hz) and asynchronously during kf-SCS. Through experiments and computational simulations, we explained the basis for and consequences of desynchronization, showing that asynchronous spikes are sufficient to mediate pain relief whereas synchronous spikes are necessary for paresthesia.", "tags": [ @@ -103905,6 +103905,10 @@ { "id": 586, "tag": "Synchronization" + }, + { + "id": 659, + "tag": "NetPyNE" } ], "timestamp_created": "2024-01-12 10:21:47.712206+00:00", @@ -127654,24 +127658,40 @@ "id": 2258, "name": "Izhikevich2004_SpikingNeurons_thresholdVariability", "repository_type": "biomodels", - "summary": "", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(O) threshold variability has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.03; b=0.25; c=-60; d=4; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", "tags": [ { - "id": 6, - "tag": "excitability" + "id": 2936, + "tag": "BioModels" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3229, + "tag": "BioModels:BIOMD0000000136" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" } ], "timestamp_created": "2025-01-22 10:51:14.014827+00:00", "timestamp_updated": "---", "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000136", "user": { - "email": "p.gleeson@gmail.com", - "first_name": "Padraig", - "id": "7089f659-90ad-4ed9-9715-2327f7e2e72f", - "last_name": "Gleeson Admin", - "username": "pgleeson" + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" }, - "user_id": "7089f659-90ad-4ed9-9715-2327f7e2e72f" + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" }, "2259": { "auto_sync": true, @@ -127721,6 +127741,51060 @@ "username": "osbadmin" }, "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2260": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2260, + "name": "Network model of the granular layer of the cerebellar cortex (Maex, De Schutter 1998)", + "repository_type": "github", + "summary": "We computed the steady-state activity of a large-scale model of the granular layer of the rat cerebellum. Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. ... The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. See paper for more and details.", + "tags": [ + { + "id": 750, + "tag": "GENESIS (web link to model)" + }, + { + "id": 590, + "tag": "I A" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 581, + "tag": "I K,Ca" + }, + { + "id": 589, + "tag": "I L high threshold" + }, + { + "id": 574, + "tag": "I Na,t" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2937, + "tag": "ModelDB:50219" + }, + { + "id": 585, + "tag": "Oscillations" + }, + { + "id": 569, + "tag": "Simplified Models" + }, + { + "id": 586, + "tag": "Synchronization" + } + ], + "timestamp_created": "2025-01-27 12:54:09.354510+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/50219", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2261": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "master", + "id": 2261, + "name": "Vertical System (VS) tangential cells network model (Trousdale et al. 2014)", + "repository_type": "github", + "summary": "Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.", + "tags": [ + { + "id": 578, + "tag": "Activity Patterns" + }, + { + "id": 855, + "tag": "Connectivity matrix" + }, + { + "id": 729, + "tag": "Invertebrate" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2938, + "tag": "ModelDB:155727" + }, + { + "id": 620, + "tag": "Python" + }, + { + "id": 569, + "tag": "Simplified Models" + }, + { + "id": 587, + "tag": "Spatio-temporal Activity Patterns" + } + ], + "timestamp_created": "2025-01-27 14:52:07.733272+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/155727", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2262": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2262, + "name": "Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment (Barlow et al., 2024)", + "repository_type": "github", + "summary": "Model code for: 'Impact on backpropagation of the spatial heterogeneity of sodium channel kinetics in the axon initial segment', provisionally accepted at PLOS Computational Biology. ", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 579, + "tag": "Active Dendrites" + }, + { + "id": 571, + "tag": "Detailed Neuronal Models" + }, + { + "id": 1531, + "tag": "I Cl, leak" + }, + { + "id": 574, + "tag": "I Na,t" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 567, + "tag": "Ion Channel Kinetics" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2939, + "tag": "ModelDB:267088" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 740, + "tag": "Na/K pump" + }, + { + "id": 620, + "tag": "Python" + }, + { + "id": 1919, + "tag": "Reaction-diffusion" + } + ], + "timestamp_created": "2025-01-27 15:28:00.222162+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267088", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2263": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2263, + "name": "An ODE model of the inspiratory & sigh rhythms (Borrus et al., 2024)", + "repository_type": "github", + "summary": "Our work aims to understand the cellular and synaptic mechanisms underlying eupnea and sigh rhythms in the brain. Part of our work benefited from a mathematical model that made predictions about key components of both rhythm generators. We show a network a neurons can generate two oscillations simultaneously via different mechanisms.", + "tags": [ + { + "id": 1527, + "tag": "Brain Rhythms" + }, + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2940, + "tag": "ModelDB:267252" + }, + { + "id": 585, + "tag": "Oscillations" + }, + { + "id": 1536, + "tag": "Respiratory control" + }, + { + "id": 759, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2025-01-27 15:28:01.244095+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267252", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2264": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2264, + "name": "D1-MSN: The effect of EAAC1 on firing frequency (Petroccione et al., 2023)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2941, + "tag": "ModelDB:267267" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:01.837749+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267267", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2265": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2265, + "name": "Biochemically detailed model of post-synaptic plasticity for computational analyses of schizophrenia (Maki-Marttunen et al. in press)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 749, + "tag": "Long-term Synaptic Plasticity" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2942, + "tag": "ModelDB:267741" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 802, + "tag": "STDP" + }, + { + "id": 725, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2025-01-27 15:28:03.893523+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/267741", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2266": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2266, + "name": "Myelin dystrophy impairs signal transmission and working memory in a multiscale model of the aging prefrontal cortex (Iba\u00f1ez, Sengupta et al., 2024)", + "repository_type": "github", + "summary": "This study aims to quantify the effects of specific myelin dystrophies, such as demyelination and remyelination, which have been observed in the aging rhesus dlPFC, on the propagation of action potentials (APs) in layer 3 pyramidal neurons. Furthermore, this study sheds light on how such age-related myelin changes affect a core cognitive function: spatial working memory.", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 2255, + "tag": "Brian 2" + }, + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2943, + "tag": "ModelDB:2014821" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 620, + "tag": "Python" + }, + { + "id": 794, + "tag": "Working memory" + } + ], + "timestamp_created": "2025-01-27 15:28:05.267194+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2014821", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2267": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2267, + "name": "Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics (Dura-Bernal et al., 2023)", + "repository_type": "github", + "summary": "\"We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.\"", + "tags": [ + { + "id": 762, + "tag": "Audition" + }, + { + "id": 1546, + "tag": "Evoked LFP" + }, + { + "id": 590, + "tag": "I A" + }, + { + "id": 581, + "tag": "I K,Ca" + }, + { + "id": 589, + "tag": "I L high threshold" + }, + { + "id": 721, + "tag": "I N" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 575, + "tag": "I T low threshold" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 856, + "tag": "Laminar Connectivity" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2944, + "tag": "ModelDB:2014832" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 659, + "tag": "NetPyNE" + }, + { + "id": 585, + "tag": "Oscillations" + } + ], + "timestamp_created": "2025-01-27 15:28:06.721268+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2014832", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2268": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2268, + "name": "Kinetics and functional consequences of BK Channels activation by N-type Ca2+ channels in the dendrite of mouse neocortical layer-5 pyramidal neurons (Bl\u00f6mer et al., 2024)", + "repository_type": "github", + "summary": "The back propagation of an action potential (AP) from the axon/soma to the dendrites plays a central role in dendritic integration. This process involves the intricate orchestration of various ion channels, yet a comprehensive understanding of the contribution of each channel type remains elusive. In this study, we leverage ultrafast membrane potential recordings (Vm) and Ca2+ imaging techniques to shed light on the involvement of N-type voltage-gated Ca2+ channels (VGCCs) in layer-5 neocortical pyramidal neurons' apical dendrites.\r\nOur findings reveal a selective interaction between N-type VGCCs and large-conductance Ca2+-activated K+ channels (BK CAKCs). Remarkably, we observe that BK CAKCs are activated within a mere 500 \u00b5s after the AP peak, preceding the peak of the Ca2+ current triggered by the AP. Consequently, when N-type VGCCs are inhibited, the early broadening of the AP shape amplifies the activity of other VGCCs, leading to an augmented total Ca2+ influx. Our NEURON model, constructed to replicate and support these experimental results, underscores the critical coupling between N-type and BK channels.\r\nThis study not only redefines the conventional role of N-type VGCCs as primarily involved in presynaptic neurotransmitter release but also establishes their distinct and essential function as activators of BK CAKCs in neuronal dendrites. Furthermore, our results provide original functional validation of a physical interaction between Ca2+ and K+ channels, elucidated through ultrafast kinetic reconstruction. This insight enhances our understanding of the intricate mechanisms governing neuronal signaling and may have far-reaching implications in the field.\r\n", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 579, + "tag": "Active Dendrites" + }, + { + "id": 565, + "tag": "Dendritic Action Potentials" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 581, + "tag": "I K,Ca" + }, + { + "id": 2162, + "tag": "I Na, slow inactivation" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 2318, + "tag": "IK Bkca" + }, + { + "id": 2319, + "tag": "IK Skca" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2945, + "tag": "ModelDB:2015410" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:08.528594+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015410", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2269": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2269, + "name": "Touch-Motor Circuit (Gradwell et al., 2024)", + "repository_type": "github", + "summary": "A modified motor network model (from Moraud et al. 2016 and Formento et al. 2018) with added cutaneous input and dPV neuron model.", + "tags": [ + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2946, + "tag": "ModelDB:2015411" + }, + { + "id": 659, + "tag": "NetPyNE" + } + ], + "timestamp_created": "2025-01-27 15:28:09.037063+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015411", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2270": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2270, + "name": "Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models (Marascoa et al., 2024)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2947, + "tag": "ModelDB:2015423" + }, + { + "id": 611, + "tag": "NEST" + }, + { + "id": 620, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:28:11.856712+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015423", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2271": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2271, + "name": "Axonal K channel inhibition promotes ectopic burst of hippocampal mossy fiber (Kamiya 2024)", + "repository_type": "github", + "summary": "Simulation of ectopic burst firings from distal axons induced by local inhibition of axonal K channels on the hippocampal mossy fibers.", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 728, + "tag": "Axonal Action Potentials" + }, + { + "id": 2325, + "tag": "Excitability" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 574, + "tag": "I Na,t" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2948, + "tag": "ModelDB:2015571" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:12.342184+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015571", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2272": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2272, + "name": "Unipolar brush cell circuits extend and diversify spiking patterns (Hariani et al., 2023)", + "repository_type": "github", + "summary": "Sensory signals are processed by the cerebellum to coordinate movements. Unipolar brush cells (UBCs) are excitatory interneurons that project to granule cells and transform their input into prolonged increases or decreases in firing, depending on their ON or OFF UBC subtype. Further extension and diversification of the input signal could be produced by UBCs that project to one another, but whether this circuitry exists was unclear. In this work we showed that UBCs innervate one another using transgenic mice and immunohistochemistry. This NEURON model explores how these feed-forward networks of UBCs could extend the length of bursts or pauses and introduce delays\u2014transformations that may be necessary for cerebellar functions from modulation of eye movements to adaptive learning across time scales.", + "tags": [ + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2949, + "tag": "ModelDB:2015953" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:12.920411+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015953", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2273": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2273, + "name": "Respiratory control model with brainstem CPG and sensory feedback adapted for COVID-19 associated silent hypoxemia (Diekman et al., 2024)", + "repository_type": "github", + "summary": "This is an updated version of a closed-loop respiratory control model incorporating a central pattern generator (CPG), the Butera-Rinzel-Smith (BRS) model, together with lung mechanics, oxygen handling, and chemosensory components (see accession number 229640). We explored model parameters consistent with the silent hypoxemia phenomenon observed in some COVID-19 patients.", + "tags": [ + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 739, + "tag": "I Na,p" + }, + { + "id": 574, + "tag": "I Na,t" + }, + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2950, + "tag": "ModelDB:2015954" + }, + { + "id": 2236, + "tag": "Pacemaking mechanism" + }, + { + "id": 1536, + "tag": "Respiratory control" + } + ], + "timestamp_created": "2025-01-27 15:28:13.426869+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2015954", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2274": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2274, + "name": "Hippocampal O-LM interneurons and hippocampo-septal neurons with simplified and detailed biophysics (Tak\u00e1cs et al, 2024)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 578, + "tag": "Activity Patterns" + }, + { + "id": 590, + "tag": "I A" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 1531, + "tag": "I Cl, leak" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 825, + "tag": "Learning" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2951, + "tag": "ModelDB:2016137" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:13.965187+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016137", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2275": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2275, + "name": "Heterogeneous Purkinje Cell model (Cirtala et al., accepted)", + "repository_type": "github", + "summary": "We propose the first computational model of cerebellar Purkinje cell with dendritic heterogeneity. Each branch in our model is an individual unit and is characterized by its own set ion channel conductance density. We model clustered parallel fiber input and we measure the peak amplitude of a response. We observe how changes in P-type calcium conductance density changes the peak amplitude response from linear to bimodal step-plateau and vice-versa for each branch of the dendritic tree. We show how the dendritic calcium spikes propagate and how Kv4 channels block spreading depolarization to nearby branches. ", + "tags": [ + { + "id": 565, + "tag": "Dendritic Action Potentials" + }, + { + "id": 571, + "tag": "Detailed Neuronal Models" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 575, + "tag": "I T low threshold" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 763, + "tag": "Intrinsic plasticity" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2952, + "tag": "ModelDB:2016138" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 596, + "tag": "Synaptic Integration" + } + ], + "timestamp_created": "2025-01-27 15:28:14.477504+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016138", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2276": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2276, + "name": "A novel mechanism for ramping bursts based on slow negative feedback in model respiratory neurons (John et al., accepted)", + "repository_type": "github", + "summary": "Recordings from pre-Botzinger complex neurons responsible for the inspiratory phase of the respiratory rhythm reveal a ramping burst pattern, starting around the time that the transition from expiration to inspiration begins, in which the spike rate gradually rises until a transition into a high-frequency burst occurs. The spike rate increase along the burst is accompanied by a gradual depolarization of the plateau potential that underlies the spikes. These effects may be functionally important for inducing the onset of inspiration and hence maintaining effective respiration; however, most mathematical models for inspiratory bursting do not capture this activity pattern. Here, we study how the modulation of spike height and afterhyperpolarization via the slow inactivation of an inward current can support various activity patterns including ramping bursts. We use dynamical systems methods designed for multiple timescale systems, such as bifurcation analysis based on timescale decomposition and averaging over fast oscillations, to generate an understanding of and predictions about the specific dynamic effects that lead to ramping bursts. We also analyze how transitions between ramping and other activity patterns may occur with parameter changes, which could be associated with experimental manipulations, environmental conditions and/or development.", + "tags": [ + { + "id": 576, + "tag": "I K" + }, + { + "id": 2162, + "tag": "I Na, slow inactivation" + }, + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2953, + "tag": "ModelDB:2016216" + }, + { + "id": 759, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2025-01-27 15:28:14.978231+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016216", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2277": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2277, + "name": "A model of rodent mechanoreceptors (LTMRs) (Medlock et al., 2024)", + "repository_type": "github", + "summary": "\"... unlike the dichotomous frequency preference characteristic of RA1 and RA2/Pacinian afferents in other species, rodent RAs fell along a continuum. Fitting generalized linear models (GLMs) to experimental data reproduced the reliability and precision of rodent RAs. The resulting model parameters highlight key mechanistic differences across the RA spectrum; specifically, the integration window of different RAs transitions from wide to narrow as tuning preferences across the population move from low to high frequencies...\"", + "tags": [ + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2954, + "tag": "ModelDB:2016218" + }, + { + "id": 764, + "tag": "Touch" + } + ], + "timestamp_created": "2025-01-27 15:28:15.460071+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016218", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2278": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2278, + "name": "Thalamocortical sleep model (Fink et. al., 2024, and Krishnan et al., 2016)", + "repository_type": "github", + "summary": "While many theoretical models of neural dynamics during sleep exist, few include the effects of neuromodulators on sleep oscillations and describe transitions between different states of vigilance. Here, we ported an established thalamocortical network model (https://doi.org/10.7554/eLife.18607) from C++ to NEURON. This model, which includes a biophysically realistic description of intrinsic and synaptic channels, allows for testing the effects of different neuromodulators, intrinsic cell properties, and synaptic connectivity on neural dynamics during sleep. ", + "tags": [ + { + "id": 576, + "tag": "I K" + }, + { + "id": 581, + "tag": "I K,Ca" + }, + { + "id": 591, + "tag": "I K,leak" + }, + { + "id": 739, + "tag": "I Na,p" + }, + { + "id": 575, + "tag": "I T low threshold" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2955, + "tag": "ModelDB:2016601" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:15.949605+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016601", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2279": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2279, + "name": "Persistent Interruption in Parvalbumin-Positive Inhibitory Interneurons: Biophysical and Mathematical Mechanisms (Upchurch et al., 2024)", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2956, + "tag": "ModelDB:2016658" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 620, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:28:16.505766+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016658", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2280": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2280, + "name": "Synaptic plasticity for hippocampal place field formation and dynamics (Savelli 2024)", + "repository_type": "github", + "summary": "The model illustrates how the plastic integration of spatially stable grid-cell inputs could contribute to hippocampal place fields' generation and dynamic character. Theoretically, the grid-to-place transformation is possible if a place cell can respond selectively to a combination of suitably aligned grids. A synaptic plasticity rule whereby postsynaptic activation gates synaptic change while presynaptic activation determines its direction can accomplish this task during rat foraging behavior. The synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time when the model is run in larger environments and the topographical/modular organization of grid inputs is considered. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization - ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models.", + "tags": [ + { + "id": 755, + "tag": "C or Cplusplus program" + }, + { + "id": 1461, + "tag": "Grid cell" + }, + { + "id": 808, + "tag": "Hebbian plasticity" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2957, + "tag": "ModelDB:2016659" + }, + { + "id": 799, + "tag": "Place cell/field" + }, + { + "id": 800, + "tag": "Spatial Navigation" + }, + { + "id": 725, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2025-01-27 15:28:17.018509+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016659", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2281": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2281, + "name": "Equivalent excitability achieved via different Nav subtypes (Xie et al., 2024)", + "repository_type": "github", + "summary": "Abstract: Nociceptive sensory neurons convey pain signals to the CNS using action potentials. Loss-of-function mutations in the voltage-gated sodium channel NaV1.7 cause insensitivity to pain (presumably by reducing nociceptor excitability) but efforts to treat pain by inhibiting NaV1.7 pharmacologically have largely failed. This may reflect the variable contribution of NaV1.7 to nociceptor excitability. Contrary to claims that NaV1.7 is necessary for nociceptors to initiate action potentials, we show that nociceptors can achieve equivalent excitability using different combinations of NaV1.3, NaV1.7, and NaV1.8. Selectively blocking one of those NaV subtypes reduces nociceptor excitability only if the other two subtypes are weakly expressed. For example, excitability relies on NaV1.8 in acutely dissociated nociceptors but responsibility shifts to NaV1.7 and NaV1.3 by the fourth day in culture. A similar shift in NaV dependence occurs in vivo after inflammation, impacting ability of the NaV1.7-selective inhibitor PF-05089771 to reduce pain in behavioral tests. Flexible use of different NaV subtypes \u2013 an example of degeneracy \u2013 compromises the reliable modulation of nociceptor excitability by subtype-selective inhibitors. Identifying the dominant NaV subtype to predict drug efficacy is not trivial. Degeneracy at the cellular level must be considered when choosing drug targets at the molecular level.", + "tags": [ + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2958, + "tag": "ModelDB:2016663" + } + ], + "timestamp_created": "2025-01-27 15:28:17.478820+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016663", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2282": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2282, + "name": "Electrical properties of dendritic spines (Popovic et al. 2015)", + "repository_type": "github", + "summary": "An electrochromic dye was used to make fast, high signal/noise ratio, linear scale optical measurements of membrane potential from spines and basal dendrites of mouse somatosensory cortex layer 5 pyramidal cells. These experiments revealed close electrical coupling, with little signal loss, for membrane potential spreading from the spine head to the adjacent dendritic shaft. Modeling was used to explore the relationship between cellular properties (anatomy and biophysics) and the degree of electrical coupling between spines and dendrites. For usage instructions, see readme.pdf", + "tags": [ + { + "id": 786, + "tag": "Electrotonus" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2959, + "tag": "ModelDB:2016666" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 596, + "tag": "Synaptic Integration" + }, + { + "id": 2924, + "tag": "Two-port analysis of electrotonus" + }, + { + "id": 2925, + "tag": "Voltage transfer ratio" + } + ], + "timestamp_created": "2025-01-27 15:28:17.953231+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016666", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2283": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2283, + "name": "Cholinergic modulation of resting state networks (Sanda et al., accepted)", + "repository_type": "github", + "summary": "Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions\r\nbetween the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.\r\n", + "tags": [ + { + "id": 755, + "tag": "C or Cplusplus program" + }, + { + "id": 571, + "tag": "Detailed Neuronal Models" + }, + { + "id": 2325, + "tag": "Excitability" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 581, + "tag": "I K,Ca" + }, + { + "id": 591, + "tag": "I K,leak" + }, + { + "id": 739, + "tag": "I Na,p" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 861, + "tag": "I_K,Na" + }, + { + "id": 1685, + "tag": "KCC2" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2960, + "tag": "ModelDB:2016670" + }, + { + "id": 740, + "tag": "Na/K pump" + }, + { + "id": 585, + "tag": "Oscillations" + }, + { + "id": 586, + "tag": "Synchronization" + } + ], + "timestamp_created": "2025-01-27 15:28:18.530067+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016670", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2284": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2284, + "name": "Markovian model for HCN-encoded current regulated by capsazepine (Wong et al., 2024)", + "repository_type": "github", + "summary": "Capsazepine (CPZ) was recognized as a synthetic inhibitor of capsaicin activation of TRPV1 channel. TRPV1 has been demonstrated to be widely distributed in endocrine or neuroendocrine cells, and different types of central neurons. However, whether and how this compound might produce any perturbations on varying types of ionic currents, other than block of capsaicin-induced TRPV1 or activation of epithelial Na+ current, remain largely unclear. In this study, we aimed to clarify the effect of CPZ on hyperpolarization-activated cationic current (Ih, or HCN-encoded current) and voltage-gated Na+ current (INa) in pituitary GH3 cells. By use of whole-cell patch-clamp recordings, the CPZ application caused a concentration-dependent inhibition of Ih amplitude or slowing in activation time course of the current with the measured IC50 or KD value of 3.1 or 3.16 \u03bcM, respectively. The steady-state activation curve of Ih during the exposure to 3 \u03bcM CPZ was shifted toward more hyperpolarized potential by approximately 20 mV; however, no change in the gating charge of the current was noticed. In this work, a modified Markovian model designed for Ih was implemented to evaluate the plausible modifications of CPZ on the hysteresis of the current; and the model was well suited to predict CPZ-mediated decrease in hysteretic strength of Ih. The INa identified in GH3 cells was also suppressed by CPZ, despite the activation or inactivation time course of the current was changed. Moreover, under cell-attached current measurements, cell exposure to CPZ resulted in a reduction of spontaneous firing. Collectively, finding the current observations suggest that CPZ-perturbed inhibition of Ih or INa appears to be direct and independent of its action on vanilloid receptor(s); hence, such actions would be a yet unidentified but important ionic mechanism underlying perturbed intrinsic membrane excitability in the in-vivo endocrine or neuroendocrine cells, or neurons.", + "tags": [ + { + "id": 594, + "tag": "I h" + }, + { + "id": 567, + "tag": "Ion Channel Kinetics" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2961, + "tag": "ModelDB:2016995" + }, + { + "id": 759, + "tag": "XPPAUT" + } + ], + "timestamp_created": "2025-01-27 15:28:19.014582+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016995", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2285": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2285, + "name": "Subthreshold conductances regulate theta-frequency LFPs and spike phase (Sinha and Narayanan, 2015)", + "repository_type": "github", + "summary": "What are the implications for the existence of subthreshold ion channels, their localization profiles, and plasticity on local field potentials (LFPs)? Here, we assessed the role of hyperpolarization-activated cyclic-nucleotide-gated (HCN) channels in altering hippocampal theta-frequency LFPs and the associated spike phase. We presented spatiotemporally randomized, balanced theta-modulated excitatory and inhibitory inputs to somatically aligned, morphologically realistic pyramidal neuron models spread across a cylindrical neuropil. We computed LFPs from seven electrode sites and found that the insertion of an experimentally constrained HCN-conductance gradient into these neurons introduced a location-dependent lead in the LFP phase without significantly altering its amplitude. Further, neurons fired action potentials at a specific theta phase of the LFP, and the insertion of HCN channels introduced large lags in this spike phase and a striking enhancement in neuronal spike-phase coherence. Importantly, graded changes in either HCN conductance or its half-maximal activation voltage resulted in graded changes in LFP and spike phases. Our conclusions on the impact of HCN channels on LFPs and spike phase were invariant to changes in neuropil size, to morphological heterogeneity, to excitatory or inhibitory synaptic scaling, and to shifts in the onset phase of inhibitory inputs. Finally, we selectively abolished the inductive lead in the impedance phase introduced by HCN channels without altering neuronal excitability and found that this inductive phase lead contributed significantly to changes in LFP and spike phase. Our results uncover specific roles for HCN channels and their plasticity in phase-coding schemas and in the formation and dynamic reconfiguration of neuronal cell assemblies. ", + "tags": [ + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 763, + "tag": "Intrinsic plasticity" + }, + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2962, + "tag": "ModelDB:2016996" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 2919, + "tag": "Phenomenological inductance" + }, + { + "id": 725, + "tag": "Synaptic Plasticity" + } + ], + "timestamp_created": "2025-01-27 15:28:19.504817+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2016996", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2286": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2286, + "name": "Mouse colorectal afferent ending (Feng et al 2015)", + "repository_type": "github", + "summary": "This model simulates the afferent neural encoding in response to mechanical colorectal stretching. A custom-built mechano-sensitive ion channel, gated by membrane tension induced by circumferential colorectal stretch, is incorporated. A lumped parametric model has been developed to calculate membrane tension from the overall colorectal stretch.", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2963, + "tag": "ModelDB:2017006" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:20.206499+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017006", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2287": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2287, + "name": "Glutamate spillover and plateau potentials (Trpevski et al., 2023)", + "repository_type": "github", + "summary": "Plateau potentials in models of striatal projection neurons were studied. Plateau potentials are supralinear dendritic voltage elevations which have: i) high somatic voltage amplitude and ii) are threshold, or all-or-none, events (like somatic action potentials). While the high somatic amplitude can sometimes be captured in models of striatal projection neurons, their all-or-none property is not usually not. In this study, we found that including glutamate spillover consistently and robustly provides all-or-none plateaus in addition to a high somatic amplitude. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to parameters in the model for the Mg2+ block of the NMDA receptors, which is the mechanism for generating plateaus. These results suggest a potentially significant role of glutamate spillover in plateau potential generation. See the related article for more details.", + "tags": [ + { + "id": 579, + "tag": "Active Dendrites" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 574, + "tag": "I Na,t" + }, + { + "id": 2396, + "tag": "Kir, inactivating" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2964, + "tag": "ModelDB:2017143" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:20.680230+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017143", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2288": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2288, + "name": "C.elegans motor and interneurons (Nicoletti at al. 2024)", + "repository_type": "github", + "summary": "Biophysically accurate models of six classes of C. elegans neurons: AIY, RIM, and AVA interneurons, and the VA, VB, and VD motor neurons. The implemented codes reproduce current-clamp and voltage-clamp experiments reported in literature and simulate knockout neurons, with the aim to identify the biophysical mechanisms at the basis of inter and motor neuron functioning. These models represent a step forward toward the modeling of C. elegans neuronal networks and virtual experiments on the nematode nervous system.", + "tags": [ + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 584, + "tag": "I Potassium" + }, + { + "id": 2318, + "tag": "IK Bkca" + }, + { + "id": 2319, + "tag": "IK Skca" + }, + { + "id": 1931, + "tag": "Kir" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2965, + "tag": "ModelDB:2017403" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 620, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:28:21.168355+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017403", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2289": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2289, + "name": "Parkinsonian Motor Network Model during Multivariable Closed-loop DBS (Fleming et al 2023)", + "repository_type": "github", + "summary": "We developed a computational model of the parkinsonian motor network to investigate multivariable closed-loop control strategies of deep brain stimulation (DBS) for Parkinson\u2019s disease (PD). The motor network model includes a model of the cortical basal ganglia coupled to a model of the motoneuron pool. The cortical basal ganglia model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode, while (iv) the motoneuron pool model includes a model of electromyography and (v) force generated due to the activation of motoneurons in the pool. The model simulates periods of elevated beta- and tremor-band activity to facilitate investigation of tremor- and beta-based closed-loop DBS control strategies, modulating DBS amplitude, pulse duration or frequency, using clinically accessible measures of tremor- (based on the measured force signal) and beta-band activity (based on the local field potential).", + "tags": [ + { + "id": 578, + "tag": "Activity Patterns" + }, + { + "id": 831, + "tag": "Deep brain stimulation" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 589, + "tag": "I L high threshold" + }, + { + "id": 582, + "tag": "I Sodium" + }, + { + "id": 575, + "tag": "I T low threshold" + }, + { + "id": 1433, + "tag": "I_AHP" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2966, + "tag": "ModelDB:2017405" + }, + { + "id": 577, + "tag": "NEURON" + }, + { + "id": 585, + "tag": "Oscillations" + }, + { + "id": 801, + "tag": "Parkinson's" + }, + { + "id": 714, + "tag": "PyNN" + }, + { + "id": 620, + "tag": "Python" + } + ], + "timestamp_created": "2025-01-27 15:28:21.690601+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2017405", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2290": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2290, + "name": "Synchronization in a realistic model of CA1 pyramidal neurons (Fiasconaro and Migliore 2024)", + "repository_type": "github", + "summary": "We study the synchronisation of neurons in a realistic model under the Hodgkin-Huxley dynamics. To focus on the role of the different locations of the excitatory synapses, we use two identical neurons where the set of input signals is grouped at two different distances from the soma. Synchronisation is studied using phase spiking correlation as a function of various parameters such as the distance from the soma of one of the synaptic groups, the inhibition weight and the associated activation delay.\r\nWe found that the neurons' spiking activity depends nonmonotonically on the relative dendritic location of the synapses and their inhibitory weight, whereas the synchronisation measure always decreases with inhibition, and strongly depends on its activation time delay.", + "tags": [ + { + "id": 736, + "tag": "Action Potentials" + }, + { + "id": 1684, + "tag": "Ca pump" + }, + { + "id": 571, + "tag": "Detailed Neuronal Models" + }, + { + "id": 590, + "tag": "I A" + }, + { + "id": 731, + "tag": "I CAN" + }, + { + "id": 583, + "tag": "I Calcium" + }, + { + "id": 576, + "tag": "I K" + }, + { + "id": 581, + "tag": "I K,Ca" + }, + { + "id": 589, + "tag": "I L high threshold" + }, + { + "id": 580, + "tag": "I M" + }, + { + "id": 574, + "tag": "I Na,t" + }, + { + "id": 575, + "tag": "I T low threshold" + }, + { + "id": 594, + "tag": "I h" + }, + { + "id": 747, + "tag": "I_KD" + }, + { + "id": 756, + "tag": "Methods" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2967, + "tag": "ModelDB:2018009" + }, + { + "id": 577, + "tag": "NEURON" + } + ], + "timestamp_created": "2025-01-27 15:28:22.273349+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2018009", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2291": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 2291, + "name": "Disinhibitory circuit for arousal effects in perceptual decision making tasks (Beerendonk et al., 2024)", + "repository_type": "github", + "summary": "This code reproduces the computational model of cortical circuits presented in Beerendonk, Mejias et al., PNAS 2024. The model describes a simplified population-level cortical circuit with pyramidal neurons and multiple interneuron types (PV, SST, VIP) which performs a simple perceptual decision making task. Notably, the model incorporates the effect of arousal signals on the circuit to reproduce the inverted-U relationship between task performance and arousal level found experimentally (and the corresponding U-shape between reaction times and arousal levels).", + "tags": [ + { + "id": 655, + "tag": "MATLAB" + }, + { + "id": 564, + "tag": "ModelDB" + }, + { + "id": 2968, + "tag": "ModelDB:2018021" + } + ], + "timestamp_created": "2025-01-27 15:28:22.773429+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/2018021", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2292": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2292, + "name": "Edelstein1996 - EPSP ACh event", + "repository_type": "biomodels", + "summary": "
Edelstein1996 - EPSP ACh event

Model of a nicotinic Excitatory Post-Synaptic Potential in a Torpedo electric organ. Acetylcholine is not represented explicitely, but by an event that changes the constants of transition from unliganded to liganded.\u00a0

This model has initially been encoded using StochSim.

This model is described in the article:

Edelstein SJ, Schaad O, Henry E, Bertrand D, Changeux JP.
Biol Cybern 1996 Nov; 75(5): 361-379

Abstract:

Nicotinic acetylcholine receptors are transmembrane oligomeric proteins that mediate interconversions between open and closed channel states under the control of neurotransmitters. Fast in vitro chemical kinetics and in vivo electrophysiological recordings are consistent with the following multi-step scheme. Upon binding of agonists, receptor molecules in the closed but activatable resting state (the Basal state, B) undergo rapid transitions to states of higher affinities with either open channels (the Active state, A) or closed channels (the initial Inactivatable and fully Desensitized states, I and D). In order to represent the functional properties of such receptors, we have developed a kinetic model that links conformational interconversion rates to agonist binding and extends the general principles of the Monod-Wyman-Changeux model of allosteric transitions. The crucial assumption is that the linkage is controlled by the position of the interconversion transition states on a hypothetical linear reaction coordinate. Application of the model to the peripheral nicotine acetylcholine receptor (nAChR) accounts for the main properties of ligand-gating, including single-channel events, and several new relationships are predicted. Kinetic simulations reveal errors inherent in using the dose-response analysis, but justify its application under defined conditions. The model predicts that (in order to overcome the intrinsic stability of the B state and to produce the appropriate cooperativity) channel activation is driven by an A state with a Kd in the 50 nM range, hence some 140-fold stronger than the apparent affinity of the open state deduced previously. According to the model, recovery from the desensitized states may occur via rapid transit through the A state with minimal channel opening, thus without necessarily undergoing a distinct recovery pathway, as assumed in the standard 'cycle' model. Transitions to the desensitized states by low concentration 'pre-pulses' are predicted to occur without significant channel opening, but equilibrium values of IC50 can be obtained only with long pre-pulse times. Predictions are also made concerning allosteric effectors and their possible role in coincidence detection. In terms of future developments, the analysis presented here provides a physical basis for constructing more biologically realistic models of synaptic modulation that may be applied to artificial neural networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2971, + "tag": "Cell surface receptor signaling pathway" + }, + { + "id": 2972, + "tag": "Neuromuscular synaptic transmission" + }, + { + "id": 2973, + "tag": "Transmission of nerve impulse" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2969, + "tag": "BioModels:BIOMD0000000001" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2970, + "tag": "Tetronarce californica" + } + ], + "timestamp_created": "2025-01-27 18:08:25.578676+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000001", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2293": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2293, + "name": "Edelstein1996 - EPSP ACh species", + "repository_type": "biomodels", + "summary": "
Edelstein1996 - EPSP ACh species

Model of a nicotinic Excitatory Post-Synaptic Potential in a Torpedo electric organ. Acetylcholine is represented explicitely as a molecular species.

This model has initially been encoded using StochSim.

This model is described in the article:

Edelstein SJ, Schaad O, Henry E, Bertrand D, Changeux JP.
Biol. Cybern. 1996 Nov; 75(5):361-79

Abstract:

Nicotinic acetylcholine receptors are transmembrane oligomeric proteins that mediate interconversions between open and closed channel states under the control of neurotransmitters. Fast in vitro chemical kinetics and in vivo electrophysiological recordings are consistent with the following multi-step scheme. Upon binding of agonists, receptor molecules in the closed but activatable resting state (the Basal state, B) undergo rapid transitions to states of higher affinities with either open channels (the Active state, A) or closed channels (the initial Inactivatable and fully Desensitized states, I and D). In order to represent the functional properties of such receptors, we have developed a kinetic model that links conformational interconversion rates to agonist binding and extends the general principles of the Monod-Wyman-Changeux model of allosteric transitions. The crucial assumption is that the linkage is controlled by the position of the interconversion transition states on a hypothetical linear reaction coordinate. Application of the model to the peripheral nicotine acetylcholine receptor (nAChR) accounts for the main properties of ligand-gating, including single-channel events, and several new relationships are predicted. Kinetic simulations reveal errors inherent in using the dose-response analysis, but justify its application under defined conditions. The model predicts that (in order to overcome the intrinsic stability of the B state and to produce the appropriate cooperativity) channel activation is driven by an A state with a Kd in the 50 nM range, hence some 140-fold stronger than the apparent affinity of the open state deduced previously. According to the model, recovery from the desensitized states may occur via rapid transit through the A state with minimal channel opening, thus without necessarily undergoing a distinct recovery pathway, as assumed in the standard 'cycle' model. Transitions to the desensitized states by low concentration 'pre-pulses' are predicted to occur without significant channel opening, but equilibrium values of IC50 can be obtained only with long pre-pulse times. Predictions are also made concerning allosteric effectors and their possible role in coincidence detection. In terms of future developments, the analysis presented here provides a physical basis for constructing more biologically realistic models of synaptic modulation that may be applied to artificial neural networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000002 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2974, + "tag": "BioModels:BIOMD0000000002" + }, + { + "id": 2971, + "tag": "Cell surface receptor signaling pathway" + }, + { + "id": 2972, + "tag": "Neuromuscular synaptic transmission" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2970, + "tag": "Tetronarce californica" + }, + { + "id": 2973, + "tag": "Transmission of nerve impulse" + } + ], + "timestamp_created": "2025-01-27 18:23:04.842593+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000002", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2294": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2294, + "name": "Goldbeter1991 - Min Mit Oscil", + "repository_type": "biomodels", + "summary": "
Goldbeter1991 - Min Mit Oscil

Minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase.

This model has been generated by MathSBML 2.4.6 (14-January-2005) 14-January-2005 18:33:39.806932.

This model is described in the article:

Goldbeter A.
Proc. Natl. Acad. Sci. U.S.A. 1991; 88(20):9107-11

Abstract:

A minimal model for the mitotic oscillator is presented. The model, built on recent experimental advances, is based on the cascade of post-translational modification that modulates the activity of cdc2 kinase during the cell cycle. The model pertains to the situation encountered in early amphibian embryos, where the accumulation of cyclin suffices to trigger the onset of mitosis. In the first cycle of the bicyclic cascade model, cyclin promotes the activation of cdc2 kinase through reversible dephosphorylation, and in the second cycle, cdc2 kinase activates a cyclin protease by reversible phosphorylation. That cyclin activates cdc2 kinase while the kinase triggers the degradation of cyclin has suggested that oscillations may originate from such a negative feedback loop [F\u00e9lix, M. A., Labb\u00e9, J. C., Dor\u00e9e, M., Hunt, T. & Karsenti, E. (1990) Nature (London) 346, 379-382]. This conjecture is corroborated by the model, which indicates that sustained oscillations of the limit cycle type can arise in the cascade, provided that a threshold exists in the activation of cdc2 kinase by cyclin and in the activation of cyclin proteolysis by cdc2 kinase. The analysis shows how miototic oscillations may readily arise from time lags associated with these thresholds and from the delayed negative feedback provided by cdc2-induced cyclin degradation. A mechanism for the origin of the thresholds is proposed in terms of the phenomenon of zero-order ultrasensitivity previously described for biochemical systems regulated by covalent modification.

This model is hosted on BioModels Database and identified by: BIOMD0000000003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2975, + "tag": "Amphibia" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2976, + "tag": "BioModels:BIOMD0000000003" + }, + { + "id": 2977, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-27 18:23:05.330401+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000003", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2295": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2295, + "name": "Goldbeter1991 - Min Mit Oscil, Expl Inact", + "repository_type": "biomodels", + "summary": "
Goldbeter1991 - Min Mit Oscil, Expl Inact

This model represents the inactive forms of CDC-2 Kinase and Cyclin Protease as separate species, unlike the ODEs in the published paper, in which the equations for the inactive forms are substituted into the equations for the active forms using a mass conservation rule M+MI=1,X+XI=1. Mass is still conserved in this model through the explicit reactions M<->MI and X<->XI. The terms in the kinetic laws are identical to the corresponding terms in the kinetic laws in the published paper.

This model has been generated by MathSBML 2.4.6 (14-January-2005) 14-January-2005 18:37:35.503857.

This model is described in the article:

Goldbeter A.
Proc. Natl. Acad. Sci. USA 1991 Oct; 88(20):9107-11

Abstract:

A minimal model for the mitotic oscillator is presented. The model, built on recent experimental advances, is based on the cascade of post-translational modification that modulates the activity of cdc2 kinase during the cell cycle. The model pertains to the situation encountered in early amphibian embryos, where the accumulation of cyclin suffices to trigger the onset of mitosis. In the first cycle ofthe bicyclic cascade model, cyclin promotes the activation of cdc2 kinase through reversible dephosphorylation, and in the second cycle, cdc2 kinase activates a cyclin protease by reversible phosphorylation. That cyclin activates cdc2 kinase while the kinase triggers the degradation of cyclin has suggested that oscillations may originate from such a negative feedback loop [F\u00e9lix, M. A., Labb\u00e9, J. C., Dor\u00e9e, M., Hunt, T. & Karsenti, E. (1990) Nature (London) 346, 379-382]. Thisconjecture is corroborated by the model, which indicates that sustained oscillations of the limit cycle type can arise in the cascade, provided that a threshold exists in the activation of cdc2 kinase by cyclin and in the activation of cyclinproteolysis by cdc2 kinase. The analysis shows how miototic oscillations may readily arise from time lags associated with these thresholds and from the delayed negative feedback provided by cdc2-induced cyclin degradation. A mechanism for theorigin of the thresholds is proposed in terms of the phenomenon of zero-order ultrasensitivity previously described for biochemical systems regulated by covalent modification.

This model is hosted on BioModels Database and identified by: BIOMD0000000004 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2975, + "tag": "Amphibia" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2979, + "tag": "BioModels:BIOMD0000000004" + }, + { + "id": 2977, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-27 18:23:05.835940+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000004", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2296": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2296, + "name": "Tyson1991 - Cell Cycle 6 var", + "repository_type": "biomodels", + "summary": "
Tyson1991 - Cell Cycle 6 var

Mathematical model of the interactions of cdc2 and cyclin.

This model is described in the article:

Tyson JJ.
Proc. Natl. Acad. Sci. U.S.A. 1991; 88(16); 7328-32

Abstract:

The proteins cdc2 and cyclin form a heterodimer (maturation promoting factor) that controls the major events of the cell cycle. A mathematical model for the interactions of cdc2 and cyclin is constructed. Simulation and analysis of the model show that the control system can operate in three modes: as a steady state with high maturation promoting factor activity, as a spontaneous oscillator, or as an excitable switch. We associate the steady state with metaphase arrest in unfertilized eggs, the spontaneous oscillations with rapid division cycles in early embryos, and the excitable switch with growth-controlled division cycles typical of nonembryonic cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000005 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2980, + "tag": "BioModels:BIOMD0000000005" + }, + { + "id": 2981, + "tag": "Cell cycle - yeast - Saccharomyces cerevisiae (budding yeast)" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 2982, + "tag": "Opisthokonta" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-27 18:23:06.415517+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000005", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2297": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2297, + "name": "Tyson1991 - Cell Cycle 2 var", + "repository_type": "biomodels", + "summary": "
Tyson1991 - Cell Cycle 2 var

Mathematical model of the interactions of cdc2 and cyclin.

Description taken from the original Cellerator version of the model ( Tyson (1991, 2 variables) at http://www.cellerator.org ).

This model is described in the article:

Tyson JJ.
Proc. Natl. Acad. Sci. U.S.A. 1991; 88(16); 7328-32

Abstract:

The proteins cdc2 and cyclin form a heterodimer (maturation promoting factor) that controls the major events of the cell cycle. A mathematical model for the interactions of cdc2 and cyclin is constructed. Simulation and analysis of the model show that the control system can operate in three modes: as a steady state with high maturation promoting factor activity, as a spontaneous oscillator, or as an excitable switch. We associate the steady state with metaphase arrest in unfertilized eggs, the spontaneous oscillations with rapid division cycles in early embryos, and the excitable switch with growth-controlled division cycles typical of nonembryonic cells.

This is a two variable reduction of the larger 6-variable model published in the same paper. The equations are:

u'= k4(v-u)(alpha+u^2)-k6*u
v'=kappa-k6*u
z= v-u
with kappa = k1[aa]/[CT]

In the present implementation, an additional variable z is introduced with z = v-u is made, so that the different variables be interpreted as follows:

u=[activeMPF]/[CT]
v=([cyclin]+[preMPF]+[activeMPF])/[CT]
z=([ cyclin]+[preMPF])/[CT]
with [CT]=[CDC2]+{CDC2P]+[preMPF]+[aMPF].

The reactions included are only to show the flows between z and u, and do not influence the species, as they all are set to boundaryCondition=True , meaning, that they are only determined by the rate rules (explicit differential equations) and assignment rules.

If you set boundaryCondition=False and remove the rate rules for v, u and the the assignment rule for z, you get the more symmetrical, but equivalent, version from the Cellerator repository:

u'= k4*z*(alpha+u^2)-k6*u
z'=kappa-z*(alpha+u^2)

This model is hosted on BioModels Database and identified by: BIOMD0000000006 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2983, + "tag": "BioModels:BIOMD0000000006" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 2982, + "tag": "Opisthokonta" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-27 18:23:07.124840+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000006", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2298": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2298, + "name": "Novak1997 - Cell Cycle", + "repository_type": "biomodels", + "summary": "
Novak1997 - Cell Cycle

Modeling the control of DNA replication in fission yeast.

This model is described in the article:

Novak B., Tyson JJ.
Proc. Natl. Acad. Sci. U.S.A. 1997:94(17):9147-52

Abstract:

A central event in the eukaryotic cell cycle is the decision to commence DNA replication (S phase). Strict controls normally operate to prevent repeated rounds of DNA replication without intervening mitoses (\"endoreplication\") or initiation of mitosis before DNA is fully replicated (\"mitotic catastrophe\"). Some of the genetic interactions involved in these controls have recently been identified in yeast. From this evidence we propose a molecular mechanism of \"Start\" control in Schizosaccharomyces pombe. Using established principles of biochemical kinetics, we compare the properties of this model in detail with the observed behavior of various mutant strains of fission yeast: wee1(-) (size control at Start), cdc13Delta and rum1(OP) (endoreplication), and wee1(-) rum1Delta (rapid division cycles of diminishing cell size). We discuss essential features of the mechanism that are responsible for characteristic properties of Start control in fission yeast, to expose our proposal to crucial experimental tests.

This model is hosted on BioModels Database and identified by: BIOMD0000000007 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2984, + "tag": "BioModels:BIOMD0000000007" + }, + { + "id": 2985, + "tag": "Cell cycle" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2986, + "tag": "Schizosaccharomyces pombe" + } + ], + "timestamp_created": "2025-01-27 18:23:07.599900+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000007", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2299": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2299, + "name": "Gardner1998 - Cell Cycle Goldbeter", + "repository_type": "biomodels", + "summary": "
Gardner1998 - Cell Cycle Goldbeter

Mathematical modeling of cell division cycle (CDC) dynamics.

The SBML file has been generated by MathSBML 2.6.0.p960929 (Prerelease Version of 29-Sept-2006) 1-October-2006 15:36:36.076517.

This model is described in the article:

Gardner TS, Dolnik M, Collins JJ.
Proc. Natl. Acad. Sci. U.S.A. 1998 Nov; 95(24): 14190-14195

Abstract:

We demonstrate, by using mathematical modeling of cell division cycle (CDC) dynamics, a potential mechanism for precisely controlling the frequency of cell division and regulating the size of a dividing cell. Control of the cell cycle is achieved by artificially expressing a protein that reversibly binds and inactivates any one of the CDC proteins. In the simplest case, such as the checkpoint-free situation encountered in early amphibian embryos, the frequency of CDC oscillations can be increased or decreased by regulating the rate of synthesis, the binding rate, or the equilibrium constant of the binding protein. In a more complex model of cell division, where size-control checkpoints are included, we show that the same reversible binding reaction can alter the mean cell mass in a continuously dividing cell. Because this control scheme is general and requires only the expression of a single protein, it provides a practical means for tuning the characteristics of the cell cycle in vivo.

This model is hosted on BioModels Database and identified by: BIOMD0000000008.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2975, + "tag": "Amphibia" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2987, + "tag": "BioModels:BIOMD0000000008" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-27 18:23:08.049820+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000008", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2300": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2300, + "name": "Huang1996 - Ultrasensitivity in MAPK cascade", + "repository_type": "biomodels", + "summary": "
Huang1996 - Ultrasensitivity in MAPK cascade

The temporal sequence of kinase activation, from MAPKKK (activated RAF) to the final effector MAPK (activated ERK), is described here. It is observed from the model that there is an increase in sensitivity along the levels of the cascade, where the activity of MAPK reaches its maximal before MAPKKK.

This model is described in the article:

Huang CY, Ferrell JE Jr
Proc. Natl. Acad. Sci. U.S.A. 1996:93(19):10078-83

Abstract:

The mitogen-activated protein kinase (MAPK) cascade is a highly conserved series of three protein kinases implicated in diverse biological processes. Here we demonstrate that the cascade arrangement has unexpected consequences for the dynamics of MAPK signaling. We solved the rate equations for the cascade numerically and found that MAPK is predicted to behave like a highly cooperative enzyme, even though it was not assumed that any of the enzymes in the cascade were regulated cooperatively. Measurements of MAPK activation in Xenopus oocyte extracts confirmed this prediction. The stimulus/response curve of the MAPK was found to be as steep as that of a cooperative enzyme with a Hill coefficient of 4-5, well in excess of that of the classical allosteric protein hemoglobin. The shape of the MAPK stimulus/ response curve may make the cascade particularly appropriate for mediating processes like mitogenesis, cell fate induction, and oocyte maturation, where a cell switches from one discrete state to another.

The species K_PP_norm, KKK_P_norm and KK_PP_norm are the relative concentrations of the active MAPK, MAPKK and MAPKKK, that is the double, or single resp. phophorylated forms divided by the total concentrations of each kinase. For MAPK additionally the also active MAPK divided by the maximal concentration of active MAPK is given by rel_K_PP_max. The parameter K_PP_norm_max, the maximal ratio of active MapK, has to be calculated for each change of parameters.

This model is hosted on BioModels Database and identified by: BIOMD0000000009 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2988, + "tag": "BioModels:BIOMD0000000009" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-27 18:23:08.518900+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000009", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2301": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2301, + "name": "Kholodenko2000 - Ultrasensitivity and negative feedback bring oscillations in MAPK cascade", + "repository_type": "biomodels", + "summary": "
Kholodenko2000 - Ultrasensitivity and negative feedback bring oscillations in MAPK cascade

The combination of ultrasensitivity and negative feedback bring sustained oscillations in the mitogen-activated protein kinase cascades.

This model is described in the article:

Kholodenko BN
Eur. J. Biochem. 2000; 267(6):1583-8

Abstract:

Functional organization of signal transduction into protein phosphorylation cascades, such as the mitogen-activated protein kinase (MAPK) cascades, greatly enhances the sensitivity of cellular targets to external stimuli. The sensitivity increases multiplicatively with the number of cascade levels, so that a tiny change in a stimulus results in a large change in the response, the phenomenon referred to as ultrasensitivity. In a variety of cell types, the MAPK cascades are imbedded in long feedback loops, positive or negative, depending on whether the terminal kinase stimulates or inhibits the activation of the initial level. Here we demonstrate that a negative feedback loop combined with intrinsic ultrasensitivity of the MAPK cascade can bring about sustained oscillations in MAPK phosphorylation. Based on recent kinetic data on the MAPK cascades, we predict that the period of oscillations can range from minutes to hours. The phosphorylation level can vary between the base level and almost 100% of the total protein. The oscillations of the phosphorylation cascades and slow protein diffusion in the cytoplasm can lead to intracellular waves of phospho-proteins.

This model is hosted on BioModels Database and identified by: BIOMD0000000010 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2991, + "tag": "BioModels:BIOMD0000000010" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-27 18:23:08.970329+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000010", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2302": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2302, + "name": "Levchenko2000_MAPK_noScaffold", + "repository_type": "biomodels", + "summary": "

MAPK cascade in solution (no scaffold)

Description
This model describes a basic 3-\t\t\t\t\t\t\tstage Mitogen Activated Protein Kinase (MAPK)\t\t\t\t\t\t\t cascade in solution. This cascade is typically expressed as RAF=\t\t\t\t\t\t\t=>MEK==>MAPK (alternative forms are K3==>K2==>\t\t\t\t\t\t\tK1 and KKK==>KK==>K)\t\t\t\t\t\t\t. The input signal is RAFK (RAF Kinase)\t\t\t\t\t\t\t and the output signal is MAPKpp (\t\t\t\t\t\t\tdoubly phosphorylated form of MAPK)\t\t\t\t\t\t\t. RAFK phosphorylates RAF once to RAFp. RAFp,\t\t\t\t\t\t\t the phosphorylated form of RAF induces two phoshporylations of MEK,\t\t\t\t\t\t\tto MEKp and MEKpp. MEKpp,\t\t\t\t\t\t\t the doubly phosphorylated form of MEK,\t\t\t\t\t\t\t induces two phosphorylations of MAPK to MAPKp and MAPKpp.
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Reaction
a10\u00a0=\u00a05. MAPKPH\u00a0+\u00a0MAPKpp\u00a0->\u00a0MAPKppMAPKPH
a1\u00a0=\u00a01. RAF\u00a0+\u00a0RAFK\u00a0->\u00a0RAFRAFK
a2\u00a0=\u00a00.5 RAFp\u00a0+\u00a0RAFPH\u00a0->\u00a0RAFpRAFPH
a3\u00a0=\u00a03.3 MEK\u00a0+\u00a0RAFp\u00a0->\u00a0MEKRAFp
a4\u00a0=\u00a010. MEKp\u00a0+\u00a0MEKPH\u00a0->\u00a0MEKpMEKPH
a5\u00a0=\u00a03.3 MEKp\u00a0+\u00a0RAFp\u00a0->\u00a0MEKpRAFp
a6\u00a0=\u00a010. MEKPH\u00a0+\u00a0MEKpp\u00a0->\u00a0MEKppMEKPH
a7\u00a0=\u00a020. MAPK\u00a0+\u00a0MEKpp\u00a0->\u00a0MAPKMEKpp
a8\u00a0=\u00a05. MAPKp\u00a0+\u00a0MAPKPH\u00a0->\u00a0MAPKpMAPKPH
a9\u00a0=\u00a020. MAPKp\u00a0+\u00a0MEKpp\u00a0->\u00a0MAPKpMEKpp
d10\u00a0=\u00a00.4 MAPKppMAPKPH\u00a0->\u00a0MAPKPH\u00a0+\u00a0MAPKpp
d1\u00a0=\u00a00.4 RAFRAFK\u00a0->\u00a0RAF\u00a0+\u00a0RAFK
d2\u00a0=\u00a00.5 RAFpRAFPH\u00a0->\u00a0RAFp\u00a0+\u00a0RAFPH
d3\u00a0=\u00a00.42 MEKRAFp\u00a0->\u00a0MEK\u00a0+\u00a0RAFp
d4\u00a0=\u00a00.8 MEKpMEKPH\u00a0->\u00a0MEKp\u00a0+\u00a0MEKPH
d5\u00a0=\u00a00.4 MEKpRAFp\u00a0->\u00a0MEKp\u00a0+\u00a0RAFp
d6\u00a0=\u00a00.8 MEKppMEKPH\u00a0->\u00a0MEKPH\u00a0+\u00a0MEKpp
d7\u00a0=\u00a00.6 MAPKMEKpp\u00a0->\u00a0MAPK\u00a0+\u00a0MEKpp
d8\u00a0=\u00a00.4 MAPKpMAPKPH\u00a0->\u00a0MAPKp\u00a0+\u00a0MAPKPH
d9\u00a0=\u00a00.6 MAPKpMEKpp\u00a0->\u00a0MAPKp\u00a0+\u00a0MEKpp
k10\u00a0=\u00a00.1 MAPKppMAPKPH\u00a0->\u00a0MAPKp\u00a0+\u00a0MAPKPH
k1\u00a0=\u00a00.1 RAFRAFK\u00a0->\u00a0RAFK\u00a0+\u00a0RAFp
k2\u00a0=\u00a00.1 RAFpRAFPH\u00a0->\u00a0RAF\u00a0+\u00a0RAFPH
k3\u00a0=\u00a00.1 MEKRAFp\u00a0->\u00a0MEKp\u00a0+\u00a0RAFp
k4\u00a0=\u00a00.1 MEKpMEKPH\u00a0->\u00a0MEK\u00a0+\u00a0MEKPH
k5\u00a0=\u00a00.1 MEKpRAFp\u00a0->\u00a0MEKpp\u00a0+\u00a0RAFp
k6\u00a0=\u00a00.1 MEKppMEKPH\u00a0->\u00a0MEKp\u00a0+\u00a0MEKPH
k7\u00a0=\u00a00.1 MAPKMEKpp\u00a0->\u00a0MAPKp\u00a0+\u00a0MEKpp
k8\u00a0=\u00a00.1 MAPKpMAPKPH\u00a0->\u00a0MAPK\u00a0+\u00a0MAPKPH
k9\u00a0=\u00a00.1 MAPKpMEKpp\u00a0->\u00a0MAPKpp\u00a0+\u00a0MEKpp
Variable IC\u00a0\u00a0 ODE
MAPK 0.3 MAPK'[t]\u00a0==\u00a0d7*MAPKMEKpp[t]\u00a0+\u00a0k8*MAPKpMAPKPH[t]\u00a0-\t\t\t\t\t\t\t\u00a0a7*MAPK[t]*MEKpp[t]
MAPKMEKpp 0 MAPKMEKpp'[t]\u00a0==\u00a0-(d7*MAPKMEKpp[t])\u00a0-\u00a0k7*MAPKMEKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0a7*MAPK[t]*MEKpp[t]
MAPKp 0 MAPKp'[t]\u00a0==\u00a0k7*MAPKMEKpp[t]\u00a0-\u00a0a8*MAPKp[t]*MAPKPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0d8*MAPKpMAPKPH[t]\u00a0+\u00a0d9*MAPKpMEKpp[t]\u00a0+\u00a0k10*\t\t\t\t\t\t\tMAPKppMAPKPH[t]\u00a0-\u00a0a9*MAPKp[t]*MEKpp[t]
MAPKPH 0.3 MAPKPH'[t]\u00a0==\u00a0-(a8*MAPKp[t]*MAPKPH[t])\u00a0+\u00a0d8*MAPKpMAPKPH[\t\t\t\t\t\t\tt]\u00a0+\u00a0k8*MAPKpMAPKPH[t]\u00a0-\u00a0a10*MAPKPH[t]*MAPKpp[t]\u00a0+\t\t\t\t\t\t\t\u00a0d10*MAPKppMAPKPH[t]\u00a0+\u00a0k10*MAPKppMAPKPH[t]
MAPKpMAPKPH 0 MAPKpMAPKPH'[t]\u00a0==\u00a0a8*MAPKp[t]*MAPKPH[t]\u00a0-\u00a0d8*\t\t\t\t\t\t\tMAPKpMAPKPH[t]\u00a0-\u00a0k8*MAPKpMAPKPH[t]
MAPKpMEKpp 0 MAPKpMEKpp'[t]\u00a0==\u00a0-(d9*MAPKpMEKpp[t])\u00a0-\u00a0k9*MAPKpMEKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0a9*MAPKp[t]*MEKpp[t]
MAPKpp 0 MAPKpp'[t]\u00a0==\u00a0k9*MAPKpMEKpp[t]\u00a0-\u00a0a10*MAPKPH[t]*MAPKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0d10*MAPKppMAPKPH[t]
MAPKppMAPKPH 0 MAPKppMAPKPH'[t]\u00a0==\u00a0a10*MAPKPH[t]*MAPKpp[t]\u00a0-\u00a0d10*\t\t\t\t\t\t\tMAPKppMAPKPH[t]\u00a0-\u00a0k10*MAPKppMAPKPH[t]
MEK 0.2 MEK'[t]\u00a0==\u00a0k4*MEKpMEKPH[t]\u00a0+\u00a0d3*MEKRAFp[t]\u00a0-\t\t\t\t\t\t\t\u00a0a3*MEK[t]*RAFp[t]
MEKp 0 MEKp'[t]\u00a0==\u00a0-(a4*MEKp[t]*MEKPH[t])\u00a0+\u00a0d4*MEKpMEKPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0k6*MEKppMEKPH[t]\u00a0+\u00a0d5*MEKpRAFp[t]\u00a0+\u00a0k3*MEKRAFp[\t\t\t\t\t\t\tt]\u00a0-\u00a0a5*MEKp[t]*RAFp[t]
MEKPH 0.2 MEKPH'[t]\u00a0==\u00a0-(a4*MEKp[t]*MEKPH[t])\u00a0+\u00a0d4*MEKpMEKPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0k4*MEKpMEKPH[t]\u00a0-\u00a0a6*MEKPH[t]*MEKpp[t]\u00a0+\u00a0d6*\t\t\t\t\t\t\tMEKppMEKPH[t]\u00a0+\u00a0k6*MEKppMEKPH[t]
MEKpMEKPH 0 MEKpMEKPH'[t]\u00a0==\u00a0a4*MEKp[t]*MEKPH[t]\u00a0-\u00a0d4*MEKpMEKPH[t]\t\t\t\t\t\t\t\u00a0-\u00a0k4*MEKpMEKPH[t]
MEKpp 0 MEKpp'[t]\u00a0==\u00a0d7*MAPKMEKpp[t]\u00a0+\u00a0k7*MAPKMEKpp[t]\u00a0+\t\t\t\t\t\t\t\u00a0d9*MAPKpMEKpp[t]\u00a0+\u00a0k9*MAPKpMEKpp[t]\u00a0-\u00a0a7*MAPK[t]*\t\t\t\t\t\t\tMEKpp[t]\u00a0-\u00a0a9*MAPKp[t]*MEKpp[t]\u00a0-\u00a0a6*MEKPH[t]*MEKpp[t]\t\t\t\t\t\t\t\u00a0+\u00a0d6*MEKppMEKPH[t]\u00a0+\u00a0k5*MEKpRAFp[t]
MEKppMEKPH 0 MEKppMEKPH'[t]\u00a0==\u00a0a6*MEKPH[t]*MEKpp[t]\u00a0-\u00a0d6*MEKppMEKPH[\t\t\t\t\t\t\tt]\u00a0-\u00a0k6*MEKppMEKPH[t]
MEKpRAFp 0 MEKpRAFp'[t]\u00a0==\u00a0-(d5*MEKpRAFp[t])\u00a0-\u00a0k5*MEKpRAFp[t]\t\t\t\t\t\t\t\u00a0+\u00a0a5*MEKp[t]*RAFp[t]
MEKRAFp 0 MEKRAFp'[t]\u00a0==\u00a0-(d3*MEKRAFp[t])\u00a0-\u00a0k3*MEKRAFp[t]\u00a0+\t\t\t\t\t\t\t\u00a0a3*MEK[t]*RAFp[t]
RAF 0.4 RAF'[t]\u00a0==\u00a0-(a1*RAF[t]*RAFK[t])\u00a0+\u00a0k2*RAFpRAFPH[t]\u00a0+\t\t\t\t\t\t\t\u00a0d1*RAFRAFK[t]
RAFK 0.1 RAFK'[t]\u00a0==\u00a0-(a1*RAF[t]*RAFK[t])\u00a0+\u00a0d1*RAFRAFK[t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*RAFRAFK[t]
RAFp 0 RAFp'[t]\u00a0==\u00a0d5*MEKpRAFp[t]\u00a0+\u00a0k5*MEKpRAFp[t]\u00a0+\t\t\t\t\t\t\t\u00a0d3*MEKRAFp[t]\u00a0+\u00a0k3*MEKRAFp[t]\u00a0-\u00a0a3*MEK[t]*RAFp[t]\t\t\t\t\t\t\t\u00a0-\u00a0a5*MEKp[t]*RAFp[t]\u00a0-\u00a0a2*RAFp[t]*RAFPH[t]\u00a0+\u00a0d2*\t\t\t\t\t\t\tRAFpRAFPH[t]\u00a0+\u00a0k1*RAFRAFK[t]
RAFPH 0.3 RAFPH'[t]\u00a0==\u00a0-(a2*RAFp[t]*RAFPH[t])\u00a0+\u00a0d2*RAFpRAFPH[t]\t\t\t\t\t\t\t\u00a0+\u00a0k2*RAFpRAFPH[t]
RAFpRAFPH 0 RAFpRAFPH'[t]\u00a0==\u00a0a2*RAFp[t]*RAFPH[t]\u00a0-\u00a0d2*RAFpRAFPH[t]\t\t\t\t\t\t\t\u00a0-\u00a0k2*RAFpRAFPH[t]
RAFRAFK 0 RAFRAFK'[t]\u00a0==\u00a0a1*RAF[t]*RAFK[t]\u00a0-\u00a0d1*RAFRAFK[t]\u00a0-\t\t\t\t\t\t\t\u00a0k1*RAFRAFK[t]

Generated by Cellerator Version 1.4.3 (6-March-2004) using Mathematica 5.0 \t\t\t\tfor Mac OS X (November 19, 2003), March 6, 2004 12:18:07, using (PowerMac,\t\t\t\tPowerPC,Mac OS X,MacOSX,Darwin)

author=B.E.Shapiro

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Elowitz2000 - Repressilator

This model describes the deterministic version of the repressilator system.

The authors of this model (see reference) use three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network that they called the repressilator. The model system was induced in Escherichia coli.

In this system, LacI (variable X is the mRNA, variable PX is the protein) inhibits the tetracycline-resistance transposon tetR (Y, PY describe mRNA and protein). Protein tetR inhibits the gene Cl from phage Lambda (Z, PZ: mRNA, protein),and protein Cl inhibits lacI expression. With the appropriate parameter values this system oscillates.

This model is described in the article:

Elowitz MB, Leibler S.
Nature. 2000 Jan; 403(6767):335-338

Abstract:

Networks of interacting biomolecules carry out many essential functions in living cells, but the 'design principles' underlying the functioning of such intracellular networks remain poorly understood, despite intensive efforts including quantitative analysis of relatively simple systems. Here we present a complementary approach to this problem: the design and construction of a synthetic network to implement a particular function. We used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli. The network periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells. The resulting oscillations, with typical periods of hours, are slower than the cell-division cycle, so the state of the oscillator has to be transmitted from generation to generation. This artificial clock displays noisy behaviour, possibly because of stochastic fluctuations of its components. Such 'rational network design may lead both to the engineering of new cellular behaviours and to an improved understanding of naturally occurring networks.

The model is based upon the equations in Box 1 of the paper; however, these equations as printed are dimensionless, and the correct dimensions have been returned to the equations, and the parameters set to reproduce Figure 1C (left).

The original model was generated by B.E. Shapiro using Cellerator version 1.0 update 2.1127 using Mathematica 4.2 for Mac OS X (June 4, 2002), November 27, 2002 12:15:32, using (PowerMac,PowerPC, Mac OS X,MacOSX,Darwin).

Nicolas Le Novere provided a corrected version generated by SBMLeditor on Sun Aug 20 00:44:05 BST 2006. This removed the EmptySet species. Ran fine on COPASI 4.0 build 18.

Bruce Shapiro revised the model with SBMLeditor on 23 October 2006 20:39 PST. This defines default units and correct reactions. The original Cellerator reactions while being mathematically correct did not accurately reflect the intent of the authors. The original notes were mostly removed because they were mostly incorrect in the revised version. Tested with MathSBML 2.6.0.

Nicolas Le Novere changed the volume to 1 cubic micrometre, to allow for stochastic simulation.

Changed by Lukas Endler to use the average livetime of mRNA instead of its halflife and a corrected value of alpha and alpha0.

Moreover, the equations used in this model were clarified, cf. below.

The equations given in box 1 of the original publication are rescaled in three respects (lowercase letters denote the rescaled, uppercase letters the unscaled number of molecules per cell):

\u03b1 in the equations should be in units of rescaled proteins per promotor and cell, and \u03b2 is the ratio of the protein to the mRNA decay rates or the ratio of the mRNA to the protein halflife.

In this version of the model \u03b1 and \u03b2 are calculated correspondingly to the article, while p and m where just replaced by P/Km resp. M/eff and all equations multiplied by 1/t_ave . Also, to make the equations easier to read, commonly used variables derived from the parameters given in the article by simple rules were introduced.

The parameters given in the article were:

promotor strength (repressed) ( tps_repr ): 5*10 -4 transcripts/(promotor*s)
promotor strength (full) ( tps_active ): 0.5 transcripts/(promotor*s)
mRNA half life, \u03c4 1/2,mRNA : 2 min
protein half life, \u03c4 1/2,prot : 10 min
K M : 40 monomers/cell
Hill coefficient n: 2

From these the following constants can be derived:

average mRNA lifetime ( t_ave ): \u03c4 1/2,mRNA /ln(2) = 2.89 min
mRNA decay rate ( kd_mRNA ): ln(2)/ \u03c4 1/2,mRNA = 0.347 min -1
protein decay rate ( kd_prot ): ln(2)/ \u03c4 1/2,prot
transcription rate ( a_tr ): tps_active*60 = 29.97 transcripts/min
transcription rate (repressed) ( a0_tr ): tps_repr*60 = 0.03 transcripts/min
translation rate ( k_tl ): eff*kd_mRNA = 6.93 proteins/(mRNA*min)
\u03b1 : a_tr*eff*\u03c4 1/2,prot /(ln(2)*K M ) = 216.4 proteins/(promotor*cell*Km)
\u03b1 0 : a0_tr*eff*\u03c4 1/2,prot /(ln(2)*K M ) = 0.2164 proteins/(promotor*cell*Km)
\u03b2 : k_dp/k_dm = 0.2

Annotation by the Kinetic Simulation Algorithm Ontology (KiSAO):

To reproduce the simulations run published by the authors, the model has to be simulated with any of two different approaches. First, one could use a deterministic method ( KISAO_0000035 ) with continuous variables ( KISAO_0000018 ). One sample algorithm to use is the CVODE solver ( KISAO_0000019 ). Second, one could simulate the system using Gillespie's direct method ( KISAO_0000029 ), which is a stochastic method ( KISAO_0000036 ) supporting adaptive timesteps ( KISAO_0000041 ) and using discrete variables ( KISAO_0000016 ).

This model is hosted on BioModels Database and identified by: BIOMD0000000012 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Applications of metabolic modelling to plant metabolism.
\t Poolman MG ,Assmus HE, Fell DA J. Exp. Bot.[2004 May; Volume: 55 (Issue: 400 )]: 1177-86 15073223,
Abstract:
In this paper some of the general concepts underpinning the computer modelling of metabolic systems are introduced. The difference between kinetic and structural modelling is emphasized, and the more important techniques from both, along with the physiological implications, are described. These approaches are then illustrated by descriptions of other work, in which they have been applied to models of the Calvin cycle, sucrose metabolism in sugar cane, and starch metabolism in potatoes.



This model describes the non oxidative Calvin cycle as depicted in Poolman et al; J Exp Bot (2004) 55:1177-1186, fig 2. Reaction E20: E4P + F6P \u2194 S7P + GAP, is depicted in the figure, but not included in the model. The light reaction: ADP + P i \u2192 ATP, is included in the model, but only mentioned in the figure caption. The parameters and initial concentrations are the same as in Poolman, 1999, Computer Modelling Applied to the Calvin Cycle, PhD Thesis, Oxford Brookes University, Appendix A (available at at http://mudshark.brookes.ac.uk/index.php/Publications/Theses/Mark)

\u00a9 Mark Poolman (mgpoolman@brookes.ac.uk) 1995-2002
Based on a description by Pettersson 1988, Eur. J. Biochem. 175, 661-672
Differences are:
1 - Reactions assumed by Pettersson to be in equilibrium have fast mass action kinetics.
2 - Introduction of the parameter PGAxpMult to modulate PGA export through TPT.
3 - Introduction of Starch phosphorylase reaction.
This file may be freely copied or translated into other formats provided:
1 - This notice is reproduced in its entirety
2 - Published material making use of (information gained from) this model cites at least:
(a) Poolman, 1999, Computer Modelling Applied to the Calvin Cycle, PhD Thesis, Oxford Brookes University
(b) Poolman, Fell, and Thomas. 2000, Modelling Photosynthesis and its control, J. Exp. Bot. 51, 319-328
or
(c) Poolman et al. 2001, Computer modelling and experimental evidence for two steady states in the photosynthetic Calvin cycle. Eur. J. Biochem. 268, 2810-2816
Further related information may be found at http://mudshark.brookes.ac.uk.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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MAPK cascade on a scaffold

\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Citation
Levchenko, A., Bruck, J., Sternberg, P.W. (2000)\t\t\t\t\t\t\t.Scaffold proteins may biphasically affect the levels of mitogen-activated protein kinase signaling and reduce its threshold properties. Proc. Natl. Acad. Sci. USA 97(11):5818-5823.\t\t\t\t\t\t\t\t\t\t\t\t\thttp://www.pnas.org/cgi/content/abstract/97/11/5818\t\t\t\t\t\t
\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Description
This model describes a basic 3-stage Mitogen Activated Protein Kinase (MAPK). Kinases in solution are written as K[3,J], K[2,J], K[1,J] for MAPKKK, MAPKK, and MAPK, respectively, J indicates the phosphorylation level, J=0,1 for K3 and J=0,1,2 for K2 and K1. Scaffolds have three slots, for MAPK, MAPKK, and MAPKKK, respectively. Bound and free scaffold are denoted as S[i,j,k], where i, j, and k indicate the binding of K[1,i], K[2,j] and K[3,k] in their respective slots. Here i,j=-1,0,1,or,2 and k=-1,0,or,1. A value of -1 means the slot is empty, 0 means the unphorphorylated kinase is bound, 1 means the singly phosphorylated kinase is bound, and 2 means the doubly phosphorylated kinase is bound. Thus S[1,-1,2] is a scaffold with K[3,1] bound in the first slot and K[1,2] in the third slot, while the second slot is empty.Note: Indices X[I,J,K] are translated into the unindexed variable X_I_J_K and so forth in the SBML. Negative indices are translated as mI, etc, thus S[1,-1,2] becomes S_1_m1_2.
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\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Reaction
a10\u00a0=\u00a05.MAPKP\u00a0+\u00a0K[1,\u00a02]\u00a0->\u00a0K_MAPKP[1,\u00a02]
a1\u00a0=\u00a01.RAFK\u00a0+\u00a0K[3,\u00a00]\u00a0->\u00a0K_RAFK[3,\u00a00]
a2\u00a0=\u00a00.5RAFP\u00a0+\u00a0K[3,\u00a01]\u00a0->\u00a0K_RAFP[3,\u00a01]
a3\u00a0=\u00a03.3K[2,\u00a00]\u00a0+\u00a0K[3,\u00a01]\u00a0->\u00a0K_K[2,\u00a00,\u00a03,\t\t\t\t\t\t\t\u00a01]
a4\u00a0=\u00a010.MEKP\u00a0+\u00a0K[2,\u00a01]\u00a0->\u00a0K_MEKP[2,\u00a01]
a5\u00a0=\u00a03.3K[2,\u00a01]\u00a0+\u00a0K[3,\u00a01]\u00a0->\u00a0K_K[2,\u00a01,\u00a03,\t\t\t\t\t\t\t\u00a01]
a6\u00a0=\u00a010.MEKP\u00a0+\u00a0K[2,\u00a02]\u00a0->\u00a0K_MEKP[2,\u00a02]
a7\u00a0=\u00a020.K[1,\u00a00]\u00a0+\u00a0K[2,\u00a02]\u00a0->\u00a0K_K[1,\u00a00,\u00a02,\t\t\t\t\t\t\t\u00a02]
a8\u00a0=\u00a05.MAPKP\u00a0+\u00a0K[1,\u00a01]\u00a0->\u00a0K_MAPKP[1,\u00a01]
a9\u00a0=\u00a020.K[1,\u00a01]\u00a0+\u00a0K[2,\u00a02]\u00a0->\u00a0K_K[1,\u00a01,\u00a02,\t\t\t\t\t\t\t\u00a02]
d10\u00a0=\u00a00.4K_MAPKP[1,\u00a02]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a02]
d1\u00a0=\u00a00.4K_RAFK[3,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0K[3,\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a00,\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a01,\u00a00]
d1a\u00a0=\u00a00S_RAFK[0,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a02,\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a00,\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a01,\u00a00]
d1a\u00a0=\u00a00S_RAFK[-1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a02,\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a00,\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a01,\u00a00]
d1a\u00a0=\u00a00S_RAFK[2,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a02,\u00a00]
d2\u00a0=\u00a00.5K_RAFP[3,\u00a01]\u00a0->\u00a0RAFP\u00a0+\u00a0K[3,\u00a01]
d3\u00a0=\u00a00.42K_K[2,\u00a00,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
d4\u00a0=\u00a00.8K_MEKP[2,\u00a01]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a01]
d5\u00a0=\u00a00.4K_K[2,\u00a01,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
d6\u00a0=\u00a00.8K_MEKP[2,\u00a02]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a02]
d7\u00a0=\u00a00.6K_K[1,\u00a00,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
d8\u00a0=\u00a00.4K_MAPKP[1,\u00a01]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a01]
d9\u00a0=\u00a00.6K_K[1,\u00a01,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
k10\u00a0=\u00a00.1K_MAPKP[1,\u00a02]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a01]
k1\u00a0=\u00a00.1K_RAFK[3,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0K[3,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a00,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a01,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[0,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[0,\u00a02,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a00,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a01,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[-1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[1,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[1,\u00a02,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a00,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a00,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a0-1,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a01,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a01,\u00a01]
k1\u00a0=\u00a00.1S_RAFK[2,\u00a02,\u00a00]\u00a0->\u00a0RAFK\u00a0+\u00a0S[2,\u00a02,\u00a01]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a00,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a01,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[0,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[0,\u00a02,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a00,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a01,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[1,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[1,\u00a02,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a00,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a00,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a01,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a01,\u00a00]
k1a\u00a0=\u00a0100RAFK\u00a0+\u00a0S[2,\u00a02,\u00a00]\u00a0->\u00a0S_RAFK[2,\u00a02,\u00a00]
k2\u00a0=\u00a00.1K_RAFP[3,\u00a01]\u00a0->\u00a0RAFP\u00a0+\u00a0K[3,\u00a00]
k3\u00a0=\u00a00.1K_K[2,\u00a00,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
k3\u00a0=\u00a00.1S[0,\u00a00,\u00a01]\u00a0->\u00a0S[0,\u00a01,\u00a01]
k3\u00a0=\u00a00.1S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[-1,\u00a01,\u00a01]
k3\u00a0=\u00a00.1S[1,\u00a00,\u00a01]\u00a0->\u00a0S[1,\u00a01,\u00a01]
k3\u00a0=\u00a00.1S[2,\u00a00,\u00a01]\u00a0->\u00a0S[2,\u00a01,\u00a01]
k4\u00a0=\u00a00.1K_MEKP[2,\u00a01]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a00]
k5\u00a0=\u00a00.1K_K[2,\u00a01,\u00a03,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0K[3,\t\t\t\t\t\t\t\u00a01]
k5a\u00a0=\u00a00.1S[0,\u00a01,\u00a01]\u00a0->\u00a0S[0,\u00a02,\u00a01]
k5a\u00a0=\u00a00.1S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[-1,\u00a02,\u00a01]
k5a\u00a0=\u00a00.1S[1,\u00a01,\u00a01]\u00a0->\u00a0S[1,\u00a02,\u00a01]
k5a\u00a0=\u00a00.1S[2,\u00a01,\u00a01]\u00a0->\u00a0S[2,\u00a02,\u00a01]
k6\u00a0=\u00a00.1K_MEKP[2,\u00a02]\u00a0->\u00a0MEKP\u00a0+\u00a0K[2,\u00a01]
k7\u00a0=\u00a00.1K_K[1,\u00a00,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
k7\u00a0=\u00a00.1S[0,\u00a02,\u00a00]\u00a0->\u00a0S[1,\u00a02,\u00a00]
k7\u00a0=\u00a00.1S[0,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\u00a0-1]
k7\u00a0=\u00a00.1S[0,\u00a02,\u00a01]\u00a0->\u00a0S[1,\u00a02,\u00a01]
k8\u00a0=\u00a00.1K_MAPKP[1,\u00a01]\u00a0->\u00a0MAPKP\u00a0+\u00a0K[1,\u00a00]
k9\u00a0=\u00a00.1K_K[1,\u00a01,\u00a02,\u00a02]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0K[2,\t\t\t\t\t\t\t\u00a02]
k9a\u00a0=\u00a00.1S[1,\u00a02,\u00a00]\u00a0->\u00a0S[2,\u00a02,\u00a00]
k9a\u00a0=\u00a00.1S[1,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\u00a0-1]
k9a\u00a0=\u00a00.1S[1,\u00a02,\u00a01]\u00a0->\u00a0S[2,\u00a02,\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a0-1,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a01,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a00]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a0-1]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[0,\u00a02,\u00a01]\u00a0->\u00a0K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[-1,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[1,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a00]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a0-1]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a00,\u00a01]\u00a0->\u00a0K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
koff\u00a0=\u00a00.5S[2,\u00a0-1,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a01,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
koff\u00a0=\u00a00.5S[2,\u00a02,\u00a00]\u00a0->\u00a0K[3,\u00a00]\u00a0+\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[1,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a01]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kon\u00a0=\u00a010K[2,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a01]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a00,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a01,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[0,\u00a02,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a00,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a01,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[1,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a00,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a01,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a00]
kon\u00a0=\u00a010K[3,\u00a00]\u00a0+\u00a0S[2,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[0,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[0,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[0,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[-1,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a00]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a0-1]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a01]\u00a0->\u00a0K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[-1,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[1,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a00,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a00]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a0-1]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a01]\u00a0->\u00a0K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a0-1,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a01,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a00]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a00]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a0-1]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a01]\u00a0->\u00a0K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a0-1]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a01]\u00a0->\u00a0K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpoff\u00a0=\u00a00.05S[2,\u00a02,\u00a01]\u00a0->\u00a0K[3,\u00a01]\u00a0+\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a01]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\u00a00]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a00,\u00a01]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\u00a00]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a01,\u00a01]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\u00a00]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[1,\u00a02]\u00a0+\u00a0S[-1,\u00a02,\u00a01]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\u00a00]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[0,\u00a0-1,\u00a01]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a00]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\u00a00]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[-1,\u00a0-1,\u00a01]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[1,\u00a0-1,\u00a01]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\u00a00]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a00]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1]
kpon\u00a0=\u00a00K[2,\u00a02]\u00a0+\u00a0S[2,\u00a0-1,\u00a01]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a00,\u00a0-1]\u00a0->\u00a0S[0,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a01,\u00a0-1]\u00a0->\u00a0S[0,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[0,\u00a02,\u00a0-1]\u00a0->\u00a0S[0,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a00,\u00a0-1]\u00a0->\u00a0S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a00,\u00a0-1]\u00a0->\u00a0S[1,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a01,\u00a0-1]\u00a0->\u00a0S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a01,\u00a0-1]\u00a0->\u00a0S[1,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[-1,\u00a02,\u00a0-1]\u00a0->\u00a0S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[1,\u00a02,\u00a0-1]\u00a0->\u00a0S[1,\u00a02,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a00,\u00a0-1]\u00a0->\u00a0S[2,\u00a00,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a0-1,\u00a0-1]\u00a0->\u00a0S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a01,\u00a0-1]\u00a0->\u00a0S[2,\u00a01,\t\t\t\t\t\t\t\u00a01]
kpon\u00a0=\u00a00K[3,\u00a01]\u00a0+\u00a0S[2,\u00a02,\u00a0-1]\u00a0->\u00a0S[2,\u00a02,\t\t\t\t\t\t\t\u00a01]
\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t
VariableIC\u00a0\u00a0ODE
MAPKP0.3MAPKP'[t]\u00a0==\u00a0-(a8*MAPKP[t]*K[1,\u00a01][t])\u00a0-\u00a0a10*MAPKP[\t\t\t\t\t\t\tt]*K[1,\u00a02][t]\u00a0+\u00a0d8*K_MAPKP[1,\u00a01][t]\u00a0+\u00a0k8*K_MAPKP[\t\t\t\t\t\t\t1,\u00a01][t]\u00a0+\u00a0d10*K_MAPKP[1,\u00a02][t]\u00a0+\u00a0k10*K_MAPKP[1,\t\t\t\t\t\t\t\u00a02][t]
MEKP0.2MEKP'[t]\u00a0==\u00a0-(a4*MEKP[t]*K[2,\u00a01][t])\u00a0-\u00a0a6*MEKP[t]*\t\t\t\t\t\t\tK[2,\u00a02][t]\u00a0+\u00a0d4*K_MEKP[2,\u00a01][t]\u00a0+\u00a0k4*K_MEKP[2,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0d6*K_MEKP[2,\u00a02][t]\u00a0+\u00a0k6*K_MEKP[2,\u00a02]\t\t\t\t\t\t\t[t]
RAFK0.1RAFK'[t]\u00a0==\u00a0-(a1*RAFK[t]*K[3,\u00a00][t])\u00a0+\u00a0d1*K_RAFK[3,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k1*K_RAFK[3,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a00,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0k1a*RAFK[t]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[0,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[1,\u00a00,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0k1a*RAFK[t]*S[1,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[2,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[-1,\u00a00,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a01,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[-1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a0-1,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[\t\t\t\t\t\t\t0,\u00a00,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[0,\u00a01,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a01,\u00a00][\t\t\t\t\t\t\tt]\u00a0+\u00a0d1a*S_RAFK[0,\u00a02,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a0-1,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a0-1,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[1,\u00a00,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a01,\u00a00][\t\t\t\t\t\t\tt]\u00a0+\u00a0k1*S_RAFK[1,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[\t\t\t\t\t\t\t2,\u00a0-1,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[2,\u00a00,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a00,\u00a00][\t\t\t\t\t\t\tt]\u00a0+\u00a0d1a*S_RAFK[2,\u00a01,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a02,\u00a00][t]\u00a0+\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a02,\u00a00][t]
RAFP0.3RAFP'[t]\u00a0==\u00a0-(a2*RAFP[t]*K[3,\u00a01][t])\u00a0+\u00a0d2*K_RAFP[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k2*K_RAFP[3,\u00a01][t]
K[1,\u00a00]0.4(K[1,\u00a00])'[t]\u00a0==\u00a0-(a7*K[1,\u00a00][t]*K[2,\u00a02][t])\u00a0+\t\t\t\t\t\t\t\u00a0d7*K_K[1,\u00a00,\u00a02,\u00a02][t]\u00a0+\u00a0k8*K_MAPKP[1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\u00a0+\u00a0koff*\t\t\t\t\t\t\tS[0,\u00a00,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a0-1][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a00]\t\t\t\t\t\t\t[t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a01][t]
K[1,\u00a01]0(K[1,\u00a01])'[t]\u00a0==\u00a0-(a8*MAPKP[t]*K[1,\u00a01][t])\u00a0-\t\t\t\t\t\t\t\u00a0a9*K[1,\u00a01][t]*K[2,\u00a02][t]\u00a0+\u00a0k7*K_K[1,\u00a00,\u00a02,\t\t\t\t\t\t\t\u00a02][t]\u00a0+\u00a0d9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0+\u00a0d8*\t\t\t\t\t\t\tK_MAPKP[1,\u00a01][t]\u00a0+\u00a0k10*K_MAPKP[1,\u00a02][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a01][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\u00a01][t]\u00a0+\u00a0kpoff*S[\t\t\t\t\t\t\t1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\t\t\t\t\t\t\t\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[1,\u00a02,\u00a01][t]
K[1,\u00a02]0(K[1,\u00a02])'[t]\u00a0==\u00a0-(a10*MAPKP[t]*K[1,\u00a02][t])\u00a0+\t\t\t\t\t\t\t\u00a0k9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0+\u00a0d10*K_MAPKP[1,\u00a02][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a02][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[2,\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
K[2,\u00a00]0.2(K[2,\u00a00])'[t]\u00a0==\u00a0-(a3*K[2,\u00a00][t]*K[3,\u00a01][t])\u00a0+\t\t\t\t\t\t\t\u00a0d3*K_K[2,\u00a00,\u00a03,\u00a01][t]\u00a0+\u00a0k4*K_MEKP[2,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a0-1][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[-1,\u00a00,\u00a00][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-\t\t\t\t\t\t\t1,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t2,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a00]\t\t\t\t\t\t\t[t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[\t\t\t\t\t\t\t2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a00,\u00a00][t]\u00a0+\u00a0koff*\t\t\t\t\t\t\tS[2,\u00a00,\u00a01][t]
K[2,\u00a01]0(K[2,\u00a01])'[t]\u00a0==\u00a0-(a4*MEKP[t]*K[2,\u00a01][t])\u00a0-\t\t\t\t\t\t\t\u00a0a5*K[2,\u00a01][t]*K[3,\u00a01][t]\u00a0+\u00a0k3*K_K[2,\u00a00,\u00a03,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0d5*K_K[2,\u00a01,\u00a03,\u00a01][t]\u00a0+\u00a0d4*\t\t\t\t\t\t\tK_MEKP[2,\u00a01][t]\u00a0+\u00a0k6*K_MEKP[2,\u00a02][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a01][t]
K[2,\u00a02]0(K[2,\u00a02])'[t]\u00a0==\u00a0-(a6*MEKP[t]*K[2,\u00a02][t])\u00a0-\t\t\t\t\t\t\t\u00a0a7*K[1,\u00a00][t]*K[2,\u00a02][t]\u00a0-\u00a0a9*K[1,\u00a01][t]*K[2,\t\t\t\t\t\t\t\u00a02][t]\u00a0+\u00a0d7*K_K[1,\u00a00,\u00a02,\u00a02][t]\u00a0+\u00a0k7*K_K[\t\t\t\t\t\t\t1,\u00a00,\u00a02,\u00a02][t]\u00a0+\u00a0d9*K_K[1,\u00a01,\u00a02,\u00a02][t]\t\t\t\t\t\t\t\u00a0+\u00a0k9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0+\u00a0k5*K_K[2,\u00a01,\t\t\t\t\t\t\t\u00a03,\u00a01][t]\u00a0+\u00a0d6*K_MEKP[2,\u00a02][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
K[3,\u00a00]0.3(K[3,\u00a00])'[t]\u00a0==\u00a0-(a1*RAFK[t]*K[3,\u00a00][t])\u00a0+\t\t\t\t\t\t\t\u00a0d1*K_RAFK[3,\u00a00][t]\u00a0+\u00a0k2*K_RAFP[3,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kon*\t\t\t\t\t\t\tK[3,\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a02,\u00a00]\t\t\t\t\t\t\t[t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*\t\t\t\t\t\t\tS[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a00,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t3,\u00a00][t]*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t3,\u00a00][t]*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[2,\u00a00,\u00a00][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[\t\t\t\t\t\t\t3,\u00a00][t]*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a02,\u00a00][t]
K[3,\u00a01]0(K[3,\u00a01])'[t]\u00a0==\u00a0-(a2*RAFP[t]*K[3,\u00a01][t])\u00a0-\t\t\t\t\t\t\t\u00a0a3*K[2,\u00a00][t]*K[3,\u00a01][t]\u00a0-\u00a0a5*K[2,\u00a01][t]*K[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0d3*K_K[2,\u00a00,\u00a03,\u00a01][t]\u00a0+\u00a0k3*K_K[\t\t\t\t\t\t\t2,\u00a00,\u00a03,\u00a01][t]\u00a0+\u00a0d5*K_K[2,\u00a01,\u00a03,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0k5*K_K[2,\u00a01,\u00a03,\u00a01][t]\u00a0+\u00a0k1*K_RAFK[3,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d2*K_RAFP[3,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]\t\t\t\t\t\t\t*S[-1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a0-1,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[-\t\t\t\t\t\t\t1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*\t\t\t\t\t\t\tK[3,\u00a01][t]*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01][\t\t\t\t\t\t\tt]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a00,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[\t\t\t\t\t\t\t1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\t\t\t\t\t\t\t\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[\t\t\t\t\t\t\t2,\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
K_K[1,\u00a00,\u00a02,\u00a02]0(K_K[1,\u00a00,\u00a02,\u00a02])'[t]\u00a0==\u00a0a7*K[1,\u00a00][t]*K[2,\t\t\t\t\t\t\t\u00a02][t]\u00a0-\u00a0d7*K_K[1,\u00a00,\u00a02,\u00a02][t]\u00a0-\u00a0k7*K_K[\t\t\t\t\t\t\t1,\u00a00,\u00a02,\u00a02][t]
K_K[1,\u00a01,\u00a02,\u00a02]0(K_K[1,\u00a01,\u00a02,\u00a02])'[t]\u00a0==\u00a0a9*K[1,\u00a01][t]*K[2,\t\t\t\t\t\t\t\u00a02][t]\u00a0-\u00a0d9*K_K[1,\u00a01,\u00a02,\u00a02][t]\u00a0-\u00a0k9*K_K[\t\t\t\t\t\t\t1,\u00a01,\u00a02,\u00a02][t]
K_K[2,\u00a00,\u00a03,\u00a01]0(K_K[2,\u00a00,\u00a03,\u00a01])'[t]\u00a0==\u00a0a3*K[2,\u00a00][t]*K[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0d3*K_K[2,\u00a00,\u00a03,\u00a01][t]\u00a0-\u00a0k3*K_K[\t\t\t\t\t\t\t2,\u00a00,\u00a03,\u00a01][t]
K_K[2,\u00a01,\u00a03,\u00a01]0(K_K[2,\u00a01,\u00a03,\u00a01])'[t]\u00a0==\u00a0a5*K[2,\u00a01][t]*K[3,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0d5*K_K[2,\u00a01,\u00a03,\u00a01][t]\u00a0-\u00a0k5*K_K[\t\t\t\t\t\t\t2,\u00a01,\u00a03,\u00a01][t]
K_MAPKP[1,\u00a01]0(K_MAPKP[1,\u00a01])'[t]\u00a0==\u00a0a8*MAPKP[t]*K[1,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0d8*K_MAPKP[1,\u00a01][t]\u00a0-\u00a0k8*K_MAPKP[1,\u00a01][t]
K_MAPKP[1,\u00a02]0(K_MAPKP[1,\u00a02])'[t]\u00a0==\u00a0a10*MAPKP[t]*K[1,\u00a02][t]\u00a0-\t\t\t\t\t\t\t\u00a0d10*K_MAPKP[1,\u00a02][t]\u00a0-\u00a0k10*K_MAPKP[1,\u00a02][t]
K_MEKP[2,\u00a01]0(K_MEKP[2,\u00a01])'[t]\u00a0==\u00a0a4*MEKP[t]*K[2,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0d4*K_MEKP[2,\u00a01][t]\u00a0-\u00a0k4*K_MEKP[2,\u00a01][t]
K_MEKP[2,\u00a02]0(K_MEKP[2,\u00a02])'[t]\u00a0==\u00a0a6*MEKP[t]*K[2,\u00a02][t]\u00a0-\t\t\t\t\t\t\t\u00a0d6*K_MEKP[2,\u00a02][t]\u00a0-\u00a0k6*K_MEKP[2,\u00a02][t]
K_RAFK[3,\u00a00]0(K_RAFK[3,\u00a00])'[t]\u00a0==\u00a0a1*RAFK[t]*K[3,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0d1*K_RAFK[3,\u00a00][t]\u00a0-\u00a0k1*K_RAFK[3,\u00a00][t]
K_RAFP[3,\u00a01]0(K_RAFP[3,\u00a01])'[t]\u00a0==\u00a0a2*RAFP[t]*K[3,\u00a01][t]\u00a0-\t\t\t\t\t\t\t\u00a0d2*K_RAFP[3,\u00a01][t]\u00a0-\u00a0k2*K_RAFP[3,\u00a01][t]
S[-1,\u00a0-1,\u00a0-1]0.1(S[-1,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0-(kon*K[1,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t])\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*\t\t\t\t\t\t\tK[2,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-\t\t\t\t\t\t\t1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\u00a0-1]\t\t\t\t\t\t\t[t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*\t\t\t\t\t\t\tK[3,\u00a01][t]*S[-1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a0-1][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a0-1,\u00a00]0(S[-1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0koff*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\u00a00]\t\t\t\t\t\t\t[t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*\t\t\t\t\t\t\tK[1,\u00a02][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\u00a00][t]\u00a0+\u00a0koff*S[-\t\t\t\t\t\t\t1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[-1,\u00a02,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a0-1,\u00a00][t]
S[-1,\u00a0-1,\u00a01]0(S[-1,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[-1,\t\t\t\t\t\t\t\u00a0-1,\u00a00][t]
S[-1,\u00a00,\u00a0-1]0(S[-1,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0koff*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a00,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a00,\u00a00]0(S[-1,\u00a00,\u00a00])'[t]\u00a0==\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*koff*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\u00a00][t]\u00a0-\u00a0kpon*K[1,\t\t\t\t\t\t\t\u00a02][t]*S[-1,\u00a00,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a00,\u00a00][t]
S[-1,\u00a00,\u00a01]0(S[-1,\u00a00,\u00a01])'[t]\u00a0==\u00a0kon*K[2,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[-1,\u00a00,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0k3*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0koff*S[-1,\u00a00,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpoff*S[-1,\u00a00,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-\t\t\t\t\t\t\t1,\u00a00,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\u00a01][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[2,\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a00,\u00a00][t]
S[-1,\u00a01,\u00a0-1]0(S[-1,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a01,\u00a00]0(S[-1,\u00a01,\u00a00])'[t]\u00a0==\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kpoff*S[-1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][\t\t\t\t\t\t\tt]*S[-1,\u00a01,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]
S[-1,\u00a01,\u00a01]0(S[-1,\u00a01,\u00a01])'[t]\u00a0==\u00a0kpon*K[2,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k3*S[-1,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a01,\u00a0-1][t]\u00a0-\u00a0k5a*S[-1,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a02*kpoff*S[-1,\u00a01,\u00a01][t]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[\t\t\t\t\t\t\t-1,\u00a01,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[2,\u00a01,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[-1,\u00a01,\u00a00][t]
S[-1,\u00a02,\u00a0-1]0(S[-1,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[1,\t\t\t\t\t\t\t\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[-1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[-1,\u00a02,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[-1,\u00a02,\u00a00]0(S[-1,\u00a02,\u00a00])'[t]\u00a0==\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[-1,\u00a02,\u00a00][t]\u00a0-\u00a0kpoff*S[-1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[-1,\u00a02,\u00a00][t]\u00a0-\u00a0kon*K[1,\u00a00][\t\t\t\t\t\t\tt]*S[-1,\u00a02,\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]
S[-1,\u00a02,\u00a01]0(S[-1,\u00a02,\u00a01])'[t]\u00a0==\u00a0kpon*K[2,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k5a*S[-1,\u00a01,\u00a01][t]\u00a0+\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[-1,\u00a02,\u00a0-1][t]\u00a0-\u00a02*kpoff*S[-1,\u00a02,\u00a01][\t\t\t\t\t\t\tt]\u00a0-\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t1,\u00a01][t]*S[-1,\u00a02,\u00a01][t]\u00a0-\u00a0kpon*K[1,\u00a02][t]*S[-1,\t\t\t\t\t\t\t\u00a02,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a02,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a02,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[-1,\u00a02,\u00a00][t]
S[0,\u00a0-1,\u00a0-1]0(S[0,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0koff*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[0,\u00a0-1,\u00a00]0(S[0,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*koff*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\t\t\t\t\t\t\t\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[0,\u00a0-1,\u00a00][t]
S[0,\u00a0-1,\u00a01]0(S[0,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpoff*S[0,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[0,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[0,\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[0,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k1*S_RAFK[0,\u00a0-1,\u00a00][t]
S[0,\u00a00,\u00a0-1]0(S[0,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*koff*S[0,\u00a00,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\t\t\t\t\t\t\t\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a00,\u00a01]\t\t\t\t\t\t\t[t]
S[0,\u00a00,\u00a00]0(S[0,\u00a00,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[0,\u00a00,\u00a0-1][t]\u00a0-\u00a03*koff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a00,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[0,\u00a00,\u00a00][t]
S[0,\u00a00,\u00a01]0(S[0,\u00a00,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[0,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a00,\u00a0-1][t]\u00a0-\u00a0k3*S[0,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a02*koff*S[0,\u00a00,\u00a01][t]\u00a0-\u00a0kpoff*S[0,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[0,\u00a00,\u00a00][t]
S[0,\u00a01,\u00a0-1]0(S[0,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[0,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpoff*S[0,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[0,\u00a01,\u00a0-1][t]\u00a0+\u00a0koff*S[0,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[0,\u00a01,\u00a01][t]
S[0,\u00a01,\u00a00]0(S[0,\u00a01,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[0,\u00a01,\u00a0-1][t]\u00a0-\u00a02*koff*S[0,\t\t\t\t\t\t\t\u00a01,\u00a00][t]\u00a0-\u00a0kpoff*S[0,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[0,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[0,\u00a01,\u00a00][t]
S[0,\u00a01,\u00a01]0(S[0,\u00a01,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[0,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k3*S[0,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k5a*S[0,\u00a01,\u00a01][t]\u00a0-\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0-\u00a02*kpoff*S[0,\u00a01,\u00a01][t]\u00a0+\u00a0k1*\t\t\t\t\t\t\tS_RAFK[0,\u00a01,\u00a00][t]
S[0,\u00a02,\u00a0-1]0(S[0,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0k7*S[0,\u00a02,\u00a0-1][t]\u00a0-\u00a0koff*S[0,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpoff*S[0,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[0,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[0,\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[0,\u00a02,\u00a01]\t\t\t\t\t\t\t[t]
S[0,\u00a02,\u00a00]0(S[0,\u00a02,\u00a00])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[0,\u00a02,\u00a0-1][t]\u00a0-\u00a0k7*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*koff*S[0,\u00a02,\u00a00][t]\u00a0-\u00a0kpoff*S[0,\t\t\t\t\t\t\t\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[0,\u00a02,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0d1a*S_RAFK[0,\u00a02,\u00a00][t]
S[0,\u00a02,\u00a01]0(S[0,\u00a02,\u00a01])'[t]\u00a0==\u00a0kon*K[1,\u00a00][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a02][t]*S[0,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k5a*S[0,\u00a01,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[0,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k7*S[0,\u00a02,\u00a01][t]\u00a0-\u00a0koff*S[0,\t\t\t\t\t\t\t\u00a02,\u00a01][t]\u00a0-\u00a02*kpoff*S[0,\u00a02,\u00a01][t]\u00a0+\u00a0k1*\t\t\t\t\t\t\tS_RAFK[0,\u00a02,\u00a00][t]
S[1,\u00a0-1,\u00a0-1]0(S[1,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[1,\u00a0-1,\u00a00]0(S[1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpoff*S[1,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][\t\t\t\t\t\t\tt]*S[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]
S[1,\u00a0-1,\u00a01]0(S[1,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[1,\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[1,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a0-1,\u00a00][t]
S[1,\u00a00,\u00a0-1]0(S[1,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpoff*S[1,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[1,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[1,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[1,\u00a00,\u00a01][t]
S[1,\u00a00,\u00a00]0(S[1,\u00a00,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a02*koff*S[1,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0-\u00a0kpoff*S[1,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[1,\u00a00,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a00,\u00a00][t]
S[1,\u00a00,\u00a01]0(S[1,\u00a00,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a00,\u00a0-1][t]\u00a0-\u00a0k3*S[1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0koff*S[1,\u00a00,\u00a01][t]\u00a0-\u00a02*kpoff*S[1,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a00,\u00a00][t]
S[1,\u00a01,\u00a0-1]0(S[1,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[1,\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[1,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a01,\u00a01]\t\t\t\t\t\t\t[t]
S[1,\u00a01,\u00a00]0(S[1,\u00a01,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[1,\u00a01,\u00a0-1][t]\u00a0-\u00a0koff*S[1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*kpoff*S[1,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[1,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a01,\u00a00][t]
S[1,\u00a01,\u00a01]0(S[1,\u00a01,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k3*S[1,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k5a*S[1,\u00a01,\u00a01][t]\u00a0-\u00a03*kpoff*S[1,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\u00a01,\u00a00][t]
S[1,\u00a02,\u00a0-1]0(S[1,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0k7*S[0,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[1,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0k9a*S[1,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a02*kpoff*S[1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[\t\t\t\t\t\t\t1,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a02,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[1,\u00a02,\u00a00][t]\u00a0+\u00a0kpoff*S[1,\u00a02,\u00a01]\t\t\t\t\t\t\t[t]
S[1,\u00a02,\u00a00]0(S[1,\u00a02,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k7*S[0,\u00a02,\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a02]\t\t\t\t\t\t\t[t]*S[1,\u00a0-1,\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[1,\u00a02,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0-\u00a0k9a*S[1,\u00a02,\u00a00][t]\u00a0-\u00a0koff*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*kpoff*S[1,\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[1,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[1,\u00a02,\u00a00][t]
S[1,\u00a02,\u00a01]0(S[1,\u00a02,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a01][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k7*S[0,\u00a02,\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a02]\t\t\t\t\t\t\t[t]*S[1,\u00a0-1,\u00a01][t]\u00a0+\u00a0k5a*S[1,\u00a01,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[1,\u00a02,\u00a0-1][t]\u00a0-\u00a0k9a*S[1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a03*kpoff*S[1,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[1,\t\t\t\t\t\t\t\u00a02,\u00a00][t]
S[2,\u00a0-1,\u00a0-1]0(S[2,\u00a0-1,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-\t\t\t\t\t\t\t1,\u00a0-1][t]\u00a0-\u00a0kpoff*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kon*K[2,\t\t\t\t\t\t\t\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[2,\u00a00,\u00a0-\t\t\t\t\t\t\t1][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a0-1][t]\u00a0+\u00a0kpoff*S[2,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]
S[2,\u00a0-1,\u00a00]0(S[2,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpoff*S[2,\u00a0-1,\u00a00][t]\t\t\t\t\t\t\t\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0kon*K[2,\u00a00][\t\t\t\t\t\t\tt]*S[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0koff*S[2,\u00a00,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]
S[2,\u00a0-1,\u00a01]0(S[2,\u00a0-1,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[2,\u00a0-1,\u00a01][t]\u00a0-\u00a0kon*K[2,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a0-1,\u00a01][t]\u00a0-\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a01][t]\t\t\t\t\t\t\t\u00a0-\u00a0kpon*K[2,\u00a02][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0koff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a01][t]\u00a0+\u00a0kpoff*\t\t\t\t\t\t\tS[2,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a0-1,\u00a00][t]
S[2,\u00a00,\u00a0-1]0(S[2,\u00a00,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a0koff*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpoff*S[2,\u00a00,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\t\t\t\t\t\t\t\u00a01][t]*S[2,\u00a00,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a00,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a00,\u00a01][t]
S[2,\u00a00,\u00a00]0(S[2,\u00a00,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a02*koff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a00][t]\u00a0-\u00a0kpoff*S[2,\u00a00,\u00a00][t]\u00a0-\u00a0k1a*\t\t\t\t\t\t\tRAFK[t]*S[2,\u00a00,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a00,\u00a00][t]
S[2,\u00a00,\u00a01]0(S[2,\u00a00,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kon*K[2,\u00a00][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a00,\u00a0-1][t]\u00a0-\u00a0k3*S[2,\u00a00,\t\t\t\t\t\t\t\u00a01][t]\u00a0-\u00a0koff*S[2,\u00a00,\u00a01][t]\u00a0-\u00a02*kpoff*S[2,\t\t\t\t\t\t\t\u00a00,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a00,\u00a00][t]
S[2,\u00a01,\u00a0-1]0(S[2,\u00a01,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\t\t\t\t\t\t\t\u00a02*kpoff*S[2,\u00a01,\u00a0-1][t]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a01,\u00a0-1][t]\u00a0-\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a01,\u00a0-1][t]\t\t\t\t\t\t\t\u00a0+\u00a0koff*S[2,\u00a01,\u00a00][t]\u00a0+\u00a0kpoff*S[2,\u00a01,\u00a01]\t\t\t\t\t\t\t[t]
S[2,\u00a01,\u00a00]0(S[2,\u00a01,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\t\t\t\t\t\t\t\u00a0kon*K[3,\u00a00][t]*S[2,\u00a01,\u00a0-1][t]\u00a0-\u00a0koff*S[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a02*kpoff*S[2,\u00a01,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*\t\t\t\t\t\t\tS[2,\u00a01,\u00a00][t]\u00a0+\u00a0d1a*S_RAFK[2,\u00a01,\u00a00][t]
S[2,\u00a01,\u00a01]0(S[2,\u00a01,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0kpon*K[2,\u00a01][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\t\t\t\t\t\t\t\u00a0k3*S[2,\u00a00,\u00a01][t]\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a01,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0-\u00a0k5a*S[2,\u00a01,\u00a01][t]\u00a0-\u00a03*kpoff*S[2,\t\t\t\t\t\t\t\u00a01,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a01,\u00a00][t]
S[2,\u00a02,\u00a0-1]0(S[2,\u00a02,\u00a0-1])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a0-1][t]\u00a0+\u00a0k9a*S[1,\u00a02,\u00a0-1][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a0-1][t]\u00a0-\u00a02*kpoff*S[2,\u00a02,\u00a0-1][\t\t\t\t\t\t\tt]\u00a0-\u00a0kon*K[3,\u00a00][t]*S[2,\u00a02,\u00a0-1][t]\u00a0-\u00a0kpon*K[\t\t\t\t\t\t\t3,\u00a01][t]*S[2,\u00a02,\u00a0-1][t]\u00a0+\u00a0koff*S[2,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpoff*S[2,\u00a02,\u00a01][t]
S[2,\u00a02,\u00a00]0(S[2,\u00a02,\u00a00])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0+\u00a0k9a*S[1,\u00a02,\u00a00][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a00][t]\u00a0+\u00a0kon*K[3,\u00a00][t]*S[2,\t\t\t\t\t\t\t\u00a02,\u00a0-1][t]\u00a0-\u00a0koff*S[2,\u00a02,\u00a00][t]\u00a0-\u00a02*\t\t\t\t\t\t\tkpoff*S[2,\u00a02,\u00a00][t]\u00a0-\u00a0k1a*RAFK[t]*S[2,\u00a02,\u00a00][t]\t\t\t\t\t\t\t\u00a0+\u00a0d1a*S_RAFK[2,\u00a02,\u00a00][t]
S[2,\u00a02,\u00a01]0(S[2,\u00a02,\u00a01])'[t]\u00a0==\u00a0kpon*K[1,\u00a02][t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a01][t]\u00a0+\u00a0k9a*S[1,\u00a02,\u00a01][t]\u00a0+\u00a0kpon*K[2,\t\t\t\t\t\t\t\u00a02][t]*S[2,\u00a0-1,\u00a01][t]\u00a0+\u00a0k5a*S[2,\u00a01,\u00a01][t]\t\t\t\t\t\t\t\u00a0+\u00a0kpon*K[3,\u00a01][t]*S[2,\u00a02,\u00a0-1][t]\u00a0-\u00a03*kpoff*\t\t\t\t\t\t\tS[2,\u00a02,\u00a01][t]\u00a0+\u00a0k1*S_RAFK[2,\u00a02,\u00a00][t]
S_RAFK[-1,\u00a0-1,\u00a00]0(S_RAFK[-1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*\t\t\t\t\t\t\tS_RAFK[-1,\u00a0-1,\u00a00][t]
S_RAFK[-1,\u00a00,\u00a00]0(S_RAFK[-1,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a00,\u00a00][t]
S_RAFK[-1,\u00a01,\u00a00]0(S_RAFK[-1,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a01,\u00a00][t]
S_RAFK[-1,\u00a02,\u00a00]0(S_RAFK[-1,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[-1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[-1,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t-1,\u00a02,\u00a00][t]
S_RAFK[0,\u00a0-1,\u00a00]0(S_RAFK[0,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a0-1,\u00a00][t]
S_RAFK[0,\u00a00,\u00a00]0(S_RAFK[0,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a00,\u00a00][t]
S_RAFK[0,\u00a01,\u00a00]0(S_RAFK[0,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a01,\u00a00][t]
S_RAFK[0,\u00a02,\u00a00]0(S_RAFK[0,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[0,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[0,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t0,\u00a02,\u00a00][t]
S_RAFK[1,\u00a0-1,\u00a00]0(S_RAFK[1,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a0-1,\u00a00][t]
S_RAFK[1,\u00a00,\u00a00]0(S_RAFK[1,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a00,\u00a00][t]
S_RAFK[1,\u00a01,\u00a00]0(S_RAFK[1,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a01,\u00a00][t]
S_RAFK[1,\u00a02,\u00a00]0(S_RAFK[1,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[1,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[1,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t1,\u00a02,\u00a00][t]
S_RAFK[2,\u00a0-1,\u00a00]0(S_RAFK[2,\u00a0-1,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a0-1,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a0-1,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a0-1,\u00a00][t]
S_RAFK[2,\u00a00,\u00a00]0(S_RAFK[2,\u00a00,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a00,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a00,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a00,\u00a00][t]
S_RAFK[2,\u00a01,\u00a00]0(S_RAFK[2,\u00a01,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a01,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a01,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a01,\u00a00][t]
S_RAFK[2,\u00a02,\u00a00]0(S_RAFK[2,\u00a02,\u00a00])'[t]\u00a0==\u00a0k1a*RAFK[t]*S[2,\u00a02,\t\t\t\t\t\t\t\u00a00][t]\u00a0-\u00a0d1a*S_RAFK[2,\u00a02,\u00a00][t]\u00a0-\u00a0k1*S_RAFK[\t\t\t\t\t\t\t2,\u00a02,\u00a00][t]
\t\t\t

Generated by Cellerator Version 1.0 update 2.1203 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), December 4, 2002 15:06:10, using (PowerMac,PowerPC,Mac \t\t\t\tOS X,MacOSX,Darwin)

\t\t\t

author=B.E.Shapiro

\t\t
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 2999, + "tag": "BioModels:BIOMD0000000014" + }, + { + "id": 3000, + "tag": "MAP-kinase scaffold activity" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:28:47.434624+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000014", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2306": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2306, + "name": "Curto1998 - purine metabolism", + "repository_type": "biomodels", + "summary": "
Curto1998 - purine metabolism

This is a purine metabolism model that is geared toward studies of gout.

The model uses Generalized Mass Action (GMA; i.e. power law) descriptions of reaction rate laws.

Such descriptions are local approximations that assume independent substrate binding.

This model is described in the article:

Curto R, Voit EO, Sorribas A, Cascante M.
Math Biosci 1998 Jul; 151(1): 1-49

Abstract:

Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy.

This model is hosted on BioModels Database and identified by: BIOMD0000000015.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3001, + "tag": "BioModels:BIOMD0000000015" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3003, + "tag": "Purine nucleobase metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:28:50.763113+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000015", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2307": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2307, + "name": "Goldbeter1995_CircClock", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3004, + "tag": "BioModels:BIOMD0000000016" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:06.367336+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000016", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2308": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2308, + "name": "Hoefnagel2002_PyruvateBranches", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Metabolic engineering of lactic acid bacteria, the combined approach: kinetic modelling, metabolic control and experimental analysis.
Hoefnagel MH, Starrenburg MJ, Martens DE, Hugenholtz J, Kleerebezem M, Van Swam II, Bongers R, Westerhoff HV, Snoep JL Microbiology2002 Apr; 148(4):1003-13 11932446,
Abstract:
Everyone who has ever tried to radically change metabolic fluxes knows that it is often harder to determine which enzymes have to be modified than it is to actually implement these changes. In the more traditional genetic engineering approaches \u2019bottle-necks\u2019 are pinpointed using qualitative, intuitive approaches, but the alleviation of suspected \u2019rate-limiting\u2019 steps has not often been successful. Here the authors demonstrate that a model of pyruvate distribution in Lactococcus lactis based on enzyme kinetics in combination with metabolic control analysis clearly indicates the key control points in the flux to acetoin and diacetyl, important flavour compounds. The model presented here (available at http://jjj.biochem.sun.ac.za/wcfs.html) showed that the enzymes with the greatest effect on this flux resided outside the acetolactate synthase branch itself. Experiments confirmed the predictions of the model, i.e. knocking out lactate dehydrogenase and overexpressing NADH oxidase increased the flux through the acetolactate synthase branch from 0 to 75% of measured product formation rates.

The paper does not have any figure to be put as a curation figure in the BioModels database. The model does reproduce the fluxes and control-coefficients given in Figure 2 and Table 4. To reproduce the results, the model was changed from the description in the article according to the model on JWS: the parameter Kmpyr was changed to 2.5 from 25. The equillibrium constant for PTA reaction (R4) was changed from 0.0281 to 0.0065. The Km for oxygen in the NOX reaction (R13) was changed from 0.01 to 0.2. Slight deviations between the values in the article and the model results may stem from different algorithms used for finding the steady state.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3007, + "tag": "BioModels:BIOMD0000000017" + }, + { + "id": 3008, + "tag": "Lactate metabolic process" + }, + { + "id": 3009, + "tag": "Lactococcus lactis" + }, + { + "id": 3010, + "tag": "Pyruvate metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:06.872760+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000017", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2309": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2309, + "name": "Morrison1989 - Folate Cycle", + "repository_type": "biomodels", + "summary": "
Morrison1989 - Folate Cycle
The model describes the folate cyclekinetics in breast cancer cells.

This model is described in the article:

Morrison PF, Allegra CJ.
J. Biol. Chem. 1989 Jun; 264(18): 10552-10566

Abstract:

A mathematical description of polyglutamated folate kinetics for human breast carcinoma cells (MCF-7) has been formulated based upon experimental folate, methotrexate (MTX), purine, and pyrimidine pool sizes as well as reaction rate parameters obtained from intact MCF-7 cells and their enzyme isolates. The schema accounts for the interconversion of highly polyglutamated tetrahydrofolate, 5-methyl-FH4, 5-10-CH2FH4, dihydrofolate (FH2), 10-formyl-FH4 (FFH4), and 10-formyl-FH2 (FFH2), as well as formation and transport of the MTX polyglutamates. Inhibition mechanisms have been chosen to reproduce all observed non-, un-, and pure competition inhibition patterns. Steady state folate concentrations and thymidylate and purine synthesis rates in drug-free intact cells were used to determine normal folate Vmax values. The resulting average-cell folate model, examined for its ability to predict folate pool behavior following exposure to 1 microM MTX over 21 h, agreed well with the experiment, including a relative preservation of the FFH4 and CH2FH4 pools. The results depend strongly on thymidylate synthase (TS) reaction mechanism, especially the assumption that MTX di- and triglutamates inhibit TS synthesis as greatly in the intact cell as they do with purified enzyme. The effects of cell cycle dependence of TS and dihydrofolate reductase activities were also examined by introducing G- to S-phase activity ratios of these enzymes into the model. For activity ratios down to at least 5%, cell population averaged folate pools were only slightly affected, while CH2FH4 pools in S-phase cells were reduced to as little as 10% of control values. Significantly, these folate pool dynamics were indicated to arise from both direct inhibition by MTX polyglutamates as well as inhibition by elevated levels of polyglutamated FH2 and FFH2.

Note: two flow BCs were converted into two downstream concentration BCs, thus removing the GAR and dUMP state variables. This dropped the number of ODEs from 21 to 19.

This model is hosted on BioModels Database and identified by: BIOMD0000000018.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3011, + "tag": "BioModels:BIOMD0000000018" + }, + { + "id": 3012, + "tag": "Folic acid metabolic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4586, + "tag": "Breast cancer" + } + ], + "timestamp_created": "2025-01-29 11:34:07.355218+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000018", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2310": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2310, + "name": "Schoeberl2002 - EGF MAPK", + "repository_type": "biomodels", + "summary": " \t
\t Schoeberl2002 - EGF MAPK\t
\t
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Computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways.

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The initial model was constructed by Ken Lau from the MATLAB source code.

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This model is described in the article:

\t \t
Schoeberl B, Eichler-Jonsson C, Gilles ED, M\u00fcller G
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Nat. Biotechnol. 2002 Apr; 20(4): 370-375
\t

Abstract:

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We present a computational model that offers an integrated quantitative, dynamic, and topological representation of intracellular signal networks, based on known components of epidermal growth factor (EGF) receptor signal pathways. The model provides insight into signal-response relationships between the binding of EGF to its receptor at the cell surface and the activation of downstream proteins in the signaling cascade. It shows that EGF-induced responses are remarkably stable over a 100-fold range of ligand concentration and that the critical parameter in determining signal efficacy is the initial velocity of receptor activation. The predictions of the model agree well with experimental analysis of the effect of EGF on two downstream responses, phosphorylation of ERK-1/2 and expression of the target gene, c-fos.

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This model does not exactly reproduce the results given in the original publication. It has, though, the same reaction graph and gives very similar time courses for the conditions depicted in the article.

\t
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Several corrections were applied to the parameters described in the paper's supplementary materials. Some parameter names were replaced by the corresponding identical ones: k(r)26 by k(r)18, k(r)27 by k(r)19, k(r)30 by k(r)20, k(r)38 by k(r)24, k(r)39 by k(r)37, k(r)46 by k(r)44, k51 by k49, k(r)54 by k(r)52 and k62 by k62. In particular the parameter values described in the column \"remark\" of supplementary table 1 override the values explicitely written in the numerical columns:

name in suppl. value used in model value used remarks
kr16 0.055 0.275
k30 7.9e6 2.1e6 as k20
kr30 0.3 0.4 as kr24
k38 3e7 1e7 as k20
kr38 0.055 0.55 as kr24
k52 1.1e5 5.34e7

k5 was used for v116, v119, v122 and v125 in addition of v107, v110 and v113 as listed in the legend of supplementary figure 2. k5 is calculated using th eformula from the matlab file not given in the supplements.

All rate constants were rescaled to minutes (k[min] = 60*k[sec]) and all second order rate constants additionally to molecules/cell with a cell volume of 1 picolitre (k[molecs/cell] = k[M]/(Vc*Na), with Vc=1e-12 l and Na = 6e23).

The association constant of internalized EGF was rescaled to molecules/endosome using an endosomal volume of 4.3 al (= 4.3*10 -18 litre).

The extracellular EGF concentration was converted to molecules per picolitre with a MW of 6045 Da.

[ng/ml] [numb/pl]
50 4962
0.5 49.6
0.125 12.4

With the initial conditions given in the paper, the results could not be reproduced at all. Therefore the initial conditions used in the MATLAB file were adopted for SHC (1.01 * 10 5 instead of 1.01 * 10 6 ) and Ras_GDP. (7.2 * 10 4 instead of 1.14 * 10 7 )

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This model is hosted on BioModels Database and identified by: BIOMD0000000019.

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To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3013, + "tag": "BioModels:BIOMD0000000019" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3015, + "tag": "Signaling by EGFR" + } + ], + "timestamp_created": "2025-01-29 11:34:07.804518+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000019", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2311": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2311, + "name": "hodgkin-huxley squid-axon 1952", + "repository_type": "biomodels", + "summary": "

This is an implementation of the Hodgkin-Huxley model of the electrical behavior of the squid axon membrane from:
A quantitative description of membrane current and its application to conduction and excitation in nerve.
A. L. Hodgkin and A. F. Huxley. (1952 ) Journal of Physiology 119(4): pp 500-544; pmID: 12991237 .

Abstract:
This article concludes a series of papers concerned with the flow of electric current through the surface membrane of a giant nerve fibre (Hodgkin,Huxley & Katz, 1952; Hodgkin & Huxley, 1952 a-c). Its general object is to discuss the results of the preceding papers (Part I), to put them into mathematical form (Part II) and to show that they will account for conduction and excitation in quantitative terms (Part III).

This SBML model uses the same formalism as the one described in the paper, contrary to modern versions:
* V describes the the membrane depolarisation relative to the resting potential of the membrane
* opposing to modern practice, depolarization is negative , not positive , so the sign of V is different
* inward transmembrane currents are considered positive (inward current positive), contrary to modern use
The changeable parameters are the equilibrium potentials( E_R, E_K, E_L, E_Na ), the membrane depolarization ( V ) and the initial sodium and potassium channel activation and inactivation coefficients ( m,h,n ). The initial values of m,h,n for the model were calculated for V = 0 using the equations from the article: n t=0 = \u03b1_n V=0 /(\u03b1_n V=0 + \u03b2_n V=0 ) and equivalent expressions for h and m .
For single excitations apply a negative membrane depolarization (V < 0). To achieve oscillatory behavior either change the resting potential to a more positive value or apply a constant negative ionic current (I < 0).
Two assignments for parameters in the model, alpha_n and alpha_m, are not defined at V=-10 resp. -25 mV. We did not change this to keep the formulas similar to the original publication and as most integrators seem not to have any problem with it. The limits at V=-10 and -25 mV are 0.1 for alpha_n resp. 1 for alpha_m.
We thank Mark W. Johnson for finding a bug in the model and his helpful comments.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3016, + "tag": "BioModels:BIOMD0000000020" + }, + { + "id": 3017, + "tag": "Loligo forbesii" + }, + { + "id": 3018, + "tag": "Neuronal action potential propagation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3019, + "tag": "Voltage-gated potassium channel activity" + }, + { + "id": 3020, + "tag": "Voltage-gated sodium channel activity" + }, + { + "id": 3021, + "tag": "giant axon" + } + ], + "timestamp_created": "2025-01-29 11:34:08.289311+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000020", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2312": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2312, + "name": "Leloup1999_CircClock", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3022, + "tag": "BioModels:BIOMD0000000021" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:08.759521+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000021", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2313": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2313, + "name": "Ueda2001_CircClock", + "repository_type": "biomodels", + "summary": "

Bruce Shapiro: Generated by Cellerator Version 1.0 update 3.0303 using Mathematica 4.1 for Microsoft Windows (June 13, 2001), April 2, 2003 16:49:13, using (PC,x86, Microsoft Windows,WindowsNT,Windows)

Bruce Shapiro: Corrected 29 March 2005

Nicolas Le Nov\u00e8re: Added Dbt and Cyc species, and the corresponding reactions. 23 April 2005


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3023, + "tag": "BioModels:BIOMD0000000022" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:09.192573+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000022", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2314": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2314, + "name": "Rohwer2001_Sucrose", + "repository_type": "biomodels", + "summary": "

SBML Level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS .

Run this model online at http://jjj.biochem.sun.ac.za .

To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3024, + "tag": "BioModels:BIOMD0000000023" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3025, + "tag": "Saccharum officinarum" + }, + { + "id": 3026, + "tag": "Sucrose biosynthetic process" + } + ], + "timestamp_created": "2025-01-29 11:34:09.731449+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000023", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2315": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2315, + "name": "Scheper1999_CircClock", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3027, + "tag": "BioModels:BIOMD0000000024" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 3028, + "tag": "Mesocricetus auratus" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:10.245915+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000024", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2316": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2316, + "name": "Smolen2002_CircClock", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3029, + "tag": "BioModels:BIOMD0000000025" + }, + { + "id": 3030, + "tag": "Circadian regulation of gene expression" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:10.706639+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000025", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2317": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2317, + "name": "Markevich2004_MAPK_orderedElementary", + "repository_type": "biomodels", + "summary": "

The model corresponds to the schemas 1 and 2 of Markevich et al 2004, as described in the figure 1 and the supplementary table S1. Phosphorylations and dephosphorylations follow distributive ordered kinetics. The phosphorylations are modeled with three elementary reactions:
E+S<=>ES->E+P
The dephosphorylations are modeled with five elementary reactions:
E+S<=>ES->EP<=>E+P

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3031, + "tag": "BioModels:BIOMD0000000026" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:34:11.293560+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000026", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2318": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2318, + "name": "Markevich2004 - MAPK double phosphorylation, ordered Michaelis-Menton", + "repository_type": "biomodels", + "summary": "
Markevich2004 - MAPK double phosphorylation,ordered Michaelis-Menton
The model corresponds to the schemas 1and 2 of Markevich et al 2004, as described in the figure 1 andmodelled using Michaelis-Menten like kinetics. Phosphorylations anddephosphorylations follow distributive ordered kinetics. Itreproduces figure 3 of the main article.

This model is described in the article:

Markevich NI, Hoek JB, Kholodenko BN.
J. Cell Biol. 2004 Feb; 164(3): 353-359

Abstract:

Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model is hosted on BioModels Database and identified by: BIOMD0000000027.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3032, + "tag": "BioModels:BIOMD0000000027" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:34:11.777247+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000027", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2319": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2319, + "name": "Markevich2004_MAPK_phosphoRandomElementary", + "repository_type": "biomodels", + "summary": "

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table S2. Phosphorylations follow distributive random kinetics, while dephosphorylations follow an ordered mechanism. The phosphorylations are modeled with three elementary reactions:
E+S<=>ES->E+P
The dephosphorylations are modeled with five elementary reactions:
E+S<=>ES->EP<=>E+P
The model reproduces figure 5 in the main article.

The model is further described in:
Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. Markevich NI, Hoek JB, Kholodenko BN. J Cell Biol. 2004 Feb 2;164(3):353-9.
PMID: 14744999 ; DOI: 10.1083/jcb.200308060
Abstract:
Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3033, + "tag": "BioModels:BIOMD0000000028" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:34:12.251379+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000028", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2320": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2320, + "name": "Markevich2004_MAPK_phosphoRandomMM", + "repository_type": "biomodels", + "summary": "

The model corresponds to the schema 3 of Markevich et al 2004, as described in the figure 2 and the supplementary table S3, and modelled using Michaelis-Menten like kinetics. Phosphorylations follow distributive random kinetics, while dephosphorylations follow an ordered mechanism.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2007 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3034, + "tag": "BioModels:BIOMD0000000029" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:34:12.707925+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000029", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2321": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2321, + "name": "Markevich2004_MAPK_AllRandomElementary", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3035, + "tag": "BioModels:BIOMD0000000030" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:34:13.163054+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000030", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2322": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2322, + "name": "Markevich2004_MAPK_orderedMM2kinases", + "repository_type": "biomodels", + "summary": "

The model describes the double phosphorylation of MAP kinase by an ordered mechanism using the Michaelis-Menten formalism. Two different enzymes, MAPKK1 and MAPKK2, successively phosphorylate the MAP kinase, but one and the same phosphatase dephosphorylates both sites.
The model reproduces figure S9 in the supplemental material of the article.

The model is further described in:
Signaling switches and bistability arising from multisite phosphorylation in protein kinase cascades. Markevich NI, Hoek JB, Kholodenko BN. J Cell Biol. 2004 Feb 2;164(3):353-9.
PMID: 14744999 ; DOI: 10.1083/jcb.200308060
Abstract:
Mitogen-activated protein kinase (MAPK) cascades can operate as bistable switches residing in either of two different stable states. MAPK cascades are often embedded in positive feedback loops, which are considered to be a prerequisite for bistable behavior. Here we demonstrate that in the absence of any imposed feedback regulation, bistability and hysteresis can arise solely from a distributive kinetic mechanism of the two-site MAPK phosphorylation and dephosphorylation. Importantly, the reported kinetic properties of the kinase (MEK) and phosphatase (MKP3) of extracellular signal-regulated kinase (ERK) fulfill the essential requirements for generating a bistable switch at a single MAPK cascade level. Likewise, a cycle where multisite phosphorylations are performed by different kinases, but dephosphorylation reactions are catalyzed by the same phosphatase, can also exhibit bistability and hysteresis. Hence, bistability induced by multisite covalent modification may be a widespread mechanism of the control of protein activity.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3036, + "tag": "BioModels:BIOMD0000000031" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-29 11:34:13.641486+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000031", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2323": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2323, + "name": "Kofahl2004_PheromonePathway", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modelling the dynamics of the yeast pheromone pathway.
Kofahl B, Klipp E Yeast[2004 Jul; Volume: 21 (Issue: 10 )] Page info: 831-50 15300679,
Abstract:
We present a mathematical model of the dynamics of the pheromone pathways in haploid yeast cells of mating type MATa after stimulation with pheromone alpha-factor. The model consists of a set of differential equations and describes the dynamics of signal transduction from the receptor via several steps, including a G protein and a scaffold MAP kinase cascade, up to changes in the gene expression after pheromone stimulation in terms of biochemical changes (complex formations, phosphorylations, etc.). The parameters entering the models have been taken from the literature or adapted to observed time courses or behaviour. Using this model we can follow the time course of the various complex formation processes and of the phosphorylation states of the proteins involved. Furthermore, we can explain the phenotype of more than a dozen well-characterized mutants and also the graded response of yeast cells to varying concentrations of the stimulating pheromone.


The model was updated on 21st October 2010, by Vijayalakshmi Chelliah.
The following changes were made: 1) The model has been converted to SBML l2v4.2) The model has been recurated and the curation figure was updated (units are in nanoMolar; but the publication has units in microMolar). Simulations were done using Copasi v4.6 (Build 32).3) Notes have been added.4) Annotation for one of the species has been corrected (Complex M).



SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

The following are the four major differences between the original publication by Kofahl et al and the model that actually is able to replicate the results as depicted in the publication (those corrections have been made in agreement with the authors):
1. Bar1 is the inactive protease present inside the cell but the publication wrongly mentions that Bar1 is also the protease that is present on the extracellular surface.
The model correctly names the protease in it's different forms by calling inactive Bar1 within the cell as Bar1, active Bar1 within the cell as Bar1a and extracellular Bar1 as Bar1aex
2. The initial amount of Alpha-factor is given as 1000nM but the model uses a value of 100nM.
3. The value of the paramenter k8 is given as 0.33 but the model uses a value of 0.033.
4. The value of the paramenter k41 is given as 0.002 but the model uses a value of 0.02.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/).(http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3037, + "tag": "BioModels:BIOMD0000000032" + }, + { + "id": 3038, + "tag": "Response to pheromone" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:14.091079+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000032", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2324": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2324, + "name": "Brown2004 - NGF and EGF signaling", + "repository_type": "biomodels", + "summary": "
Brown2004 - NGF and EGF signaling

This model is described in the article:

Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA.
Phys Biol 2004 Dec; 1(3-4): 184-195

Abstract:

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'


The figures in the paper show results from computationsperformed over an ensemble of all parameter sets that fit theavailable data. This file contains only the best fit parameters.The full ensemble of parameters is available athttp://www.lassp.cornell.edu/sethna/GeneDynamics/PC12DataFiles/(Also, the best-fit parameter set produces a curve for DN Rap1 thatis less \"peakish\" than the ensemble average.)

The conversion factors for EGF and NGF concentrations accountfor their molecular weights and the density of cells in the culturedish. These concentrations are saturating, so the exact values arenot critical.

Because the Erk data fit to measure only fold changes inactivity, there is no absolute scale for the y-axes. Thus thecurves from this file have different magnitudes than thosepublished.

To reproduce the figures from the paper:
2a) For EGF stimulation, set the initial concentration of EGFto 100 ng/ml * 100020 (molecule/cell)/(ng/ml) = 10002000.
For NGF stimulation, set the initial concentration of NGF to50 ng/ml * 4560 (molecule/cell)/(ng/ml) = 456000
5a) To simulate LY294002 addition, set kPI3KRas and kPI3K to0.
5b) To simulate a dominant negative Rap1, set kRap1ToBRaf to0.
To simulate a dominant negative Ras, set kRasToRaf1 andkPI3KRas to 0.

Almost all the data fit with this model by the authors arefrom Western blots. Given the uncertainties in antibodyeffectiveness and other factors, one can't a priori derive aconversion between the arbitrary units for a given set of data andmolecules per cell. So the authors used an adjustable \"scalefactor\" that converts between molecules per cell and Western blotunits.

For the EGF stimulation data in figure 2a) the scale factorconversion is 1.414e-05 (U/mg)/(molecule/cell). For the NGFstimulation data in figure 2a) it is 7.135e-06(U/mg)/(molecule/cell).

This model is hosted on BioModels Database and identified by: BIOMD0000000033.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3040, + "tag": "BioModels:BIOMD0000000033" + }, + { + "id": 3041, + "tag": "ERK1 and ERK2 cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:14.567318+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000033", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2325": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2325, + "name": "Smolen2004_CircClock", + "repository_type": "biomodels", + "summary": "

No inititial conditions are specified in the paper. Because there is a basal rate of transcription for each gene, it doesn't matter much. With the agreement of Paul Smolen, I put all the initial concentration at 0.001 nanomoles. N Le Nov\u00e8re.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3043, + "tag": "BioModels:BIOMD0000000034" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:15.031836+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000034", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2326": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2326, + "name": "Vilar2002_Oscillator", + "repository_type": "biomodels", + "summary": "

Minimal Model for Circadian Oscillations

Citation
Vilar JMG, Kueh HY, Barkai N, Leibler S,\t\t\t\t\t\t\t (2002)\t\t\t\t\t\t\t. Mechanisms of noise resistance in genetic oscillators,\t\t\t\t\t\t\t PNAS, 99(9):5988-5992. http://www.pnas.org/cgi/content/abstract/\t\t\t\t\t\t\t99/9/5988
Description
A minimal model of genomically based oscillation,\t\t\t\t\t\t\t based on two mutually interacting genes,\t\t\t\t\t\t\t an activator and a repressor. Postive feedback is provided by the activator protein,\t\t\t\t\t\t\t which binds to the promotors of both the activator and the repressor genes. Negative feedback is provided by the repressor protein which binds to the activator protein.
Rate\u00a0constant\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Reaction
alphaA\u00a0=\u00a050 DA\u00a0->\u00a0DA\u00a0+\u00a0MA
alphaAp\u00a0=\u00a0500 DAp\u00a0->\u00a0DAp\u00a0+\u00a0MA
alphaR\u00a0=\u00a00.01 DR\u00a0->\u00a0DR\u00a0+\u00a0MR
alphaRp\u00a0=\u00a050 DRp\u00a0->\u00a0DRp\u00a0+\u00a0MR
betaA\u00a0=\u00a050 MA\u00a0->\u00a0A\u00a0+\u00a0MA
betaR\u00a0=\u00a05 MR\u00a0->\u00a0MR\u00a0+\u00a0R
gammaA\u00a0=\u00a01 A\u00a0+\u00a0DA\u00a0->\u00a0DAp
gammaC\u00a0=\u00a02 A\u00a0+\u00a0R\u00a0->\u00a0C
gammaR\u00a0=\u00a01 A\u00a0+\u00a0DR\u00a0->\u00a0DRp
deltaA\u00a0=\u00a01 A\u00a0->\u00a0EmptySet
deltaA\u00a0=\u00a01 C\u00a0->\u00a0R
deltaMA\u00a0=\u00a010 MA\u00a0->\u00a0EmptySet
deltaMR\u00a0=\u00a00.5 MR\u00a0->\u00a0EmptySet
deltaR\u00a0=\u00a00.2 R\u00a0->\u00a0EmptySet
thetaA\u00a0=\u00a050 DAp\u00a0->\u00a0A\u00a0+\u00a0DA
thetaR\u00a0=\u00a0100 DRp\u00a0->\u00a0A\u00a0+\u00a0DR
Variable IC\u00a0\u00a0 ODE
A 0 A'[t]\u00a0==\u00a0-(deltaA*A[t])\u00a0-\u00a0gammaA*A[t]*DA[t]\u00a0+\u00a0thetaA*DAp[\t\t\t\t\t\t\tt]\u00a0-\u00a0gammaR*A[t]*DR[t]\u00a0+\u00a0thetaR*DRp[t]\u00a0+\u00a0betaA*MA[t]\u00a0-\t\t\t\t\t\t\t\u00a0gammaC*A[t]*R[t]
C 0 C'[t]\u00a0==\u00a0-(deltaA*C[t])\u00a0+\u00a0gammaC*A[t]*R[t]
DA 1 DA'[t]\u00a0==\u00a0-(gammaA*A[t]*DA[t])\u00a0+\u00a0thetaA*DAp[t]
DAp 0 DAp'[t]\u00a0==\u00a0gammaA*A[t]*DA[t]\u00a0-\u00a0thetaA*DAp[t]
DR 1 DR'[t]\u00a0==\u00a0-(gammaR*A[t]*DR[t])\u00a0+\u00a0thetaR*DRp[t]
DRp 0 DRp'[t]\u00a0==\u00a0gammaR*A[t]*DR[t]\u00a0-\u00a0thetaR*DRp[t]
MA 0 MA'[t]\u00a0==\u00a0alphaA*DA[t]\u00a0+\u00a0alphaAp*DAp[t]\u00a0-\u00a0deltaMA*MA[t]
MR 0 MR'[t]\u00a0==\u00a0alphaR*DR[t]\u00a0+\u00a0alphaRp*DRp[t]\u00a0-\u00a0deltaMR*MR[t]
R 0 R'[t]\u00a0==\u00a0deltaA*C[t]\u00a0+\u00a0betaR*MR[t]\u00a0-\u00a0deltaR*R[t]\u00a0-\t\t\t\t\t\t\t\u00a0gammaC*A[t]*R[t]

Generated by Cellerator Version 1.0 update 2.1127 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), November 27, 2002 12:17:46, using (PowerMac,PowerPC,\t\t\t\tMac OS X,MacOSX,Darwin)

author=B.E.Shapiro

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3044, + "tag": "BioModels:BIOMD0000000035" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:15.483666+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000035", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2327": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2327, + "name": "Tyson1999_CircClock", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3045, + "tag": "BioModels:BIOMD0000000036" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:15.981174+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000036", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2328": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2328, + "name": "Marwan2003 - Genetics, regulatory hierarchy between genes", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3046, + "tag": "BioModels:BIOMD0000000037" + }, + { + "id": 3047, + "tag": "Far-red light signaling pathway" + }, + { + "id": 3048, + "tag": "Physarum polycephalum" + }, + { + "id": 3049, + "tag": "Regulation of sporulation resulting in formation of a cellular spore" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:16.439484+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000037", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2329": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2329, + "name": "Rohwer2000_Phosphotransferase_System", + "repository_type": "biomodels", + "summary": "

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3050, + "tag": "BioModels:BIOMD0000000038" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 3051, + "tag": "Phosphoenolpyruvate-dependent sugar phosphotransferase system" + }, + { + "id": 3052, + "tag": "Regulation of glucose transport" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:16.928745+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000038", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2330": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2330, + "name": "Marhl2000_CaOscillations", + "repository_type": "biomodels", + "summary": "

In order to reproduce the model, the volume of all compartment is set to 1, and the stoichiometry of CaER and CaM has been set to 0.25, corresponding to betaER/rhoER and betaM/rhoM described in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Field-Noyes Model of BZ Reaction

Citation
R.J.Field and R.M.Noyes,J.Chem.Phys.60,1877 (1974)
Description
Field Noyes Version of Belousov-Zhabotinsky Reaction. BrO3 is held constant; HOBr is typically ignored,\t\t\t\t\t\t\t and can be replaced by an empty-set. The stoichiometry f is typically taken as 1/2 or 1.\t\t\t\t\t\t\t.

Initially Generated by Cellerator Version 1.0 update 2.1220 using Mathematica 4.2 for \t\t\t\tMac OS X (June 4, 2002), December 26, 2002 10:43:53, using (PowerMac,PowerPC,\t\t\t\tMac OS X,MacOSX,Darwin). author=B.E.Shapiro

Modified with SBMLeditor by Nicolas Le Nov\u00e8re, to fit the original article.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3054, + "tag": "BioModels:BIOMD0000000040" + }, + { + "id": 3055, + "tag": "Rhythmic behavior" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:17.899168+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000040", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2332": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2332, + "name": "Kongas2007 - Creatine Kinase in energy metabolic signaling in muscle", + "repository_type": "biomodels", + "summary": "
Kongas2007 - Creatine Kinase in energy metabolic signaling in muscle

This model is described in the article:

Olav Kongas and Johannes H. G. M. van Beek
Available from Nature Precedings

Abstract:

There has been much debate on the mechanism of regulation of mitochondrial ATP synthesis to balance ATP consumption during changing cardiac workloads. A key role of creatine kinase (CK) isoenzymes in this regulation of oxidative phosphorylation and in intracellular energy transport had been proposed, but has in the mean time been disputed for many years. It was hypothesized that high-energy phosphorylgroups are obligatorily transferred via CK; this is termed the phosphocreatine shuttle. The other important role ascribed to the CK system is its ability to buffer ADP concentration in cytosol near sites of ATP hydrolysis.

Almost all of the experiments to determine the role of CK had been done in the steady state, but recently the dynamic response of oxidative phosphorylation to quick changes in cytosolic ATP hydrolysis has been assessed at various levels of inhibition of CK. Steady state models of CK function in energy transfer existed but were unable to explain the dynamic response with CK inhibited.

The aim of this study was to explain the mode of functioning of the CK system in heart, and in particular the role of different CK isoenzymes in the dynamic response to workload steps. For this purpose we used a mathematical model of cardiac muscle cell energy metabolism containing the kinetics of the key processes of energy production, consumption and transfer pathways. The model underscores that CK plays indeed a dual role in the cardiac cells. The buffering role of CK system is due to the activity of myofibrillar CK (MMCK) while the energy transfer role depends on the activity of mitochondrial CK (MiCK). We propose that this may lead to the differences in regulation mechanisms and energy transfer modes in species with relatively low MiCK activity such as rabbit in comparison with species with high MiCK activity such as rat.

The model needed modification to explain the new type of experimental data on the dynamic response of the mitochondria. We submit that building a Virtual Muscle Cell is not possible without continuous experimental tests to improve the model. In close interaction with experiments we are developing a model for muscle energy metabolism and transport mediated by the creatine kinase isoforms which now already can explain many different types of experiments.

The model has been designed according to the spirit of the paper. The list of rate in the appendix has been corrected as follow:

  1. d[ATP]/dt = (-Vhyd -Vmmck +Jatp) / Vcyt
  2. d[ADP]/dt = ( Vhyd +Vmmck +Jadp) / Vcyt
  3. d[PCr]/dt = ( Vmmck +Jpcr ) / Vcyt
  4. d[Cr]/dt = (-Vmmck +Jpcr ) / Vcyt
  5. d[Pi]/dt = ( Vhyd + Jpi ) / Vcyt
  6. d[ATPi]/dt = (+Vsyn -Vmick -Jatp) / Vims
  7. d[ADPi]/dt = (-Vsyn +Vmick -Jadp) / Vims
  8. d[PCri]/dt = ( Vmick -Jpcr ) / Vims
  9. d[Cri]/dt = (-Vmick -Jpcr ) / Vims
  10. d[Pii]/dt = (-Vsyn -Jpi ) / Vims

This model is hosted on BioModels Database and identified by: BIOMD0000000041 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3056, + "tag": "ATP metabolic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3057, + "tag": "BioModels:BIOMD0000000041" + }, + { + "id": 3058, + "tag": "Creatine metabolic process" + }, + { + "id": 3059, + "tag": "Oryctolagus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:18.349598+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000041", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2333": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2333, + "name": "Nielsen1998_Glycolysis", + "repository_type": "biomodels", + "summary": "

This model was automatically converted from model BIOMD0000000042 by using libSBML .

According to the BioModels Database terms of use , this generated model is not related with model BIOMD0000000042 any more.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3060, + "tag": "BioModels:BIOMD0000000042" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:19.045837+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000042", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2334": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2334, + "name": "Borghans1997 - Calcium Oscillation - Model 1", + "repository_type": "biomodels", + "summary": "
Borghans1997 - Calcium Oscillation - Model 1

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis (see below). This model corresponds to the first hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6622689184 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3062, + "tag": "BioModels:BIOMD0000000043" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3064, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:19.494979+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000043", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2335": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2335, + "name": "Borghans1997 - Calcium Oscillation - Model 2", + "repository_type": "biomodels", + "summary": "
Borghans1997 - Calcium Oscillation - Model 2

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis (see below). This model corresponds to the second hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6622948601 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3066, + "tag": "BioModels:BIOMD0000000044" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3064, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:19.962934+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000044", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2336": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2336, + "name": "Borghans1997 - Calcium Oscillation - Model 3", + "repository_type": "biomodels", + "summary": "
Borghans1997 - Calcium Oscillation - Model 3

A theoretical expoloration of possible mechanisms of intracellular calcium oscillations has been studied, considering three hypothesis. This model corresponds to the third hypothesis.

This model is described in the article:

Borghans JM, Dupont G, Goldbeter A.
Biophys. Chem. 1997 May; 66(1): 25-41

Abstract:

Intracellular Ca(2+) oscillations are commonly observed in a large number of cell types in response to stimulation by an extracellular agonist. In most cell types the mechanism of regular spiking is well understood and models based on Ca(2+)-induced Ca(2+) release (CICR) can account for many experimental observations. However, cells do not always exhibit simple Ca(2+) oscillations. In response to given agonists, some cells show more complex behaviour in the form of bursting, i.e. trains of Ca(2+) spikes separated by silent phases. Here we develop several theoretical models, based on physiologically plausible assumptions, that could account for complex intracellular Ca(2+) oscillations. The models are all based on one- or two-pool models based on CICR. We extend these models by (i) considering the inhibition of the Ca(2+)-release channel on a unique intracellular store at high cytosolic Ca(2+) concentrations, (ii) taking into account the Ca(2+)-activated degradation of inositol 1,4,5-trisphosphate (IP(3)), or (iii) considering explicity the evolution of the Ca(2+) concentration in two different pools, one sensitive and the other one insensitive to IP(3). Besides simple periodic oscillations, these three models can all account for more complex oscillatory behaviour in the form of bursting. Moreover, the model that takes the kinetics of IP(3) into account shows chaotic behaviour.

This model is hosted on BioModels Database and identifiedby: MODEL6623009547 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3067, + "tag": "BioModels:BIOMD0000000045" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3064, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:20.437758+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000045", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2337": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2337, + "name": "Olsen2003_peroxidase", + "repository_type": "biomodels", + "summary": "

Notes of the BioModels curators:

The current model reproduce the figure 7, panel B of the paper. Note that there is a typo in the figure. The ordinates represent the concentration of peroxyde, as stated in the legend, and not of oxygen. The model has been tested in COPASI (http://www.copasi.org/, build 13).

Notes of the original version of the SBML file:

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3068, + "tag": "Armoracia rusticana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3069, + "tag": "BioModels:BIOMD0000000046" + }, + { + "id": 3070, + "tag": "NADH peroxidase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:20.900179+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000046", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2338": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2338, + "name": "Oxhamre2005_Ca_oscillation", + "repository_type": "biomodels", + "summary": "

The model should reproduce the figure 1C of the article (successfully reproduced in MathSBML). If your software does not support the variable \"time\", you can replace the assignmentRule:
n = n0 * [ exp(-kbN*time) + kappa * (1 - exp(-kbN*time))]
by
n = n0 * kappa


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Kholodenko1999 - EGFR signaling

This model has been generated by the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based \t modelling using JWS Online , Bioinformatics, 20:2143-2144

This model is described in the article:

Kholodenko BN, Demin OV, Moehren G, Hoek JB
J. Biol. Chem. 1999 Oct; 274(42): 30169-30181

Abstract:

During the past decade, our knowledge of molecular mechanisms involved in growth factor signaling has proliferated almost explosively. However, the kinetics and control of information transfer through signaling networks remain poorly understood. This paper combines experimental kinetic analysis and computational modeling of the short term pattern of cellular responses to epidermal growth factor (EGF) in isolated hepatocytes. The experimental data show transient tyrosine phosphorylation of the EGF receptor (EGFR) and transient or sustained response patterns in multiple signaling proteins targeted by EGFR. Transient responses exhibit pronounced maxima, reached within 15-30 s of EGF stimulation and followed by a decline to relatively low (quasi-steady-state) levels. In contrast to earlier suggestions, we demonstrate that the experimentally observed transients can be accounted for without requiring receptor-mediated activation of specific tyrosine phosphatases, following EGF stimulation. The kinetic model predicts how the cellular response is controlled by the relative levels and activity states of signaling proteins and under what conditions activation patterns are transient or sustained. EGFR signaling patterns appear to be robust with respect to variations in many elemental rate constants within the range of experimentally measured values. On the other hand, we specify which changes in the kinetic scheme, rate constants, and total amounts of molecular factors involved are incompatible with the experimentally observed kinetics of signal transfer. Quantitation of signaling network responses to growth factors allows us to assess how cells process information controlling their growth and differentiation.

The model correctly reproduces all the figures from the paper. The curation has been done using SBMLodeSolver.

This model is hosted on BioModels Database and identified by: BIOMD0000000048 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Prediction and validation of the distinct dynamics of transient and sustained ERK activation.
Sasagawa S, Ozaki Y, Fujita K, Kuroda S Nat. Cell Biol.[2005 Apr; Volume: 7 (Issue: 4 )]: 365-73 15793571,
Abstract:
To elucidate the hidden dynamics of extracellular-signal-regulated kinase (ERK) signalling networks, we developed a simulation model of ERK signalling networks by constraining in silico dynamics based on in vivo dynamics in PC12 cells. We predicted and validated that transient ERK activation depends on rapid increases of epidermal growth factor and nerve growth factor (NGF) but not on their final concentrations, whereas sustained ERK activation depends on the final concentration of NGF but not on the temporal rate of increase. These ERK dynamics depend on Ras and Rap1 dynamics, the inactivation processes of which are growth-factor-dependent and -independent, respectively. Therefore, the Ras and Rap1 systems capture the temporal rate and concentration of growth factors, and encode these distinct physical properties into transient and sustained ERK activation, respectively.

Dynamics of active Ras, active Rap1 and phosphorylated ERK were correctly reproduced with CellDesigner 3.0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3074, + "tag": "BioModels:BIOMD0000000049" + }, + { + "id": 3041, + "tag": "ERK1 and ERK2 cascade" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:22.342233+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000049", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2341": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2341, + "name": "Martins2003_AmadoriDegradation", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Kinetic modelling of Amadori N-(1-deoxy-D-fructos-1-yl)-glycine degradation pathways. Part II--kinetic analysis.
Martins SI, Van Boekel MA. Carbohydr Res2003 Jul;338(16):1665-78. 12873422,
Abstract:
A kinetic model for N-(1-deoxy-Image -fructos-1-yl)-glycine (DFG) thermal decomposition was proposed. Two temperatures (100 and 120 \u00b0C) and two pHs (5.5 and 6.8) were studied. The measured responses were DFG, 3-deoxyosone, 1-deoxyosone, methylglyoxal, acetic acid, formic acid, glucose, fructose, mannose and melanoidins. For each system the model parameters, the rate constants, were estimated by non-linear regression, via multiresponse modelling. The determinant criterion was used as the statistical fit criterion. Model discrimination was performed by both chemical insight and statistical tests (Posterior Probability and Akaike criterion). Kinetic analysis showed that at lower pH DFG 1,2-enolization is favoured whereas with increasing pH 2,3-enolization becomes a more relevant degradation pathway. The lower amount observed of 1-DG is related with its high reactivity. It was shown that acetic acid, a main degradation product from DFG, was mainly formed through 1-DG degradation. Also from the estimated parameters 3-DG was found to be the main precursor in carbohydrate fragments formation, responsible for colour formation. Some indication was given that as the reaction proceeded other compounds besides DFG become reactants themselves with the formation among others of methylglyoxal. The multiresponse kinetic analysis was shown to be both helpful in deriving relevant kinetic parameters as well as in obtaining insight into the reaction mechanism.

Model was intially tested in Jarnac.

The model was recently updated on 9th July 2010. The reference publication has reported two models M1 and M2, where the parameter values are given for conditions A) 100oC, pH5.5, B) 120oC, pH5.5, C) 100oC, pH6.8 and D) 120oC, pH6.8.

This model corresponds to the model M2 with condition 100oC, pH6.8

The model reproduces Figure 6 of the reference publication. The curation figure was recently added

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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The model reproduces Figures 4,5 and 6 of the publication. The analytical functions for cometabolites Catp, Camp, Cnadph, and Cnadp slightly differ from the equations given in the paper. These changes were made in consultation with Dr. Christophe Chassagnole and are essential for reproducing the figures. The dependency of the rate of change of extracellular glucose concentration on the ratio of biomass concentration to specific weight of biomass (Cx*rPTS/Rhox) is taken into account by appropriately adjusting the stoichiometries of the species involved in the phosphotransferase system (rPTS). The rmax values for the various reactions are obtained from experiments and are not provided in the paper. However, these were personally communicated to the JWS repository. The model has been successfully tested on MathSBML.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3077, + "tag": "BioModels:BIOMD0000000051" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:23.598621+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000051", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2343": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2343, + "name": "Brands2002 - Monosaccharide-casein systems", + "repository_type": "biomodels", + "summary": "
Brands2002 - Monosaccharide-casein systems

A kinetic model of the Maillard reaction occurring in heated monosaccharide-casein system.

This model is described in the article:

Brands CM, van Boekel MA
Journal of Agricultural and Food Chemistry. 2002, 50(23):6725-6739

Abstract:

In the present study, a kinetic model of the Maillard reaction occurring in heated monosaccharide-casein systems was proposed. Its parameters, the reaction rate constants, were estimated via multiresponse modeling. The determinant criterion was used as the statistical fit criterion instead of the familiar least squares to avoid statistical problems. The kinetic model was extensively tested by varying the reaction conditions. Different sugars (glucose, fructose, galactose, and tagatose) were studied regarding their effect on the reaction kinetics. This study has shown the power of multiresponse modeling for the unraveling of complicated reaction routes as occur in the Maillard reaction. The iterative process of proposing a model, confronting it with experiments, and criticizing the model was passed through four times to arrive at a model that was largely consistent with all results obtained. A striking difference was found between aldose and ketose sugars as suggested by the modeling results: not the ketoses themselves but only their reaction products were found to be reactive in the Maillard reaction.

This model is hosted on BioModels Database and identifiedby: MODEL8177704759 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3078, + "tag": "BioModels:BIOMD0000000052" + }, + { + "id": 3079, + "tag": "Monosaccharide binding" + }, + { + "id": 3080, + "tag": "Monosaccharide biosynthetic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:24.035507+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000052", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2344": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2344, + "name": "Ferreira2003_CML_generation2", + "repository_type": "biomodels", + "summary": "

The model should reproduce the figure 2F of the article.

The equation 7 has been split into equations 7a-7c, in order to take into account the different flux rates of Lysine and CML formation from Schiff.

The model was tested in Jarnac (SBML L2 V1) and Copasi (SBML L2 V3).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3081, + "tag": "BioModels:BIOMD0000000053" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + }, + { + "id": 4587, + "tag": "Amyloidosis" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-29 11:34:24.542740+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000053", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2345": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2345, + "name": "Ataullahkhanov1996_Adenylate", + "repository_type": "biomodels", + "summary": "

The model reproduces ion and adenylate pool concentration corresponding to line 2 of Fig 3 of the publication. This model was tested successfully on Jarnac


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3082, + "tag": "BioModels:BIOMD0000000054" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:25.014984+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000054", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2346": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2346, + "name": "Locke2005 - Circadian Clock", + "repository_type": "biomodels", + "summary": "
Locke2005 - Circadian Clock

SBML model of the interlocked feedback loop network

The model describes the circuit depicted in Fig. 4 and reproduces the simulations in Figure 5A and 5B. It provides initial conditions, parameter values and rules for the production rates of the following species: LHY mRNA (cLm), cytoplasmic LHY (cLc), nuclear LHY (cLn), TOC1 mRNA (cTm), cytoplasmic TOC1 (cTc), nuclear TOC1 (cTn),X mRNA (cXm), cytoplasmic X (cXc), nuclear X (cXn), Y mRNA (cYm), cytoplasmic Y (cYc), nuclear Y (cYn), nuclear P (cPn). This model was successfully tested on MathSBML and SBML ODE Solver.

Fig 5B is not in the right phase. However, the data is correct relative to the light/dark bars at the top of the figure.

This model is described in the article:

Locke JC, Southern MM, Kozma-Bogn\u00e1r L, Hibberd V, Brown PE, Turner MS, Millar AJ
Molecular Systems Biology [2005; 1: 2005.0013]

Abstract:

Circadian clocks involve feedback loops that generate rhythmic expression of key genes. Molecular genetic studies in the higher plant Arabidopsis thaliana have revealed a complex clock network. The first part of the network to be identified, a transcriptional feedback loop comprising TIMING OF CAB EXPRESSION 1 (TOC1), LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), fails to account for significant experimental data. We develop an extended model that is based upon a wider range of data and accurately predicts additional experimental results. The model comprises interlocking feedback loops comparable to those identified experimentally in other circadian systems. We propose that each loop receives input signals from light, and that each loop includes a hypothetical component that had not been explicitly identified. Analysis of the model predicted the properties of these components, including an acute light induction at dawn that is rapidly repressed by LHY and CCA1. We found this unexpected regulation in RNA levels of the evening-expressed gene GIGANTEA (GI), supporting our proposed network and making GI a strong candidate for this component.

This model is hosted on BioModels Database and identified by: BIOMD0000000055 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3084, + "tag": "BioModels:BIOMD0000000055" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:25.479805+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000055", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2347": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2347, + "name": "Chen2004 - Cell Cycle Regulation", + "repository_type": "biomodels", + "summary": "
Chen2004 - Cell Cycle Regulation

This is a hypothetical model of cell cycle that describes the molecular mechanism for regulating DNA synthesis, bud emergence, mitosis, and cell division in budding yeast.

This model is described in the article:

Chen KC, Calzone L, Csikasz-Nagy A, Cross FR, Novak B, Tyson JJ
Mol. Biol. Cell. [2004 Aug; Volume: 15 (Issue: 8 )] Page info: 3841-62

Abstract:

The adaptive responses of a living cell to internal and external signals are controlled by networks of proteins whose interactions are so complex that the functional integration of the network cannot be comprehended by intuitive reasoning alone. Mathematical modeling, based on biochemical rate equations, provides a rigorous and reliable tool for unraveling the complexities of molecular regulatory networks. The budding yeast cell cycle is a challenging test case for this approach, because the control system is known in exquisite detail and its function is constrained by the phenotypic properties of >100 genetically engineered strains. We show that a mathematical model built on a consensus picture of this control system is largely successful in explaining the phenotypes of mutants described so far. A few inconsistencies between the model and experiments indicate aspects of the mechanism that require revision. In addition, the model allows one to frame and critique hypotheses about how the division cycle is regulated in wild-type and mutant cells, to predict the phenotypes of new mutant combinations, and to estimate the effective values of biochemical rate constants that are difficult to measure directly in vivo.

The model reproduces the time profiles of the different species in Figure 2 of the paper. The figure depicts the cycle of a daughter cell. Since the Mass Doubling Time (MDT) is 90 minutes, time t=90 from the model simulation will correspond to time t=0 in the paper. The model was successfully tested using MathSBML and SBML odeSolver.

To create a valid SBML file, a local parameter k=1 was added in the reaction 'Inactivation_274_CDC20'. Also, in order to annotate the protein and to have the interaction in the reaction graph to match figure 1 of the article, the reaction rate constants k_{mad2}, k_{bub2} and k_{lte1} are considered as species and renamed as MAD2, BUB2 and LTE1 in the model.

This model is hosted on BioModels Database and identified by: BIOMD0000000056 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3085, + "tag": "BioModels:BIOMD0000000056" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:25.934983+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000056", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2348": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2348, + "name": "Sneyd2002_IP3_Receptor", + "repository_type": "biomodels", + "summary": "

This model was successfully tested on Jarnac and MathSBML. The model reproduces the time profile of \"Open Probability\" of the receptor as shown in Figure 4 of the publication. The value of calcium ion concentration \"c\" in this model is 10 microM.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3086, + "tag": "BioModels:BIOMD0000000057" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3064, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 3087, + "tag": "Rattus rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:26.377802+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000057", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2349": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2349, + "name": "Bindschadler2001_coupled_Ca_oscillators", + "repository_type": "biomodels", + "summary": "

The model reproduces the same amplitude antiphase calcium oscillations of coupled cells depicted in Figure 5B of the publication. This model was successfully tested on Jarnac and MathSBML. The values of \"h1\" and \"h2\" are not given in the publication, but the antiphase oscillations are reproduced over a narrow range of values of h1, h2,c1,c2,D and p. The values of D and p are given, while the other values were plugged in, in order to simulate the time profiles shown in the Figure. The time t=0 in the figure may have been fixed after the system was allowed to settle, and hence does not correspond to the t=0 of the simulation.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3088, + "tag": "BioModels:BIOMD0000000058" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:26.867806+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000058", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2350": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2350, + "name": "Fridlyand2003_Calcium_flux", + "repository_type": "biomodels", + "summary": "

The model reproduces block A of Fig 5 and also Fig 3 (without the inclusion of Tg action). The model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3089, + "tag": "BioModels:BIOMD0000000059" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:27.366898+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000059", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2351": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2351, + "name": "Keizer1996_Ryanodine_receptor_adaptation", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of Open probability of the ryanodine receptor as shown in Fig 2A and 2B of the paper. The model was successfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3091, + "tag": "BioModels:BIOMD0000000060" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:27.821147+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000060", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2352": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2352, + "name": "Hynne2001_Glycolysis", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 6 of the paper. The stoichiometry and rate of reactions involving uptake of metabolites from extracellular medium have been changed corresponding to Yvol (ratio of extracellular volume to cytosolic volume) mentioned in the publication. The extracellular and cytosolic compartments have been set to 1. Concentration of extracellular glucose, GlcX, is set to 6.7 according to the equation for cellular glucose uptake rate in Table 7 of the paper. The model was successfully tested on MathSBML and Jarnac

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3092, + "tag": "BioModels:BIOMD0000000061" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:28.259086+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000061", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2353": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2353, + "name": "Bhartiya2003_Tryptophan_operon", + "repository_type": "biomodels", + "summary": "

SBML level 2 code originaly generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

BioModels Curation : The model reproduces Fig 3 of the publication. By substituting a value of 1.4 for Tex it is possible to reproduce Fig 3C and 3D(iii), Fig 3A and 3D(i), are obtained by setting Tex=0. Also, note that the tryptophan concentrations have been normalized by 82 micromolar in the figures; the normalized concetrations can be obtained via the parameters To/s/t_norm. The model was successfully tested on MathSBML and Copasi.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3093, + "tag": "BioModels:BIOMD0000000062" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3094, + "tag": "Tryptophan biosynthetic process" + } + ], + "timestamp_created": "2025-01-29 11:34:28.704710+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000062", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2354": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2354, + "name": "Galazzo1990_FermentationPathwayKinetics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Fermentation pathway kinetics and metabolic flux control in suspended and immobilized Saccharomyces cerevisiae
Jorge L. Galazzo and James E. Bailey Enzyme and Microbial TechnologyVolume 12, Issue 3, 1990, Pages 162-172.
DOI:10.1016/0141-0229(90)90033-M
Abstract:
Measurements of rates of glucose uptake and of glycerol and ethanol formation combined with knowledge of the metabolic pathways involved in S. cerevisiae were employed to obtain in vivo rates of reaction catalysed by pathway enzymes for suspended and alginate-entrapped cells at pH 4.5 and 5.5. Intracellular concentrations of substrates and effectors for most key pathway enzymes were estimated from in vivo phosphorus-31 nuclear magnetic resonance measurements. These data show the validity in vivo of kinetic models previously proposed for phosphofructokinase and pyruvate kinase based on in vitro studies. Kinetic representations of hexokinase, glycogen synthetase, and glyceraldehyde 3-phosphate dehydrogenase, which incorporate major regulatory properties of these enzymes, are all consistent with the in vivo data. This detailed model of pathway kinetics and these data on intracellular metabolite concentrations allow evaluation of flux-control coefficients for all key enzymes involved in glucose catabolism under the four different cell environments examined. This analysis indicates that alginate entrapment increases the glucose uptake rate and shifts the step most influencing ethanol production from glucose uptake to phosphofructokinase. The rate of ATP utilization in these nongrowing cells strongly limits ethanol production at pH 5.5 but is relatively insignificant at pH 4.5.

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

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Biomodels Curation: The model reproduces Fig 2 of the paper. However, it appears that the figures are swapped, hence the plot for V/Vmax vs Glucose actually represnts V/Vmax vs ATP and the vice versa is true for the other figure. The rate of hexokinase reaction that is obtained upon simulation of the model is 17.24 mM/min, therefore V/Vmax has a value of 17.24/68.5=0.25. For steady state values of Glucose and ATP (0.038 and 1.213 mM respectively), the V/Vmax values correctly correspond to 0.25, if we were to assume that the figures are swapped.

BioModels Curation updated on 25th November 2010: Figure 3 of the reference publication has been reproduced and added as a curation figure for the model.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3095, + "tag": "BioModels:BIOMD0000000063" + }, + { + "id": 3096, + "tag": "Glycolytic fermentation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:29.214860+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000063", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2355": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2355, + "name": "Teusink2000_Glycolysis", + "repository_type": "biomodels", + "summary": "

Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry.
Teusink,B et al.: Eur J Biochem 2000 Sep;267(17):5313-29.
The model reproduces the steady-state fluxes and metabolite concentrations of the branched model as given in Table 4 of the paper. It is derived from the model on JWS online, but has the ATP consumption in the succinate branch with the same stoichiometrie as in the publication. The model was successfully tested on copasi v.4.4(build 26).
For Vmax values, please note that there is a conversion factor of approx. 270 to convert from U/mg-protein as shown in Table 1 of the paper to mmol/(min*L_cytosol). The equilibrium constant for the ADH reaction in the paper is given for the reverse reaction (Keq = 1.45*10 4 ). The value used in this model is for the forward reaction: 1/Keq = 6.9*10 -5 .
Vmax parameters values used (in [mM/min] except VmGLT):
VmGLT 97.264 mmol/min
VmGLK 226.45
VmPGI 339.667
VmPFK 182.903
VmALD 322.258
VmGAPDH_f 1184.52
VmGAPDH_r 6549.68
VmPGK 1306.45
VmPGM 2525.81
VmENO 365.806
VmPYK 1088.71
VmPDC 174.194
VmG3PDH 70.15
The result of the G6P steady state concentration (marked in red) differs slightly from the one given in table 4. of the publication
Results for steady state:
orig. article this model
Fluxes[mM/min] \u00a0
Glucose\u00a0 88\u00a0 88\u00a0
Ethanol\u00a0 129\u00a0 129\u00a0
Glycogen\u00a0 6\u00a0 6\u00a0
Trehalose\u00a0 4.8\u00a0 4.8\u00a0 (G6P flux through trehalose branch)
Glycerol\u00a0 18.2\u00a0 18.2\u00a0
Succinate\u00a0 3.6\u00a0 3.6\u00a0
Conc.[mM] \u00a0
G6P\u00a0 1.07\u00a0 1.03\u00a0
F6P\u00a0 0.11\u00a0 0.11\u00a0
F1,6P\u00a0 0.6\u00a0 0.6\u00a0
DHAP\u00a0 0.74\u00a0 0.74\u00a0
3PGA\u00a0 0.36\u00a0 0.36\u00a0
2PGA\u00a0 0.04\u00a0 0.04\u00a0
PEP\u00a0 0.07\u00a0 0.07\u00a0
PYR\u00a0 8.52\u00a0 8.52\u00a0
AcAld\u00a0 0.17\u00a0 0.17\u00a0
ATP\u00a0 2.51\u00a0 2.51\u00a0
ADP\u00a0 1.29\u00a0 1.29\u00a0
AMP\u00a0 0.3\u00a0 0.3\u00a0
NAD\u00a0 1.55\u00a0 1.55\u00a0
NADH\u00a0 0.04\u00a0 0.04\u00a0
Authors of the publication also mentioned a few misprints in the original article:
in the kinetic law for ADH :

  1. the species a should denote NAD and b Ethanol
  2. the last term in the equation should read bpq /( K ib K iq K p )
in the kinetic law for PFK :
  1. R = 1 + \u03bb 1 + \u03bb 2 + g r \u03bb 1 \u03bb 2
  2. equation L should read: L = L0*(..) 2 *(..) 2 *(..) 2 not L = L0*(..) 2 *(..) 2 *(..)
To make the model easier to curate, the species ATP , ADP and AMP were added. These are calculated via assignment rules from the active phosphate species, P , and the sum of all AXP , SUM_P .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3097, + "tag": "BioModels:BIOMD0000000064" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:29.657122+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000064", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2356": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2356, + "name": "Yildirim2003_Lac_Operon", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Feedback regulation in the lactose operon: a mathematical modeling study and comparison with experimental data.
Yildirim N, Mackey MC Biophys. J. 2003 12719218 ,
Abstract:
A mathematical model for the regulation of induction in the lac operon in Escherichia coli is presented. This model takes into account the dynamics of the permease facilitating the internalization of external lactose; internal lactose; beta-galactosidase, which is involved in the conversion of lactose to allolactose, glucose and galactose; the allolactose interactions with the lac repressor; and mRNA. The final model consists of five nonlinear differential delay equations with delays due to the transcription and translation process. We have paid particular attention to the estimation of the parameters in the model. We have tested our model against two sets of beta-galactosidase activity versus time data, as well as a set of data on beta-galactosidase activity during periodic phosphate feeding. In all three cases we find excellent agreement between the data and the model predictions. Analytical and numerical studies also indicate that for physiologically realistic values of the external lactose and the bacterial growth rate, a regime exists where there may be bistable steady-state behavior, and that this corresponds to a cusp bifurcation in the model dynamics.

The model reproduces the time profile of beta-galactosidase activity as shown in Fig 3 of the paper. The delay functions for transcription (M) and translation (B and P) have been implemented by introducing intermediates ( I1, I2 and I3) in the reaction scheme which then give their respective products (I1-> M, I2 ->B and I3 ->P) after an appropriate length of time. The steady state values, attained upon simulation of model equations, for Allolactose (A), mRNA (M), beta-galactosidase (B), Lactose (L), and Permease (P) match with those predicted by the paper. The model was successfully tested on Jarnac, MathSBML and COPASI

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3098, + "tag": "BioModels:BIOMD0000000065" + }, + { + "id": 3099, + "tag": "Carbon catabolite regulation of transcription" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:30.100424+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000065", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2357": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2357, + "name": "Chassagnole2001_Threonine Synthesis", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation: The model reproduces Fig 2f of the paper. The Vmax values for different reactions are obtained by multiplying the specific activites given in Table 3 of the paper with the protein concentration and an assay correction factor that was provided by the authors. The protein concentration is 202 mg/litre. The specific activities that need to be taken into consideration are those given for \"variable threonine\" in Table 3. The following are the assay correction factors provided by the authors: vak1=1.49; vak3=1.12; vasd=1.14; vhsd=1.42; vts=1.15; vhk=1.13. The model was successfully tested on MathSBML and Jarnac

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3100, + "tag": "BioModels:BIOMD0000000066" + }, + { + "id": 3101, + "tag": "Escherichia coli (strain K12)" + }, + { + "id": 3102, + "tag": "Glycine, serine and threonine metabolism" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:30.901525+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000066", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2358": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2358, + "name": "Fung2005_Metabolic_Oscillator", + "repository_type": "biomodels", + "summary": "

A Synthetic Gene-Metabolic Oscillator

Reference: Fung et al; Nature (2005) 435:118-122
Name of kinetic law Reaction
Glycolytic flux, V_gly: nil -> AcCoA;
Flux to TCA cycle/ETOH, V_TCA: AcCoA -> TCA/EtOH;
HOAc ex/import,reversible, V_out: HOAc -> HOAc_E
V_Pta: AcCoA + Pi -> AcP + CoA
reversible, V_Ack: AcP + ADP -> OAc + ATP
V_Acs: OAC + ATP -> AcCoA +PPi
Acetic acid-base equillibrium, reversible, V_Ace: OAC + H -> HOAc
Expression of LacI, R_LacI: nil -> LacI
Expression of Acs, R_Acs: nil -> Acs
Expression of Pta, R_Pta: nil -> Pta
LacI degradation, R_dLacI: LacI -> nil
Acs degradation, R_dAcs: Acs -> nil
Pta degradation, R_dPta: Pta -> nil

For this model the differential equation for V_Ace was changed from:
C*(AcP*H-K_eq*OAC) with C = 100 in the supplemental material
to C*(OAc*H-K_eq*HOAc) with C = 100, as in Bulter et. al; PNAS(2004),101,2299-2304 , and a value for K_eq of 5*10^-4 after communication with the authors.

translated to SBML by:
Lukas Endler(luen at tbi.univie.ac.at), Christoph Flamm (xtof at tbi.univie.ac.at)

Biomodels Curation The model reproduces 3a of the paper for glycolytic flux Vgly = 0.5. The authors have agreed that the values on Y-axis are marked wrong and hence there is a discrepancy between model simulation results and the figure. Also, note that the values of concentration and time are in dimensionless units. The model was successfully tested on MathSBML and Jarnac.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3103, + "tag": "BioModels:BIOMD0000000067" + }, + { + "id": 3099, + "tag": "Carbon catabolite regulation of transcription" + }, + { + "id": 3101, + "tag": "Escherichia coli (strain K12)" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:31.372916+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000067", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2359": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2359, + "name": "Curien2003_MetThr_synthesis", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A kinetic model of the branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana.
Curien G, Ravanel S, Dumas R Eur. J. Biochem. 2003 Dec; Volume: 270 (Issue: 23 )]:4615-27 14622248 ,
Abstract:
This work proposes a model of the metabolic branch-point between the methionine and threonine biosynthesis pathways in Arabidopsis thaliana which involves kinetic competition for phosphohomoserine between the allosteric enzyme threonine synthase and the two-substrate enzyme cystathionine gamma-synthase. Threonine synthase is activated by S-adenosylmethionine and inhibited by AMP. Cystathionine gamma-synthase condenses phosphohomoserine to cysteine via a ping-pong mechanism. Reactions are irreversible and inhibited by inorganic phosphate. The modelling procedure included an examination of the kinetic links, the determination of the operating conditions in chloroplasts and the establishment of a computer model using the enzyme rate equations. To test the model, the branch-point was reconstituted with purified enzymes. The computer model showed a partial agreement with the in vitro results. The model was subsequently improved and was then found consistent with flux partition in vitro and in vivo. Under near physiological conditions, S-adenosylmethionine, but not AMP, modulates the partition of a steady-state flux of phosphohomoserine. The computer model indicates a high sensitivity of cystathionine flux to enzyme and S-adenosylmethionine concentrations. Cystathionine flux is sensitive to modulation of threonine flux whereas the reverse is not true. The cystathionine gamma-synthase kinetic mechanism favours a low sensitivity of the fluxes to cysteine. Though sensitivity to inorganic phosphate is low, its concentration conditions the dynamics of the system. Threonine synthase and cystathionine gamma-synthase display similar kinetic efficiencies in the metabolic context considered and are first-order for the phosphohomoserine substrate. Under these conditions outflows are coordinated.


SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

Biomodels Curation The model simulates the flux for TS and CGS under conditions given in Table 2 and reproduces the dotted lines given in Table 3 of the paper. There is a typo in the equation for the apparent specificity constant for Phser, Kts (equation13). This was changed after communication with the authors to be: Kts = 5.9E-4+6.2E-2*pow(AdoMet,2.9)/(pow(32,2.9)+pow(AdoMet,2.9)). The model was successfully tested on Jarnac and Copasi. Due to a suggestion from Pedro Mendez the parameter AdoMet, TS and CGS where made constant species.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3105, + "tag": "BioModels:BIOMD0000000068" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:31.909151+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000068", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2361": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2361, + "name": "Holzhutter2004_Erythrocyte_Metabolism", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144

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Biomodels CurationThe model simulates the flux values as given for \"kinetic model\" in Table 1 of the paper. The model was successfully tested on Jarnac.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3108, + "tag": "BioModels:BIOMD0000000070" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:32.788190+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000070", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2362": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2362, + "name": "Bakker2001_Glycolysis", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation: The paper refers to the model equations present in Bakker et al's \" Glycolysis in bloodstream from Trypanosoma brucei can be understood in terms of the kinetics of glycolytic enzymes\" (Pubmed ID: 9013556), also, the authors claim that some of the modifications in these equations were made based on the experimental results from the paper \"Contribution of glucose transport in the control of glycolytic flux in Trypanosoma brucei\" (Pubmed ID: 10468568). The model reproduces the various flux values in Fig 3 for 100% TPI activity. It also matches with the values provided in Table 2 of the paper. The model was successfully tested with Copasi and SBML ODE Solver.
The volumes are set to the values containing 1 mg of total protein per microlitre total cell volume. To change the protein concentration use Vt , the total cell volume in micro litre per mg protein.
To change the TPI activity use the global parameter TPIact .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3109, + "tag": "BioModels:BIOMD0000000071" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-29 11:34:33.221701+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000071", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2363": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2363, + "name": "Yi2003_GproteinCycle", + "repository_type": "biomodels", + "summary": "

The paper describes both wild-type and mutant cells of G protein cycle by using different values of G protein deactivation. We chosed the wild-type, k=0.11 s-1.

The unit of the concentration for the proteins are numbers of molecules per cell.

Figure5(A) was reproduced with COPASI 4.0 (Build 18) and SBML_odeSolver. Figure5(B) was reproduced with COPASI 4.0 (Build 18).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3111, + "tag": "BioModels:BIOMD0000000072" + }, + { + "id": 3112, + "tag": "Heterotrimeric G-protein complex cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:33.735191+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000072", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2364": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2364, + "name": "Leloup2003_CircClock_DD", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Toward a detailed computational model for the mammalian circadian clock . In this model only interlocked negative and positive regulation of Per, Cry, Bmal gene are involved. Some initial values were not provided, therefore they were chosen to fit the curve from the paper.

Figure2A re-produced by Copasi 4.0.19 and roadRunner online.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3113, + "tag": "BioModels:BIOMD0000000073" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:34.232120+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000073", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2365": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2365, + "name": "Leloup2003_CircClock_DD_REV-ERBalpha", + "repository_type": "biomodels", + "summary": "

This is model in continous darkness (DD) described in the article Toward a detailed computational model for the mammalian circadian clock

This model features the full interlocked negative and positive regulation of Per,Cry,Bmal and REV-ERBalpha. The model exhibits robust oscillations quite independent of the initial conditions for teh parameters given. Each species is assigned zero as initial value, and the graph started at time=120h.

Simulation results could be reproduced using Copasi 4.0.19(development) and roadRunner online.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3115, + "tag": "BioModels:BIOMD0000000074" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:34.662635+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000074", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2366": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2366, + "name": "Xu2003 - Phosphoinositide turnover", + "repository_type": "biomodels", + "summary": "
Xu2003 - Phosphoinositide turnover

The model reproduces the percentage change of PIP_PM, PIP2_PM and IP3_Cyt as depicted in Figure 1 of the paper. The model also contains the equations for the analysis of PH-GFP experiments, however the initial value of PH_GFP has been set to zero to more accurately reproduce Figure 1. The units of cytosolic species are given in molecules/um^3. In order to convert them to uM, divide the concentration by 602. For the analysis of PH_GFP experiments, one should plug in the values of PH_GFP, IP3_PHGFP and PIP2_PHGFP from Table AI in the appendix. The model was successfully tested on MathSBML.

This model has been generated by VCell

This model is described in the article:

Xu C, Watras J, Loew LM.
J. Cell Biol. 2003 May; 161(4): 779-791

Abstract:

We studied the bradykinin-induced changes in phosphoinositide composition of N1E-115 neuroblastoma cells using a combination of biochemistry, microscope imaging, and mathematical modeling. Phosphatidylinositol-4,5-bisphosphate (PIP2) decreased over the first 30 s, and then recovered over the following 2-3 min. However, the rate and amount of inositol-1,4,5-trisphosphate (InsP3) production were much greater than the rate or amount of PIP2 decline. A mathematical model of phosphoinositide turnover based on this data predicted that PIP2 synthesis is also stimulated by bradykinin, causing an early transient increase in its concentration. This was subsequently confirmed experimentally. Then, we used single-cell microscopy to further examine phosphoinositide turnover by following the translocation of the pleckstrin homology domain of PLCdelta1 fused to green fluorescent protein (PH-GFP). The observed time course could be simulated by incorporating binding of PIP2 and InsP3 to PH-GFP into the model that had been used to analyze the biochemistry. Furthermore, this analysis could help to resolve a controversy over whether the translocation of PH-GFP from membrane to cytosol is due to a decrease in PIP2 on the membrane or an increase in InsP3 in cytosol; by computationally clamping the concentrations of each of these compounds, the model shows how both contribute to the dynamics of probe translocation.

This model is hosted on BioModels Database and identified by: BIOMD0000000075 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3116, + "tag": "BioModels:BIOMD0000000075" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3117, + "tag": "Phosphatidylinositol metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:35.102459+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000075", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2367": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2367, + "name": "Cronwright2002_Glycerol_Synthesis", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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Biomodels Curation The model reproduces the flux value of \"Gpp p\" (rate of Glycerol synthesis) as depicted in Fig 3 of the paper. The model reproduces the flux for early exponential phase , however it can be used to reproduce the values for other phases by plugging in appropriate values for maximal rates as given in Table 1 and metabolite concentrations as given in Table 2 of the paper. The model was succesfully reproduced using Jarnac.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3118, + "tag": "BioModels:BIOMD0000000076" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:35.533145+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000076", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2368": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2368, + "name": "Blum2000_LHsecretion_1", + "repository_type": "biomodels", + "summary": "

A mathematical model quantifying GnRH-induced LH secretion from gonadotropes by Blum et al (2000)

This paper includes three stages, and the model does not include the third stage. Also an event is included which remove the hormone GnRH at time=5min. Figure 1 and Figure 2 of the paper are reproduced, using SBML odeSolver. We choose to encode the model with the concentration of GnRH equal to 1.0nM.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3119, + "tag": "BioModels:BIOMD0000000077" + }, + { + "id": 3120, + "tag": "Response to gonadotropin-releasing hormone" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:35.965871+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000077", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2369": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2369, + "name": "Leloup2003_CircClock_LD", + "repository_type": "biomodels", + "summary": "

This model is described in the paper Toward a detailed computational model for the mammalian circadian clock . In this model only interlocked negative and positive regulation of Per, Cry, Bmal gene are involved. Some initial values were not provided, therefore they were chosen to fit the curves from the paper.

Figure2C is re-produced by odeSolver.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3121, + "tag": "BioModels:BIOMD0000000078" + }, + { + "id": 3122, + "tag": "Entrainment of circadian clock" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:36.421772+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000078", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2371": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2371, + "name": "Thomsen1989_AdenylateCyclase", + "repository_type": "biomodels", + "summary": "

This model reproduces figure 5 and figure 4(B)of the paper, with Kinh represented by [G-GTP]. We arbitrarily chosed to set the initial concentration of D to 31 micorMolar based on legend of figure 4. [R] was not given anywhere in the paper and was chosen to calibrate the sigmoid response to an increased [GTP]. THe figure 5 in the model was successfully simulated on COPASI 4.0 ,the figure 4(B) was sucessfully simulated on both COPASI and SBML_odeSolver.

There are two curves for Kinh in the absence and presence of NaCl in the figure obtained from simulations of the model using parameters of set C and set D.Here in the model the initial value given is from set D.The parameters in set C :k7=0.5, k10=1.0,k5=0.1,the others are the same with set D.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3125, + "tag": "BioModels:BIOMD0000000080" + }, + { + "id": 3112, + "tag": "Heterotrimeric G-protein complex cycle" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3126, + "tag": "Negative regulation of adenylate cyclase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:37.417020+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000080", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2372": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2372, + "name": "Suh2004_KCNQ_Regulation", + "repository_type": "biomodels", + "summary": "

The model reproduces FIG 11A and FIG 11B of the paper. However, please note that FIG 11B is a plot of normalised amounts versus time. The \"stoichiometry\" field has been used to convert fluxes from membrane species to volume species. The value of 0.0009967 is a product of (Surface to Volume_M*(1/Avagadro's number)*1E21. 0.6 is the surface to volume ratio of the plasma membrane, 1E21 is required for a unit surface to volume ratio and the Avagadro's number is present in the denominator to convert molecules to moles. The model was successfully tested using MathSBML and SBML ODESolver.
All the kinetic laws have the unit items per second , which requires the one reaction taking place in the cytoplasm - IP3Phosphatase - to include an explicit conversion factor both in the kinetic law and the stoichiometry of IP3_C . The kinetic law is multiplied and the stoichiometry divided by the number of molecules per micro-mole. This conversion factor is only required for correct units and can be replaced by 1, if it should lead to numerical problems.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3127, + "tag": "BioModels:BIOMD0000000081" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:37.852872+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000081", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2373": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2373, + "name": "Thomsen1988_AdenylateCyclase_Inhibition", + "repository_type": "biomodels", + "summary": "

This model was created according to the paper Inhibition of Adenylate Cyclase Is Mediated by the High Affinity Conformation of the alpha2-Adrenergic Receptor published in 1988.

The figure4 (steady state curve) in the paper has been simulated having the same plot with Copasi 4.0.19 (development) and roadRunner(online).Because the initial concentration of R and D were not given in the paper ,so we gave it 1e-9 Mol/L and 1e-8 Mol/L respectively.

Pay attention that the simulations of steady state concentration of species in arbitrary units are shown for figure4 and figure6 in the paper.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3128, + "tag": "BioModels:BIOMD0000000082" + }, + { + "id": 3112, + "tag": "Heterotrimeric G-protein complex cycle" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3126, + "tag": "Negative regulation of adenylate cyclase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:38.313353+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000082", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2374": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2374, + "name": "Leloup2003_CircClock_LD_REV-ERBalpha", + "repository_type": "biomodels", + "summary": "

This is model is according to the paperToward a detailed computational model for the mammalian circadian clock

In this model interlocked negative and positive regulation of Per,Cry,Bmal,REV-ERBalpha genes are all involved.The model is actually robust so the initial conditions are unimportant.We gave every entity zero as initial value,and start the graph at time=132h.

The simulation results in figure 8B can be reproduced by roadRunner online and Copasi. We use a ceiling function to simulate the day-light cycle.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3129, + "tag": "BioModels:BIOMD0000000083" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:38.791659+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000083", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2375": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2375, + "name": "Hornberg2005_ERKcascade", + "repository_type": "biomodels", + "summary": "

SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

Biomodels Curation The model reproduces the time series depicted in Fig 2 of the paper. Also, by varying the values of Vmax for the second kinase (k5) the time series of X3P as shown in Fig3 can be reproduced. The model was successfully tested on MathSBML and Jarnac.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3130, + "tag": "BioModels:BIOMD0000000084" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:39.229429+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000084", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2376": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2376, + "name": "Maurya2005_GTPaseCycle_reducedOrder", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Reduced-order modeling of biochemical networks: application to the GTPase-cycle signalling module by Maurya et al 2006.The figure 4c is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z at various concentrations of R and GAP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3131, + "tag": "BioModels:BIOMD0000000085" + }, + { + "id": 3132, + "tag": "Regulation of G-protein coupled receptor protein signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:39.654330+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000085", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2377": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2377, + "name": "Bornheimer2004_GTPaseCycle", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Computational modeling reveals how interplay between components of a GTPase-cycle module regulates signal transduction by Bornheimer et al 2004.The figure 3 is reproduced by Copasi 4.0.19 (development) .It is three-dimensional logarithmic plots show the output of simulations of Z and v at various concentrations of R and GAP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3133, + "tag": "BioModels:BIOMD0000000086" + }, + { + "id": 3132, + "tag": "Regulation of G-protein coupled receptor protein signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:40.167878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000086", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2378": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2378, + "name": "Proctor2006_telomere", + "repository_type": "biomodels", + "summary": "

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3134, + "tag": "BioModels:BIOMD0000000087" + }, + { + "id": 3135, + "tag": "Cell cycle checkpoint" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:40.614917+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000087", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2379": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2379, + "name": "Maeda2006_MyosinPhosphorylation", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2B, D, F, and 2H. The dynamics correspond to a stimulus of 1 U/ml of thrombin which is equal to 0.01 uM. Phosphorylated MLC is the sum of pMLC (s359) and ppMLC (s360). A slight discrepancy in peak values of species between the figure in the paper and simulation result might be due to different initial conditions in the two sets. The model was successfully tested on MathSBML. It is possible to simulate the model on other software that do not support \"Events\" at this time by removing the \"listOfEvents\" and substituting a value of 0.01 for thrombin (s2). This does not change the model very much. With the latter format, the model was also successfully tested on Copasi.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3136, + "tag": "BioModels:BIOMD0000000088" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:41.055302+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000088", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2380": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2380, + "name": "Locke2006_CircClock_LL", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Experimental validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana.
Locke JC, Kozma-Bogn\u00e1r L, Gould PD, Feh\u00e9r B, Kevei E, Nagy F, Turner MS, Hall A, Millar AJ Mol. Syst. Biol.2006;Volume:2;Page:59 17102804,
Abstract:
Our computational model of the circadian clock comprised the feedback loop between LATE ELONGATED HYPOCOTYL (LHY), CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and TIMING OF CAB EXPRESSION 1 (TOC1), and a predicted, interlocking feedback loop involving TOC1 and a hypothetical component Y. Experiments based on model predictions suggested GIGANTEA (GI) as a candidate for Y. We now extend the model to include a recently demonstrated feedback loop between the TOC1 homologues PSEUDO-RESPONSE REGULATOR 7 (PRR7), PRR9 and LHY and CCA1. This three-loop network explains the rhythmic phenotype of toc1 mutant alleles. Model predictions fit closely to new data on the gi;lhy;cca1 mutant, which confirm that GI is a major contributor to Y function. Analysis of the three-loop network suggests that the plant clock consists of morning and evening oscillators, coupled intracellularly, which may be analogous to coupled, morning and evening clock cells in Drosophila and the mouse.


The model describes a three loops model of the Arabidopsis circadian clock. It provides initial conditions, parameter values and reactions for the production rates of the following species: LHY mRNA (cLm), cytoplasmic LHY (cLc), nuclear LHY (cLn), TOC1 mRNA (cTm), cytoplasmic TOC1 (cTc), nuclear TOC1 (cTn), X mRNA (cXm), cytoplasmic X (cXc), nuclear X (cXn), Y mRNA (cYm), cytoplasmic Y (cYc), nuclear Y (cYn), nuclear P (cPn), APRR7/9 mRNA, cytoplasmic APRR7/9, and nuclear APRR7/9.

The paper describes the behaviour of the model in constant light (LL) and day-night cycle (LD). However, the current model only contains the LL cycle. Some parameter values should be changed from the wild-type (WT) ones in order to simulate the effect of mutations. These changes are listed in the notes of relevant parameters.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3137, + "tag": "BioModels:BIOMD0000000089" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:41.630147+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000089", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2381": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2381, + "name": "Wolf2001_Respiratory_Oscillations", + "repository_type": "biomodels", + "summary": "

This model by Jana Wolf et al. 2001 is the first mechanistic model of respiratory oscillations in Saccharomyces cerevisae. It is based on the assumption that feedback inhibition of cysteine on the sulfate transporters leads to oscillations in this pathway and causes oscillations in respiratory activity via inhibition of cytochrome c oxidase by hydrogen disulfide. The model is qualitative/semi-quantitative and reproduces the respiratory oscillation pattern quite well. It is based on very coarse-grained representations of the mitochondrial tricarboxylic acid cycle and the mitochondrial electron transport chain (oxidative phosphorylation). The sulfate assimilatory pathways also contains some significant simplifcations.

The model corresponds to Fig. 2B of the paper, with a slight phase shift of the oscillations. No initial conditions were given in the paper, and thus they were chosen arbitrarily in a range that lies within the basin of attraction of the limit cycle oscillations. Species IDs correspond to IDs used by the authors, while SBML names are more common abbreviations.

Caveats:
1) Equilibrated transport:
The model assumes fast equilibration between mitochondria and cytoplasm for the metabolites NADH, NAD+, H2S and Acetyl-CoA.
2) Cytosolic mass conservation ATP/ADP:
The model uses mass conservation for cytosolic adenosine nucleotides with is however not encoded in the stoichiometry, but is implied by the lumped reaction v4. This reaction combines the enzymatic reactions of phosphoadenylyl-sulfate reductase (thioredoxin) (yeast protein Met16p, EC 1.8.4.8) and sulfite reductase (NADPH) (subunits Met5p and Met10p, EC 1.8.1.2). EC 1.8.4.8 also has adenosine-3',5'-bismonophosphate (PAP, not to confuse with ID pap in this model, standing for PAPS) as a product. PAP is the substrate for enzyme 3'(2'),5'-bisphosphate nucleotidase (Met22p, EC:3.1.3.7) which would revover AMP (and Pi). Then AMP can be assumed to be equilibrated with ATP and ADP via adenylate kinase, as often used in metabolic models. This AMP production is implied in the mass conservation for cytosolic adenosine phosphates. Accounting for these reactions explicitly does not change the dynamics of the model significantly. An according version can be obtained from the SBML creator (Rainer Machne, mailto:raim@tbi.univie.ac.at).
3) Redox balance:
The enzyme sulfite reductase (NADPH) (subunits Met5p and Met10p, EC 1.8.1.2, part of reaction v4) actually uses NADPH, and the authors assume equilibration of NADH and NADPH. But actually S. cerevisiae specifically is missing the according enzyme transhydrogenase (EC 1.6.1.1 or EC 1.6.1.2). EC 1.8.4.8 also oxidizes thioredoxin and would actually require an additional NADPH for thioredoxin recovery (reduction). This would slightly affect the redox balance of the model.
4) Energy balance:
Reaction v7 lumps NAD-dependent alcohol dehydrogenase (EC 1.1.1.1), aldehyde dehydrogenase (NAD+) (EC 1.2.1.3) and acetyl-CoA synthase (EC 6.2.1.1). The latter reaction would actually consume ATP as a co-factor, producing AMP+PPi, and this is not included in the model. This would slightly bias the model's energy balance.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3138, + "tag": "BioModels:BIOMD0000000090" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3139, + "tag": "Sulfate assimilation, phosphoadenylyl sulfate reduction by phosphoadenylyl-sulfate reductase (thioredoxin)" + } + ], + "timestamp_created": "2025-01-29 11:34:42.630149+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000090", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2382": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2382, + "name": "Proctor2005 - Actions of chaperones and their role in ageing", + "repository_type": "biomodels", + "summary": "
Proctor2005 - Actions of chaperones and theirrole in ageing

This model is described in the article:

Proctor CJ, Soti C, Boys RJ, Gillespie CS, Shanley DP, Wilkinson DJ, Kirkwood TB.
Mech. Ageing Dev. 2005 Jan; 126(1): 119-131

Abstract:

Many molecular chaperones are also known as heat shock proteins because they are synthesised in increased amounts after brief exposure of cells to elevated temperatures. They have many cellular functions and are involved in the folding of nascent proteins, the re-folding of denatured proteins, the prevention of protein aggregation, and assisting the targeting of proteins for degradation by the proteasome and lysosomes. They also have a role in apoptosis and are involved in modulating signals for immune and inflammatory responses. Stress-induced transcription of heat shock proteins requires the activation of heat shock factor (HSF). Under normal conditions, HSF is bound to heat shock proteins resulting in feedback repression. During stress, cellular proteins undergo denaturation and sequester heat shock proteins bound to HSF, which is then able to become transcriptionally active. The induction of heat shock proteins is impaired with age and there is also a decline in chaperone function. Aberrant/damaged proteins accumulate with age and are implicated in several important age-related conditions (e.g. Alzheimer's disease, Parkinson's disease, and cataract). Therefore, the balance between damaged proteins and available free chaperones may be greatly disturbed during ageing. We have developed a mathematical model to describe the heat shock system. The aim of the model is two-fold: to explore the heat shock system and its implications in ageing; and to demonstrate how to build a model of a biological system using our simulation system (biology of ageing e-science integration and simulation (BASIS)).

This model is hosted on BioModels Database and identified by: BIOMD0000000091.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3140, + "tag": "BioModels:BIOMD0000000091" + }, + { + "id": 3141, + "tag": "Cell aging" + }, + { + "id": 3142, + "tag": "Chaperone mediated protein folding requiring cofactor" + }, + { + "id": 3143, + "tag": "Response to heat" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4589, + "tag": "Cataract" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-29 11:34:43.663339+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000091", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2383": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2383, + "name": "Fuentes2005_ZymogenActivation", + "repository_type": "biomodels", + "summary": "

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SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online , Bioinformatics, 20:2143-2144

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The model reproduces Fig 1A of the paper. The model was successfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3144, + "tag": "BioModels:BIOMD0000000092" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:44.578819+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000092", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2384": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2384, + "name": "Yamada2003_JAK_STAT_pathway", + "repository_type": "biomodels", + "summary": "

NCBS Curation Comments This model shows the control mechanism of Jak-Stat pathway, here SOCS1 (Suppressor of cytokine signaling-I) was identified as the negative regulator of Jak and STAT signal transduction pathway. Note: There are a few ambiguities in the paper like initial concentration of IFN and some reactions were missing in the paper that were employed for obtaining the results. The graphs are almost similar to the graphs as shown in the paper but still some ambiguities regarding the concentration are there. Thanks to Dr Satoshi Yamada for clarifying some of those ambiguities and providing the values used in simulations.

Biomodels Curation Comments The model reproduces Fig 2 (A,C,E,G,I,K,M) of the paper. The set of equations present in the paper are inadequate to reproduce the figures mentioned . The model appears to have been fine tuned after correspondence between the curators at NCBS and the authors. There is however a slight discrepancy between the simulation results and the plots in the paper. The model was tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3145, + "tag": "BioModels:BIOMD0000000093" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:45.290626+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000093", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2385": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2385, + "name": "Yamada2003_JAK_STAT_SOCS1_knockout", + "repository_type": "biomodels", + "summary": "

NCBS Curation Comments: This model shows the control mechanism of Jak-Stat pathway, here SOCS1 (Suppressor of cytokine signaling-I) was identified as the negative regulator of Jak and STAT signal transduction pathway. This is the knockout version of Jak-Stat pathway in this model the SOCS1 has been knocked out i.e it formation is not shown. The graphs are almost similar to the graphs as shown in the paper but STAT1n graph has some ambiguities. Thanks to Dr Satoshi Yamada for clarifying some of those ambiguities and providing the values used in simulations.

Biomodels Curation Comments: The model reproduces the figures 2 (B,D,F,H,J,L,N) corresponding to JAK/STAT activation in SOCS1 knock out cells. The model was successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2006 The BioModels Team.
For more information see the terms of use .

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3146, + "tag": "BioModels:BIOMD0000000094" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:45.719664+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000094", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2386": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2386, + "name": "Zeilinger2006_PRR7-PRR9-Y", + "repository_type": "biomodels", + "summary": "

The model reproduces the circadian charecteristics as given in Table 1 for the PRR7-PRR9-Y model. The model makes use of the event section to introduce light at 30 hours. The Zeitgeber (ZT) times for species shown in Table 1 can be reproduced by looking at the time it takes for species to reach peak values after the introduction of light. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3147, + "tag": "BioModels:BIOMD0000000095" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:46.150415+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000095", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2387": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2387, + "name": "Zeilinger2006_PRR7-PRR9light-Y", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of cYm and cTm under light-dark cycles as depicted in Fig 4 and Fig 5 respectively. 12 hour light-dark cycles are accomplished using a simple algorithm in the event section. The model was successfully tested using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3148, + "tag": "BioModels:BIOMD0000000096" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:46.589209+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000096", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2388": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2388, + "name": "Zeilinger2006_PRR7-PRR9light-Yprime", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of TOC1 and Y mRNA for a 8:16 cycle as depicted in Fig7A and 7B. A simple algorithm in the event section accomplishes the 8 hour light and 16 hour dark cycle. The model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3149, + "tag": "BioModels:BIOMD0000000097" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:47.061058+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000097", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2389": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2389, + "name": "Goldbeter1990_CalciumSpike_CICR", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of cytosolic and intracellular calcium as depicted in the upper panel of Fig 2 in the paper. The model was successfully tested on MathSBML and Jarnac.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3150, + "tag": "BioModels:BIOMD0000000098" + }, + { + "id": 3151, + "tag": "Calcium ion transport" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3152, + "tag": "Inositol 1,4,5-trisphosphate-sensitive calcium-release channel activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:47.498310+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000098", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2390": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2390, + "name": "Laub1998_SpontaneousOscillations", + "repository_type": "biomodels", + "summary": "

This is model according to the paper \"A Molecular Network That Produces Spontaneous Oscillations in Excitalbe Cells of Dictyostelium. Figure 3 has been reproduced by Copasi 4.0.20(development) \". However four of the parameters have been changed , see details in notes.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3153, + "tag": "BioModels:BIOMD0000000099" + }, + { + "id": 3154, + "tag": "CAMP-mediated signaling" + }, + { + "id": 3155, + "tag": "Dictyostelium" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:47.944946+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000099", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2391": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2391, + "name": "Rozi2003_GlycogenPhosphorylase_Activation", + "repository_type": "biomodels", + "summary": "

The model reproduces the temporal evolution of Glycogen phosphorylase for a vale of Vm5=30 as depicted in Fig 1a of the paper. The model makes use of calcium oscillations from the Borghans model to stimulate the activation of glycogen phosphorylase. Hence, this is a simple extension of the Borghans model. The model was succesfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3156, + "tag": "BioModels:BIOMD0000000100" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3064, + "tag": "Inositol phosphate-mediated signaling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:48.447188+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000100", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2392": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2392, + "name": "Vilar2006_TGFbeta", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 5A of the paper. The ligand concentration is increased from 3E-5 to 0.01 at time t=2500 to ensure that the system reaches steady state. Hence, the time t=0 of the paper corresponds to t=2500 in the model. The peak value of the active ligand receptor complex is off by a value of 1.25, the authors have stated that this discrepancy is due to the fact that the figure in the paper corresponds to a slightly different parameter set. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3157, + "tag": "BioModels:BIOMD0000000101" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3158, + "tag": "Transforming growth factor beta receptor signaling pathway" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:49.114022+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000101", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2393": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2393, + "name": "Legewie2006_apoptosis_WT", + "repository_type": "biomodels", + "summary": "

The model reproduces active Caspase-3 time profile corresponding to the total Apaf-1 value of 20 nM as depicted in Fig 2-A . The model was successfully tested on MathSBML.

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3160, + "tag": "BioModels:BIOMD0000000102" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:49.693008+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000102", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2394": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2394, + "name": "Legewie2006_apoptosis_NC", + "repository_type": "biomodels", + "summary": "

This model represents the non-competitive binding of XIAP to Casapase-3 and Caspase-9. In other words, XIAP mediated feedback is abolished in this model. The authors state that this leads to bistable-reversible behaviour as depicted in Fig 4C. The wild-type model displays a bistable-irreversible profile. This shows that irreversibility requires XIAP mediated feedback. The model was tested on MathSBML. However, please note that the paper does not contain any figure that corresponds to simulation of the Non-Competitive model.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3161, + "tag": "BioModels:BIOMD0000000103" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:50.124817+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000103", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2395": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2395, + "name": "Klipp2002_MetabolicOptimization_linearPathway(n=2)", + "repository_type": "biomodels", + "summary": "
Klipp2002_MetabolicOptimization_linearPathway(n=2)

The model describes time dependent gene expression as a means to enable cells to adapt metabolic activity optimally based on environmental conditions. It uses a simple unbranched pathway and a constraint of fixed total enzyme. It calculates enzyme profiles at different times which optimise a performance function, and compares them to experimental data. The initial model is cell-type agnostic, while the experimeental data is from yeast.

This model is described in the article:

Klipp E, Heinrich R, Holzh\u00fctter HG.
Eur. J. Biochem. 2002 Nov; 269(22): 5406-5413

Abstract:

A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time- dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.

This model is from the paper Prediction of temporal gene expression metabolic optimization by re-distribution of enzyme activities. The model describes optimal enzyme profiles and metabolite time courses for a simple linear metabolic pathway (n=2). Figure 1 was reproduced using roadRunner. The values of k1 and k2 were not explicitly stated in the publication, but calculations were performed for equal catalytic efficiencies of the enzymes (ki=k), hence the curator assigned k1=k2=1. Also enzyme concentrations are given in units of Etot; times are given in units of 1/(k*Etot) in the papaer, for simplicity , we use defalut units of the SBML to present the concentration and time.

This model is hosted on BioModels Database and identifiedby: MODEL4931762955 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3162, + "tag": "BioModels:BIOMD0000000104" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:50.561737+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000104", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2396": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2396, + "name": "Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system", + "repository_type": "biomodels", + "summary": "
Proctor2007 - Age related decline of proteolysis, ubiquitin-proteome system

This is a stochastic model of the ubiquitin-proteasome system for a generic pool of native proteins (NatP), which have a half-life of about 10 hours under normal conditions. It is assumed that these proteins are only degraded after they have lost their native structure due to a damage event. This is represented in the model by the misfolding reaction which depends on the level of reactive oxygen species (ROS) in the cell. Misfolded proteins (MisP) are first bound by an E3 ubiquitin ligase. Ubiquitin (Ub) is activated by E1 (ubiquitin-activating enzyme) and then passed to E2 (ubiquitin-conjugating enzyme). The E2 enzyme then passes the ubiquitin molecule to the E3/MisP complex with the net effect that the misfolded protein is monoubiquitinated and both E2 and E3 are released. Further ubiquitin molecules are added in a step-wise manner. When the chain of ubiquitin molecules is of length 4 or more, the polyubiquitinated misfolded protein may bind to the proteasome. The model also includes de-ubiquitinating enzymes (DUB) which cleave ubiquitin molecules from the chain in a step-wise manner. They work on chains attached to misfolded proteins both unbound and bound to the proteasomes. Misfolded proteins bound to the proteasome may be degraded releasing ubiquitin. Misfolded proteins including ubiquitinated proteins may also aggregate. Aggregates (AggP) may be sequestered (Seq_AggP) which takes them out of harm's way or they may bind to the proteasome (AggP_Proteasome). Proteasomes bound by aggregates are no longer available for protein degradation.

Figure 2 and Figure 3 has been simulated using Gillespie2.

This model is described in the article:

Proctor CJ, Tsirigotis M, Gray DA.
BMC Syst Biol 2007; 1: 17

Abstract:

BACKGROUND: The ubiquitin-proteasome system is responsible for homeostatic degradation of intact protein substrates as well as the elimination of damaged or misfolded proteins that might otherwise aggregate. During ageing there is a decline in proteasome activity and an increase in aggregated proteins. Many neurodegenerative diseases are characterised by the presence of distinctive ubiquitin-positive inclusion bodies in affected regions of the brain. These inclusions consist of insoluble, unfolded, ubiquitinated polypeptides that fail to be targeted and degraded by the proteasome. We are using a systems biology approach to try and determine the primary event in the decline in proteolytic capacity with age and whether there is in fact a vicious cycle of inhibition, with accumulating aggregates further inhibiting proteolysis, prompting accumulation of aggregates and so on. A stochastic model of the ubiquitin-proteasome system has been developed using the Systems Biology Mark-up Language (SBML). Simulations are carried out on the BASIS (Biology of Ageing e-Science Integration and Simulation) system and the model output is compared to experimental data wherein levels of ubiquitin and ubiquitinated substrates are monitored in cultured cells under various conditions. The model can be used to predict the effects of different experimental procedures such as inhibition of the proteasome or shutting down the enzyme cascade responsible for ubiquitin conjugation. RESULTS: The model output shows good agreement with experimental data under a number of different conditions. However, our model predicts that monomeric ubiquitin pools are always depleted under conditions of proteasome inhibition, whereas experimental data show that monomeric pools were depleted in IMR-90 cells but not in ts20 cells, suggesting that cell lines vary in their ability to replenish ubiquitin pools and there is the need to incorporate ubiquitin turnover into the model. Sensitivity analysis of the model revealed which parameters have an important effect on protein turnover and aggregation kinetics. CONCLUSION: We have developed a model of the ubiquitin-proteasome system using an iterative approach of model building and validation against experimental data. Using SBML to encode the model ensures that it can be easily modified and extended as more data become available. Important aspects to be included in subsequent models are details of ubiquitin turnover, models of autophagy, the inclusion of a pool of short-lived proteins and further details of the aggregation process.

This model is hosted on BioModels Database and identified by: BIOMD0000000105.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3164, + "tag": "BioModels:BIOMD0000000105" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 3166, + "tag": "Proteasome-mediated ubiquitin-dependent protein catabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4591, + "tag": "Neurodegenerative disease" + } + ], + "timestamp_created": "2025-01-29 11:34:51.028968+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000105", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2397": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2397, + "name": "Yang2007_ArachidonicAcid", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Dynamic Simulation on the Arachidonic Acid Metabolic Network . Figure 2A has been reproduced by SBML ode solver on line. In the original model, all the reactions are presented as ODE directly. So curator rewrite each reaction according to the semantics of the paper. In this paper, the authors used quict complex kinetics law to describe the catalysis in the network, curators did not necessarily know all the complete meanings of the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3167, + "tag": "Arachidonic acid metabolic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3168, + "tag": "BioModels:BIOMD0000000106" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:52.305917+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000106", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2398": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2398, + "name": "Novak1993 - Cell cycle M-phase control", + "repository_type": "biomodels", + "summary": "
Novak1993 - Cell cycle M-phase control
The model reproduces Figure 9 of the paper. Please note that active MPF and cyclin concentrations in the paper are given relative to total cdc2 concentration (100nM). Active MPF (dimer_p) is the cyclin-cdc2 complex that is phosphorylated at Thr161. The earlier versions of the model was successfully tested on MathSBML and Jarnac, and the current version was checked in Copasi.

This model is described in the article:

Novak B, Tyson JJ.
J. Cell. Sci. 1993 Dec; 106 ( Pt 4): 1153-1168

Abstract:

To contribute to a deeper understanding of M-phase control in eukaryotic cells, we have constructed a model based on the biochemistry of M-phase promoting factor (MPF) in Xenopus oocyte extracts, where there is evidence for two positive feedback loops (MPF stimulates its own production by activating Cdc25 and inhibiting Wee1) and a negative feedback loop (MPF stimulates its own destruction by indirectly activating the ubiquitin pathway that degrades its cyclin subunit). To uncover the full dynamical possibilities of the control system, we translate the regulatory network into a set of differential equations and study these equations by graphical techniques and computer simulation. The positive feedback loops in the model account for thresholds and time lags in cyclin-induced and MPF-induced activation of MPF, and the model can be fitted quantitatively to these experimental observations. The negative feedback loop is consistent with observed time lags in MPF-induced cyclin degradation. Furthermore, our model indicates that there are two possible mechanisms for autonomous oscillations. One is driven by the positive feedback loops, resulting in phosphorylation and abrupt dephosphorylation of the Cdc2 subunit at an inhibitory tyrosine residue. These oscillations are typical of oocyte extracts. The other type is driven by the negative feedback loop, involving rapid cyclin turnover and negligible phosphorylation of the tyrosine residue of Cdc2. The early mitotic cycles of intact embryos exhibit such characteristics. In addition, by assuming that unreplicated DNA interferes with M-phase initiation by activating the phosphatases that oppose MPF in the positive feedback loops, we can simulate the effect of addition of sperm nuclei to oocyte extracts, and the lengthening of cycle times at the mid-blastula transition of intact embryos.

This model is hosted on BioModels Database and identified by: BIOMD0000000107.

To cite BioModels Database, please use: BioModels: ten-year anniversary .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2975, + "tag": "Amphibia" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3169, + "tag": "BioModels:BIOMD0000000107" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:53.454655+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000107", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2399": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2399, + "name": "Kowald2006_SOD", + "repository_type": "biomodels", + "summary": "

This model is according to the paper from Axel Kowald Alternative pathways as mechanism for the negative effects associated with overexpression of superoxide dismutase.

Reactions from 1 to 17 are listed in the paper, note that for clarity species whose concentrations are assumed to be constant (e.g.water, oxygen,protons, metal ions) are omitted from the diagram. In the paper, v16 is a fast reaction, but we do not use fast reaction in the model.

Figure2 has been reproduced by both SBMLodeSolver and Copasi4.0.20(development) . Figure 3 has been obtained with Copasi4.0.20(development) using parameter scan.

The steady-state of [LOO*] a little bit lower than showed on the paper, I guess it may be the simulation method used in the paper use fast reaction and also the reaction (5) listed on Page 831 on the paper is slightly different from equation (2) on Page 832. The rest of them are the quite the same.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3170, + "tag": "BioModels:BIOMD0000000108" + }, + { + "id": 3171, + "tag": "Regulation of superoxide metabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3172, + "tag": "Superoxide dismutase activity" + }, + { + "id": 3042, + "tag": "cellular organisms" + }, + { + "id": 4592, + "tag": "Down syndrome" + } + ], + "timestamp_created": "2025-01-29 11:34:53.897701+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000108", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2400": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2400, + "name": "Haberichter2007_cellcycle", + "repository_type": "biomodels", + "summary": "

This model is according to the paper A systems biology dynamical model of mammalian G1 cell cycle progression. Supplementary Figure 2A has been reproduced by the MathSBML and CellDesigner. All the data of this model are from the set 2 of Supplementary talbe2.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3173, + "tag": "BioModels:BIOMD0000000109" + }, + { + "id": 3174, + "tag": "G1 phase" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:54.411119+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000109", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2401": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2401, + "name": "Qu2003_CellCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Dynamics of the cell cycle: checkpoints, sizers, and timers.
\t Qu Z, MacLellan WR, Weiss JN Biophys. J.2003 Dec; 85(6): 3600-11 14645053,
Abstract:
We have developed a generic mathematical model of a cell cycle signaling network in higher eukaryotes that can be used to simulate both the G1/S and G2/M transitions. In our model, the positive feedback facilitated by CDC25 and wee1 causes bistability in cyclin-dependent kinase activity, whereas the negative feedback facilitated by SKP2 or anaphase-promoting-complex turns this bistable behavior into limit cycle behavior. The cell cycle checkpoint is a Hopf bifurcation point. These behaviors are coordinated by growth and division to maintain normal cell cycle and size homeostasis. This model successfully reproduces sizer, timer, and the restriction point features of the eukaryotic cell cycle, in addition to other experimental findings.


Figure6B has been reproduced by both SBMLodeSolver online and MathSBML. We do not include the synthesis of cyclins is proportional to cell size (Equation 2 in Page3604 of the paper) in this model. The author of the paper keep all the variables and parameters dimensionless. But in the model, we choose to use default units of SBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3175, + "tag": "BioModels:BIOMD0000000110" + }, + { + "id": 3176, + "tag": "Cell cycle G1/S phase transition" + }, + { + "id": 3177, + "tag": "Cell cycle G2/M phase transition" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:54.868090+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000110", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2402": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2402, + "name": "Novak2001_FissionYeast_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time evolution of several species as depicted in Fig 4 of the paper. Events have been used to reset cell mass when the value of M-phase promoting factor (MPF) decreases through 0.1. The model was successfully tested on Cell Designer.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3178, + "tag": "BioModels:BIOMD0000000111" + }, + { + "id": 3179, + "tag": "Cell cycle - yeast - Schizosaccharomyces pombe (fission yeast)" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3180, + "tag": "Schizosaccharomycetaceae" + } + ], + "timestamp_created": "2025-01-29 11:34:55.310763+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000111", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2403": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2403, + "name": "Clarke2006_Smad_signalling", + "repository_type": "biomodels", + "summary": "

The model reproduces the temporal evolution of four variables depicted in Fig 2a. The solution is generated for median parameter values as given in Table 3. Result shown was generated by MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3181, + "tag": "BioModels:BIOMD0000000112" + }, + { + "id": 3182, + "tag": "Neovison vison" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:55.791706+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000112", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2404": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2404, + "name": "Dupont1992_Ca_dpt_protein_phospho", + "repository_type": "biomodels", + "summary": "

Model reproduces Fig 4 of the paper. For fraction of phosphorylated protein, W_star, the model reproduces panel b in the same figure. Model successfully tested on MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3183, + "tag": "BioModels:BIOMD0000000113" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:56.356776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000113", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2405": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2405, + "name": "Somogyi1990_CaOscillations", + "repository_type": "biomodels", + "summary": "

This model encoded according to the paper Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simple One Pool Model. The values of parameters a and alpha are varioused inorder to simulate results in different situations. For Figure 3A, a=3.5,alpha=1.2 ; Figure 3B, a=3,alpha=5 ; Figure 3C a= 0.95, alpha=1.5; Figure3D, a=1, alpha=5. Keep in mind that the value for the xy axies are arbitrary value. Figures3 in the paper are reproduced by COPASI 4.0.20(development) , and SBMLodeSolver online.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3184, + "tag": "BioModels:BIOMD0000000114" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3185, + "tag": "Hormone-mediated signaling pathway" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 3186, + "tag": "Regulation of calcium ion transport" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:56.808047+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000114", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2406": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2406, + "name": "Somogyi1990_CaOscillations_SingleCaSpike", + "repository_type": "biomodels", + "summary": "

Another model from Hormone induced Calcium Oscillations in Liver Cells Can Be Explained by a Simply One Pool Model. Anatomy of a single Ca2+ spike. Figure4A has been simulated by COPASI4.0.20(development). However, the simulated figure is slightly different from the paper, single spike of Ca2+ is around \"6\" time arbitrary units instead \"9\" time arbitrary units displayed in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3187, + "tag": "BioModels:BIOMD0000000115" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 3185, + "tag": "Hormone-mediated signaling pathway" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 3186, + "tag": "Regulation of calcium ion transport" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:34:57.255476+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000115", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2407": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2407, + "name": "McClean2007_CrossTalk", + "repository_type": "biomodels", + "summary": "

This model encoded according to the paper Cross-talk and decision making in MAP kinase pathways. Supplementary Figure 2 has been reproduced by COPASI4.0.20 (development) using parameter scan method. You probably need to uncheck \"always use initial conditions\" in copasi when you simulate for the second run in order to get the figure. S1 scale from 0 to 12. Keep in mind that the y axis is the fractions of excited X3 and Y3, meaning that X3P and Y3P are normalized by total concentration X3T and Y3T.

The results from modeling the pathway in Supplementary Figure1a, including both activation and inhibition. According to the paper, the value of ka and kd should in the orange region (ka belongs [0,1], kd belongs [1,10]) so assigned ka=0, kd=1.

The author made the simplifying assumption that the interactions between the pathways are symmetric. Thus the k12xy=k12yx=ka, k33xy=k33yx=kd.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3188, + "tag": "BioModels:BIOMD0000000116" + }, + { + "id": 3189, + "tag": "MAPK cascade involved in osmosensory signaling pathway" + }, + { + "id": 3038, + "tag": "Response to pheromone" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-29 11:34:57.688278+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000116", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2408": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2408, + "name": "Dupont1991_CaOscillation", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Signal-induced Ca2+ oscillations: Properties of a model based on Ca2+-induced Ca2+ release. Figure4B in the paper has been reproduced by RoadRunner and MathSBML. Damped Ca2+ oscillations elicited by a transient pulse of InsP3 applied intracellularly to a resting, non-oscillatory cell.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3190, + "tag": "BioModels:BIOMD0000000117" + }, + { + "id": 3191, + "tag": "Calcium-induced calcium release activity" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:34:58.125451+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000117", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2409": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2409, + "name": "Golomb2006_SomaticBursting", + "repository_type": "biomodels", + "summary": "

Model is according to the paper Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cell: Combined Experimental. Figure6Da has been reproduced by MathSBML. The original model from ModelDB. http://senselab.med.yale.edu/modeldb/


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3192, + "tag": "BioModels:BIOMD0000000118" + }, + { + "id": 3018, + "tag": "Neuronal action potential propagation" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3019, + "tag": "Voltage-gated potassium channel activity" + }, + { + "id": 3020, + "tag": "Voltage-gated sodium channel activity" + } + ], + "timestamp_created": "2025-01-29 11:34:58.564260+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000118", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2410": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2410, + "name": "Golomb2006_SomaticBursting_nonzero[Ca]", + "repository_type": "biomodels", + "summary": "

Model is according to the paper Contribution of Persistent Na+ Current and M-Type K+ Current to Somatic Bursting in CA1 Pyramidal Cell: Combined Experimental. This is the second model from this paper for the non-zero [Ca2+] initial value, parameters and the kinetics quations from Table2 in the paper. Figure9Aa has been reproduced by MathSBML. The original model from ModelDB. http://senselab.med.yale.edu/modeldb/


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3193, + "tag": "BioModels:BIOMD0000000119" + }, + { + "id": 3018, + "tag": "Neuronal action potential propagation" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3019, + "tag": "Voltage-gated potassium channel activity" + }, + { + "id": 3020, + "tag": "Voltage-gated sodium channel activity" + } + ], + "timestamp_created": "2025-01-29 11:34:59.024652+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000119", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2411": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2411, + "name": "Chan2004_TCell_receptor_activation", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 3a of the paper. Please note that the authors mention that they used a value of 2 for n, n being the power in the positive feedback function for kinase autocatalysis, however the model here has n=1.95 because this results in a simulation that is identical to Fig 3a. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3194, + "tag": "BioModels:BIOMD0000000120" + }, + { + "id": 3195, + "tag": "Regulation of T cell receptor signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3196, + "tag": "T cell receptor signaling pathway - Homo sapiens (human)" + }, + { + "id": 3042, + "tag": "cellular organisms" + }, + { + "id": 3810, + "tag": "Bacterial infectious disease" + } + ], + "timestamp_created": "2025-01-29 11:34:59.921930+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000120", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2412": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2412, + "name": "Clancy2001_Kchannel", + "repository_type": "biomodels", + "summary": "

This model is according to the paper Cellular consequences of HEGR mutations in the long QT syndrome: precursors to sudden cardiac death. The author used Markovian model of cardiac Ikr in the paper. Figure4B in the paper has been reproduced using CellDesigner3.5.1. The cell is depolarized to the indicated test potential for 250ms (from 50ms to 300ms) from a holding potential of -40mV and then repolarized to -40mV. Change the value for vtest from -30,-20,-10,0,10,20,30,40 for each simulation in order to produce the different cureve in the paper.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3197, + "tag": "BioModels:BIOMD0000000121" + }, + { + "id": 3198, + "tag": "Cavia porcellus" + }, + { + "id": 3199, + "tag": "Delayed rectifier potassium channel activity" + }, + { + "id": 3200, + "tag": "Heart process" + }, + { + "id": 3201, + "tag": "Regulation of potassium ion transport" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4593, + "tag": "Andersen-Tawil syndrome" + } + ], + "timestamp_created": "2025-01-29 11:35:00.364487+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000121", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2413": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2413, + "name": "Fisher2006_Ca_Oscillation_dpdnt_NFAT_dynamics", + "repository_type": "biomodels", + "summary": "

The model reproduces the calcium oscillation dependent activation-deactivation kinetics of nuclear factor of activated T cells (NFAT) as depicted in Fig 4a of the paper. A simple algorithm in the events section takes care of the calcium oscillation. The model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3202, + "tag": "BioModels:BIOMD0000000122" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3203, + "tag": "Regulation of T cell mediated immunity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:00.818035+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000122", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2414": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2414, + "name": "Fisher2006_NFAT_Activation", + "repository_type": "biomodels", + "summary": "

The model reproduces the kinetics of the nuclear factor of activated cells (NFAT) as depicted in Figure 3a of the paper. Model was successfully tested on Jarnac and MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3204, + "tag": "BioModels:BIOMD0000000123" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3203, + "tag": "Regulation of T cell mediated immunity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:01.370849+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000123", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2415": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2415, + "name": "Wu2006_K+Channel", + "repository_type": "biomodels", + "summary": "

The model is described in the paper by Wu and Chang (2006). Diethyl pyrocarbonate, a histidine-modifying agent, directly stimulates activity of ATP-sensitive potassium channels in pituitary GH3 cells. Biochem Pharmacol. 71(5): 615-23.

The unit of time is ms, and the simulation time is 80 s, that is 8e4 ms. Therfore, you probably need to increase the maximum steps for your simulator.

The figure 7 has been reproduced by MathSBML. Application of DEPC as indicated at horizontal bar was mimicked by an increase of maximal conductance of Katp-channels from 500 to 530 ps at t=30 s.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3205, + "tag": "ATP-activated inward rectifier potassium channel activity" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3206, + "tag": "BioModels:BIOMD0000000124" + }, + { + "id": 3207, + "tag": "Histone modification" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:01.936725+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000124", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2416": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2416, + "name": "Komarova2005_TheoreticalFramework_BasicArchitecture", + "repository_type": "biomodels", + "summary": "

This model according to the paper A Theoretical Framework for Specificity in Cell Signalling The model is \"basic architecture\" of Figure2A. Figure2B, Figure2C have been reproduced by MathSBML. The reproduced figures are slightly different from the original ones in the paper, the peak of [x2] is higher than 1 and is not decreasing dramatically when [x0]=0. And I think maybe the author shift the or scale the curves.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3208, + "tag": "BioModels:BIOMD0000000125" + }, + { + "id": 2971, + "tag": "Cell surface receptor signaling pathway" + }, + { + "id": 3209, + "tag": "Cellular response to stimulus" + }, + { + "id": 2982, + "tag": "Opisthokonta" + }, + { + "id": 3210, + "tag": "Receptor signaling complex scaffold activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:02.459051+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000125", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2417": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2417, + "name": "Clancy2002_CardiacSodiumChannel_WT", + "repository_type": "biomodels", + "summary": "

The model is according to the paper Na+ Channel Mutation That Causes Both Brugada and Long-QT Syndrome Phenotypes: A Simulation Study of Mechanism Original model comes from ModelDB with accession number: 62661. This is the wide type model. All the values and reactions obtained from Data Supplement6: Appendix of the paper. Figure3 has been reproduced by MathSBML. The stimulus v=-30mV during the time from 5ms to 20 ms displayed in the event. The meaning for the keyword, C: Close states; O: Open states; IF: Fast inactivation states; IC: Closed-Inactivation states; IM: Intermediat Inactivation states.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3211, + "tag": "BioModels:BIOMD0000000126" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3200, + "tag": "Heart process" + }, + { + "id": 3213, + "tag": "Regulation of sodium ion transport" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3214, + "tag": "Sodium channel activity" + }, + { + "id": 4594, + "tag": "Brugada syndrome" + } + ], + "timestamp_created": "2025-01-29 11:35:02.962812+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000126", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2418": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2418, + "name": "Izhikevich2003_SpikingNeuron", + "repository_type": "biomodels", + "summary": "

The model is according to the paper Simple Model of Spiking Neurons In this paper, a simple spiking model is presented that is as biologically plausible as the Hodgkin-Huxley model, yet as computationally efficient as the integrate-and-fire model. Known types of neurons correspond to different values of the parameters a,b,c,d in the model. Figure2RS,IB,CH,FS,LTS have been simulated by MathSBML.

RS: a=0.02, b=0.2, c=-65, d=8.

IB: a=0.02,b=0.2,c=-55,d=4

CH: a=0.02,b=0.2,c=-50,d=2

FS:a=0.1b=0.2c=-65,d=2

LTS:a=0.02,b=0.25,c=-65,d=2


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3215, + "tag": "Action potential" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3216, + "tag": "BioModels:BIOMD0000000127" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 3217, + "tag": "Regulation of membrane potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:03.469963+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000127", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2419": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2419, + "name": "Bertram2006_Endothelin", + "repository_type": "biomodels", + "summary": "

The model is according to the paper Endothelin Action on Pituitary Lactotrophs: One Receptor, Many GTP-Binding Proteins Figure 1 has been simulated by MathSBML. The figure for the [Ca2+]i and [Ca2+]ER have been normalized in the paper.Original model comes from http://www.math.fsu.edu/~bertram/software/pituitary

The units for parameters and species are varied from one to another, so I omit the unit definition here . Conductances in pS; currents in fA; Ca concentrations in uM; time in ms


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3218, + "tag": "Adenylate cyclase-modulating G-protein coupled receptor signaling pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3219, + "tag": "BioModels:BIOMD0000000128" + }, + { + "id": 3220, + "tag": "Endothelin receptor activity" + }, + { + "id": 3221, + "tag": "G-protein coupled receptor binding" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:03.931479+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000128", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2420": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2420, + "name": "Izhikevich2004_SpikingNeurons_inhibitionInducedSpiking", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(S) inhibition-induced spiking has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=-0.02; b=-1; c=-60; d=8; V=-63.8; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3222, + "tag": "BioModels:BIOMD0000000129" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:04.374776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000129", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2421": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2421, + "name": "Izhikevich2004_SpikingNeurons_integrator", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich, EM Neural Networks, IEEE Transactions on2004:15(5):1063-1070 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(L) integrator has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.02; b=-0.1; c=-55; d=6; V=-60; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3223, + "tag": "BioModels:BIOMD0000000130" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:04.831715+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000130", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2422": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2422, + "name": "Izhikevich2004_SpikingNeurons_reboundBurst", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(N) rebound burst has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.03; b=0.25; c=-52; d=0;V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3224, + "tag": "BioModels:BIOMD0000000131" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:05.276413+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000131", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2423": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2423, + "name": "Izhikevich2004_SpikingNeurons_reboundSpike", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(M) rebound spike has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.03; b=0.25; c=-60; d=4; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3225, + "tag": "BioModels:BIOMD0000000132" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:05.729711+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000132", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2424": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2424, + "name": "Izhikevich2004_SpikingNeurons_resonator", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw. 2004 Sep;15(5):1063-70. 15484883 ,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paper Which Model to Use for Cortical Spiking Neurons? Figure1(K) resonator has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking Neurons The original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. a=0.1; b=0.26; c=-60; d=-1; V=-62; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3226, + "tag": "BioModels:BIOMD0000000133" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:06.183430+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000133", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2425": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2425, + "name": "Izhikevich2004_SpikingNeurons_SpikeLatency", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(I) spike latency has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events. In this model a=0.02; b=0.2; c=-65; d=6; V=-70; u=b*V=0.2*(-70);

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3227, + "tag": "BioModels:BIOMD0000000134" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:06.805609+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000134", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2426": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2426, + "name": "Izhikevich2004_SpikingNeurons_subthresholdOscillations", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(J) subthreshold oscillations has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.05; b=0.26; c=-60; d=0; V=-62; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3228, + "tag": "BioModels:BIOMD0000000135" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:07.325752+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000135", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2427": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2427, + "name": "Sedaghat2002_InsulinSignalling_noFeedback", + "repository_type": "biomodels", + "summary": "

Model reproduces the various plots in Figure 6 and 7 of the paper. It was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3230, + "tag": "BioModels:BIOMD0000000137" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-29 11:35:08.397733+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000137", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2428": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2428, + "name": "Tabak2007_dopamine", + "repository_type": "biomodels", + "summary": "

The model is encoded according to the paper Low dose of dopamine may stimulate prolactin secretion by increasing fast potassium currents Figure5 has been reproduced by MathSBML. One need to change the value of ga in order to get the three correct results.

the xppaut file of the model is avaiable on the following address offered by the author , http://www.math.fsu.edu/%7Ebertram/software/pituitary/JCNS_07.ode


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3231, + "tag": "BioModels:BIOMD0000000138" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3232, + "tag": "Dopamine secretion" + }, + { + "id": 3233, + "tag": "Potassium channel activity" + }, + { + "id": 3234, + "tag": "Regulation of prolactin signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:08.866794+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000138", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2429": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2429, + "name": "Hoffmann2002_KnockOut_IkBNFkB_Signaling", + "repository_type": "biomodels", + "summary": "

The model corresponds to the knock out model of beta-/-, epsilon -/- and reproduces the upper panel in Fig 2C. In order to reproduce the other knock out models the transcription rate of the species that are not present must be set to zero and the rate of the one that is present must be set as seven times its corresponding value for the wild type model. This is done so as to compensate for the loss of other isoforms. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3235, + "tag": "BioModels:BIOMD0000000139" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:09.297799+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000139", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2430": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2430, + "name": "Hoffmann2002_WT_IkBNFkB_Signaling", + "repository_type": "biomodels", + "summary": "

This model corresponds to the IkB-NFkB signaling in wild type cells and reproduces the dynamics of the species as depicted in Figure 2 F of the paper. The authors mention that the simulation is carried out in three phases, where the steady state values of the species in one phase are fed to the succeding phase. This model captures the simulation dynamics of two phases and makes use of the event section to introduce the stimulus and thereby transition to the next phase. Accordingly, a few terms have been introduced that make this transition possible, this in no way compromises the original model. Also, the simulation plots are not an exact reproduction of the figures in the paper, they do however match the simulation results that the authors shared with us. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3236, + "tag": "BioModels:BIOMD0000000140" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:09.751584+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000140", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2431": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2431, + "name": "Izhikevich2004_SpikingNeurons_Class1Excitable", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(G) Class 1 excitable has been reproduced by MathSBML. The ODE and the parameters values are originally taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.02; b=-0.1; c=-55; d=6; V=-60; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3237, + "tag": "BioModels:BIOMD0000000141" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:10.279283+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000141", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2432": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2432, + "name": "Izhikevich2004_SpikingNeurons_Class2Excitable", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Which model to use for cortical spiking neurons?
Izhikevich EM. IEEE Trans Neural Netw.2004 Sep;15(5):1063-70. 15484883,
Abstract:
We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.

The model is according to the paperWhich Model to Use for Cortical Spiking Neurons? Figure1(H) Class 2 excitable has been reproduced by MathSBML. The ODE and the parameters values are taken from the a paper Simple Model of Spiking NeuronsThe original format of the models are encoded in the MATLAB format existed in the ModelDB with Accession number 39948

Figure1 are the simulation results of the same model with different choices of parameters and different stimulus function or events.a=0.2; b=0.26; c=-65; d=0; V=-64; u=b*V;

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3238, + "tag": "BioModels:BIOMD0000000142" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 460, + "tag": "Neuronal action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:10.710663+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000142", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2433": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2433, + "name": "Olsen2003_neutrophil_oscillatory_metabolism", + "repository_type": "biomodels", + "summary": "
Olsen2003_neutrophil_oscillatory_metabolism

This model is described in the article:

Olsen LF, Kummer U, Kindzelskii AL, Petty HR.
Biophys. J. 2003 Jan; 84(1): 69-81

Abstract:

We present a two-compartment model to explain the oscillatory behavior observed experimentally in activated neutrophils. Our model is based mainly on the peroxidase-oxidase reaction catalyzed by myeloperoxidase with melatonin as a cofactor and NADPH oxidase, a major protein in the phagosome membrane of the leukocyte. The model predicts that after activation of a neutrophil, an increase in the activity of the hexose monophosphate shunt and the delivery of myeloperoxidase into the phagosome results in oscillations in oxygen and NAD(P)H concentration. The period of oscillation changes from >200 s to 10-30 s. The model is consistent with previously reported oscillations in cell metabolism and oxidant production. Key features and predictions of the model were confirmed experimentally. The requirement of the hexose monophosphate pathway for 10 s oscillations was verified using 6-aminonicotinamide and dexamethasone, which are inhibitors of glucose-6-phosphate dehydrogenase. The role of the NADPH oxidase in promoting oscillations was confirmed by dose-response studies of the effect of diphenylene iodonium, an inhibitor of the NADPH oxidase. Moreover, the model predicted an increase in the amplitude of NADPH oscillations in the presence of melatonin, which was confirmed experimentally. Successful computer modeling of complex chemical dynamics within cells and their chemical perturbation will enhance our ability to identify new antiinflammatory compounds.

This model is hosted on BioModels Database and identified by: BIOMD0000000143.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3239, + "tag": "BioModels:BIOMD0000000143" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3240, + "tag": "Melatonin metabolic process" + }, + { + "id": 3070, + "tag": "NADH peroxidase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:11.172215+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000143", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2434": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2434, + "name": "Calzone2007_CellCycle", + "repository_type": "biomodels", + "summary": "

This is the Dynamical model of nuclear division cycles during early embryogenesis of Drosophila, without StringT regulation. so ksstg=kdstg=0. Figure1B has been simulated by MathSBML. Curator changed model from only one compartment into two compartments according to the paper. Detail explaination of the models are in the supplement information of the paper.The author didn't specify which compartment Xm, Stgm, Xp are located, we assume that they locate in cytoplasm.

Some of the parameter values for the equations are dimensionless parameters.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3241, + "tag": "BioModels:BIOMD0000000144" + }, + { + "id": 132, + "tag": "Drosophila" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 3242, + "tag": "Regulation of nuclear division" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:11.633405+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000144", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2435": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2435, + "name": "Wang2007 - ATP induced intracellular Calcium Oscillation", + "repository_type": "biomodels", + "summary": "
Wang2007 - ATP induced intracellular Calicum Oscillation

The model simulate the ATP-induced intracellular Ca2+ oscillations and the quantitative effect of ATP concentration on the oscillation characteristics such as the duration, peak concentration of intracellular Ca2+ and average interval.

This model is described in the article:

Wang J, Huang X, Huang W.
J. Theor. Biol. 2007 Apr; 245(3): 510-519

Abstract:

A quantitative kinetic model is proposed to simulate the ATP-induced intracellular Ca(2+) oscillations. The quantitative effect of ATP concentration upon the oscillations was successfully simulated. Our simulation results support previous experimental explanations that the Ca(2+) oscillations are mainly due to interaction of Ca(2+) release from the endoplasmic reticulum (ER) and the ATP-dependent Ca(2+) pump back into the ER, and the oscillations are prolonged by extracellular Ca(2+) entry that maintains the constant Ca(2+) supplies to its intracellular stores. The model is also able to simulate the sudden disappearance phenomenon of the Ca(2+) oscillations observed in some cell types by taking into account of the biphasic characteristic of the Ca(2+) release from the endoplasmic reticulum (ER). Moreover, the model simulation results for the Ca(2+) oscillations characteristics such as duration, peak [Ca(2+)](cyt), and average interval, etc., lead to prediction of some possible factors responsible for the variations of Ca(2+) oscillations in different types of cells.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000145.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3243, + "tag": "BioModels:BIOMD0000000145" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-29 11:35:12.112958+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000145", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2436": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2436, + "name": "Hatakeyama2003_MAPK", + "repository_type": "biomodels", + "summary": "

Figure4 and Figure5 can be simulated by Copasi. Figure4 can be simulated in MathSBML as well. There are some typos in the paper:K29=234, is it should k_29; Table2, reaction17, is there are \"slash\" missing in between the rate equation; reaction 33,\"Akt-PI-PP\" in the last term of denominator instead of \"AktPI-P\" . For plotting figure4, we create another extra parameter *_percent, and use assignment rule calculate percentage of each species.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3244, + "tag": "BioModels:BIOMD0000000146" + }, + { + "id": 3245, + "tag": "Cricetinae" + }, + { + "id": 3246, + "tag": "ERBB signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-29 11:35:12.631756+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000146", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2437": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2437, + "name": "ODea2007_IkappaB", + "repository_type": "biomodels", + "summary": "

O'Dea, E.L., Barken, D., Peralta, R.Q., Tran K.T., Werner, S.L., Kearns, J.D., Levchenko, A., Hoffmann, A. A homeostatic model of IkB metabolism to control constitutive activity. Molecular Systems Biology, 3:111, pp. 1-7. 2007

Questions concerning the paper should be addressed to the corresponding author. Alexander Hoffmann (ahoffmann@ucsd.edu)


The original model was written and simulated within MathWorks MatLab 2006a using the ode15s (stiff/NDF) solver. It is highly recommended that those wanting to model this system use the MatLab version which we will freely provide upon request. As always, simulation results vary according to the numerical solver used.

Translation to SBML Level 2.1 was performed via reconstruction of the model within MathWorks SimBiology Desktop (version 2.1) followed by an Export to SBML. Please address questions about this SBML model to Jeff Kearns (jkearns@ucsd.edu).

BioModels DB curation: The model reproduces the values of diffferent species depicted in Fig 3A and 3B (wt) of the paper corresponding to Model1.1. To depict the the total IkB alpha, beta epsilon species, three additional parameters and their corresponding assignment rules have been introduced in this model by the creator. Model succesfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.
Komarova SV, Smith RJ, Dixon SJ, Sims SM, Wahl LM Bone2003 Aug;33(2):206-15 14499354,
Abstract:
Bone remodeling occurs asynchronously at multiple sites in the adult skeleton and involves resorption by osteoclasts, followed by formation of new bone by osteoblasts. Disruptions in bone remodeling contribute to the pathogenesis of disorderssuch as osteoporosis, osteoarthritis, and Paget's disease. Interactions among cells of osteoblast and osteoclast lineages are critical in the regulation of bone remodeling. We constructed a mathematical model of autocrine and paracrine interactions among osteoblasts and osteoclasts that allowed us to calculate cell population dynamics and changes in bone mass at a discrete site of bone remodeling. Themodel predicted different modes of dynamic behavior: a single remodeling cycle in response to an external stimulus, a series of internally regulated cycles of bone remodeling, or unstable behavior similar to pathological bone remodeling in Paget's disease. Parametric analysis demonstrated that the mode of dynamic behaviorin the system depends strongly on the regulation of osteoclasts by autocrine factors, such as transforming growth factor beta. Moreover, simulations demonstratedthat nonlinear dynamics of the system may explain the differing effects of immunosuppressants on bone remodeling in vitro and in vivo. In conclusion, the mathematical model revealed that interactions among osteoblasts and osteoclasts result in complex, nonlinear system behavior, which cannot be deduced from studies of each cell type alone. The model will be useful in future studies assessing the impact of cytokines, growth factors, and potential therapies on the overall process ofremodeling in normal bone and in pathological conditions such as osteoporosis and Paget's disease.

The model reproduces Fig 2A and Fig 2B of the paper. Note that the Y-axis scale is not right, the osteoblast steadystate is approximatley 212 and not 0 as depicted in the figure. Also, there is atypo in the equation for x2_bar which has been corrected here. Model successfully tested on MathSBML.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Kim2007 - Crosstalk between Wnt and ERK pathways

Experimental studies have shown that both Wnt and the MAPK pathways are involved in the pathogenesis of various kinds of cancers (eg. colorectal cancer). The crosstalk between the two pathways have also been identified. Here, Kim et al., (2007) have integrated the experimental evidences on crosstalk mechanisms between the two pathways into a pathway model, and have identified the existence of a hidden positive feedback loop and suggest that this positive feedback loop might participate in the pathogenesis of colorectal cancer.

This model is described in the article:

Kim D, Rath O, Kolch W, Cho KH.
Oncogene 2007 Jul; 26(31): 4571-4579

Abstract:

The Wnt and the extracellular signal regulated-kinase (ERK) pathways are both involved in the pathogenesis of various kinds of cancers. Recently, the existence of crosstalk between Wnt and ERK pathways was reported. Gathering all reported results, we have discovered a positive feedback loop embedded in the crosstalk between the Wnt and ERK pathways. We have developed a plausible model that represents the role of this hidden positive feedback loop in the Wnt/ERK pathway crosstalk based on the integration of experimental reports and employing established basic mathematical models of each pathway. Our analysis shows that the positive feedback loop can generate bistability in both the Wnt and ERK signaling pathways, and this prediction was further validated by experiments. In particular, using the commonly accepted assumption that mutations in signaling proteins contribute to cancerogenesis, we have found two conditions through which mutations could evoke an irreversible response leading to a sustained activation of both pathways. One condition is enhanced production of beta-catenin, the other is a reduction of the velocity of MAP kinase phosphatase(s). This enables that high activities of Wnt and ERK pathways are maintained even without a persistent extracellular signal. Thus, our study adds a novel aspect to the molecular mechanisms of carcinogenesis by showing that mutational changes in individual proteins can cause fundamental functional changes well beyond the pathway they function in by a positive feedback loop embedded in crosstalk. Thus, crosstalk between signaling pathways provides a vehicle through which mutations of individual components can affect properties of the system at a larger scale.

Figure 6 of the reference publication has been reproduced. The model as such reproduces the plots corresponding to the normal conditions. To obtain simulations under 1) beta-cataenin mutation; set V12=0.846 (two-fold of the beta-catenin synthetic rate than the normal system. i.e. 2*0.426), 2) PP2A mutation; set Vmax4=Vmax5=33.75 (three-fourths of the PP2A activity that the normal system. i.e. (3/4)*45). The simulation was performed using Copasi 4.10 (Build 55).

This model is hosted on BioModels Database and identifiedby: BIOMD0000000149 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Notes from the original DOCQS curator:
In this version of the CDK2/Cyclin A complex activation there is discrepancy in the first curve which plots the binding reaction of CDK2 and Cyclin A expressed in E. coli. With the published rate constants the simulation does not match the published graph (Fig.1B) in Morris MC. et al. J Biol Chem. 277(26):23847-53 .

Notes from BioModels DB curator:
Although the parameters are those reported in the table I for CDK2/Cyclin A, the total fluorescence follows exactly the curve reported in the paper for CDK2/Cyclin H in figure 1B. Either the plot legend or the table is wrong.

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The model reproduces Fig 2 , Fig3A and Fig 3B of the paper. The ODE for x1(gp180) and x3 (gp 130) is wrong and the authors have communicated to the curator that the species ought to have a constant value. There are a few other differences from the paper and these were made in consultation with the authors. Model was successfully tested on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces Fig 6B of the paper for model 3. The model was reproduced using XPP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3258, + "tag": "BioModels:BIOMD0000000154" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:19:04.884766+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000154", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2445": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2445, + "name": "Zatorsky2006_p53_Model6", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 6B of the paper for model 6. The model was reproduced using XPP.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3259, + "tag": "BioModels:BIOMD0000000155" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:19:05.327744+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000155", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2446": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2446, + "name": "Zatorsky2006_p53_Model5", + "repository_type": "biomodels", + "summary": "

The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 5. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3260, + "tag": "BioModels:BIOMD0000000156" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:19:05.752262+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000156", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2447": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2447, + "name": "Zatorsky2006_p53_Model4", + "repository_type": "biomodels", + "summary": "

The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 4. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces time profile of p53 and Mdm2 as depicted in Fig 6B of the paper for Model 2. Results obtained using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3262, + "tag": "BioModels:BIOMD0000000158" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:19:06.719048+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000158", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2449": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2449, + "name": "Zatorsky2006_p53_Model1", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of p53 and Mdm2 as depicted in Fig 6B of the plot for model 1. Results obtained on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3263, + "tag": "BioModels:BIOMD0000000159" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:19:07.238368+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000159", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2450": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2450, + "name": "Xie2007_CircClock", + "repository_type": "biomodels", + "summary": "

The model reproduces the oscillations for mRNA and protein species as depicted in Fig 3 of the plot. The model differs slightly from that given in the paper and this was made after a communication from the authors. The values of parameters tcvriclkp, tcdvpmt and dccpt are slightly different. Also, although it is not given in the paper, rate laws for reactions re20, re28, re35, re42, re43 and re45 are multiplied by a specie. Model was successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3264, + "tag": "BioModels:BIOMD0000000160" + }, + { + "id": 3005, + "tag": "Drosophila melanogaster" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:19:07.680657+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000160", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2451": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2451, + "name": "Eungdamrong2007_Ras_Activation", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of Golgi Ras-GTP and plasma membrane Ras-GTP, subjected to a palmitoylation rate of 0.00015849 second inverse. This is depicted in Fig 5a and 5b for various palmitolylation rates, however the value used in this model is not present in the figure in the paper but corresponds to Fig S2 of the supplement. Model successfully reproduced using MathSBML. Please note that the units of volumetric species in this model are molecules/micrometer cubed, to convert this to microMolar as given in the paper, multiply the simulation result by 1/602.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3265, + "tag": "BioModels:BIOMD0000000161" + }, + { + "id": 3266, + "tag": "MAPK signaling pathway - Homo sapiens (human)" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:33:42.472134+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000161", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2452": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2452, + "name": "Hernjak2005_Calcium_Signaling", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of Calcium in the spine and dendrites as depicted in Fig 8 and Fig 9 of the paper for CF activation.

The model was reproduced using MathSBML.

Please note that the units of volume species is molecules/micrometer cubed as against the units of microMolar given in the paper. To convert the units to microMolar multiply the species concentration by the conversion factor 1/602.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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The model reproduces the time profiles of Total Smad2 in the nucleus as well as the cytoplasm as depicted in 2D and also the other time profiles as depicted in Fig 2. Two parameters that are not present in the paper are introduced here for illustration purposes and they are Total Smad2n and Total Smad2c. The term kr_EE*LRC_EE has not been included in the ODE's for T1R_surf, T2R_surf and TGFbeta in the paper but is included in this model. MathSBML was used to reproduce the simulation result.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3268, + "tag": "BioModels:BIOMD0000000163" + }, + { + "id": 3269, + "tag": "Regulation of transforming growth factor beta receptor signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:33:43.539324+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000163", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2454": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2454, + "name": "SmithAE2002_RanTransport", + "repository_type": "biomodels", + "summary": "

The model reproduces the compartmental model for Ran transport as depicted in Fig 3 of the paper. Model reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3270, + "tag": "BioModels:BIOMD0000000164" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3271, + "tag": "Nucleocytoplasmic transport" + }, + { + "id": 3272, + "tag": "Ran protein signal transduction" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:43.970225+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000164", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2455": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2455, + "name": "Saucerman2006_PKA", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2B of the paper. Model successfully tested on MathSBML


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3273, + "tag": "Activation of protein kinase A activity" + }, + { + "id": 3218, + "tag": "Adenylate cyclase-modulating G-protein coupled receptor signaling pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3274, + "tag": "BioModels:BIOMD0000000165" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 737, + "tag": "Heart disease" + } + ], + "timestamp_created": "2025-01-30 13:33:44.412713+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000165", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2456": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2456, + "name": "Zhu2007_TF_modulated_by_Calcium", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A theoretical study on activation of transcription factor modulated by intracellular Ca2+ oscillations.
Zhu CL, Zheng Y, Jia Y Biophys. Chem.[2007 Aug:129(1):49-55 17560007,
Abstract:
This work presents both deterministic and stochastic models of genetic expression modulated by intracellular calcium (Ca2+) oscillations, based on macroscopic differential equations and chemical Langevin equations, respectively. In deterministic case, the oscillations of intracellular Ca2+ decrease the effective Ca2+ threshold for the activation of transcriptional activator (TF-A). The average activation of TF-A increases with the increase of the average amplitude of intracellular Ca2+ oscillations, but decreases with the increase of the period of intracellular Ca2+ oscillations, which are qualitatively consistent with the experimental results on the gene expression in lymphocytes. In stochastic case, it is found that a large internal fluctuation of the biochemical reaction can enhance gene expression efficiency specifically at a low level of external stimulations or at a small rate of TF-A dimer phosphorylation activated by Ca2+, which reduces the threshold of the average intracellular Ca2+ concentration for gene expression.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M.(2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3275, + "tag": "BioModels:BIOMD0000000166" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:33:44.925933+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000166", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2457": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2457, + "name": "Mayya2005_STATmodule", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2B of the paper. Model successfully reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3276, + "tag": "BioModels:BIOMD0000000167" + }, + { + "id": 3277, + "tag": "Regulation of tyrosine phosphorylation of STAT protein" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:33:45.371844+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000167", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2458": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2458, + "name": "Obeyesekere1999_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of the different species depicted in Fig 3a of the paper. Model successfully reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3278, + "tag": "BioModels:BIOMD0000000168" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:45.822197+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000168", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2459": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2459, + "name": "Aguda1999_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profiles of p27, E2F and aE/cdk2 as depicted in Figure 5 c of the paper. Model was simulated on MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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The model reproduces the time profile of the species as depicted in Fig 3A of the paper. Model successfully tested on MathSBML and Jarnac.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3281, + "tag": "BioModels:BIOMD0000000170" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:47.044537+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000170", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2461": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2461, + "name": "Leloup1998_CircClock_LD", + "repository_type": "biomodels", + "summary": "

Leloup and Goldbeter, 1998

This model was created after the article by Leloup and Goldbeter, J Biol Rhythms 1998, Vol:13(1),pp70-87, pubmedID: 9486845
A Model for Circadian Rhythms in Drosophila Incorporating the Formation of a Complex between the PER and TIM Proteins
The parameters and initial concentrations are taken to reproduce figs. 4 D,E,F in the publication.
For a simulation without light dependent degradation of TIM_pp, change the the parameter v_dT_fac to 1.
The light/dark phases length can be set using the parameter l_d .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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from:
Schemes of fluc control in a model of Saccharomyces cerevisiae glycolysis

Pritchard, L and Kell, DB Eur. J. Biochem. 269(2002), 3894-3904.
It represents a modified version of Teusink et al. Eur. J. Biochem. 267(2000), 5313-5329.
The model is a translation from the GEPASI file encoded by Leighton Pritchard.
This version uses the Vmaxes found by the best fit (R1) of Table 1 of the \tPritchard and Kell paper and simulates a decrease of external glucose concentration from 100 to 2 mM.
To reproduce the values in table 2 of the publication, set GLCo to 50 mM and compute the steady state.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This sbml file describes the RECI model from:
\t \"Mathematical modeling identifies Smad nucleocytoplasmic shuttling as a dynamic signal-interpreting system\" by Bernhard Schmierer, Alexander L. Tournier, Paul A. Bates and Caroline S. Hill, Proc Natl Acad Sci U S A. 2008 May 6;105(18):6608-13.
\t All parameter and species names are as in Figure S3 of the original publication. The original model was done in copasi.
SB-431542 addition to a concentration of 10000 nM is set at 2700 sec. The initial concentration of SB, the time point of addition and the final concentration can be set by altering the parameters SB_0, t_SB and SB_end.
This model file has been used to reproduce Figures 2D and 5A from the research paper using SBMLodesolver. To get the results for the figures, sum the corresponding concentrations:
fig 2D: nuclear EGFP-Smad2 = G_n + pG_n + G2_n + G4_n + 2* GG_n
fig 5A (either n or c for nucleus or cytosol):
monomeric Smad2 = S2_n/c + G_n/c
monomeric P-Smad2 = pS2_n/c + pG_n/c
Smad2/Smad4 complexes = S24_n/c + G4_n/c
Smad2/Smad2 complexes = S22_n/c + G2_n/c + GG_n/c

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Membrane identity and GTPase cascades regulated by toggle and cut-out switches
\t Perla Del Conte-Zerial, Lutz Brusch, Jochen C Rink, Claudio Collinet, Yannis Kalaidzidis, Marino Zerial, and Andreas Deutsch: Molecular Systems Biology4:206 15 July 2008, doi:10.1038/msb.2008.45

This is the cut-out switch model for the Rab5 - Rab7 transition, also referred to as model 2 in the original publication.
This model is not completely described in all details in the publication. Thanks go to Barbara Szomolay and Lutz Brusch for finding and clarifying this. According to Dr. Brusch this model represents the mechanism identified by the qualitative analysis in the article in the scenario deemed most useful by the authors. For the time-course simulations it was necessary to add a time dependency to one of the parameters, which is only verbally described in the article.
As argued in the publication the switch between early and late endosomes can be triggered by a parameter change. While with fixed parameter values each switch just converges to one steady state from its initial conditions and stays there, endosomes should switch between two different states. These changes would in reality of course depend on many different factors, such as cargo composition and amount in the specific endosome, its location and some additional cellular control mechanisms and encompass many different parameters. To keep the model simple the authors chose to add a time dependency to only one reaction - ke in the activation of RAB5 is multiplied with a term monotonously increasing over time from 0 to 1. They also hard coded a time dependence in this term, 100 minutes, to make the switch occur after several hundred minutes. As long as this modulating term remains monotonic all resulting time courses should look similar, with the switching behavior depending on the initial conditions and whether the term is increasing or decreasing. Monotonic increase is a reasonable assumption for the described mechanism of cargo accumulation.
Not explicitly described in the article:activation of Rab5 (time) : r*ke*time/(100+time) /(1+e(kg-R)*kf) instead of r*ke/(1+e(kg-R)*kf)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Increased glycolytic flux as an outcome of whole-genome duplication in yeast.
Conant GC, Wolfe KH Mol. Syst. Biol. [2007 ; Volume: 3 (Issue: )]: 129 17667951 ,
Abstract:
After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.



The original model submitted by the authors was slightly altered and now comprises the models originally submitted as MODEL2426780967, MODEL2427021978, MODEL2427095802. It reproduces figures 2A,3A and 3B from the publication.

This model uses the glycolysis model from Pritchard and Kell (2002) with an additional parameter, WGD_E , to adjust for the differing enzyme conzentrations before the whole genome duplication (WGD) and parameters fV_xxx that adjust the Vmax of the different reactions (xxx eg. HXT or PYK).
Figure 3A from the article can be reproduced by changing the value of the parameters fV_xxx to 0.9 indiviually, with xxx signifying the different enzymes (HXT, HXK ...)
Figure 3B from the publication can be reproduced by setting the parameter WGD_E to 0.75 and individually setting the parameters fV_xxx to 1.333.
To reproduce figure 2A from the article change the parameter WGD_E in the range between 0.65 and 1.0.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Increased glycolytic flux as an outcome of whole-genome duplication in yeast.
Conant GC, Wolfe KH Mol. Syst. Biol. [2007 ; Volume: 3 (Issue: )]: 129 17667951 ,
Abstract:
After whole-genome duplication (WGD), deletions return most loci to single copy. However, duplicate loci may survive through selection for increased dosage. Here, we show how the WGD increased copy number of some glycolytic genes could have conferred an almost immediate selective advantage to an ancestor of Saccharomyces cerevisiae, providing a rationale for the success of the WGD. We propose that the loss of other redundant genes throughout the genome resulted in incremental dosage increases for the surviving duplicated glycolytic genes. This increase gave post-WGD yeasts a growth advantage through rapid glucose fermentation; one of this lineage's many adaptations to glucose-rich environments. Our hypothesis is supported by data from enzyme kinetics and comparative genomics. Because changes in gene dosage follow directly from post-WGD deletions, dosage selection can confer an almost instantaneous benefit after WGD, unlike neofunctionalization or subfunctionalization, which require specific mutations. We also show theoretically that increased fermentative capacity is of greatest advantage when glucose resources are both large and dense, an observation potentially related to the appearance of angiosperms around the time of WGD.



This model reproduces fig. 2C from the corrigendum to the publication
The parameter Vmax_PDH was corrected by a factor 60 from 6.32 mM/min in the publication to 379.2 mM/min in accordance with the authors.
see the corrigendum at msb or its pubmed entry (pmid:18594520)

This model comprises the glycolysis model from Pritchard and Kell (2002) with an extension for the metabolisation of pyruvate in the mitochondria by pyruvate dehydrogenase and an additional parameter, WGD_E , to adjust for the differing enzyme concentrations before the whole genome duplication (WGD).
To switch off transport of pyruvate to the mitochondria, set the parameter t_m = 0.
Figure 2C from the article can be reproduced by manually changing the value of parameter WGD_E in the range between 0.65 and 1.0 and calculating the ratios of ratio of PDC/PDH fluxes in the altered model to the one of the model with WGD_E = 1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Lebeda2008 - BoTN Paralysis (4 stepmodel)
The onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction is described in this model. This model is the extended model of\u00a0 BIOMD0000000267 , which itself is the reduced form of the model developed by Simpson 1980; PMID\u00a0 6243359

This model is described in the article:

Lebeda FJ, Adler M, Erickson K, Chushak Y.
J Pharmacokinet Pharmacodyn 2008 Jun; 35(3): 251-267

Abstract:

Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000178.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3291, + "tag": "BioModels:BIOMD0000000178" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3292, + "tag": "Metalloendopeptidase activity" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3293, + "tag": "Negative regulation of synaptic transmission, cholinergic" + }, + { + "id": 3294, + "tag": "Neurotoxicity of clostridium toxins" + }, + { + "id": 3295, + "tag": "Pathogenesis" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 3296, + "tag": "Receptor-mediated endocytosis" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4599, + "tag": "Botulism" + } + ], + "timestamp_created": "2025-01-30 13:33:50.755374+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000178", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2469": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2469, + "name": "Kim2007_CellularMemory_AsymmetricModel", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Interlinked mutual inhibitory positive feedbacks induce robust cellular memory effects.
Kim TH, Jung SH, Cho KH FEBS Lett.2007 Oct; 581(25) 17892872,
Abstract:
Mutual inhibitory positive feedback (MIPF), or double-negative feedback, is a key regulatory motif of cellular memory with the capability of maintaining switched states for transient stimuli. Such MIPFs are found in various biological systems where they are interlinked in many cases despite a single MIPF can still realize such a memory effect. An intriguing question then arises about the advantage of interlinking MIPFs instead of exploiting an isolated single MIPF to realize the memory effect. We have investigated the advantages of interlinked MIPF systems through mathematical modeling and computer simulations. Our results revealed that interlinking MIPFs expands the parameter range of achieving the memory effect, or the memory region, thereby making the system more robust to parameter perturbations. Moreover, the minimal duration and amplitude of an external stimulus required for off-to-on state transition are increased and, as a result, external noises can more effectively be filtered out. Hence, interlinked MIPF systems can realize more robust cellular memories with respect to both parameter perturbations and external noises. Our study suggests that interlinked MIPF systems might be an evolutionary consequence acquired for a more reliable memory effect by enhancing robustness against noisy cellular environments.

Note: The model reproduces the simulation result for an asymmetric model as depicted in Fig 3G of the paper. Model successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3297, + "tag": "BioModels:BIOMD0000000179" + }, + { + "id": 3298, + "tag": "Regulation of cellular component movement" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:33:51.205814+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000179", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2470": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2470, + "name": "Kim2007_CellularMemory_SymmetricModel", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Interlinked mutual inhibitory positive feedbacks induce robust cellular memory effects.
Kim TH, Jung SH, Cho KH FEBS Lett.2007 Oct; 581(25) 17892872,
Abstract:
Mutual inhibitory positive feedback (MIPF), or double-negative feedback, is a key regulatory motif of cellular memory with the capability of maintaining switched states for transient stimuli. Such MIPFs are found in various biological systems where they are interlinked in many cases despite a single MIPF can still realize such a memory effect. An intriguing question then arises about the advantage of interlinking MIPFs instead of exploiting an isolated single MIPF to realize the memory effect. We have investigated the advantages of interlinked MIPF systems through mathematical modeling and computer simulations. Our results revealed that interlinking MIPFs expands the parameter range of achieving the memory effect, or the memory region, thereby making the system more robust to parameter perturbations. Moreover, the minimal duration and amplitude of an external stimulus required for off-to-on state transition are increased and, as a result, external noises can more effectively be filtered out. Hence, interlinked MIPF systems can realize more robust cellular memories with respect to both parameter perturbations and external noises. Our study suggests that interlinked MIPF systems might be an evolutionary consequence acquired for a more reliable memory effect by enhancing robustness against noisy cellular environments.

Note: The model reproduces the simulation result for the symmetric model as depicted in Fig 3H of the paper. Model successfully tested on MathSBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3299, + "tag": "BioModels:BIOMD0000000180" + }, + { + "id": 3298, + "tag": "Regulation of cellular component movement" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:33:51.677171+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000180", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2471": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2471, + "name": "Sriram2007_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of species depicted in Figure 12a and 12 b. The authors communicated to the curator that there is a typo in the paper, the values of kd1 and kd2 are reversed. Model successfully reproduced using MathSBML.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3300, + "tag": "BioModels:BIOMD0000000181" + }, + { + "id": 2982, + "tag": "Opisthokonta" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:52.220695+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000181", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2472": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2472, + "name": "Neves2008 - Role of cell shape and size in controlling intracellular signalling", + "repository_type": "biomodels", + "summary": "
Neves2008 - Role of cell shape and size in controlling intracellular signalling

The role of cell shape and size in the flow of spatial information from the cell surface receptor to downstream components within the cell has been studied on the \u03b2-adrenergic receptor to MAPK-signalling network.

This model is described in the article:

Neves SR, Tsokas P, Sarkar A, Grace EA, Rangamani P, Taubenfeld SM, Alberini CM, Schaff JC, Blitzer RD, Moraru II, Iyengar R
Cell. 2008, 133(4):666-680

Abstract:

The role of cell size and shape in controlling local intracellular signaling reactions, and how this spatial information originates and is propagated, is not well understood. We have used partial differential equations to model the flow of spatial information from the beta-adrenergic receptor to MAPK1,2 through the cAMP/PKA/B-Raf/MAPK1,2 network in neurons using real geometries. The numerical simulations indicated that cell shape controls the dynamics of local biochemical activity of signal-modulated negative regulators, such as phosphodiesterases and protein phosphatases within regulatory loops to determine the size of microdomains of activated signaling components. The model prediction that negative regulators control the flow of spatial information to downstream components was verified experimentally in rat hippocampal slices. These results suggest a mechanism by which cellular geometry, the presence of regulatory loops with negative regulators, and key reaction rates all together control spatial information transfer and microdomain characteristics within cells.

This model is hosted on BioModels Database and identifiedby: MODEL8609366518 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3301, + "tag": "BioModels:BIOMD0000000182" + }, + { + "id": 3302, + "tag": "Cell morphogenesis" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:52.709828+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000182", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2473": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2473, + "name": "Stefan2008 - calmodulin allostery", + "repository_type": "biomodels", + "summary": " \t
\t Stefan2008 - calmodulin allostery\t
\t
\t

An allosteric model for calmodulin activation, in which binding to calcium facilitates the transition between a low-affinity [tense (T)] and a high-affinity [relaxed (R)] state.

\t
\t
\t

This model is described in the article:

\t \t
Stefan MI, Edelstein SJ, Le Nov\u00e8re N
\t
Proc. Natl. Acad. Sci. U.S.A. 2008 Aug; 105(31): 10768-10773
\t

Abstract:

\t
\t

Calmodulin plays a vital role in mediating bidirectional synaptic plasticity by activating either calcium/calmodulin-dependent protein kinase II (CaMKII) or protein phosphatase 2B (PP2B) at different calcium concentrations. We propose an allosteric model for calmodulin activation, in which binding to calcium facilitates the transition between a low-affinity [tense (T)] and a high-affinity [relaxed (R)] state. The four calcium-binding sites are assumed to be nonidentical. The model is consistent with previously reported experimental data for calcium binding to calmodulin. It also accounts for known properties of calmodulin that have been difficult to model so far, including the activity of nonsaturated forms of calmodulin (we predict the existence of open conformations in the absence of calcium), an increase in calcium affinity once calmodulin is bound to a target, and the differential activation of CaMKII and PP2B depending on calcium concentration.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000183.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3303, + "tag": "BioModels:BIOMD0000000183" + }, + { + "id": 3304, + "tag": "Calmodulin binding" + }, + { + "id": 3305, + "tag": "Calmodulin-dependent protein kinase activity" + }, + { + "id": 3306, + "tag": "Detection of calcium ion" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3307, + "tag": "Positive regulation of synaptic plasticity" + }, + { + "id": 3308, + "tag": "Protein serine/threonine phosphatase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:53.168500+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000183", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2474": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2474, + "name": "Lavrentovich2008_Ca_Oscillations", + "repository_type": "biomodels", + "summary": "

The model reproduces the time profile of cytoplasmic Calcium as depicted in Fig 3 of the paper. Model successfully reproduced using Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3309, + "tag": "BioModels:BIOMD0000000184" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:53.613536+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000184", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2475": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2475, + "name": "Locke2008_Circadian_Clock", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 2A of the paper. Model successfully reproduced using Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3310, + "tag": "BioModels:BIOMD0000000185" + }, + { + "id": 3311, + "tag": "Circadian behavior" + }, + { + "id": 3030, + "tag": "Circadian regulation of gene expression" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:54.070936+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000185", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2476": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2476, + "name": "Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Dissociation variant", + "repository_type": "biomodels", + "summary": "
Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Dissociation variant

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism. This model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. MCC:APC dissociation is described by two alternatives models, namely the \"Dissociation\" and the \"Convey\" model variants. Both these model are available in BioModels Database. This model corresponds to the \"Dissociation\" variant.

This model is described in the article:

Ibrahim B, Diekmann S, Schmitt E, Dittrich P
PLoS One. 2008 Feb 6;3(2):e1555.

Abstract:

BACKGROUND: The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

PRINCIPLE FINDINGS: We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

CONCLUSION: Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.

This model describes the controlled dissociation variant of the mitotic spindle assembly checkpoint. If the tool you use has problems with events, you can uncomment the assignment rules for u and u_prime and comment out the list of events.

In accordance with the authors due to typos in the original publication some initial conditions and parameters were slightly changed in the model:
article model
[O-Mad2] 1.5e-7 M 1.3e-7 M
[BubR1:Bub3] 1.30e-7 M 1.27e-7 M
k -4 0.01 M -1 s -1 0.02 M -1 s -1
k -5 0.1 M -1 s -1 0.2 M -1 s -1

This model is hosted on BioModels Database and identified by: MODEL6655615431 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3312, + "tag": "BioModels:BIOMD0000000186" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3313, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3314, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:54.532472+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000186", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2477": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2477, + "name": "Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Convey variant", + "repository_type": "biomodels", + "summary": "
Ibrahim2008 - Mitotic Spindle Assembly Checkpoint - Convey variant

The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism. This model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. MCC:APC dissociation is described by two alternatives models, namely the \"Dissociation\" and the \"Convey\" model variants. Both these model are available in BioModels Database. This model corresponds to the \"Convey\" variant.

This model is described in the article:

Ibrahim B, Diekmann S, Schmitt E, Dittrich P
PLoS One. 2008 Feb 6;3(2):e1555.

Abstract:

BACKGROUND: The Mitotic Spindle Assembly Checkpoint ((M)SAC) is an evolutionary conserved mechanism that ensures the correct segregation of chromosomes by restraining cell cycle progression from entering anaphase until all chromosomes have made proper bipolar attachments to the mitotic spindle. Its malfunction can lead to cancer.

PRINCIPLE FINDINGS: We have constructed and validated for the human (M)SAC mechanism an in silico dynamical model, integrating 11 proteins and complexes. The model incorporates the perspectives of three central control pathways, namely Mad1/Mad2 induced Cdc20 sequestering based on the Template Model, MCC formation, and APC inhibition. Originating from the biochemical reactions for the underlying molecular processes, non-linear ordinary differential equations for the concentrations of 11 proteins and complexes of the (M)SAC are derived. Most of the kinetic constants are taken from literature, the remaining four unknown parameters are derived by an evolutionary optimization procedure for an objective function describing the dynamics of the APC:Cdc20 complex. MCC:APC dissociation is described by two alternatives, namely the \"Dissociation\" and the \"Convey\" model variants. The attachment of the kinetochore to microtubuli is simulated by a switching parameter silencing those reactions which are stopped by the attachment. For both, the Dissociation and the Convey variants, we compare two different scenarios concerning the microtubule attachment dependent control of the dissociation reaction. Our model is validated by simulation of ten perturbation experiments.

CONCLUSION: Only in the controlled case, our models show (M)SAC behaviour at meta- to anaphase transition in agreement with experimental observations. Our simulations revealed that for (M)SAC activation, Cdc20 is not fully sequestered; instead APC is inhibited by MCC binding.

This model describes the controlled dissociation variant of the mitotic spindle assembly checkpoint. If the tool you use has problems with events, you can uncomment the assignment rules for u and u_prime and comment out the list of events.

In accordance with the authors due to typos in the original publication some initial conditions and parameters were slightly changed in the model:
article model
[O-Mad2] 1.5e-7 M 1.3e-7 M
[BubR1:Bub3] 1.30e-7 M 1.27e-7 M
k -4 0.01 M -1 s -1 0.02 M -1 s -1
k -5 0.1 M -1 s -1 0.2 M -1 s -1

This model is hosted on BioModels Database and identified by: MODEL6655578762 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3315, + "tag": "BioModels:BIOMD0000000187" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3313, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3314, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:55.071528+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000187", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2478": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2478, + "name": "Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM", + "repository_type": "biomodels", + "summary": "
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by ATM

This model is described in the article:

Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75

Abstract:

BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000188.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3316, + "tag": "BioModels:BIOMD0000000188" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3317, + "tag": "Regulation of DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3318, + "tag": "Stabilization of p53" + } + ], + "timestamp_created": "2025-01-30 13:33:55.520043+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000188", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2479": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2479, + "name": "Proctor2008 - p53/Mdm2 circuit - p53 stablisation by p14ARF", + "repository_type": "biomodels", + "summary": "
Proctor2008 - p53/Mdm2 circuit - p53 stabilisation by p14ARF

This model is described in the article:

Proctor CJ, Gray DA.
BMC Syst Biol 2008; 2: 75

Abstract:

BACKGROUND: In individual living cells p53 has been found to be expressed in a series of discrete pulses after DNA damage. Its negative regulator Mdm2 also demonstrates oscillatory behaviour. Attempts have been made recently to explain this behaviour by mathematical models but these have not addressed explicit molecular mechanisms. We describe two stochastic mechanistic models of the p53/Mdm2 circuit and show that sustained oscillations result directly from the key biological features, without assuming complicated mathematical functions or requiring more than one feedback loop. Each model examines a different mechanism for providing a negative feedback loop which results in p53 activation after DNA damage. The first model (ARF model) looks at the mechanism of p14ARF which sequesters Mdm2 and leads to stabilisation of p53. The second model (ATM model) examines the mechanism of ATM activation which leads to phosphorylation of both p53 and Mdm2 and increased degradation of Mdm2, which again results in p53 stabilisation. The models can readily be modified as further information becomes available, and linked to other models of cellular ageing. RESULTS: The ARF model is robust to changes in its parameters and predicts undamped oscillations after DNA damage so long as the signal persists. It also predicts that if there is a gradual accumulation of DNA damage, such as may occur in ageing, oscillations break out once a threshold level of damage is acquired. The ATM model requires an additional step for p53 synthesis for sustained oscillations to develop. The ATM model shows much more variability in the oscillatory behaviour and this variability is observed over a wide range of parameter values. This may account for the large variability seen in the experimental data which so far has examined ARF negative cells. CONCLUSION: The models predict more regular oscillations if ARF is present and suggest the need for further experiments in ARF positive cells to test these predictions. Our work illustrates the importance of systems biology approaches to understanding the complex role of p53 in both ageing and cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000188.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3319, + "tag": "BioModels:BIOMD0000000189" + }, + { + "id": 3320, + "tag": "DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:55.954928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000189", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2480": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2480, + "name": "Rodriguez-Caso2006_Polyamine_Metabolism", + "repository_type": "biomodels", + "summary": "

SBML creators: Armando Reyes-Palomares * , Carlos Rodr\u00edguez-Caso +, Raul Monta\u00f1ez * , Marta Cascante $, Francisca S\u00e1nchez-Jim\u00e9nez * , Miguel A. Medina *

* ProCel Group, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga and CIBER de Enfermedades Raras (CIBER-ER). + Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB). $ Department of Biochemistry and Molecular Biology, Faculty of Biology, Universitat de Barcelona.

http://asp.uma.es

Metabolic modeling of polyamine metabolism in mammals.
Rodr\u00edguez-Caso,C et al.: J Biol Chem 2006 : 281:21799-812.
The model reproduces the dynamical behavior of the polyamine metabolism in mammals. In this model there are some additions and corrections to the publication. All perturbations and analysis have produced results very close to the published experiments. The model was successfully tested on CoPaSi v.4.4 (build 26).

Parameters not included in the publication:

1. Parameters for SSAT kinetic constants:

KmAcCoA = 1.5 \u00b5M

KmCoA = 40 \u00b5M

2. Parameters for equation MAT (table 1):

Vmax_MAT = 0.45 \u00b5M/min

Km_MAT = 41 \u00b5M

Ki_MET_MAT = 50 \u00b5M

3. Erratum.: The corrected ODE for time-dependent variable Antz is:

KsANTZ*(1-1/(1+Keq*0.01*([D]+[S])))-KdANTZ*[Antz]

According to these modifications the new steady-state analysis results are:

Metabolites:

[P]= 104.681 \u00b5M

[D]= 76.7492 \u00b5M

[S]= 58.0135 \u00b5M

[SAM]= 52.327 \u00b5M

[A]= 0.0101962 \u00b5M

[aS]= 0.0245375 \u00b5M

[aD]= 0.832236 \u00b5M

Time-dependent global parameters:

[Antz] = 0.574038 \u00b5M

Vmaxodc = 1.28315 \u00b5M/min

Vmaxssat = 0.673814 \u00b5M/min

Vmaxsamdc = 0.36829 \u00b5M/min


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3321, + "tag": "BioModels:BIOMD0000000190" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3322, + "tag": "Polyamine metabolic process" + }, + { + "id": 3323, + "tag": "Rodentia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:56.404879+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000190", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2481": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2481, + "name": "Monta\u00f1ez2008_Arginine_catabolism", + "repository_type": "biomodels", + "summary": "

SBML creators: Armando Reyes-Palomares * , Raul Monta\u00f1ez *, Carlos Rodriguez-Caso +, Francisca Sanchez-Jimenez * , Miguel A. Medina *

* ProCel Group, Department of Molecular Biology and Biochemistry, Faculty of Sciences, Campus de Teatinos, University of Malaga and CIBER de Enfermedades Raras (CIBER-ER). + Complex Systems Lab (ICREA-UPF), Barcelona Biomedical Research Park (PRBB-GRIB).

http://asp.uma.es

In silico analysis of arginine catabolism as a source of nitric oxide or polyamines in endothelial cells.
Monta\u00f1ez, R et al.: Amino Acids. 2008 Feb;34(2):223-9.
The model reproduces the dynamical behavior of the arginine catabolism and transport in relation to the nitric oxide production. In this model there are some additions and corrections to the publication. All perturbations and analysis have produced results very close to the published experiments. The model was successfully tested on CoPaSi v.4.4 (build 26).

Erratum: parameters values modificated respect to the publication to reach the steady-state:

Kmodc=90 \u00b5M (60 \u00b5M in the paper)

Kiornhat (is equivalent to the parameter Kmefflhat Eq ) = 360 \u00b5M (380 \u00b5M in the paper)


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3324, + "tag": "BioModels:BIOMD0000000191" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:56.907561+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000191", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2482": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2482, + "name": "G\u00f6rlich2003_RanGTP_gradient", + "repository_type": "biomodels", + "summary": " \t This model represents a concentration gradient of RanGTP across the nuclear envelope. This gradient is generated by distribution of regulators of RanGTPase. We have taken a log linear plot of graphs generated by GENESIS and compared with the experimental graphs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2008 The BioModels Team.
For more information see the terms of use.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3325, + "tag": "BioModels:BIOMD0000000192" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3326, + "tag": "Ran GTPase binding" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:57.341586+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000192", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2483": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2483, + "name": "Ibrahim2008_MCC_assembly_model_KDM", + "repository_type": "biomodels", + "summary": " BioSystems (2007), doi:10.1016/j.biosystems.2008.06.007

In-silico study of kinetochore control, amplification, and inhibition effects in MCC assembly


Bashar Ibrahim, Eberhard Schmitt, Peter Dittrich, Stephan Diekmann
This is the kinetochore dependent MCC model (KDM) from the article. For the kinetochore independent MCC model (KIM) replace u*k4f in R4 by k4f and u*k5f in R5 by k5f .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3327, + "tag": "BioModels:BIOMD0000000193" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3313, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3314, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:57.875421+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000193", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2484": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2484, + "name": "Ibrahim2008_Cdc20_Sequestring_Template_Model", + "repository_type": "biomodels", + "summary": " Biophysical Chemistry 134 (2008) 93-100

Mad2 binding is not sufficient for complete Cdc20 sequestering in mitotic transition control (an in silico study)


Bashar Ibrahim, Peter Dittrich, Stephan Diekmann, Eberhard Schmitt

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3328, + "tag": "BioModels:BIOMD0000000194" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3313, + "tag": "Mitotic Spindle Checkpoint" + }, + { + "id": 3314, + "tag": "Mitotic spindle assembly checkpoint" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:58.302655+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000194", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2485": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2485, + "name": "Tyson2001_Cell_Cycle_Regulation", + "repository_type": "biomodels", + "summary": "

This model describes the budding yeast cell cycle model used in fig 8 a in
Regulation of the eukaryotic cell cycle: molecular antagonism, hysteresis, and irreversible transitions.
Tyson JJ and Novak B., J Theor Biol 2001 May;210(2):249-63.
It consitsts of the equations (2)-(8), with mu=0.005 min -1 . It was taken from Cell Cycle DB ( file ) and only slightly altered.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3329, + "tag": "BioModels:BIOMD0000000195" + }, + { + "id": 3107, + "tag": "Cell Cycle, Mitotic" + }, + { + "id": 3330, + "tag": "Cell cycle - yeast" + }, + { + "id": 3331, + "tag": "Regulation of cell cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:33:58.741901+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000195", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2486": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2486, + "name": "Srividhya2006_CellCycle", + "repository_type": "biomodels", + "summary": "

In this model the values of \"free CDK\" (Id: x2), \"cdc25_P\" (x4) \"Wee1_P\" (Id: y5) and \"APC\" (Id: y6) are assigned using the parameters describing the total concentrations totcdk (Id: c)), totcdc5, totwee1 and totAPC. So if you want to change the levels of these proteins, you need to change the values ofthese parameters.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3332, + "tag": "BioModels:BIOMD0000000196" + }, + { + "id": 3107, + "tag": "Cell Cycle, Mitotic" + }, + { + "id": 3333, + "tag": "Cell cycle process" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:33:59.180717+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000196", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2487": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2487, + "name": "Bartholome2007_MDCKII", + "repository_type": "biomodels", + "summary": "

SBML model exported from PottersWheel on 2007-09-19 15:35:47.

The values for parameters and the inital concentrations of this model where directly provided by the main author:
Parameter values
parameter value unit
p1 0.0025 1/min
p2 0.0784 1/min
p3 0.0013 1/min
p4 0.0827 1/min
p5 0.0091 1/min
p6 0.000064 1/(nmole*min)
p7 0.0397 1/min
p8 1000 nmole
p9 0.0098 1/(nmole*min)
p10 1.6 1/min
p11 1000 nmole
p12 0.0003 ml/min
The basal chamber volume was taken as 1 ml, the apical as 1.5. As starting values x1 was set to 88 nmole, all other species to 0.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Stone1996 - activation of soluble guanylatecyclase by nitric oxide
This features the two step binding ofNO to soluble Guanylyl Cyclase as proposed by StoneJR, Marletta MA. Biochemistry (1996) 35(4):1093-9 . There is afast step binding scheme and a slow step binding scheme. Thedifference lies in the binding of a NO to a non-heme site on sGC,which may not necessarily be the same site of binding during theinitial binding. The rates have been directly used models.

This model is described in the article:

Stone JR, Marletta MA.
Biochemistry 1996 Jan; 35(4): 1093-1099

Abstract:

The soluble form of guanylate cyclase (sGC) is the only definitive receptor for the signaling agent nitric oxide (.NO). The enzyme is a heterodimer of homologous subunits in which each subunit binds 1 equiv of 5-coordinate high-spin heme. .NO increases the Vmax of sGC up to 400-fold and has previously been shown to bind to the heme to form a 5-coordinate complex. Using stopped-flow spectrophotometry, it is demonstrated that the binding of .NO to the heme of sGC is a complex process. .NO first binds to the heme to form a 6-coordinate nitrosyl complex, which then converts to a 5-coordinate nitrosyl complex through one of two ways. For 28 +/- 4% of the heme, the 6-coordinate nitrosyl complex rapidly (approximately 20 s-1) converts to the 5-coordinate complex. For the remaining 72 +/- 4% of the heme, the conversion of the 6-coordinate nitrosyl complex to a 5-coordinate nitrosyl complex is slow (0.1-1.0 s-1) and is dependent upon the interaction of .NO with an unidentified non-heme site on the protein. The heme (200 nM) was completely converted to the 5-coordinate state with as little as 500 nM .NO, and the equilibrium dissociation constant of .NO for activating the enzyme was determined to be < or = 250 nM. Gel-filtration analysis indicates that the binding of .NO to the heme has no effect on the native molecular mass of the protein. Correlation of electronic absorption spectra with activity measurements indicates that the 5-coordinate nitrosyl form of the enzyme is activated relative to the resting 5-coordinate ferrous form of the enzyme.

This model is hosted on BioModels Database and identified by: BIOMD0000000198.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3336, + "tag": "BioModels:BIOMD0000000198" + }, + { + "id": 3337, + "tag": "Bos taurus" + }, + { + "id": 3338, + "tag": "Nitric oxide mediated signal transduction" + }, + { + "id": 3339, + "tag": "Regulation of guanylate cyclase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:00.103442+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000198", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2489": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2489, + "name": "Santolini2001_nNOS_Mechanism_Regulation", + "repository_type": "biomodels", + "summary": " \t This is a model of neuronal Nitric Oxide Synthase expressed in Escherichia coli based on Santolini J. et al. J Biol Chem. (2001) 276(2):1233-43.
Differing from the article, oxygen explicitly included in the reaction 2, 5 and 10 (numbers as in scheme 1 in the article). In the article the assumed oxygen concentration of 140 uM was included in the pseudo first order rate constant.
Fig 2E in the article shows different time courses for citrulline and NO than the ones produced by this model. Dr. Santolini, one of the authors of the article, wrote that the legends in fig. 2E might be mixed up and should rather denote NO and NO3 instead of citrulline and NO.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3342, + "tag": "BioModels:BIOMD0000000200" + }, + { + "id": 3343, + "tag": "Chemotaxis" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:01.043177+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000200", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2491": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2491, + "name": "Goldbeter2008_Somite_Segmentation_Clock_Notch_Wnt_FGF", + "repository_type": "biomodels", + "summary": "

This is a model of the coupled Natch, Wnt and FGF modules as described in:
A. Goldbeter and O. Pourqui\u00e9 , Modeling the segmentation clock as a network of coupled oscillations in the Notch, Wnt and FGF signaling pathways. J Theor Biol. 2008 Jun 7;252(3):574-85, pubmed ID: 18308339
To uncouple the modules remove the reaction MAx_trans_Xa and set vsFK=vsF .
The SBML version of the model was converted from the CellML version by Catherine Lloyd for the CellML repository .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3344, + "tag": "Amniota" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3345, + "tag": "BioModels:BIOMD0000000201" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:01.707824+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000201", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2492": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2492, + "name": "ChenXF2008_CICR", + "repository_type": "biomodels", + "summary": "

The model reproduces the plots in Figures 1 and 2. Note that the units of the time scale \"A\" are not right in the paper, it was corrected by the curator. Model successfully tested on MathSBML.

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Chickarmane2006 - Stem cell switch reversible

Kinetic modeling approach of the transcriptional dynamics of the embryonic stem cell switch.

This model is described in the article:

Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C
PLoS Computational Biology. 2006; 2(9):e123

Abstract:

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.

This model is hosted on BioModels Database and identified by: MODEL7957907314 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3349, + "tag": "BioModels:BIOMD0000000203" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:34:02.610244+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000203", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2494": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2494, + "name": "Chickarmane2006 - Stem cell switch irreversible", + "repository_type": "biomodels", + "summary": "
Chickarmane2006 - Stem cell switch irreversible

Kinetic modeling approach of the transcriptional dynamics of the embryonic stem cell switch.

This model is described in the article:

Chickarmane V, Troein C, Nuber UA, Sauro HM, Peterson C
PLoS Computational Biology. 2006; 2(9):e123

Abstract:

Recent ChIP experiments of human and mouse embryonic stem cells have elucidated the architecture of the transcriptional regulatory circuitry responsible for cell determination, which involves the transcription factors OCT4, SOX2, and NANOG. In addition to regulating each other through feedback loops, these genes also regulate downstream target genes involved in the maintenance and differentiation of embryonic stem cells. A search for the OCT4-SOX2-NANOG network motif in other species reveals that it is unique to mammals. With a kinetic modeling approach, we ascribe function to the observed OCT4-SOX2-NANOG network by making plausible assumptions about the interactions between the transcription factors at the gene promoter binding sites and RNA polymerase (RNAP), at each of the three genes as well as at the target genes. We identify a bistable switch in the network, which arises due to several positive feedback loops, and is switched on/off by input environmental signals. The switch stabilizes the expression levels of the three genes, and through their regulatory roles on the downstream target genes, leads to a binary decision: when OCT4, SOX2, and NANOG are expressed and the switch is on, the self-renewal genes are on and the differentiation genes are off. The opposite holds when the switch is off. The model is extremely robust to parameter changes. In addition to providing a self-consistent picture of the transcriptional circuit, the model generates several predictions. Increasing the binding strength of NANOG to OCT4 and SOX2, or increasing its basal transcriptional rate, leads to an irreversible bistable switch: the switch remains on even when the activating signal is removed. Hence, the stem cell can be manipulated to be self-renewing without the requirement of input signals. We also suggest tests that could discriminate between a variety of feedforward regulation architectures of the target genes by OCT4, SOX2, and NANOG.

This model is hosted on BioModels Database and identified by: MODEL7957942740 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3351, + "tag": "BioModels:BIOMD0000000204" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:34:03.055763+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000204", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2495": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2495, + "name": "Ung2008_EGFR_Endocytosis", + "repository_type": "biomodels", + "summary": "

Model reproduces the various plots in the publication for \"Control\" concentrations. Model successfully tested on MathSBML.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3352, + "tag": "BioModels:BIOMD0000000205" + }, + { + "id": 3353, + "tag": "Endocytosis" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:03.518602+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000205", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2496": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2496, + "name": "Wolf2000_Glycolytic_Oscillations", + "repository_type": "biomodels", + "summary": "

Model reproduces the dynamics of ATP and NADH as depicted in Fig 4 of the paper. Model successfully tested on Jarnac and MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3354, + "tag": "BioModels:BIOMD0000000206" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:34:03.970755+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000206", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2497": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2497, + "name": "Romond1999_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 3 of the paper. Model successfully reproduced using MathSBML and Jarnac.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3355, + "tag": "BioModels:BIOMD0000000207" + }, + { + "id": 2977, + "tag": "Cell cycle - Homo sapiens (human)" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:04.412825+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000207", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2498": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2498, + "name": "Deineko2003_CellCycle", + "repository_type": "biomodels", + "summary": "

The model reproduces Fig 3 of the paper corresponding to the transition to S phase. Units have not been defined for this model because the paper mentions the use of arbitrary units for the various species and parameters. Model reproduced using MathSBML.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Chickarmane2008 - Stem cell lineage determination

In this work, a dynamical model of lineage determination based upon a minimal circuit, as discussed in PMID: 17215298 , which contains the Oct4/Sox2/Nanog core as well its interaction with a few other key genes is discussed.

This model is described in the article:

Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478

Abstract:

BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/

PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.

This model is hosted on BioModels Database and identifiedby: MODEL8390025091 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Chickarmane2008 - Stem cell lineage - NANOG GATA-6 switch

In this work, a dynamical model of lineage determination based upon a minimal circuit, as discussed in PMID: 17215298 , which contains the Oct4/Sox2/Nanog core as well its interaction with a few other key genes is discussed.

This model is described in the article:

Chickarmane V, Peterson C
PloS one. 2008, 3(10):e3478

Abstract:

BACKGROUND: Recent studies have associated the transcription factors, Oct4, Sox2 and Nanog as parts of a self-regulating network which is responsible for maintaining embryonic stem cell properties: self renewal and pluripotency. In addition, mutual antagonism between two of these and other master regulators have been shown to regulate lineage determination. In particular, an excess of Cdx2 over Oct4 determines the trophectoderm lineage whereas an excess of Gata-6 over Nanog determines differentiation into the endoderm lineage. Also, under/over-expression studies of the master regulator Oct4 have revealed that some self-renewal/pluripotency as well as differentiation genes are expressed in a biphasic manner with respect to the concentration of Oct4. METHODOLOGY/

PRINCIPAL FINDINGS: We construct a dynamical model of a minimalistic network, extracted from ChIP-on-chip and microarray data as well as literature studies. The model is based upon differential equations and makes two plausible assumptions; activation of Gata-6 by Oct4 and repression of Nanog by an Oct4-Gata-6 heterodimer. With these assumptions, the results of simulations successfully describe the biphasic behavior as well as lineage commitment. The model also predicts that reprogramming the network from a differentiated state, in particular the endoderm state, into a stem cell state, is best achieved by over-expressing Nanog, rather than by suppression of differentiation genes such as Gata-6.

CONCLUSIONS: The computational model provides a mechanistic understanding of how different lineages arise from the dynamics of the underlying regulatory network. It provides a framework to explore strategies of reprogramming a cell from a differentiated state to a stem cell state through directed perturbations. Such an approach is highly relevant to regenerative medicine since it allows for a rapid search over the host of possibilities for reprogramming to a stem cell state.

This model is hosted on BioModels Database and identifiedby: MODEL8389825246 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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This model is from the article:
Experimental and in silico analyses of glycolytic flux control in bloodstream form Trypanosoma brucei.
Albert MA, Haanstra JR, Hannaert V, Van Roy J, Opperdoes FR, Bakker BM, Michels PA. J Biol Chem2005 Aug 5;280(31):28306-15. 15955817,
Abstract:
A mathematical model of glycolysis in bloodstream form Trypanosoma brucei was developed previously on the basis of all available enzyme kinetic data (Bakker, B. M., Michels, P. A. M., Opperdoes, F. R., and Westerhoff, H. V. (1997) J. Biol. Chem. 272, 3207-3215). The model predicted correctly the fluxes and cellular metabolite concentrations as measured in non-growing trypanosomes and the major contribution to the flux control exerted by the plasma membrane glucose transporter. Surprisingly, a large overcapacity was predicted for hexokinase (HXK), phosphofructokinase (PFK), and pyruvate kinase (PYK). Here, we present our further analysis of the control of glycolytic flux in bloodstream form T. brucei. First, the model was optimized and extended with recent information about the kinetics of enzymes and their activities as measured in lysates of in vitro cultured growing trypanosomes. Second, the concentrations of five glycolytic enzymes (HXK, PFK, phosphoglycerate mutase, enolase, and PYK) in trypanosomes were changed by RNA interference. The effects of the knockdown of these enzymes on the growth, activities, and levels of various enzymes and glycolytic flux were studied and compared with model predictions. Data thus obtained support the conclusion from the in silico analysis that HXK, PFK, and PYK are in excess, albeit less than predicted. Interestingly, depletion of PFK and enolase had an effect on the activity (but not, or to a lesser extent, expression) of some other glycolytic enzymes. Enzymes located both in the glycosomes (the peroxisome-like organelles harboring the first seven enzymes of the glycolytic pathway of trypanosomes) and in the cytosol were affected. These data suggest the existence of novel regulatory mechanisms operating in trypanosome glycolysis.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model described in the article:
Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters.
Curien G, Bastien O, Robert-Genthon M, Cornish-Bowden A, C\u00e1rdenas ML, Dumas R. Mol Syst Biol. 2009;5:271. Epub 2009 May 19. PMID: 19455135 , doi: 10.1038/msb.2009.29
Abstract:
The aspartate-derived amino-acid pathway from plants is well suited for analysing the function of the allosteric network of interactions in branched pathways. For this purpose, a detailed kinetic model of the system in the plant model Arabidopsis was constructed on the basis of in vitro kinetic measurements. The data, assembled into a mathematical model, reproduce in vivo measurements and also provide non-intuitive predictions. A crucial result is the identification of allosteric interactions whose function is not to couple demand and supply but to maintain a high independence between fluxes in competing pathways. In addition, the model shows that enzyme isoforms are not functionally redundant, because they contribute unequally to the flux and its regulation. Another result is the identification of the threonine concentration as the most sensitive variable in the system, suggesting a regulatory role for threonine at a higher level of integration.

The limiting rates for the tRNA synthetase reactions, V_Lys_RS, V_Thr_RS and V_Ile_RS, are all assigned a joined value, Vmax_AA_RS, to facilitate reproduction of the results in the publication. To alter these rates seperately these assignments have to be changed or removed.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is an SBML version of the folate cycle model model from:
A mathematical model of the folate cycle: new insights into folate homeostasis.
Nijhout HF, Reed MC, Budu P, Ulrich CM J. Biol. Chem.,2004, 279 (53),55008-16
pubmedID: 15496403
Abstract:
A mathematical model is developed for the folate cycle based on standard biochemical kinetics. We use the model to provide new insights into several different mechanisms of folate homeostasis. The model reproduces the known pool sizes of folate substrates and the fluxes through each of the loops of the folate cycle and has the qualitative behavior observed in a variety of experimental studies. Vitamin B(12) deficiency, modeled as a reduction in the V(max) of the methionine synthase reaction, results in a secondary folate deficiency via the accumulation of folate as 5-methyltetrahydrofolate (the \"methyl trap\"). One form of homeostasis is revealed by the fact that a 100-fold up-regulation of thymidylate synthase and dihydrofolate reductase (known to occur at the G(1)/S transition) dramatically increases pyrimidine production without affecting the other reactions of the folate cycle. The model also predicts that an almost total inhibition of dihydrofolate reductase is required to significantly inhibit the thymidylate synthase reaction, consistent with experimental and clinical studies on the effects of methotrexate. Sensitivity to variation in enzymatic parameters tends to be local in the cycle and inversely proportional to the number of reactions that interconvert two folate substrates. Another form of homeostasis is a consequence of the nonenzymatic binding of folate substrates to folate enzymes. Without folate binding, the velocities of the reactions decrease approximately linearly as total folate is decreased. In the presence of folate binding and allosteric inhibition, the velocities show a remarkable constancy as total folate is decreased.
This model was encoded by Michal Galdzicki from a MatLab file send to him by Prof. Michael Reed. There some differences in this model compared to the one described in the article, possible due to typos in the publication:
1) reaction NE (THF + H2CO <=> 5,10-CH2-THF) in the article has H2C=O as areactant and is mentioned to display pseudo first order mass actionkinetics, while in the matlab file formic acid, also used in reaction FTS, is included in the rate law for the forward reaction.
2) the reaction MS is modeled after Reed et al. 2004, which is notexplicitly mentioned in the article, although Kd and the parametersfrom Reed et al. 2004 are given.
3) in the kinetic law of the SHTM reaction (THF + Ser <=>5,10-CH2-THF + Gly), there are separate values given for Km,Glyand Km,5,10-CH2-THF in the article. in the matlab file and the SBMLmodel Km,Ser and Km,THF are used instead of Km,Gly and Km,5,10-CH2-THFfor the backwards reaction.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model 2 described in the supplement of the article below. It is parameterized for the WT at 24\u00b0C. To reproduce figure 6 the results have to be rescaled to circadian time by multiplying time by 24/tau, with tau being the period of the free-running oscillator. For the wild-type parameter set tau is equal to 22.7149.
Article:
Isoform switching facilitates period control in the Neurospora crassa circadian clock.
Akman OE, Locke JC, Tang S, Carr\u00e9 I, Millar AJ, Rand DA. Mol Syst Biol. 2008;4:164. Epub 2008 Feb 12. PMID: 18277380, doi:10.1038/msb.2008.5
Abstract:
A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Sequential polarization and imprinting of type 1 T helper lymphocytes by interferon-gamma and interleukin-12.
Schulz EG, Mariani L, Radbruch A, H\u00f6fer T. Immunity.2009;30(5):666-8. 19409816,
Abstract:
Differentiation of naive T lymphocytes into type I T helper (Th1) cells requires interferon-gamma and interleukin-12. It is puzzling that interferon-gamma induces the Th1 transcription factor T-bet, whereas interleukin-12 mediates Th1 cell lineage differentiation. We use mathematical modeling to analyze the expression kinetics of T-bet, interferon-gamma, and the IL-12 receptor beta2 chain (IL-12Rbeta2) during Th1 cell differentiation, in the presence or absence of interleukin-12 or interferon-gamma signaling. We show that interferon-gamma induced initial T-bet expression, whereas IL-12Rbeta2 was repressed by T cell receptor (TCR) signaling. The termination of TCR signaling permitted upregulation of IL-12Rbeta2 by T-bet and interleukin-12 signaling that maintained T-bet expression. This late expression of T-bet, accompanied by the upregulation of the transcription factors Runx3 and Hlx, was required to imprint the Th cell for interferon-gamma re-expression. Thus initial polarization and subsequent imprinting of Th1 cells are mediated by interlinked, sequentially acting positive feedback loops of TCR-interferon-gamma-Stat1-T-bet and interleukin-12-Stat4-T-bet signaling.


The original model was created by:
Edda G. Schulz
schulz@drfz.de
Theoretical Biophysics, Institute of Biology, Humboldt Universit\u00e4t, Invalidenstrasse 42, 10115 Berlin, Germany.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Minimum criteria for DNA damage-induced phase advances in circadian rhythms.
Hong CI, Z\u00e1mborszky J, Csik\u00e1sz-Nagy A. PLoS Comput Biol. 2009 May;5(5):e1000384. 19424508,
Abstract:
Robust oscillatory behaviors are common features of circadian and cell cycle rhythms. These cyclic processes, however, behave distinctively in terms of their periods and phases in response to external influences such as light, temperature, nutrients, etc. Nevertheless, several links have been found between these two oscillators. Cell division cycles gated by the circadian clock have been observed since the late 1950s. On the other hand, ionizing radiation (IR) treatments cause cells to undergo a DNA damage response, which leads to phase shifts (mostly advances) in circadian rhythms. Circadian gating of the cell cycle can be attributed to the cell cycle inhibitor kinase Wee1 (which is regulated by the heterodimeric circadian clock transcription factor, BMAL1/CLK), and possibly in conjunction with other cell cycle components that are known to be regulated by the circadian clock (i.e., c-Myc and cyclin D1). It has also been shown that DNA damage-induced activation of the cell cycle regulator, Chk2, leads to phosphorylation and destruction of a circadian clock component (i.e., PER1 in Mus or FRQ in Neurospora crassa). However, the molecular mechanism underlying how DNA damage causes predominantly phase advances in the circadian clock remains unknown. In order to address this question, we employ mathematical modeling to simulate different phase response curves (PRCs) from either dexamethasone (Dex) or IR treatment experiments. Dex is known to synchronize circadian rhythms in cell culture and may generate both phase advances and delays. We observe unique phase responses with minimum delays of the circadian clock upon DNA damage when two criteria are met: (1) existence of an autocatalytic positive feedback mechanism in addition to the time-delayed negative feedback loop in the clock system and (2) Chk2-dependent phosphorylation and degradation of PERs that are not bound to BMAL1/CLK.

The original xpp file of the model is available as a supplement of the article (Text S1).\t

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3369, + "tag": "BioModels:BIOMD0000000216" + }, + { + "id": 3370, + "tag": "Circadian rhythm" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:08.776829+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000216", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2507": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2507, + "name": "Bruggeman2005_AmmoniumAssimilation", + "repository_type": "biomodels", + "summary": "

This a model from the article:
The multifarious short-term regulation of ammonium assimilation of Escherichia coli: dissection using an in silico replica.
Bruggeman FJ, Boogerd FC, Westerhoff HV. FEBS J. 2005 Apr;272(8):1965-85. 15819889 ,
Abstract:
Ammonium assimilation in Escherichia coli is regulated through multiple mechanisms (metabolic, signal transduction leading to covalent modification, transcription, and translation), which (in-)directly affect the activities of its two ammonium-assimilating enzymes, i.e. glutamine synthetase (GS) and glutamate dehydrogenase (GDH). Much is known about the kinetic properties of the components of the regulatory network that these enzymes are part of, but the ways in which, and the extents to which the network leads to subtle and quasi-intelligent regulation are unappreciated. To determine whether our present knowledge of the interactions between and the kinetic properties of the components of this network is complete - to the extent that when integrated in a kinetic model it suffices to calculate observed physiological behaviour - we now construct a kinetic model of this network, based on all of the kinetic data on the components that is available in the literature. We use this model to analyse regulation of ammonium assimilation at various carbon statuses for cells that have adapted to low and high ammonium concentrations. We show how a sudden increase in ammonium availability brings about a rapid redirection of the ammonium assimilation flux from GS/glutamate synthase (GOGAT) to GDH. The extent of redistribution depends on the nitrogen and carbon status of the cell. We develop a method to quantify the relative importance of the various regulators in the network. We find the importance is shared among regulators. We confirm that the adenylylation state of GS is the major regulator but that a total of 40% of the regulation is mediated by ADP (22%), glutamate (10%), glutamine (7%) and ATP (1%). The total steady-state ammonium assimilation flux is remarkably robust against changes in the ammonium concentration, but the fluxes through GS and GDH are completely nonrobust. Gene expression of GOGAT above a threshold value makes expression of GS under ammonium-limited conditions, and of GDH under glucose-limited conditions, sufficient for ammonium assimilation.

This version of the model originates from JWS online . The original model can be retrieved here .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one ofthe two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This the model used in the article:
Quantitative analysis of pathways controlling extrinsic apoptosis in single cells.
Albeck JG, Burke JM, Aldridge BB, Zhang M, Lauffenburger DA, Sorger PK. Mol Cell. 2008 Apr 11;30(1):11-25. PMID: 18406323 , doi: 10.1016/j.molcel.2008.02.012
Abstract:
Apoptosis in response to TRAIL or TNF requires the activation of initiator\tcaspases, which then activate the effector caspases that dismantle\tcells and cause death. However, little is known about the dynamics\tand regulatory logic linking initiators and effectors. Using a combination\tof live-cell reporters, flow cytometry, and immunoblotting, we find\tthat initiator caspases are active during the long and variable delay\tthat precedes mitochondrial outer membrane permeabilization (MOMP)\tand effector caspase activation. When combined with a mathematical\tmodel of core apoptosis pathways, experimental perturbation of regulatory\tlinks between initiator and effector caspases reveals that XIAP and\tproteasome-dependent degradation of effector caspases are important\tin restraining activity during the pre-MOMP delay. We identify conditions\tin which restraint is impaired, creating a physiologically indeterminate\tstate of partial cell death with the potential to generate genomic\tinstability. Together, these findings provide a quantitative picture\tof caspase regulatory networks and their failure modes.
The mitochondrial compartment is just added as a logical partition and its volume is not used in the mathematical formulas, to stick closer to the expressions used in the matlab files distributed with the original publication. There only the rate constants for bimolecular reactions are adapted by division by v , the ration of the volumes of the mitochondrial compartment and the total cell.
For BCL2 overexpression in figure 5, the initial BCL2 amount was increased by a factor 12 to 2.4*10 5 . For siRNA downregulation of XIAP its amount was multiplied by 0.13 to 1.3*10 4 .


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one ofthe two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drugtargets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amountof active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t described in: Systems-level interactions between insulin-EGF networks amplify mitogenic signaling.
\t Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN.;Mol Syst Biol. 2009;5:256. Epub 2009 Apr 7. PMID:19357636; doi:10.1038/msb.2009.19
Abstract:
\t Crosstalk mechanisms have not been studied as thoroughly as individual signaling pathways. We exploit experimental and computational approaches to reveal how a concordant interplay between the insulin and epidermal growth factor (EGF) signaling networks can potentiate mitogenic signaling. In HEK293 cells, insulin is a poor activator of the Ras/ERK (extracellular signal-regulated kinase) cascade, yet it enhances ERK activation by low EGF doses. We find that major crosstalk mechanisms that amplify ERK signaling are localized upstream of Ras and at the Ras/Raf level. Computational modeling unveils how critical network nodes, the adaptor proteins GAB1 and insulin receptor substrate (IRS), Src kinase, and phosphatase SHP2, convert insulin-induced increase in the phosphatidylinositol-3,4,5-triphosphate (PIP(3)) concentration into enhanced Ras/ERK activity. The model predicts and experiments confirm that insulin-induced amplification of mitogenic signaling is abolished by disrupting PIP(3)-mediated positive feedback via GAB1 and IRS. We demonstrate that GAB1 behaves as a non-linear amplifier of mitogenic responses and insulin endows EGF signaling with robustness to GAB1 suppression. Our results show the feasibility of using computational models to identify key target combinations and predict complex cellular responses to a mixture of external cues. \t

\t An extracellular compartment with 34 times the volume of the cell was added and the association rate as well as the dissociation constants for Insulin and EGF binding were altered (kon'=34*kon, KD'=KD/34). This was done to allow using the concentrations for those species given in the article and retaining the same dynamics and Ligand depletion as in the matlab file the SBML file was exported from. \t

SBML model exported from PottersWheel on 2008-10-14 16:26:44.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Calcium spiking.
Meyer T, Stryer L Annu Rev Biophys Biophys Chem1991:20:153-74 1867714,
Abstract:
No Abstract Available

The IP3-Ca2+ Crosscoupling Model (ICC) is reviewed by Meyer and Stryer in 1991, originally from Meyer and Stryer, 1988. PMID - 2455890 Parameters refer to figures 5 and 6 of the article which were reproduced by using Copasi 4.5 (Build 30).Species CaI and IP3 are buffered to 1% and 50% percent, respectively.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the basic model described in eq. 1 of the article:
A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.
Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9
Abstract:
We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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NFkB model M(14,25,28) - Lipniacky's NFkB model

This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,created to demonstrate application of model reduction methods proposed in

This a model from the article:
Robust simplifications of multiscale biochemical networks.
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A. BMC Syst Biol2008:2:86 18854041,
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology with potential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

This model is originally proposed by Lipniacki 2004 (Lipniacki T, Paszek P, Brasier AR, Luxon B, Kimmel M.(2004). Mathematical model of NF-kappaB regulatory module. J. Theor. Biol. 228 (2): 195-215. 15094015

The models are provided in CellDesigner v3.5format. The name of the model M(x,y,z) should bedeciphered as following:

x - number of speciesy - number of reactionsz - number of parameters

Simulation protocol:The model can be simulated in CellDesignerdirectly, or in any simulator supportingevents. The simulation period should beset up in 20 hours (t=72000 sec). This model reproduces Figure 3b (M(14,25,28)) of the publication.

For additional information please contactAndrei.Zinovyev at curie.fr

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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NFkB model M(39,65,90) - most complex model

This is a model of NFkB pathway functioning from hierarchy of models of decreasing complexity,created to demonstrate application of model reduction methods proposed in

Robust simplifications of multiscale biochemical networks.
Radulescu O, Gorban A., Zinovyev A., Lilienbaum. A. BMC Syst Biol2008:2:86 18854041,
Abstract:
BACKGROUND: Cellular processes such as metabolism, decision making in development and differentiation, signalling, etc., can be modeled as large networks of biochemical reactions. In order to understand the functioning of these systems, there is a strong need for general model reduction techniques allowing to simplify models without loosing their main properties. In systems biology we also need to compare models or to couple them as parts of larger models. In these situations reduction to a common level of complexity is needed. RESULTS: We propose a systematic treatment of model reduction of multiscale biochemical networks. First, we consider linear kinetic models, which appear as \"pseudo-monomolecular\" subsystems of multiscale nonlinear reaction networks. For such linear models, we propose a reduction algorithm which is based on a generalized theory of the limiting step that we have developed in 1. Second, for non-linear systems we develop an algorithm based on dominant solutions of quasi-stationarity equations. For oscillating systems, quasi-stationarity and averaging are combined to eliminate time scales much faster and much slower than the period of the oscillations. In all cases, we obtain robust simplifications and also identify the critical parameters of the model. The methods are demonstrated for simple examples and for a more complex model of NF-kappaB pathway. CONCLUSION: Our approach allows critical parameter identification and produces hierarchies of models. Hierarchical modeling is important in \"middle-out\" approaches when there is need to zoom in and out several levels of complexity. Critical parameter identification is an important issue in systems biology withpotential applications to biological control and therapeutics. Our approach also deals naturally with the presence of multiple time scales, which is a general property of systems biology models.

The models are provided in CellDesigner v3.5format. The name of the model M(x,y,z) should bedeciphered as following:

x - number of speciesy - number of reactionsz - number of parameters

Simulation protocol:The model can be simulated in CellDesignerdirectly, or in any simulator supportingevents. The simulation period should beset up in 40 hours (t=144000 sec).The 'signal' event applies signal to thepathway at the moment t=20 hours=72000 sec. This model reproduces Figure 7c (M(39,65,90)) of the publication.

For additional information please contactAndrei.Zinovyev at curie.fr

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the extended model described the article:
Bifurcation analysis of the regulatory modules of the mammalian G1/S transition.
Swat M, Kel A, Herzel H. Bioinformatics 2004 Jul 10;20(10):1506-11. PMID: 15231543 , doi: 10.1093/bioinformatics/bth110
Abstract:
MOTIVATION: Mathematical models of the cell cycle can contribute to an understanding of its basic mechanisms. Modern simulation tools make the analysis of key components and their interactions very effective. This paper focuses on the role of small modules and feedbacks in the gene-protein network governing the G1/S transition in mammalian cells. Mutations in this network may lead to uncontrolled cell proliferation. Bifurcation analysis helps to identify the key components of this extremely complex interaction network.
RESULTS: We identify various positive and negative feedback loops in the network controlling the G1/S transition. It is shown that the positive feedback regulation of E2F1 and a double activator-inhibitor module can lead to bistability. Extensions of the core module preserve the essential features such as bistability. The complete model exhibits a transcritical bifurcation in addition to bistability. We relate these bifurcations to the cell cycle checkpoint and the G1/S phase transition point. Thus, core modules can explain major features of the complex G1/S network and have a robust decision taking function.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Quantifying robustness of biochemical network models.
Ma L, Iglesias PA. BMC Bioinformatics.2002 Dec 13;3:38. 12482327,
Abstract:
BACKGROUND: Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed. RESULTS: Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary. CONCLUSION: The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.


This model is originally proposed by Laub and Loomis (1998).[Laub MT, Loomis WF (1998). A molecular network that produces spontaneous oscillations in excitable cells of Dictyostelium. Mol Biol Cell. 9(12):3521-32. PubMED: 12482327.
The parameters used in this model (Ma and Iglesias, 2002), are different from that used in the original model (Laub and Loomis, 1998), because of the typographical errors in the original paper. The parameters used in the model presented by Ma and Iglesias, are obtained directly from the authors of original publication (Laub and Loomis, 1998). These parameters are also used in the website for the Laub-Loomis model, http://www-biology.ucsd.edu/labs/loomis/network/laubloomis.html.
By using this model, Kim et al., 2006 [Kim J, Bates DG, Postlethwaite I, Ma L, Iglesias PA. (2006) Robustness analysis of biochemical network models. Syst Biol (Stevenage). 153(3):96-104. PubMED: 16984084], validate and extend the analysis approach proposed by Ma and Iglesias (2002), by showing how hybrid optimisation can be used to compute worst-case parameter combinations in the model.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Sensitivity analysis of parameters controlling oscillatory signalling in the NF-kappaB pathway: the roles of IKK and IkappaBalpha.
Ihekwaba AE, Broomhead DS, Grimley RL, Benson N, Kell DB Syst Biol (Stevenage) [2004 Jun;1(1):93-103 17052119 ,
Abstract:
Analysis of cellular signalling interactions is expected to create an enormous informatics challenge, perhaps even greater than that of analysing the genome. A key step in the evolution towards a more quantitative understanding of signalling is to specify explicitly the kinetics of all chemical reaction steps in a pathway. We have reconstructed a model of the nuclear factor, kappaB (NF-kappaB) signalling pathway, containing 64 parameters and 26 variables, including steps in which the activation of the NF-kappaB transcription factor is intimately associated with the phosphorylation and ubiquitination of its inhibitor kappaB by a membrane-associated kinase, and its translocation from the cytoplasm to the nucleus. We apply sensitivity analysis to the model. This identifies those parameters in this (IkappaB)/NF-kappaB signalling system (containing only induced IkappaBalpha isoform) that most affect the oscillatory concentration of nuclear NF-kappaB (in terms of both period and amplitude). The intention is to provide guidance on which proteins are likely to be most significant as drug targets or should be exploited for further, more detailed experiments. The sensitivity coefficients were found to be strongly dependent upon the magnitude of the parameter change studied, indicating the highly non-linear nature of the system. Of the 64 parameters in the model, only eight to nine exerted a major control on nuclear NF-kappaB oscillations, and each of these involved as reaction participants either the IkappaB kinase (IKK) or IkappaBalpha, directly. This means that the dominant dynamics of the pathway can be reflected, in addition to that of nuclear NF-kappaB itself, by just two of the other pathway variables. This is conveniently observed in a phase-plane plot.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A kinetic study of a ternary cycle between adenine nucleotides.
Valero E, Var\u00f3n R, Garc\u00eda-Carmona F FEBS J. [2006 Aug;273(15):3598-613 16884499 ,
Abstract:
In the present paper, a kinetic study is made of the behavior of a moiety-conserved ternary cycle between the adenine nucleotides. The system contains the enzymes S-acetyl coenzyme A synthetase, adenylate kinase and pyruvate kinase, and converts ATP into AMP, then into ADP and finally back to ATP. L-Lactate dehydrogenase is added to the system to enable continuous monitoring of the progress of the reaction. The cycle cannot work when the only recycling substrate in the reaction medium is AMP. A mathematical model is proposed whose kinetic behavior has been analyzed both numerically by integration of the nonlinear differential equations describing the kinetics of the reactions involved, and analytically under steady-state conditions, with good agreement with the experimental results being obtained. The data obtained showed that there is a threshold value of the S-acetyl coenzyme A synthetase/adenylate kinase ratio, above which the cycle stops because all the recycling substrate has been accumulated as AMP, never reaching the steady state. In addition, the concept of adenylate energy charge has been applied to the system, obtaining the enabled values of the rate constants for a fixed adenylate energy charge value and vice versa.

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This a model from the article:
Mitochondrial energetic metabolism: a simplified model of TCA cycle with ATP production.
Nazaret C, Heiske M, Thurley K, Mazat JP J. Theor. Biol. 2009 Jun;258(3):455-64 19007794 ,
Abstract:
Mitochondria play a central role in cellular energetic metabolism. The essential parts of this metabolism are the tricarboxylic acid (TCA) cycle, the respiratory chain and the adenosine triphosphate (ATP) synthesis machinery. Here a simplified model of these three metabolic components with a limited set of differential equations is presented. The existence of a steady state is demonstrated and results of numerical simulations are presented. The relevance of a simple model to represent actual in vivo behavior is discussed.

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This a model from the article:
The smallest chemical reaction system with bistability
Thomas Wilhelm BMC Systems Biology2009;Sep 8;3:90. 19737387,
Abstract:
Background
Bistability underlies basic biological phenomena, such as cell division, differentiation, cancer onset, and apoptosis. So far biologists identified two necessary conditions for bistability: positive feedback and ultrasensitivity.
Results
Biological systems are based upon elementary mono- and bimolecular chemical reactions. In order to definitely clarify all necessary conditions for bistability we here present the corresponding minimal system. According to our definition, it contains the minimal number of (i) reactants, (ii) reactions, and (iii) terms in the corresponding ordinary differential equations (decreasing importance from i-iii). The minimal bistable system contains two reactants and four irreversible reactions (three bimolecular, one monomolecular).We discuss the roles of the reactions with respect to the necessary conditions for bistability: two reactions comprise the positive feedback loop, a third reaction filters out small stimuli thus enabling a stable 'off' state, and the fourth reaction prevents explosions. We argue that prevention of explosion is a third general necessary condition for bistability, which is so far lacking discussion in the literature.Moreover, in addition to proving that in two-component systems three steady states are necessary for bistability (five for tristability, etc.), we also present a simple general method to design such systems: one just needs one production and three different degradation mechanisms (one production, five degradations for tristability, etc.). This helps modelling multistable systems and it is important for corresponding synthetic biology projects.
Conclusion
The presented minimal bistable system finally clarifies the often discussed question for the necessary conditions for bistability. The three necessary conditions are: positive feedback, a mechanism to filter out small stimuli and a mechanism to prevent explosions. This is important for modelling bistability with simple systems and for synthetically designing new bistable systems. Our simple model system is also well suited for corresponding teaching purposes.


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This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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PURPOSE: This tumor response pharmacodynamic model aims to describe primary lesion shrinkage in non-small cell lung cancer over time and determine if concentration-based exposure metrics for gemcitabine or that of its metabolites,n2',2'-difluorodeoxyuridine or gemcitabine triphosphate, are better than gemcitabine dose for prediction of individual response. EXPERIMENTAL DESIGN: Gemcitabine was given thrice weekly on days 1 and 8 in combination with carboplatin, which was given only on day 1 of every cycle. Gemcitabine amount in the body and area under the concentration-time curves of plasma gemcitabine, 2',2'-difluorodeoxyuridine, and intracellular gemcitabine triphosphate in white cells were compared to determine which best describes tumor shrinkage over time. Tumor growth kinetics were described using a Gompertz-like model.RESULTS: The apparent half-life for the effect of gemcitabine was 7.67 weeks. The tumor turnover time constant was 21.8 week.cm. Baseline tumor size and gemcitabine amount in the body to attain 50% of tumor shrinkage were estimated to be 6.66 cm and 10,600 mg. There was no evidence of relapse during treatment.

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This a model from the article:
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network.
K\u00fchn C, Wierling C, K\u00fchn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ. BMC Syst Biol.2009 Aug 23;3:83. 19698179,
Abstract:
BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.

The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the extended model described in eq. 2 of the article:
A model of phosphofructokinase and glycolytic oscillations in the pancreatic beta-cell.
Westermark PO and Lansner A. Biophys J. 2003 Jul;85(1):126-39. PMID: 12829470, doi:10.1016/S0006-3495(03)74460-9
Abstract:
We have constructed a model of the upper part of the glycolysis in the pancreatic beta-cell. The model comprises the enzymatic reactions from glucokinase to glyceraldehyde-3-phosphate dehydrogenase (GAPD). Our results show, for a substantial part of the parameter space, an oscillatory behavior of the glycolysis for a large range of glucose concentrations. We show how the occurrence of oscillations depends on glucokinase, aldolase and/or GAPD activities, and how the oscillation period depends on the phosphofructokinase activity. We propose that the ratio of glucokinase and aldolase and/or GAPD activities are adequate as characteristics of the glucose responsiveness, rather than only the glucokinase activity. We also propose that the rapid equilibrium between different oligomeric forms of phosphofructokinase may reduce the oscillation period sensitivity to phosphofructokinase activity. Methodologically, we show that a satisfying description of phosphofructokinase kinetics can be achieved using the irreversible Hill equation with allosteric modifiers. We emphasize the use of parameter ranges rather than fixed values, and the use of operationally well-defined parameters in order for this methodology to be feasible. The theoretical results presented in this study apply to the study of insulin secretion mechanisms, since glycolytic oscillations have been proposed as a cause of oscillations in the ATP/ADP ratio which is linked to insulin secretion.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
A modelling approach to quantify dynamic crosstalk between the pheromone and the starvation pathway in baker's yeast.
Schaber J, Kofahl B, Kowald A, Klipp E FEBS J.2006 Aug; 273(15):3520-33 16884493,
Abstract:
Cells must be able to process multiple information in parallel and, moreover, they must also be able to combine this information in order to trigger the appropriate response. This is achieved by wiring signalling pathways such that they can interact with each other, a phenomenon often called crosstalk. In this study, we employ mathematical modelling techniques to analyse dynamic mechanisms and measures of crosstalk. We present a dynamic mathematical model that compiles current knowledge about the wiring of the pheromone pathway and the filamentous growth pathway in yeast. We consider the main dynamic features and the interconnections between the two pathways in order to study dynamic crosstalk between these two pathways in haploid cells. We introduce two new measures of dynamic crosstalk, the intrinsic specificity and the extrinsic specificity. These two measures incorporate the combined signal of several stimuli being present simultaneously and seem to be more stable than previous measures. When both pathways are responsive and stimulated, the model predicts that (a) the filamentous growth pathway amplifies the response of the pheromone pathway, and (b) the pheromone pathway inhibits the response of filamentous growth pathway in terms of mitogen activated protein kinase activity and transcriptional activity, respectively. Among several mechanisms we identified leakage of activated Ste11 as the most influential source of crosstalk. Moreover, we propose new experiments and predict their outcomes in order to test hypotheses about the mechanisms of crosstalk between the two pathways. Studying signals that are transmitted in parallel gives us new insights about how pathways and signals interact in a dynamical way, e.g., whether they amplify, inhibit, delay or accelerate each other.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
PKPD model of interleukin-21 effects on thermoregulation in monkeys--application and evaluation of stochastic differential equations.
Overgaard RV, Holford N, Rytved KA, Madsen H. Pharm Res.2007 Feb;24(2):298-309. PUBMED,
Abstract:
PURPOSE: To describe the pharmacodynamic effects of recombinant human interleukin-21 (IL-21) on core body temperature in cynomolgus monkeys using basic mechanisms of heat regulation. A major effort was devoted to compare the use of ordinary differential equations (ODEs) with stochastic differential equations (SDEs) in pharmacokinetic pharmacodynamic (PKPD) modelling. METHODS: A temperature model was formulated including circadian rhythm, metabolism, heat loss, and a thermoregulatory set-point. This model was formulated as a mixed-effects model based on SDEs using NONMEM. RESULTS: The effects of IL-21 were on the set-point and the circadian rhythm of metabolism. The model was able to describe a complex set of IL-21 induced phenomena, including 1) disappearance of the circadian rhythm, 2) no effect after first dose, and 3) high variability after second dose. SDEs provided a more realistic description with improved simulation properties, and further changed the model into one that could not be falsified by the autocorrelation function. CONCLUSIONS: The IL-21 induced effects on thermoregulation in cynomolgus monkeys are explained by a biologically plausible model. The quality of the model was improved by the use of SDEs.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Jiang2007 - GSIS system, Pancreatic Beta Cells
Description of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells.

This model is described in the article:

Jiang N, Cox RD, Hancock JM.
Mamm Genome 2007 Jul; 18(6-7):508-20.

Abstract:

The construction and characterization of a core kinetic model of the glucose-stimulated insulin secretion system (GSIS) in pancreatic beta cells is described. The model consists of 44 enzymatic reactions, 59 metabolic state variables, and 272 parameters. It integrates five subsystems: glycolysis, the TCA cycle, the respiratory chain, NADH shuttles, and the pyruvate cycle. It also takes into account compartmentalization of the reactions in the cytoplasm and mitochondrial matrix. The model shows expected behavior in its outputs, including the response of ATP production to starting glucose concentration and the induction of oscillations of metabolite concentrations in the glycolytic pathway and in ATP and ADP concentrations. Identification of choke points and parameter sensitivity analysis indicate that the glycolytic pathway, and to a lesser extent the TCA cycle, are critical to the proper behavior of the system, while parameters in other components such as the respiratory chain are less critical. Notably, however, sensitivity analysis identifies the first reactions of nonglycolytic pathways as being important for the behavior of the system. The model is robust to deletion of malic enzyme activity, which is absent in mouse pancreatic beta cells. The model represents a step toward the construction of a model with species-specific parameters that can be used to understand mouse models of diabetes and the relationship of these mouse models to the human disease state.

The model reproduces Figure 2 of the paper, and is built using files 'ModelNNT11.xml' and 'changed.m' available from http://www.har.mrc.ac.uk/research/bioinformatics/research_areas/systems_biology.html .

A couple of small errors in the model (in the original SBML file 'ModelNNT11.xml') have been corrected. The errors are:

This model is hosted on BioModels Database and identified by: BIOMD0000000239 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Transient heterogeneity in extracellular protease production by Bacillus subtilis.
Veening JW, Igoshin OA, Eijlander RT, Nijland R, Hamoen LW, Kuipers OP Mol. Syst. Biol. 2008 ; Volume: 4 : 184 18414485,
Abstract:
The most sophisticated survival strategy Bacillus subtilis employs is the differentiation of a subpopulation of cells into highly resistant endospores. To examine the expression patterns of non-sporulating cells within heterogeneous populations, we used buoyant density centrifugation to separate vegetative cells from endospore-containing cells and compared the transcriptome profiles of both subpopulations. This demonstrated the differential expression of various regulons. Subsequent single-cell analyses using promoter-gfp fusions confirmed our microarray results. Surprisingly, only part of the vegetative subpopulation highly and transiently expresses genes encoding the extracellular proteases Bpr (bacillopeptidase) and AprE (subtilisin), both of which are under the control of the DegU transcriptional regulator. As these proteases and their degradation products freely diffuse within the liquid growth medium, all cells within the clonal population are expected to benefit from their activities, suggesting that B. subtilis employs cooperative or even altruistic behavior. To unravel the mechanisms by which protease production heterogeneity within the non-sporulating subpopulation is established, we performed a series of genetic experiments combined with mathematical modeling. Simulations with our model yield valuable insights into how population heterogeneity may arise by the relatively long and variable response times within the DegU autoactivating pathway.


This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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\t described in: Pharmacokinetic-pharmacodynamic modeling of caffeine: Tolerance to pressor effects
\t Shi J, Benowitz NL, Denaro CP and Sheiner LB. ;Clin. Pharmacol. Ther. 1993 Jan;53(1):6-14. PMID:8422743;
Abstract:
\t We propose a parametric pharmacokinetic-pharmacodynamic model for caffeine that quantifies the development of tolerance to the pressor effect of the drug and characterizes the mean behavior and inter-individual variation of both pharmacokinetics and pressor effect. Our study in a small group of subjects indicates that acute tolerance develops to the pressor effect of caffeine and that both the pressor effect and tolerance occur after some time delay relative to changes in plasma caffeine concentration. The half-life of equilibration of effect with plasma caffeine concentration is about 20 minutes. The half-life of development and regression of tolerance is estimated to be about 1 hour, and the model suggests that tolerance, at its fullest, causes more than a 90 percent reduction of initial (nontolerant) effect. Whereas tolerance to the pressor effect of caffeine develops in habitual coffee drinkers, the pressor response is regained after relatively brief periods of abstinence. Because of the rapid development and regression of tolerance, the pressor response to caffeine depends on how much caffeine is consumed, the schedule of consumption, and the elimination half-life of caffeine. \t

Caffeine intake in this version is modelled as cups of coffee drunk at regular intervals (parameter t_interval). The amount of caffeine per cup is determined by the parameter cupsize. The body weight of the person drinking is given by the parameter bodyweight.
The even coffee cup occures delayed to the drinking of each cup, as the availability of the caffeine in the digestive tract is assumed to be delayed to the ingestion by the time t_lag.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Theoretical and experimental evidence for hysteresis in cell proliferation.
Bai S, Goodrich D, Thron CD, Tecarro E, Obeyesekere M. Cell Cycle. 2003 Jan-Feb;2(1):46-52. 12695688 ,
Abstract:
We propose a mathematical model for the regulation of the G1-phase of the mammalian cell cycle taking into account interactions of cyclin D/cdk4, cyclin E/cdk2, Rb and E2F. Mathematical analysis of this model predicts that a change in the proliferative status in response to a change in concentrations of serum growth factors will exhibit the property of hysteresis: the concentration of growth factors required to induce proliferation is higher than the concentration required to maintain proliferation. We experimentally confirmed this prediction in mouse embryonic fibroblasts in vitro. In agreement with the mathematical model, this indicates that changes in proliferative mode caused by small changes in concentrations of growth factors are not easily reversible. Based on this study, we discuss the importance of proliferation hysteresis for cell cycle regulation.


The original model was taken from the Cell Cycle DataBase (CCDB).

Variable added: assignment rule for denoting phosphorylated Rb (Rb_phosphorylated i.e(RT-RS-R)) created.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This is the reduced model (model 8) described in: Dynamics within the CD95 death-inducing signaling complex decide life and death of cells.
Leo Neumann, Carina Pforr, Joel Beaudouin, Alexander Golks, Peter H. Krammer, Inna N. Lavrik and Roland Eils (German Cancer Research Center (DKFZ), http://www.dkfz.de);Mol Sys Biol2010;6:352. doi:10.1038/msb.2010.6;

Abstract:
This study explores the dilemma in cellular signaling that triggering of CD95 (Fas/APO-1) in some situations results in cell death and in others leads to the activation of NF-\u03baB. We established an integrated kinetic mathematical model for CD95-mediated apoptotic and NF-\u03baB signaling. Systematic model reduction resulted in a surprisingly simple model well approximating experimentally observed dynamics. The model postulates a new link between c-FLIPL cleavage in the death-inducing signaling complex (DISC) and the NF-\u03baB pathway. We validated experimentally that CD95 stimulation resulted in an interaction of p43-FLIP with the IKK complex followed by its activation. Furthermore, we showed that the apoptotic and NF-\u03baB pathways diverge already at the DISC. Model and experimental analysis of DISC formation showed that a subtle balance of c-FLIPL and procaspase-8 determines life/death decisions in a nonlinear manner. We present an integrated model describing the complex dynamics of CD95-mediated apoptosis and NF-\u03baB signaling.

The original was taken from the MSB article supplementary material site msb20106-s2.xml. All the species ids were changed since the model was not a valid SBML with its original ids - Lukas.

Notes added to the species [L] (the initial concentration of Anti-CD95), regarding changes to be made in the initial concentration of [L], to obtain figure 5D.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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\t This is the model described in: Bacterial adaptation through distributed sensing of metabolic fluxes
\tOliver Kotte, Judith B Zaugg and Matthias Heinemann;Mol Sys Biol2010;6:355. doi:10.1038/msb.2010.10;
Abstract:
\t The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.\t

In its current form this SBML model is parametrized for the glucose to acetate transition and to simulate the extended diauxic shift as shown in figure 3 and scenario 6 of the attached matlab file. In this scenario the cells first are grown from an OD600 (BM) of 0.03 with a starting glucose concentration of 0.5 g/l for 8.15 h (29340 sec). Then a medium containing 5 g/l acetate is inoculated with these cells to an OD600 of 0.03 and grown for another 19.7 hours (70920 sec). Finally the cells are shifted to a medium containing both glucose and acetate at a concentration of 3 g/l with a starting OD600 of 0.0005.
The shifts where implemented using events triggering at the times determined by the parameters shift1 and shift2 (in hours). To simulate other scenarios the initial conditions need to be changed as described in the supplemental materials (supplement 1)
The original SBML model and the MATLAB file used for the calculations can be down loaded as supplementary materials of the publication from the MSB website. (supplement 2).

The units of the external metabolites are in [g/l], those of the biomass in optical density,OD600, taken as dimensionless, and [micromole/(gramm dry weight)] for all intracellular metabolites. As the latter cannot be implemented in SBML, it was chosen to be micromole only and the units of the parameters are left mostly undefined.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This the model from the article:
A biochemically structured model for Saccharomyces cerevisiae.
Lei F, Rotb\u00f8ll M, J\u00f8rgensen SB. J Biotechnol. 2001 Jul 12;88(3):205-21. \t PMID: 11434967 ,DOI: 10.1016/S0168-1656(01)00269-3

Abstract:
A biochemically structured model for the aerobic growth of Saccharomyces cerevisiae on glucose and ethanol is presented. The model focuses on the pyruvate and acetaldehyde branch points where overflow metabolism occurs when the growth changes from oxidative to oxido-reductive. The model is designed to describe the onset of aerobic alcoholic fermentation during steady-state as well as under dynamical conditions, by triggering an increase in the glycolytic flux using a key signalling component which is assumed to be closely related to acetaldehyde. An investigation of the modelled process dynamics in a continuous cultivation revealed multiple steady states in a region of dilution rates around the transition between oxidative and oxido-reductive growth. A bifurcation analysis using the two external variables, the dilution rate, D, and the inlet concentration of glucose, S(f), as parameters, showed that a fold bifurcation occurs close to the critical dilution rate resulting in multiple steady-states. The region of dilution rates within which multiple steady states may occur depends strongly on the substrate feed concentration. Consequently a single steady state may prevail at low feed concentrations, whereas multiple steady states may occur over a relatively wide range of dilution rates at higher feed concentrations.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This the single cell model from the article:
A multiscale model to investigate circadian rhythmicity of pacemaker neurons in the suprachiasmatic nucleus.
Vasalou C, Henson MA. PLoS Comput Biol 2010 Mar 12;6(3):e1000706.\t PMID: 20300645 , DOI: 10.1371/journal.pcbi.1000706 ;

Abstract:
The suprachiasmatic nucleus (SCN) of the hypothalamus is a multicellular system that drives daily rhythms in mammalian behavior and physiology. Although the gene regulatory network that produces daily oscillations within individual neurons is well characterized, less is known about the electrophysiology of the SCN cells and how firing rate correlates with circadian gene expression. We developed a firing rate code model to incorporate known electrophysiological properties of SCN pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels.

Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the model with unfitted parameters described in the article
Dynamic rerouting of the carbohydrate flux is key to counteracting oxidative stress
Markus Ralser, Mirjam M Wamelink, Axel Kowald, Birgit Gerisch, Gino Heeren, Eduard A Struys, Edda Klipp, Cornelis Jakobs, Michael Breitenbach, Hans Lehrach and Sylvia Krobitsch, J Biol 2007 6(4):10; PMID: 18154684 , doi: 10.1186/jbiol61
Abstract:
BACKGROUND: Eukaryotic cells have evolved various response mechanisms to counteract the deleterious consequences of oxidative stress. Among these processes, metabolic alterations seem to play an important role.
RESULTS: We recently discovered that yeast cells with reduced activity of the key glycolytic enzyme triosephosphate isomerase exhibit an increased resistance to the thiol-oxidizing reagent diamide. Here we show that this phenotype is conserved in Caenorhabditis elegans and that the underlying mechanism is based on a redirection of the metabolic flux from glycolysis to the pentose phosphate pathway, altering the redox equilibrium of the cytoplasmic NADP(H) pool. Remarkably, another key glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), is known to be inactivated in response to various oxidant treatments, and we show that this provokes a similar redirection of the metabolic flux.
CONCLUSION: The naturally occurring inactivation of GAPDH functions as a metabolic switch for rerouting the carbohydrate flux to counteract oxidative stress. As a consequence, altering the homoeostasis of cytoplasmic metabolites is a fundamental mechanism for balancing the redox state of eukaryotic cells under stress conditions.

Different realtive enzyme velocities can be simulated by varying the parameters k_rel_TPI and k_rel_GAPDH .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This file describes the SBML version of the mathematical model in the following journal article: Linking Pulmonary Oxygen Uptake, Muscle Oxygen Utilization and Cellular Metabolism during Exercise, Ann Biomed Eng. 2007 Jun;35(6):956-69. (Pubmed ID: 17380394). This mathematical model simulates oxygen transport and metabolism in skeletal muscle in response to a step change from a warm-up steady state to a higher work rate corresponding to exercise at different levels of intensity: moderate (M), heavy (H) and very heavy (VH). The model parameter values are listed in the tables of this article. The parameter values that are independent of the exercise level are reported in Table 2. The parameter values that depend on the exercise level are reported in Tables 1A, 3 and 4. The model simulations (Figures 2, 3, 4 and 5) were obtained for a representative subject with a set of parameter values different from those in Table 1A, 3 and 4. In the sbml model, these model parameters are used to simulate exercise at a very heavy (VH) intensity for the representative subject. Additionally, the parameter values needed to simulate exercise at moderate (M) and heavy (H) intensity are reported in the list of parameters of the file. The model simulates dynamics of (1) the concentrations of free (F) and total (T) oxygen concentration in blood (CFcap, CTcap) and tissue (CFtis, CTtis), Adenosine Triphosphate (ATP), Adenosine Diphosphate (ADP), Phosphocreatine (PCr) and Creatine (Cr); (2) the metabolic flux of oxidative phosphorylation, creatine kinase and ATPase; (3) the oxygen uptake in blood and oxygen transport rate from blood to tissue during exercise. The simulation also computes muscle oxygen saturation (StO2m) and relative muscle oxygen saturation (RStO2m) in order to compare simulated and experimental responses of human muscle oxygenation during exercise. The model was successfully tested with Roadrunner of the Systems Biology Workbench (SBW). The model simulations obtained with Roadrunner match those obtained with the mathematical model represented in Fortran and Matlab for relative and absolute tolerance smaller than 10-7.

To allow for simulations at varying levels of exercise, the parameter exercise_level was introduced. A value of 1 means medium, 2 heavy and 3 very heavy exercise. Setting this parameter assigns the parameters Vmax, KatpaseE, dQMm and tauQm with the relevant parameters. The warmup steady state is influenced by the parameter changes for this representative subject and the model has to be brought into steady state after each change of exercise level.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\ufffd\ufffdre N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Restif2006 - Whooping cough

This model is described in the article:

Restif O, Grenfell BT.
Proc. Biol. Sci. 2006 Feb; 273(1585): 409-416

Abstract:

Models for the diversity and evolution of pathogens have branched into two main directions: the adaptive dynamics of quantitative life-history traits (notably virulence) and the maintenance and invasion of multiple, antigenically diverse strains that interact with the host's immune memory. In a first attempt to reconcile these two approaches, we developed a simple modelling framework where two strains of pathogens, defined by a pair of life-history traits (infectious period and infectivity), interfere through a given level of cross-immunity. We used whooping cough as a potential example, but the framework proposed here could be applied to other acute infectious diseases. Specifically, we analysed the effects of these parameters on the invasion dynamics of one strain into a population, where the second strain is endemic. Whereas the deterministic version of the model converges towards stable coexistence of the two strains in most cases, stochastic simulations showed that transient epidemic dynamics can cause the extinction of either strain. Thus ecological dynamics, modulated by the immune parameters, eventually determine the adaptive value of different pathogen genotypes. We advocate an integrative view of pathogen dynamics at the crossroads of immunology, epidemiology and evolution, as a way towards efficient control of infectious diseases.

This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size\u00a0 N\u00a0= 1. The model reproduces the deterministic time courses. Stochastic interpretation with Copasi UI gave results similar to the article, but was not extensively tested. The initial conditions for competition simulations can be derived by equilibrating the system for one pathogen and then adding a starting concentration for the other.

Originally created by libAntimony v1.3 (using libSBML 4.1.0-b1)

This model is hosted on BioModels Database and identified by: BIOMD0000000249.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. This mechanistic model comprises the explicit reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. In the below article, two different mechanistic models have been studied, the first one based on previously known interactions but failing to account for the experimental data and the second one including additional interactions which were discovered and confirmed by new experiments. The mechanistic model encoded here is the second one, the extended and at the time of creation most complete model of cell fate decision making in response to different doses of EGF or HRG stimulation. The encoded parameter set corresponds to 10mM HRG stimulation as shown in Fig.1 of the article. The Supplementary Methods of the article provide further parameter sets that allow simulations for different ligands and different doses. A corresponding core model is available from http://www.ebi.ac.uk/biomodels/ as MODEL1003170000.

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010 , doi: 10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

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This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. The model comprises lumped reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. The parameter set implemented here corresponds to that used for generating Figs. 4 B,C,D (red curves for 10nM HRG) of the below article in Cell (2010). Moreover, we found that the same model described well the dynamics in different cell types (MCF-7 and PC-12), of different ligands (EGF and HRG) and at different doses (0.1nM, 1nM, 10nM) for a unique set of parameter values (as implemented here and reported in Table SD4_1 of the article) except for four parameters characterising the input, cytoplasmic ppERK. These four parameters K1, K2, tau1 and tau2 are used in the two equations involving species x1 and x2. These two equations define a phenomenological input module to describe the ligand-, dose- and cell type-dependent dynamics of ppERKc which are not modelled in mechanistic detail here. The four parameter values can be adjusted to model a specific ligand, dose and cell type. 8 parameter sets for different experiments are given in Table SD4_2 of the article. This SBML file, however, carries just one such parameter set. We have chosen that of MCF-7 cells stimulated by 10nM of HRG. To reproduce all simulations from the article, please replace the parameter values for K1, K2, tau1, tau2 as needed.

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010, doi:10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Stress-specific response of the p53-Mdm2 feedback loop
Alexander Hunziker, Mogens H Jensen and Sandeep Krishna BMC Systems Biology 2010, Jul 12;4(1):94 20624280,
Abstract:
ABSTRACT: BACKGROUND: The p53 signalling pathway has hundreds of inputs and outputs. It can trigger cellular senescence, cell-cycle arrest and apoptosis in response to diverse stress conditions, including DNA damage, hypoxia and nutrient deprivation. Signals from all these inputs are channeled through a single node, the transcription factor p53. Yet, the pathway is flexible enough to produce different downstream gene expression patterns in response to different stresses. RESULTS: We construct a mathematical model of the negative feedback loop involving p53 and its inhibitor, Mdm2, at the core of this pathway, and use it to examine the effect of different stresses that trigger p53. In response to DNA damage, hypoxia, etc., the model exhibits a wide variety of specific output behaviour -- steady states with low or high levels of p53 and Mdm2, as well as spiky oscillations with low or high average p53 levels. CONCLUSIONS: We show that even a simple negative feedback loop is capable of exhibiting the kind of flexible stress-specific response observed in the p53 system. Further, our model provides a framework for predicting the differences in p53 response to different stresses and single nucleotide polymorphisms.

The parameters of the model corresponds to the resting state, with delta = 11hr-1, gamma = 0.2hr-1, kt = 0.03nM-1hr-1 and kf = 5000nM-1hr-1.

To simulate different stress conditions as in figure 2A (also look at the curation figure of this model) of the reference publication, the above parameter should be changed. The parameter values corresponding to different stress conditions are shown in the following table.


Stress Condition/Parameter delta gamma kt kf
Nutlin 11hr-1 0.2hr-1 0.03nM-1hr-1 500nM-1hr-1
Oncogene 2hr-10.2hr-10.03nM-1hr-15000nM-1hr-1
DNA damage 2hr-10.5hr-10.03nM-1hr-12500nM-1hr-1
Hypoxia 2hr-10.2hr-10.01nM-1hr-15000nM-1hr-1

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the model described in the article:
The danger of metabolic pathways with turbo design
Teusink B, Walsh MC, van Dam K, Westerhoff HV Trends Biochem. Sci. 1998 May; Volume: 23 (Issue: 5 ): 162-9 9612078 ,
Abstract:
Many catabolic pathways begin with an ATP-requiring activation step, after which further metabolism yields a surplus of ATP. Such a 'turbo' principle is useful but also contains an inherent risk. This is illustrated by a detailed kinetic analysis of a paradoxical Saccharomyces cerevisiae mutant; the mutant fails to grow on glucose because of overactive initial enzymes of glycolysis, but is defective only in an enzyme (trehalose 6-phosphate synthase) that appears to have little relevance to glycolysis. The ubiquity of pathways that possess an initial activation step, suggests that there might be many more genes that, when deleted, cause rather paradoxical regulation phenotypes (i.e. growth defects caused by enhanced utilization of growth substrate).

The model represents the wild-type cell: 'guarded' glycolysis, which is the inhibition of the HK module by hexose monophosphate. The model reproduces figures 3c and 3d of the reference publication.

To reproduce unguarded glycolysis, set parameter wild_type to '0'.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
How yeast cells synchronize their glycolytic oscillations: a perturbation analytic treatment
Bier M, Bakker BM, Westerhoff HV. Biophys. J2000 Mar;78(3):1087-93. 10692299,
Abstract:
Of all the lifeforms that obtain their energy from glycolysis, yeast cells are among the most basic. Under certain conditions the concentrations of the glycolytic intermediates in yeast cells can oscillate. Individual yeast cells in a suspension can synchronize their oscillations to get in phase with each other. Although the glycolytic oscillations originate in the upper part of the glycolytic chain, the signaling agent in this synchronization appears to be acetaldehyde, a membrane-permeating metabolite at the bottom of the anaerobic part of the glycolytic chain. Here we address the issue of how a metabolite remote from the pacemaking origin of the oscillation may nevertheless control the synchronization. We present a quantitative model for glycolytic oscillations and their synchronization in terms of chemical kinetics. We show that, in essence, the common acetaldehyde concentration can be modeled as a small perturbation on the \"pacemaker\" whose effect on the period of the oscillations of cells in the same suspension is indeed such that a synchronization develops.

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This is A431 IERMv1.0 model described in the article
Input-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic data.
William W Chen, Birgit Schoeberl, Paul J Jasper, Mario Niepel, Ulrik B Nielsen, Douglas A Lauffenburger and Peter K Sorger. Molecular Systems Biology 2009; 5:239. PMID: 19156131 , DOI: 10.1038/msb.2008.74

Abstract:
The ErbB signaling pathways, which regulate diverse physiological responses such as cell survival, proliferation and motility, have been subjected to extensive molecular analysis. Nonetheless, it remains poorly understood how different ligands induce different responses and how this is affected by oncogenic mutations. To quantify signal flow through ErbB-activated pathways we have constructed, trained and analyzed a mass action model of immediate-early signaling involving ErbB1-4 receptors (EGFR, HER2/Neu2, ErbB3 and ErbB4), and the MAPK and PI3K/Akt cascades. We find that parameter sensitivity is strongly dependent on the feature (e.g. ERK or Akt activation) or condition (e.g. EGF or heregulin stimulation) under examination and that this context dependence is informative with respect to mechanisms of signal propagation. Modeling predicts log-linear amplification so that significant ERK and Akt activation is observed at ligand concentrations far below the K(d) for receptor binding. However, MAPK and Akt modules isolated from the ErbB model continue to exhibit switch-like responses. Thus, key system-wide features of ErbB signaling arise from nonlinear interaction among signaling elements, the properties of which appear quite different in context and in isolation.

The sbml model is available as supplemental material to the article and at http://www.cdpcenter.org/resources/models/chen-et-al-2008/ . It was slightly changed to make it valid SBML and to incorporate the step functions, described in the readme file and needed for inhibitor preincubation. the equilibration processes end at 1800 sec, so to reproduce the dynamics shown in the publication and supplemental material, only the time points after 1800 need to be considered. The parameter set is the hand fitted one used for Sfigure 3 in the supplemental materials. All species are in molecules, apart from HRG, EGF and Inh, which are in M.

The results shown in SFigure 3 can be calculated dividing the parameters ERK_PP , AKT_PP and ERB_B1_P_tot by ERK_t , AKT_t and EGFR_t , respectively. Somehow we did not find the right scaleing factor for the phosphorylated ErbB1 receptor. Therefore the model does only qualitatively reproduces the timecourses shown in the first row of Sfigure 3.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\ufffd\ufffd\ufffd\ufffd\ufffd\ufffdre N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This is the standard model described in the article:
Systems analysis of effector caspase activation and its control by X-linked inhibitor of apoptosis protein.
Rehm M, Huber HJ, Dussmann H, Prehn JH. EMBO J. 2006 Sep 20;25(18):4338-49. Epub 2006 Aug 24. PMID:16932741, doi:10.1038/sj.emboj.7601295;
Abstract:
Activation of effector caspases is a final step during apoptosis. Single-cell imaging studies have demonstrated that this process may occur as a rapid, all-or-none response, triggering a complete substrate cleavage within 15 min. Based on biochemical data from HeLa cells, we have developed a computational model of apoptosome-dependent caspase activation that was sufficient to remodel the rapid kinetics of effector caspase activation observed in vivo. Sensitivity analyses predicted a critical role for caspase-3-dependent feedback signalling and the X-linked-inhibitor-of-apoptosis-protein (XIAP), but a less prominent role for the XIAP antagonist Smac. Single-cell experiments employing a caspase fluorescence resonance energy transfer substrate verified these model predictions qualitatively and quantitatively. XIAP was predicted to control this all-or-none response, with concentrations as high as 0.15 microM enabling, but concentrations >0.30 microM significantly blocking substrate cleavage. Overexpression of XIAP within these threshold concentrations produced cells showing slow effector caspase activation and submaximal substrate cleavage. Our study supports the hypothesis that high levels of XIAP control caspase activation and substrate cleavage, and may promote apoptosis resistance and sublethal caspase activation in vivo.\t

This model is slightly altered from the description in the article. Cytochrome C and SMAC release from the mitochondrion is modelled as simple first order kinetics, giving the same form as the (integrated) equations in the supplement of the article. The apoptosome formation is modelled equally - and independent of the Cytochrome C release. The speed is either limited by the Apaf1 or ProCaspase9 concentration, whichever is higher, symbolised via the parameter with the ID apolim.
Also, once the substrate concentration falls below 1 percent, the event Production_Breakdown is triggered, leading to a breakdown of XIAP and procaspase3 production and turning off of the enhanced/proteosomal degradation (degradation rate for reactions 38,39,40,43,44,46,48,50,51 changes from 0.0347 to 0.0058).

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the self maintaining metabolism model described in the article:
A Simple Self-Maintaining Metabolic System: Robustness, Autocatalysis, Bistability.
Piedrafita G, Montero F, Mor\u00e1n F, C\u00e1rdenas ML, Cornish-Bowden A, PLoS Computational Biology 2010, 6(8):e1000872. doi:10.1371/journal.pcbi.1000872
Abstract:
A living organism must not only organize itself from within; it must also maintain its organization in the face of changes in its environment and degradation of its components. We show here that a simple (M,R)-system consisting of three interlocking catalytic cycles, with every catalyst produced by the system itself, can both establish a non-trivial steady state and maintain this despite continuous loss of the catalysts by irreversible degradation. As long as at least one catalyst is present at a sufficient concentration in the initial state, the others can be produced and maintained. The system shows bistability, because if the amount of catalyst in the initial state is insufficient to reach the non-trivial steady state the system collapses to a trivial steady state in which all fluxes are zero. It is also robust, because if one catalyst is catastrophically lost when the system is in steady state it can recreate the same state. There are three elementary flux modes, but none of them is an enzyme-maintaining mode, the entire network being necessary to maintain the two catalysts

As this is a theoretical model and no units are given in the article, the standard units (mol, seconds and litre) are used for the parameters. k8 and k11 are set equal to k4.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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Ortega2006 - bistability from doublephosphorylation in signal transduction

This model is described in the article:

Ortega F, Garc\u00e9s JL, Mas F, Kholodenko BN, Cascante M.
FEBS J. 2006 Sep; 273(17): 3915-3926

Abstract:

Previous studies have suggested that positive feedback loops and ultrasensitivity are prerequisites for bistability in covalent modification cascades. However, it was recently shown that bistability and hysteresis can also arise solely from multisite phosphorylation. Here we analytically demonstrate that double phosphorylation of a protein (or other covalent modification) generates bistability only if: (a) the two phosphorylation (or the two dephosphorylation) reactions are catalyzed by the same enzyme; (b) the kinetics operate at least partly in the zero-order region; and (c) the ratio of the catalytic constants of the phosphorylation and dephosphorylation steps in the first modification cycle is less than this ratio in the second cycle. We also show that multisite phosphorylation enlarges the region of kinetic parameter values in which bistability appears, but does not generate multistability. In addition, we conclude that a cascade of phosphorylation/dephosphorylation cycles generates multiple steady states in the absence of feedback or feedforward loops. Our results show that bistable behavior in covalent modification cascades relies not only on the structure and regulatory pattern of feedback/feedforward loops, but also on the kinetic characteristics of their component proteins.

This model is hosted on BioModels Database and identified by: BIOMD0000000258.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which cancatalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Deficient condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which cancatalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Adequate condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This a model from the article:
Systems analysis of iron metabolism: the network of iron pools and fluxes
Tiago JS Lopes, Tatyana Luganskaja, Maja Vujic-Spasic, Matthias W Hentze, Martina U Muckenthaler, Klaus Schumann and Jens G Reich BMC Systems Biology2010, Aug 13;4(1):112. 20704761,
Abstract:
Background
Every cell of the mammalian organism needs iron in numerous oxido-reductive processes as well as for transport and storage of oxygen. The versatility of ionic iron makes it a toxic entity which can catalyze the production of radicals that damage vital membranous and macromolecular assemblies in the cell. The mammalian organism maintains therefore a complex regulatory network of iron uptake, excretion and intra-body distribution. Intracellular regulation in different cell types is intertwined with a global hormonal signaling structure. Iron deficiency as well as excess of iron are frequent and serious human disorders. They can affect every cell, but also the organism as a whole.
Results
Here, we present a kinematic model of the dynamic system of iron pools and fluxes. It is based on ferrokinetic data and chemical measurements in C57BL6 wild-type mice maintained on iron-deficient, iron-adequate, or iron-loaded diet. The tracer iron levels in major tissues and organs (16 compartment) were followed for 28 days. The evaluation resulted in a whole-body model of fractional clearance rates. The analysis permits calculation of absolute flux rates in the steady-state, of iron distribution into different organs, of tracer-accessible pool sizes and of residence times of iron in the different compartments in response to three states of iron-repletion induced by the dietary regime.
Conclusions
This mathematical model presents a comprehensive physiological picture of mice under three different diets with varying iron contents. The quantitative results reflect systemic properties of iron metabolism: dynamic closedness, hierarchy of time scales, switch-over response and dynamics of iron storage in parenchymal organs. Therefore, we could assess which parameters will change under dietary perturbations and study in quantitative terms when those changes take place.

This model corresponds to the Iron Loaded condition - Mice

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use.
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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EGF dependent Akt pathway model

made by Kazuhiro A. Fujita.

This is the EGF dependent Akt pathway model described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters EGF_conc_pulse , EGF_conc_step and EGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value EGF_conc_pulse , a 60 second step with EGF_conc_step or a signal increasing from 0 to EGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter are set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3462, + "tag": "BioModels:BIOMD0000000262" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:31.160327+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000262", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2553": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2553, + "name": "Fujita2010_Akt_Signalling_NGF", + "repository_type": "biomodels", + "summary": "

NGF dependent Akt pathway model

made by Kazuhiro A. Fujita.

This is the NGF dependent Akt pathway model described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters NGF_conc_pulse , NGF_conc_step and NGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value NGF_conc_pulse , a 60 second step with NGF_conc_step or a signal increasing from 0 to NGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter is set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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Akt pathway model with EGFR inhibitor

made by Kazuhiro A. Fujita.

This is the Akt pathway model with an EGFR inhibitor described in:
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics.
Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810
Abstract:
In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)\u2013dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors.

The different versions of input, step, pulse and ramp, can be simulated using the parameters EGF_conc_pulse , EGF_conc_step and EGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value EGF_conc_pulse , a 60 second step with EGF_conc_step or a signal increasing from 0 to EGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter are set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time .

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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This model is from the article:
Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex.
Conradie R, Bruggeman FJ, Ciliberto A, Csik\u00e1sz-Nagy A, Nov\u00e1k B, Westerhoff HV, Snoep JL FEBS J.2010 Jan; 277(2): 357-67 20015233,
Abstract:
Numerous top-down kinetic models have been constructed to describe the cell cycle. These models have typically been constructed, validated and analyzed using model species (molecular intermediates and proteins) and phenotypic observations, and therefore do not focus on the individual model processes (reaction steps). We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. The reactions in the model with the highest RP control were those involved in: (a) the interplay between retinoblastoma protein and E2F transcription factor; (b) those synthesizing the delayed response genes and cyclin D/Cdk4 in response to growth signals; (c) the E2F-dependent cyclin E/Cdk2 synthesis reaction; as well as (d) p27 formation reactions. Nine of the 23 intermediates were shown to have a good correlation between their concentration control and RP control. Sensitivity and co-control analysis indicated that the strongest control of the RP is mediated via the cyclin E/Cdk2:p27 complex concentration. Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP.

The rate constants presented in the paper have units [per tenth of an hour] and have been changed here to [per hour] (e.g. k16 = 0.25 not 0.025); for further confirmation of the correctness of this change, see the original model (Novak, J Theor Biol 2004 230:563).

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3465, + "tag": "BioModels:BIOMD0000000265" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:32.586531+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000265", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2556": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2556, + "name": "Voit2003 - Trehalose Cycle", + "repository_type": "biomodels", + "summary": "
Voit2003 - Trehalose Cycle

This model is described in the article:

Voit EO.
J. Theor. Biol. 2003 Jul; 223(1): 55-78

Abstract:

The physiological hallmark of heat-shock response in yeast is a rapid, enormous increase in the concentration of trehalose. Normally found in growing yeast cells and other organisms only as traces, trehalose becomes a crucial protector of proteins and membranes against a variety of stresses, including heat, cold, starvation, desiccation, osmotic or oxidative stress, and exposure to toxicants. Trehalose is produced from glucose 6-phosphate and uridine diphosphate glucose in a two-step process, and recycled to glucose by trehalases. Even though the trehalose cycle consists of only a few metabolites and enzymatic steps, its regulatory structure and operation are surprisingly complex. The article begins with a review of experimental observations on the regulation of the trehalose cycle in yeast and proposes a canonical model for its analysis. The first part of this analysis demonstrates the benefits of the various regulatory features by means of controlled comparisons with models of otherwise equivalent pathways lacking these features. The second part elucidates the significance of the expression pattern of the trehalose cycle genes in response to heat shock. Interestingly, the genes contributing to trehalose formation are up-regulated to very different degrees, and even the trehalose degrading trehalases show drastically increased activity during heat-shock response. Again using the method of controlled comparisons, the model provides rationale for the observed pattern of gene expression and reveals benefits of the counterintuitive trehalase up-regulation.

Toinduce a heat shock, set the parameter heat_shock from 0 to 1. Thischanges the parameter values of X8 to X19 from 1 to the valuesgiven in table 3 of the original publication.
Asthis is an S-systems model, it does not contain any reactionsencoded in SBML.

This model is hosted on BioModels Database and identified by: BIOMD0000000266.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3466, + "tag": "BioModels:BIOMD0000000266" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3467, + "tag": "Trehalose metabolic process" + } + ], + "timestamp_created": "2025-01-30 13:34:33.048461+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000266", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2557": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2557, + "name": "Lebeda2008 - BoNT paralysis (3 step model)", + "repository_type": "biomodels", + "summary": "
Lebeda2008 - BoNT paralysis (3 step model)
The onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction is describing in the model. This is the 3-step model described in the paper. This model is the reduced form of the model developed my Simpson 1980; PMID:\u00a0\u00a0 6243359 \u00a0, i.e., it omits three unknown parameters that represents the binding sites for each species of the toxin. The extension to this model,\u00a0i.e. the 4-step model described in the paper is BIOMD0000000178.

This model is described in the article:

Lebeda FJ, Adler M, Erickson K, Chushak Y.
J Pharmacokinet Pharmacodyn 2008 Jun; 35(3): 251-267

Abstract:

Experimental studies have demonstrated that botulinum neurotoxin serotype A (BoNT/A) causes flaccid paralysis by a multi-step mechanism. Following its binding to specific receptors at peripheral cholinergic nerve endings, BoNT/A is internalized by receptor-mediated endocytosis. Subsequently its zinc-dependent catalytic domain translocates into the neuroplasm where it cleaves a vesicle-docking protein, SNAP-25, to block neurally evoked cholinergic neurotransmission. We tested the hypothesis that mathematical models having a minimal number of reactions and reactants can simulate published data concerning the onset of paralysis of skeletal muscles induced by BoNT/A at the isolated rat neuromuscular junction (NMJ) and in other systems. Experimental data from several laboratories were simulated with two different models that were represented by sets of coupled, first-order differential equations. In this study, the 3-step sequential model developed by Simpson (J Pharmacol Exp Ther 212:16-21,1980) was used to estimate upper limits of the times during which anti-toxins and other impermeable inhibitors of BoNT/A can exert an effect. The experimentally determined binding reaction rate was verified to be consistent with published estimates for the rate constants for BoNT/A binding to and dissociating from its receptors. Because this 3-step model was not designed to reproduce temporal changes in paralysis with different toxin concentrations, a new BoNT/A species and rate (k(S)) were added at the beginning of the reaction sequence to create a 4-step scheme. This unbound initial species is transformed at a rate determined by k(S) to a free species that is capable of binding. By systematically adjusting the values of k(S), the 4-step model simulated the rapid decline in NMJ function (k(S) >or= 0.01), the less rapid onset of paralysis in mice following i.m. injections (k (S) = 0.001), and the slow onset of the therapeutic effects of BoNT/A (k(S) < 0.001) in man. This minimal modeling approach was not only verified by simulating experimental results, it helped to quantitatively define the time available for an inhibitor to have some effect (t(inhib)) and the relation between this time and the rate of paralysis onset. The 4-step model predicted that as the rate of paralysis becomes slower, the estimated upper limits of (t(inhib)) for impermeable inhibitors become longer. More generally, this modeling approach may be useful in studying the kinetics of other toxins or viruses that invade host cells by similar mechanisms, e.g., receptor-mediated endocytosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000267.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3468, + "tag": "BioModels:BIOMD0000000267" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3292, + "tag": "Metalloendopeptidase activity" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3293, + "tag": "Negative regulation of synaptic transmission, cholinergic" + }, + { + "id": 3294, + "tag": "Neurotoxicity of clostridium toxins" + }, + { + "id": 3295, + "tag": "Pathogenesis" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 3296, + "tag": "Receptor-mediated endocytosis" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4599, + "tag": "Botulism" + } + ], + "timestamp_created": "2025-01-30 13:34:33.521297+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000267", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2558": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2558, + "name": "Reed2008_Glutathione_Metabolism", + "repository_type": "biomodels", + "summary": "

This is the model described in the article:
A mathematical model of glutathione metabolism.
Michael C Reed, Rachel L Thomas, Jovana Pavisic, S. Jill James, Cornelia M Ulrich and H. Frederik Nijhout, Theor Biol Med Model 2008,5:8; PubmedID:18442411 ; DOI:10.1186/1742-4682-5-8;
Abstract:
BACKGROUND: Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.
APPROACH: We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.
CONCLUSION: We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.\t

parameter orig. article this model
Vm_CBS 700000 420000
Vm_GNMT 245 260
K_sam_GNMT 32 63
Vr_MTD(mito) 600000 595000
V_CBS kinetic law rearranged
V_bmetc 913 913.4
Vm_GR 8925 892.5

This version of the model contains a feeding rhythm as used in figure 5 of the original article. Four parameters, breakfast, lunch dinner and fasting, describe the relative level of amino acids, described by the parameter aa_input or Aminoacid_input, in the blood. To remove the daily feeding rhythm, either set the parameters for meals and fasting to 1 (or for figure 3 to 0.333), or remove the assignment rule for the Aminoacid_input. For the steady state evaluations for figure 6, the mealtime parameters were set to one, which, while making Copasi complain about explicit time dependency, still gives valid results.

This version of the model differs slightly from the version described in the supplement, in which contains some typos. It was corrected using the version of JWS-online, created using the original matlab files, thankfully provided by the articles authors. Many thanks to Jacky Snoep for his help and support.

In the SBML version of the model the volumes of the mitochondrion, the cytoplasm and the cell were all set to one to obtain the same equations as described in the supplemental materials of the article. The total folate is equally split between the cytosol and the mitochondrion and divided by 3/4 for the cytosol and 1/4 for the mitochondrion, respectively. To obtain an SBML model in which the volumes of the compartments, cytosol and mito, are used, the model needs to be altered as follows:

While the concentrations stay the same after these alteration, the reaction fluxes change by a factor of cytosol and mito for cytosolic and mitchondrial reactions, respectively.

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3469, + "tag": "BioModels:BIOMD0000000268" + }, + { + "id": 3470, + "tag": "Glutathione metabolic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3471, + "tag": "hepatocyte" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4602, + "tag": "Autistic disorder" + }, + { + "id": 4603, + "tag": "Cancer" + }, + { + "id": 4604, + "tag": "Cardiovascular system disease" + }, + { + "id": 4592, + "tag": "Down syndrome" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:34:34.015665+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000268", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2559": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2559, + "name": "Liu2010_Hormonal_Crosstalk_Arabidopsis", + "repository_type": "biomodels", + "summary": "

This is the single cell model for analysis of hormonal crosstalk in Arabidopsis described in the article:
Modelling and experimental analysis of hormonal crosstalk in Arabidopsis.
Liu J, Mehdi S, Topping J, Tarkowski P and Lindsey K. Mol Syst Biol. 2010 Jun 8;6:373; PmID: 20531403 , DOI: 10.1038/msb.2010.26
Abstract:
An important question in plant biology is how genes influence the crosstalk between hormones to regulate growth. In this study, we model POLARIS (PLS) gene function and crosstalk between auxin, ethylene and cytokinin in Arabidopsis. Experimental evidence suggests that PLS acts on or close to the ethylene receptor ETR1, and a mathematical model describing possible PLS-ethylene pathway interactions is developed, and used to make quantitative predictions about PLS-hormone interactions. Modelling correctly predicts experimental results for the effect of the pls gene mutation on endogenous cytokinin concentration. Modelling also reveals a role for PLS in auxin biosynthesis in addition to a role in auxin transport. The model reproduces available mutants, and with new experimental data provides new insights into how PLS regulates auxin concentration, by controlling the relative contribution of auxin transport and biosynthesis and by integrating auxin, ethylene and cytokinin signalling. Modelling further reveals that a bell-shaped dose-response relationship between endogenous auxin and root length is established via PLS. This combined modelling and experimental analysis provides new insights into the integration of hormonal signals in plants.

This model was originally created using Copasi and taken from the supplementary materials of the MSB article. It uses equation 5 for the auxin biosynthesis and was altered to also contain the reactions for ACC, IAA and cytokinine import. Different from the supplementary material, the parameters for the auxin synthesis, v2, are set to k2c = 0.01 uM and k2=0.2 uM_per_sec and for the WT PLS transcription k6=0.3 . To obtain the model described in the first table of the supplementary materials, set k2c=k2=0 and k6=0.9 . For the pls and PLSox mutants, k6 should be set to 0 and 0.45, respectively.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.
For more information see the terms of use .
To cite BioModels Database, please use Le Nov\u00e8re N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 3472, + "tag": "Auxin-activated signaling pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3473, + "tag": "BioModels:BIOMD0000000269" + }, + { + "id": 3474, + "tag": "Cytokinin-activated signaling pathway" + }, + { + "id": 3475, + "tag": "Ethylene-activated signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:34.532282+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000269", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2560": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2560, + "name": "Schilling2009 - ERK distributive", + "repository_type": "biomodels", + "summary": "
Schilling2009 - ERK distributive

This model has been exported from PottersWheel on 2009-04-20 18:57:44.\u00a0 The PottersWheel Model Definition file can be obtained from the curation tab.

This model is described in the article:

Schilling M, Maiwald T, Hengl S, Winter D, Kreutz C, Kolch W, Lehmann WD, Timmer J, Klingm\u00fcller U.
Mol. Syst. Biol. 2009; 5: 334

Abstract:

Cell fate decisions are regulated by the coordinated activation of signalling pathways such as the extracellular signal-regulated kinase (ERK) cascade, but contributions of individual kinase isoforms are mostly unknown. By combining quantitative data from erythropoietin-induced pathway activation in primary erythroid progenitor (colony-forming unit erythroid stage, CFU-E) cells with mathematical modelling, we predicted and experimentally confirmed a distributive ERK phosphorylation mechanism in CFU-E cells. Model analysis showed bow-tie-shaped signal processing and inherently transient signalling for cytokine-induced ERK signalling. Sensitivity analysis predicted that, through a feedback-mediated process, increasing one ERK isoform reduces activation of the other isoform, which was verified by protein over-expression. We calculated ERK activation for biochemically not addressable but physiologically relevant ligand concentrations showing that double-phosphorylated ERK1 attenuates proliferation beyond a certain activation level, whereas activated ERK2 enhances proliferation with saturation kinetics. Thus, we provide a quantitative link between earlier unobservable signalling dynamics and cell fate decisions.

This model is hosted on BioModels Database and identified by: BIOMD0000000270.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3476, + "tag": "BioModels:BIOMD0000000270" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:34.992076+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000270", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2561": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2561, + "name": "Becker2010_EpoR_CoreModel", + "repository_type": "biomodels", + "summary": "

This is the core model described in the article:
Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor
Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingm\u00fcller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988
Abstract:
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

SBML model exported from PottersWheel.

% PottersWheel model definition filefunction m = BeckerSchilling2010_EpoR_CoreModel()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'BeckerSchilling2010_EpoR_CoreModel';m.name        = 'BeckerSchilling2010_EpoR_CoreModel';m.description = 'BeckerSchilling2010_EpoR_CoreModel';m.authors     = {'Verena Becker',' Marcel Schilling'};m.dates       = {'2010'};m.type        = 'PW-2-0-42';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'EpoR'     ,     516, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo'      , 2030.19, 'global', 1890,  2310,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo_EpoR' ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Epo_EpoRi',       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dEpoi'    ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dEpoe'    ,       0, 'fix'   ,    0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {            }, {'EpoR'      }, {  }, 'C' , [] , 'k1*k2', {'kt','Bmax'}, [], 'reaction0001');m = pwAddR(m, {'EpoR'      }, {            }, {  }, 'MA', [] , []     , {'kt'       }, [], 'reaction0002');m = pwAddR(m, {'Epo','EpoR'}, {'Epo_EpoR'  }, {  }, 'MA', [] , []     , {'kon'      }, [], 'reaction0003');m = pwAddR(m, {'Epo_EpoR'  }, {'Epo','EpoR'}, {  }, 'MA', [] , []     , {'koff'     }, [], 'reaction0004');m = pwAddR(m, {'Epo_EpoR'  }, {'Epo_EpoRi' }, {  }, 'MA', [] , []     , {'ke'       }, [], 'reaction0005');m = pwAddR(m, {'Epo_EpoRi' }, {'Epo','EpoR'}, {  }, 'MA', [] , []     , {'kex'      }, [], 'reaction0006');m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoi'     }, {  }, 'MA', [] , []     , {'kdi'      }, [], 'reaction0007');m = pwAddR(m, {'Epo_EpoRi' }, {'dEpoe'     }, {  }, 'MA', [] , []     , {'kde'      }, [], 'reaction0008');%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'kt'  , 0.0329366 , 'global', 1e-007, 1000);m = pwAddK(m, 'Bmax', 516       , 'fix'   , 492   , 540 );m = pwAddK(m, 'kon' , 0.00010496, 'global', 1e-007, 1000);m = pwAddK(m, 'koff', 0.0172135 , 'global', 1e-007, 1000);m = pwAddK(m, 'ke'  , 0.0748267 , 'global', 1e-007, 1000);m = pwAddK(m, 'kex' , 0.00993805, 'global', 1e-007, 1000);m = pwAddK(m, 'kdi' , 0.00317871, 'global', 1e-007, 1000);m = pwAddK(m, 'kde' , 0.0164042 , 'global', 1e-007, 1000);%% Default sampling time pointsm.t = 0:3:99;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Epo + dEpoe'      , 'Epo_extracellular_obs');m = pwAddY(m, 'Epo_EpoR'         , 'Epo_cellsurface_obs'  );m = pwAddY(m, 'Epo_EpoRi + dEpoi', 'Epo_intracellular_obs');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_Epo_extracellular_obs', 1, 'fix', 0, 100);m = pwAddS(m, 'scale_Epo_cellsurface_obs'  , 1, 'fix', 0, 100);m = pwAddS(m, 'scale_Epo_intracellular_obs', 1, 'fix', 0, 100);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3477, + "tag": "BioModels:BIOMD0000000271" + }, + { + "id": 3478, + "tag": "Erythropoietin receptor activity" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3479, + "tag": "hematopoietic cell line" + } + ], + "timestamp_created": "2025-01-30 13:34:35.446040+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000271", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2562": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2562, + "name": "Becker2010_EpoR_AuxiliaryModel", + "repository_type": "biomodels", + "summary": "

This is the auxiliary model described in the article:
Covering a Broad Dynamic Range: Information Processing at the Erythropoietin Receptor
Verena Becker, Marcel Schilling, Julie Bachmann, Ute Baumann, Andreas Raue, Thomas Maiwald, Jens Timmer and Ursula Klingm\u00fcller; Science Published Online May 20, 2010; DOI: 10.1126/science.1184913 PMID: 20488988
Abstract:
Cell surface receptors convert extracellular cues into receptor activation, thereby triggering intracellular signaling networks and controlling cellular decisions. A major unresolved issue is the identification of receptor properties that critically determine processing of ligand-encoded information. We show by mathematical modeling of quantitative data and experimental validation that rapid ligand depletion and replenishment of cell surface receptor are characteristic features of the erythropoietin (Epo) receptor (EpoR). The amount of Epo-EpoR complexes and EpoR activation integrated over time corresponds linearly to ligand input, covering a broad range of ligand concentrations. This relation solely depends on EpoR turnover independent of ligand binding, suggesting an essential role of large intracellular receptor pools. These receptor properties enable the system to cope with basal and acute demand in the hematopoietic system.

SBML model exported from PottersWheel.

% PottersWheel model definition filefunction m = BeckerSchilling2010_EpoR_AuxiliaryMode()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'BeckerSchilling2010_EpoR_AuxiliaryMode';m.name        = 'BeckerSchilling2010_EpoR_AuxiliaryModel';m.description = 'BeckerSchilling2010_EpoR_AuxiliaryModel';m.authors     = {'Verena Becker',' Marcel Schilling'};m.dates       = {'2010'};m.type        = 'PW-2-0-42';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'EpoR'     ,      76, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv'      , 999.293, 'global', 900,  1100,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv_EpoR' ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SAv_EpoRi',       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dSAvi'    ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'dSAve'    ,       0, 'fix'   ,   0, 10000,   [], 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {            }, {'EpoR'      }, {  }, 'C' , [] , 'k1*k2', {'kt','Bmax_SAv'}, [], 'reaction0001');m = pwAddR(m, {'EpoR'      }, {            }, {  }, 'MA', [] , []     , {'kt'           }, [], 'reaction0002');m = pwAddR(m, {'SAv','EpoR'}, {'SAv_EpoR'  }, {  }, 'MA', [] , []     , {'kon_SAv'      }, [], 'reaction0003');m = pwAddR(m, {'SAv_EpoR'  }, {'SAv','EpoR'}, {  }, 'MA', [] , []     , {'koff_SAv'     }, [], 'reaction0004');m = pwAddR(m, {'SAv_EpoR'  }, {'SAv_EpoRi' }, {  }, 'MA', [] , []     , {'kt'           }, [], 'reaction0005');m = pwAddR(m, {'SAv_EpoRi' }, {'SAv'       }, {  }, 'MA', [] , []     , {'kex_SAv'      }, [], 'reaction0006');m = pwAddR(m, {'SAv_EpoRi' }, {'dSAvi'     }, {  }, 'MA', [] , []     , {'kdi'          }, [], 'reaction0007');m = pwAddR(m, {'SAv_EpoRi' }, {'dSAve'     }, {  }, 'MA', [] , []     , {'kde'          }, [], 'reaction0008');%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'kt'      , 0.0329366   , 'global', 1e-007, 1000);m = pwAddK(m, 'Bmax_SAv', 76          , 'fix'   , 61    , 91  );m = pwAddK(m, 'kon_SAv' , 2.29402e-006, 'global', 1e-007, 1000);m = pwAddK(m, 'koff_SAv', 0.00679946  , 'global', 1e-007, 1000);m = pwAddK(m, 'kex_SAv' , 0.01101     , 'global', 1e-007, 1000);m = pwAddK(m, 'kdi'     , 0.00317871  , 'global', 1e-007, 1000);m = pwAddK(m, 'kde'     , 0.0164042   , 'global', 1e-007, 1000);%% Default sampling time pointsm.t = 0:3:99;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'SAv + dSAve'      , 'SAv_extracellular_obs');m = pwAddY(m, 'SAv_EpoR'         , 'SAv_cellsurface_obs'  );m = pwAddY(m, 'SAv_EpoRi + dSAvi', 'SAv_intracellular_obs');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_SAv_extracellular_obs', 1, 'fix', 0, 100);m = pwAddS(m, 'scale_SAv_cellsurface_obs'  , 1, 'fix', 0, 100);m = pwAddS(m, 'scale_SAv_intracellular_obs', 1, 'fix', 0, 100);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3480, + "tag": "BioModels:BIOMD0000000272" + }, + { + "id": 3478, + "tag": "Erythropoietin receptor activity" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3479, + "tag": "hematopoietic cell line" + } + ], + "timestamp_created": "2025-01-30 13:34:35.936736+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000272", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2563": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2563, + "name": "Pokhilko2010_CircClock", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model.
Pokhilko A, Hodge SK, Stratford K, Knox K, Edwards KD, Thomson AW, Mizuno T, Millar AJ. Mol Syst Biol.2010 Sep 21;6:416. 20865009,
Abstract:
Circadian clocks generate 24-h rhythms that are entrained by the day/night cycle. Clock circuits include several light inputs and interlocked feedback loops, with complex dynamics. Multiple biological components can contribute to each part of the circuit in higher organisms. Mechanistic models with morning, evening and central feedback loops have provided a heuristic framework for the clock in plants, but were based on transcriptional control. Here, we model observed, post-transcriptional and post-translational regulation and constrain many parameter values based on experimental data. The model's feedback circuit is revised and now includes PSEUDO-RESPONSE REGULATOR 7 (PRR7) and ZEITLUPE. The revised model matches data in varying environments and mutants, and gains robustness to parameter variation. Our results suggest that the activation of important morning-expressed genes follows their release from a night inhibitor (NI). Experiments inspired by the new model support the predicted NI function and show that the PRR5 gene contributes to the NI. The multiple PRR genes of Arabidopsis uncouple events in the late night from light-driven responses in the day, increasing the flexibility of rhythmic regulation.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3481, + "tag": "BioModels:BIOMD0000000273" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:36.434652+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000273", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2564": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2564, + "name": "Rattanakul2003_BoneFormationModel", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling of bone formation and resorption mediated by parathyroid hormone: response to estrogen/PTH therapy.
\tRattanakul C, Lenbury Y, Krishnamara N, Wollkind DJ. Biosystems 2003:70(1):55-72. 12753937,
Abstract:
Bone, a major reservoir of body calcium, is under the hormonal control of the parathyroid hormone (PTH). Several aspects of its growth, turnover, and mechanism, occur in the absence of gonadal hormones. Sex steroids such as estrogen, nonetheless, play an important role in bone physiology, and are extremely essential to maintain bone balance in adults. In order to provide a basis for understanding the underlying mechanisms of bone remodeling as it is mediated by PTH, we propose here a mathematical model of the process. The nonlinear system model is then utilized to study the temporal effect of PTH as well as the action of estrogen replacement therapy on bone turnover. Analysis of the model is done on the assumption, supported by reported clinical evidence, that the process is characterized by highly diversified dynamics, which warrants the use of singular perturbation arguments. The model is shown to exhibit limit cycle behavior, which can develop into chaotic dynamics for certain ranges of the system's parametric values. Effects of estrogen and PTH administrations are then investigated by extending on the core model. Analysis of the model seems to indicate that the paradoxical observation that intermittent PTH administration causes net bone deposition while continuous administration causes net bone loss, and certain other reported phenomena may be attributed to the highly diversified dynamics which characterizes this nonlinear remodeling process.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: rattananakul, lenbury, krishnamara, wollkind. (2003) - version01
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3482, + "tag": "BioModels:BIOMD0000000274" + }, + { + "id": 3483, + "tag": "Bone resorption" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3484, + "tag": "Ossification" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:36.894667+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000274", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2565": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2565, + "name": "Goldbeter2007_Somitogenesis_Switch", + "repository_type": "biomodels", + "summary": "

This is the simple model without diffusion described in th epublication
Sharp developmental thresholds defined through bistability by antagonistic gradients of retinoic acid and FGF signaling.
Goldbeter A, Gonze D, Pourqui\u00e9 O. Dev Dyn. 2007 Jun;236(6):1495-508. PMID: 17497689, doi:10.1016/j.jtbi.2008.01.006
Abstract:
The establishment of thresholds along morphogen gradients in the embryo is poorly understood. Using mathematical modeling, we show that mutually inhibitory gradients can generate and position sharp morphogen thresholds in the embryonic space. Taking vertebrate segmentation as a paradigm, we demonstrate that the antagonistic gradients of retinoic acid (RA) and Fibroblast Growth Factor (FGF) along the presomitic mesoderm (PSM) may lead to the coexistence of two stable steady states. Here, we propose that this bistability is associated with abrupt switches in the levels of FGF and RA signaling, which permit the synchronized activation of segmentation genes, such as mesp2, in successive cohorts of PSM cells in response to the segmentation clock, thereby defining the future segments. Bistability resulting from mutual inhibition of RA and FGF provides a molecular mechanism for the all-or-none transitions assumed in the \"clock and wavefront\" somitogenesis model. Given that mutually antagonistic signaling gradients are common in development, such bistable switches could represent an important principle underlying embryonic patterning.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3485, + "tag": "BioModels:BIOMD0000000275" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3486, + "tag": "Somitogenesis" + }, + { + "id": 3487, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-01-30 13:34:37.355224+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000275", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2566": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2566, + "name": "Shrestha2010_HypoCalcemia_PTHresponse", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.
Shrestha RP, Hollot CV, Chipkin SR, Schmitt CP, Chait Y. Math Biosci.2010 Jul;226(1):46-57. 20406649,
Abstract:
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.

The model corresponds to hypocalcemic clamp test explained in the paper and parameter values used in the model are that of \"subject 1\". In order to obtain the plots corresponding to \"subject 2\" and \"subject 3\" the following parameters to be changed: lambda_1, lambda_2, m1, m2, R, beta, x1_n, x2_n, x2_min, x2_max, Ca0, Ca1, t0 and alpha.

parameter Subject 1 Subject 2 Subject 3
lambda_1 0.0125 0.0122 0.0269
lambda_2 0.5595 0.4642 0.4935
m1 112.5200 150.0000 90.8570
m2 15.0000 15.0000 15.0000
R 1.2162 1.1627 1.1889
beta 10e+06 10e+06 10e+06
x1_n 490.7800 452.8200 298.8200
x2_n 6.6290 9.5894 5.4600
x2_min 0.6697 1.4813 0.8287
x2_max 14.0430 17.8710 15.1990
Ca0 1.2550 1.2369 1.2475
Ca1 0.1817 0.2211 0.1985
t0 575 577 575
alpha 0.0442 0.0488 0.0472

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3488, + "tag": "BioModels:BIOMD0000000276" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3489, + "tag": "Response to parathyroid hormone" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4605, + "tag": "Calcium metabolism disease" + } + ], + "timestamp_created": "2025-01-30 13:34:37.818036+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000276", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2567": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2567, + "name": "Shrestha2010_HyperCalcemia_PTHresponse", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of parathyroid hormone response to acute changes in plasma ionized calcium concentration in humans.
Shrestha RP, Hollot CV, Chipkin SR, Schmitt CP, Chait Y. Math Biosci.2010 Jul;226(1):46-57. 20406649,
Abstract:
A complex bio-mechanism, commonly referred to as calcium homeostasis, regulates plasma ionized calcium (Ca(2+)) concentration in the human body within a narrow range which is crucial for maintaining normal physiology and metabolism. Taking a step towards creating a complete mathematical model of calcium homeostasis, we focus on the short-term dynamics of calcium homeostasis and consider the response of the parathyroid glands to acute changes in plasma Ca(2+) concentration. We review available models, discuss their limitations, then present a two-pool, linear, time-varying model to describe the dynamics of this calcium homeostasis subsystem, the Ca-PTH axis. We propose that plasma PTH concentration and plasma Ca(2+) concentration bear an asymmetric reverse sigmoid relation. The parameters of our model are successfully estimated based on clinical data corresponding to three healthy subjects that have undergone induced hypocalcemic clamp tests. In the first validation of this kind, with parameters estimated separately for each subject we test the model's ability to predict the same subject's induced hypercalcemic clamp test responses. Our results demonstrate that a two-pool, linear, time-varying model with an asymmetric reverse sigmoid relation characterizes the short-term dynamics of the Ca-PTH axis.

The model corresponds to hypercalcemic clamp test explained in the paper and parameter values used in the model are that of \"subject 1\". In order to obtain the plots corresponding to \"subject 2\" and \"subject 3\" the following parameters to be changed: lambda_1, lambda_2, m1, m2, R, beta, x1_n, x2_n, x2_min, x2_max, t0, Ca0, Ca1 and alpha.

parameter Subject 1 Subject 2 Subject 3
lambda_1 0.0125 0.0122 0.0269
lambda_2 0.5595 0.4642 0.4935
m1 112.5200 150.0000 90.8570
m2 15.0000 15.0000 15.0000
R 1.2162 1.1627 1.1889
beta 10e+06 10e+06 10e+06
x1_n 490.7800 452.8200 298.8200
x2_n 6.6290 9.5894 5.4600
x2_min 0.6697 1.4813 0.8287
x2_max 14.0430 17.8710 15.1990
Ca0 1.2200 1.2513 1.2480
Ca1 0.2624 0.2267 0.2132
t0 575 575 575
alpha 0.0569 0.0563 0.0421

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modeling the interactions between osteoblast and osteoclast activities in bone remodeling.
Lemaire V, Tobin FL, Greller LD, Cho CR, Suva LJ. J Theor Biol.2004 Aug 7;229(3):293-309. 15234198,
Abstract:
We propose a mathematical model explaining the interactions between osteoblasts and osteoclasts, two cell types specialized in the maintenance of the bone integrity. Bone is a dynamic, living tissue whose structure and shape continuously evolves during life. It has the ability to change architecture by removal of old bone and replacement with newly formed bone in a localized process called remodeling. The model described here is based on the idea that the relative proportions of immature and mature osteoblasts control the degree of osteoclastic activity. In addition, osteoclasts control osteoblasts differentially depending on their stage of differentiation. Despite the tremendous complexity of the bone regulatory system and its fragmentary understanding, we obtain surprisingly good correlations between the model simulations and the experimental observations extracted from the literature. The model results corroborate all behaviors of the bone remodeling system that we have simulated, including the tight coupling between osteoblasts and osteoclasts, the catabolic effect induced by continuous administration of PTH, the catabolic action of RANKL, as well as its reversal by soluble antagonist OPG. The model is also able to simulate metabolic bone diseases such as estrogen deficiency, vitamin D deficiency, senescence and glucocorticoid excess. Conversely, possible routes for therapeutic interventions are tested and evaluated. Our model confirms that anti-resorptive therapies are unable to partially restore bone loss, whereas bone formation therapies yield better results. The model enables us to determine and evaluate potential therapies based on their efficacy. In particular, the model predicts that combinations of anti-resorptive and anabolic therapies provide significant benefits compared with monotherapy, especially for certain type of skeletal disease. Finally, the model clearly indicates that increasing the size of the pool of preosteoblasts is an essential ingredient for the therapeutic manipulation of bone formation. This model was conceived as the first step in a bone turnover modeling platform. These initial modeling results are extremely encouraging and lead us to proceed with additional explorations into bone turnover and skeletal remodeling.

This model corresponds to the core model published in the paper. There is no corresponding plot to reproduce for this model.To obtain each of the 9 plots in the Figure 2 of the reference publication, there are some changes to be made to the core model.The curation figure reproduces figure 2 of the reference publication. There is a corresponding SBML and Copasi files for each of the plot. See curation tab for more details.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3491, + "tag": "BioModels:BIOMD0000000278" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3249, + "tag": "Regulation of bone remodeling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4606, + "tag": "Glucocorticoid-remediable aldosteronism" + } + ], + "timestamp_created": "2025-01-30 13:34:38.846378+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000278", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2569": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2569, + "name": "Komarova2005_PTHaction_OsteoclastOsteoblastCoupling", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Mathematical model of paracrine interactions between osteoclasts and osteoblasts predicts anabolic action of parathyroid hormone on bone.
Komarova SV. Endocrinology.2005 Aug;146(8):3589-95. 15860557,
Abstract:
To restore falling plasma calcium levels, PTH promotes calcium liberation from bone. PTH targets bone-forming cells, osteoblasts, to increase expression of the cytokine receptor activator of nuclear factor kappaB ligand (RANKL), which then stimulates osteoclastic bone resorption. Intriguingly, whereas continuous administration of PTH decreases bone mass, intermittent PTH has an anabolic effect on bone, which was proposed to arise from direct effects of PTH on osteoblastic bone formation. However, antiresorptive therapies impair the ability of PTH to increase bone mass, indicating a complex role for osteoclasts in the process. We developed a mathematical model that describes the actions of PTH at a single site of bone remodeling, where osteoclasts and osteoblasts are regulated by local autocrine and paracrine factors. It was assumed that PTH acts only to increase the production of RANKL by osteoblasts. As a result, PTH stimulated osteoclasts upon application, followed by compensatory osteoblast activation due to the coupling of osteoblasts to osteoclasts through local paracrine factors. Continuous PTH administration resulted in net bone loss, because bone resorption preceded bone formation at all times. In contrast, over a wide range of model parameters, short application of PTH resulted in a net increase in bone mass, because osteoclasts were rapidly removed upon PTH withdrawal, enabling osteoblasts to rebuild the bone. In excellent agreement with experimental findings, increase in the rate of osteoclast death abolished the anabolic effect of PTH on bone. This study presents an original concept for the regulation of bone remodeling by PTH, currently the only approved anabolic treatment for osteoporosis.

The model reproduces Figures 1B and 2A of the reference publication. To obtain the figures 1B, the parameter g21 needs changes. To obtain the figures 1A, the parameters g21, g12 and k2 need to changed. For details look at the curation tab.

The initial concentration of Osteoclasts (x1) is corrected to 1.06066 from 10.06066.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: CellMLdetails
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3492, + "tag": "BioModels:BIOMD0000000279" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3249, + "tag": "Regulation of bone remodeling" + }, + { + "id": 3489, + "tag": "Response to parathyroid hormone" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4606, + "tag": "Glucocorticoid-remediable aldosteronism" + } + ], + "timestamp_created": "2025-01-30 13:34:39.320246+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000279", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2570": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2570, + "name": "Morris1981_MuscleFibre_Voltage_reduced", + "repository_type": "biomodels", + "summary": "

This is the reduced model of the voltage oscillations in barnacle muscle fibers, generally known as the Morris-Lecar model (eg. wikipedia), described in the article:
Voltage oscillations in the barnacle giant muscle fiber.
\tMorris C, Lecar H. Biophys J. 1981 Jul;35(1):193-213. PubmedID:7260316; DOI:10.1016/S0006-3495(81)84782-0
\tAbstract:
\tBarnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.

The model consists of the differential equations (9) and (2) given on pages 205 and 196 of the article. There seems to be a typo in the figure caption of figure 9. Using V2 = 15 instead of -15 allows to reproduce the results.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This model is described inthe article:
Metabolic control mechanisms. 5. A solution for the equations representing interaction between glycolysis and respiration in ascites tumor cells.
Britton Chance, David Garfinkel, Joseph Higgins and Benno Hess, J Biol Chem. 1960 35:2426-2439. PubmedID: 13692276
Abstract:
The other papers of this series present experimentalevidence for possible relationships between the kinetics of oxygen,glucose, adenosine diphosphate, adenosine triphosphate, andphosphate and those of the cytochromes and pyridine nucleotidesof the ascites tumor cell. From these general experiments weare able to formulate, under the law of mass action, a minimumhypothesis under which the four metabolic regulations previouslydescribed can be observed. In brief, the system can be represented by the known enzyme systems, a relatively higher ADP affinity in respiration than in glycolysis, the mitochondrial membrane, a segregation of ATP into two compartments, and an ATP-utilizing system that is responsive to small decreases of the intracellular ADP level. The chemical equations for the pathway from glucose to oxygen are solved by a digital computer method so that the responses of the chemical equations and of the living cell can be accurately compared.
For reasons already described, we greatly prefer a com-puter representation based upon a physical or chemical lawrepresenting the action of the system to a model simulating theoperation of the chemical system but not based upon funda-mental laws for the reactions involved; such a representationwould not adequately represent the kinetics of the system, as inan electric circuit network or in some types of hydraulic ana-logues.

The model gives solutions of the reaction kinetics for three types of metabolism:

  1. 0 - 64s, metabolism of endogenous substrate
  2. 64s - 119s, metabolism of added glucose, illustrating the activated and inhibited aspects of glucose metabolism
  3. 119s - 153s, relief of glucose and oxygen inhibition by the addition of an uncoupling agent

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is described in the article:
The mechanism of catalase action. II. Electric analog computer studies.
Britton Chance, David S Greenstein, Joseph Higgins, CC Yang, Arch Biochem. 1952 37:322-39. PubmedID:14953444
Summary:
An electric analog computer has been constructed for a study of the kinetics of catalase action. This computer gives results for the formation and disappearance of the catalase-hydrogen peroxide complex that are in good agreement with the experimental data. The computer study verifies an approximate method for the computation of the velocity constant for the combination of hydrogen peroxide and catalase and justifies the simple formula used previously to compute the velocity constant for the reaction of the catalase-hydrogen peroxide complex with donor molecules. Finally, the computer data show that the binding of peroxide to catalase is a practically irreversible reaction.

The reaction of the enzyme-substrate complex, p, with the electron donor, a, is bimolecular, although in the article, as a is assumed to be constant, it is modelled using an apparent rate constant consisting of the product of the rate constant, k4, and the concentration of a. In this implementation, the concentration of a is set to 1 and the value of k4 just adapted so that the product equals the values given for k4*a in the article. The specific parameter values are taken from Fig 3. The graphs do not exactly match those in the paper, this may be due to the different simulators used.

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Default parameter values are those in the right hand panel of Fig 12. The other panels may be obtained by setting X to 1, 2 or 4, and K3 to 0, 1/2 or 1.

This model is described in:
The kinetics of the enzyme-substrate compound of peroxidase.
Britton Chance, Journal of Biological Chemistry, 151, 553-577, 1943. PDF at JBC
reprinted in: Adv Enzymol Relat Areas Mol Biol. 1999;73:3-23. PubmedID:10218104>
Abstract:
Under the narrow range of experimental conditions, and at a temperature of approximately 25 degrees, the following data were obtained. 1. The equilibrium constant of peroxidase and hydrogen peroxide has a minimum value of 2 x 10(-8). 2. The velocity constant for the formation of peroxidase-H2O2 Complex I is 1.2 x 10(7) liter mole-1 sec.-1, +/- 0.4 x 10(7). 3. The velocity constant for the reversible breakdown of peroxidase-H2O2 Complex I is a negligible factor in the enzyme-substrate kinetics and is calculated to be less than 0.2 sec.-1. 4. The velocity constant, k3, for the enzymatic breakdown of peroxidase-H2O2 Complex I varies from nearly zero to higher than 5 sec.-1, depending upon the acceptor and its concentration. The quotient of k3 and the leucomalachite green concentration is 3.0 x 10(4) liter mole-1 sec.-1. For ascorbic acid this has a value of 1.8 x 10(5) liter mole-1 sec.-1. 5. For a particular acceptor concentration, k3 is determined solely from the enzyme-substrate kinetics and is found to be 4.2 sec.-1. 6. For the same conditions, k3 is determined from a simple relationship derived from mathematical solutions of the Michaelis theory and is found to be 5.2 sec.-1. 7. For the same conditions, k3 is determined from the over-all enzyme action and is found to be 5.1 sec.-1. 8. The Michaelis constant determined from kinetic data alone is found to be 0.44 x 10(-6). 9. The Michaelis constant determined from steady state measurements is found to be 0.41 x 10(-6). 10. The Michaelis constant determined from measurement of the overall enzyme reaction is found to be 0.50 x 10(-6). 11. The kinetics of the enzyme-substrate compound closely agree with mathematical solutions of an extension of the Michaelis theory obtained for experimental values of concentrations and reaction velocity constants. 12. The adequacy of the criteria by which experiment and theory were correlated has been examined critically and the mathematical solutions have been found to be sensitive to variations in the experimental conditions. 13. The critical features of the enzyme-substrate kinetics are Pmax, and curve shape, rather than t1/2. t1/2 serves as a simple measure of dx/dt. 14. A second order combination of enzyme and substrate to form the enzyme-substrate compound, followed by a first order breakdown of the compound, describes the activity of peroxidase for a particular acceptor concentration. 15. The kinetic data indicate a bimolecular combination of acceptor and enzyme-substrate compound.

This model is the one described in the appendix of the article. It reproduces, amongst others, figure 12. The parameters and concentrations used are rescaled as stated in the article. K2 and K3 stand for k2 and k3, respectively, divided by k1.

", + "tags": [ + { + "id": 3068, + "tag": "Armoracia rusticana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3500, + "tag": "BioModels:BIOMD0000000283" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:41.557472+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000283", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2574": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2574, + "name": "Hofmeyer1986_SeqFb_Proc_AA_Synthesis", + "repository_type": "biomodels", + "summary": "

This model is the reaction sequence SEQFB, a model pathway of a branched system with sequential feedback interactions found in bacterial amino acid synthesis. Its steady state is presented in Fig 4.

The model is described in:
METAMOD: software for steady-state modelling and control analysis of metabolic pathways on the BBC microcomputer.
JHS Hofmeyr and KJ van der Merwe, Comput Appl Biosci 1986 2:243-9; PubmedID: 3450367
Abstract:
METAMOD, a BBC microcomputer-based software package for steady-state modelling and control analysis of model metabolic pathways, is described, The package consists of two programs. METADEF allows the user to define the pathway in terms of reactions, rate equations and initial concentrations of metabolites. METACAL uses one of two algorithms to calculate the steady-state concentrations and fluxes. One algorithm uses the current ratio of production and consumption rates of variable metabolites to adjust iteratively their concentrations in such a way that they converge towards the steady state. The other algorithm solves the roots of the system equations by means of a quasi-Newtonian procedure. Control analysis allows the calculation of elasticity, control and response coefficients, by means of finite difference approximation. METAMOD is interactive and easy to use, and suitable for teaching and research purposes.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novere N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Experimental and computational analysis of polyglutamine-mediated cytotoxicity.
Tang MY, Proctor CJ, Woulfe J, Gray DA. PLoS Comput Biol.2010 Sep 23;6(9). 20885783,
Abstract:
Expanded polyglutamine (polyQ) proteins are known to be the causative agents of a number of human neurodegenerative diseases but the molecular basis of their cytoxicity is still poorly understood. PolyQ tracts may impede the activity of the proteasome, and evidence from single cell imaging suggests that the sequestration of polyQ into inclusion bodies can reduce the proteasomal burden and promote cell survival, at least in the short term. The presence of misfolded protein also leads to activation of stress kinases such as p38MAPK, which can be cytotoxic. The relationships of these systems are not well understood. We have used fluorescent reporter systems imaged in living cells, and stochastic computer modeling to explore the relationships of polyQ, p38MAPK activation, generation of reactive oxygen species (ROS), proteasome inhibition, and inclusion body formation. In cells expressing a polyQ protein inclusion, body formation was preceded by proteasome inhibition but cytotoxicity was greatly reduced by administration of a p38MAPK inhibitor. Computer simulations suggested that without the generation of ROS, the proteasome inhibition and activation of p38MAPK would have significantly reduced toxicity. Our data suggest a vicious cycle of stress kinase activation and proteasome inhibition that is ultimately lethal to cells. There was close agreement between experimental data and the predictions of a stochastic computer model, supporting a central role for proteasome inhibition and p38MAPK activation in inclusion body formation and ROS-mediated cell death.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3504, + "tag": "BioModels:BIOMD0000000285" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3295, + "tag": "Pathogenesis" + }, + { + "id": 3505, + "tag": "Protein polyglutamylation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4591, + "tag": "Neurodegenerative disease" + } + ], + "timestamp_created": "2025-01-30 13:34:42.541905+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000285", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2576": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2576, + "name": "Proctor2010 - a link between GSK3 and p53 in Alzheimer's Disease", + "repository_type": "biomodels", + "summary": "

This is the model described the article:
GSK3 and p53 - is there a link in Alzheimer's disease?
Carole J Proctor and Douglas A Gray Molecular Neurodegeneration 2010, 5:7; doi: 10.1186/1750-1326-5-7
Abstract:
Background: Recent evidence suggests that glycogen synthase kinase-3beta (GSK3beta) is implicated in both sporadic and familial forms of Alzheimer's disease. The transcription factor, p53 also plays a role and has been linked to an increase in tau hyperphosphorylation although the effect is indirect. There is also evidence that GSK3beta and p53 interact and that the activity of both proteins is increased as a result of this interaction. Under normal cellular conditions, p53 is kept at low levels by Mdm2 but when cells are stressed, p53 is stabilised and may then interact with GSK3beta. We propose that this interaction has an important contribution to cellular outcomes and to test this hypothesis we developed a stochastic simulation model.
Results: The model predicts that high levels of DNA damage leads to increased activity of p53 and GSK3beta and low levels of aggregation but if DNA damage is repaired, the aggregates are eventually cleared. The model also shows that over long periods of time, aggregates may start to form due to stochastic events leading to increased levels of ROS and damaged DNA. This is followed by increased activity of p53 and GSK3beta and a vicious cycle ensues.
Conclusions: Since p53 and GSK3beta are both involved in the apoptotic pathway, and GSK3beta overactivity leads to increased levels of plaques and tangles, our model might explain the link between protein aggregation and neuronal loss in neurodegeneration.


Notes: The original model submitted by the author had events in it. Since, this model is intended for Stochastic Simulation run and Copasi cannot handle events in Stochastic run, I have replaced the events with piecewise assignment rule. -Viji

This model is an extension of Proctor_p53_Mdm2_ATM ( BIOMD0000000188 ).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This is the model described in: Feedback between p21 and reactive oxygen production is necessary for cell senescence.
\tPassos JF, Nelson G, Wang C, Richter T, Simillion C, Proctor CJ, Miwa S, Olijslagers S, Hallinan J, Wipat A, Saretzki G, Rudolph KL, Kirkwood TB, von Zglinicki T. ;Mol Sys Biol2010;6:347. Epub 2010 Feb 16. PMID:20160708 doi:10.1038/msb.2010.5;
Abstract:
\t Cellular senescence--the permanent arrest of cycling in normally proliferating cells such as fibroblasts--contributes both to age-related loss of mammalian tissue homeostasis and acts as a tumour suppressor mechanism. The pathways leading to establishment of senescence are proving to be more complex than was previously envisaged. Combining in-silico interactome analysis and functional target gene inhibition, stochastic modelling and live cell microscopy, we show here that there exists a dynamic feedback loop that is triggered by a DNA damage response (DDR) and, which after a delay of several days, locks the cell into an actively maintained state of 'deep' cellular senescence. The essential feature of the loop is that long-term activation of the checkpoint gene CDKN1A (p21) induces mitochondrial dysfunction and production of reactive oxygen species (ROS) through serial signalling through GADD45-MAPK14(p38MAPK)-GRB2-TGFBR2-TGFbeta. These ROS in turn replenish short-lived DNA damage foci and maintain an ongoing DDR. We show that this loop is both necessary and sufficient for the stability of growth arrest during the establishment of the senescent phenotype. \t

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
PI3K-dependent cross-talk interactions converge with Ras as quantifiable inputs integrated by Erk.
Wang CC, Cirit M, Haugh JM Mol. Syst. Biol. 2009;5:246. 19225459 ,
Abstract:
Although it is appreciated that canonical signal-transduction pathways represent dominant modes of regulation embedded in larger interaction networks, relatively little has been done to quantify pathway cross-talk in such networks. Through quantitative measurements that systematically canvas an array of stimulation and molecular perturbation conditions, together with computational modeling and analysis, we have elucidated cross-talk mechanisms in the platelet-derived growth factor (PDGF) receptor signaling network, in which phosphoinositide 3-kinase (PI3K) and Ras/extracellular signal-regulated kinase (Erk) pathways are prominently activated. We show that, while PI3K signaling is insulated from cross-talk, PI3K enhances Erk activation at points both upstream and downstream of Ras. The magnitudes of these effects depend strongly on the stimulation conditions, subject to saturation effects in the respective pathways and negative feedback loops. Motivated by those dynamics, a kinetic model of the network was formulated and used to precisely quantify the relative contributions of PI3K-dependent and -independent modes of Ras/Erk activation.


This model is parameterized with the median of the estimated parameters given in the supplementary material of the original publication's (doi: 10.1038/msb.2009.4 ) supplement on pages 8 and 9.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is system 1, the model with linear antigen uptake by pAPCs, described in the article:
Self-tolerance and Autoimmunity in a Regulatory T Cell Model.
Alexander HK, Wahl LM. Bull Math Biol. 2010 Mar 2. PMID:20195912, doi:10.1007/s11538-010-9519-2;
Abstract:
The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R (0) for each system. When R (0) < 1, only the trivial equilibrium exists and is stable; when R (0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

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This is system 2, the model with Michelis Menten type antigen uptake by pAPCs, described in the article:
Self-tolerance and Autoimmunity in a Regulatory T Cell Model.
Alexander HK, Wahl LM. Bull Math Biol. 2010 Mar 2. PMID: 20195912 , doi: 10.1007/s11538-010-9519-2 ;
Abstract:
The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R (0) for each system. When R (0) < 1, only the trivial equilibrium exists and is stable; when R (0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This a model from the article:
Mathematical model of binding of albumin-bilirubin complex to the surface of carbon pyropolymer.
Nikolaev AV, Rozhilo YA, Starozhilova TK, Sarnatskaya VV, Yushko LA, Mikhailovskii SV, Kholodov AS, Lobanov AI. Bull Exp Biol Med2005 Sep;140(3):365-9. 16307060,
Abstract:
We proposed a mathematical model and estimated the parameters of adsorption of albumin-bilirubin complex to the surface of carbon pyropolymer. Design data corresponded to the results of experimental studies. Our findings indicate that modeling of this process should take into account fractal properties of the surface of carbon pyropolymer.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3519, + "tag": "Bilirubin conjugation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3520, + "tag": "BioModels:BIOMD0000000291" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4607, + "tag": "Liver disease" + } + ], + "timestamp_created": "2025-01-30 13:34:45.372856+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000291", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2582": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2582, + "name": "Rovers1995_Photsynthetic_Oscillations", + "repository_type": "biomodels", + "summary": "

This is the model described in the article:
Photosynthetic oscillations and the interdependence of photophosphorylation and electron transport as studied by a mathematical model.
Rovers W, Giersch C. Biosystems. 1995;35(1):63-73. PMID: 7772723
Abstract:
A simple mathematical model of photosynthetic carbon metabolism as driven by ATP and NADPH has been formulated to analyse photosynthetic oscillations. Two essential assumptions of this model are: (i) reduction of 3-phosphoglycerate to triosephosphate in the Clavin cycle is limited by ATP, not by NADPH, and (ii) photophosphorylation is affected by the availability of both ADP and NADP, while electron transport is limited by NADP only. The model produces oscillations of observed damping and period in ATP and NADP concentrations which are about 180 degrees out of phase, while three alternative proposals regarding coupling of electron transport and photophosphorylation do not produce oscillatory model solutions. The phases of ATP and NADPH are in reasonable agreement with the available experimental data. The model (which assumes that redox control of photophosphorylation is part of the oscillatory mechanism) is compared with an alternative proposal (that oscillations are due to interdependence of turnover of adenylates and Calvin cycle intermediates). From the similarity of the mathematical structures of both models it is inviting to speculate that both models are partial aspects of the oscillatory mechanism.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modelling the Role of UCH-L1 on Protein Aggregation inAge-Related Neurodegeneration.
Proctor CJ, Tangeman PJ, Ardley HC. PLoS One. 2010 Oct 6;5(10):e13175 20949132 ,
Abstract:
Overexpression of the de-ubiquitinating enzyme UCH-L1 leads to inclusion formation in response to proteasome impairment. These inclusions contain components of the ubiquitin-proteasome system and \u03b1-synuclein confirming that the ubiquitin-proteasome system plays an important role in protein aggregation. The processes involved are very complex and so we have chosen to take a systems biology approach to examine the system whereby we combine mathematical modelling with experiments in an iterative process. The experiments show that cells are very heterogeneous with respect to inclusion formation and so we use stochastic simulation. The model shows that the variability is partly due to stochastic effects but also depends on protein expression levels of UCH-L1 within cells. The model also indicates that the aggregation process can start even before any proteasome inhibition is present, but that proteasome inhibition greatly accelerates aggregation progression. This leads to less efficient protein degradation and hence more aggregation suggesting that there is a vicious cycle. However, proteasome inhibition may not necessarily be the initiating event. Our combined modelling and experimental approach show that stochastic effects play an important role in the aggregation process and could explain the variability in the age of disease onset. Furthermore, our model provides a valuable tool, as it can be easily modified and extended to incorporate new experimental data, test hypotheses and make testable predictions.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Restif2007 - Vaccination invasion

This model is described in the article:

Restif O, Grenfell BT.
J R Soc Interface 2007 Feb; 4(12): 143-153

Abstract:

Vaccines exert strong selective pressures on pathogens, favouring the spread of antigenic variants. We propose a simple mathematical model to investigate the dynamics of a novel pathogenic strain that emerges in a population where a previous strain is maintained at low endemic level by a vaccine. We compare three methods to assess the ability of the novel strain to invade and persist: algebraic rate of invasion; deterministic dynamics; and stochastic dynamics. These three techniques provide complementary predictions on the fate of the system. In particular, we emphasize the importance of stochastic simulations, which account for the possibility of extinctions of either strain. More specifically, our model suggests that the probability of persistence of an invasive strain (i) can be minimized for intermediate levels of vaccine cross-protection (i.e. immune protection against the novel strain) and (ii) is lower if cross-immunity acts through a reduced infectious period rather than through reduced susceptibility.

This version of the model can be used for both the stochastic and the deterministic simulations described in the article. For deterministic interpretations with infinite population sizes, set the population size\u00a0 N\u00a0= 1. The model does reproduces the deterministic time course. The initial values are set to the steady state values for a latent infection with strain 1 with an invading infection of strain 2 (I2=1e-06), 100 percent vaccination with a susceptibility reduction \u03c4=0.7 at birth (p=1), and all other parameters as in figure 3 of the publication.\u00a0

To be compatible with older software tools, the english letter names instead of the greek symbols were used for parameter names:

parameter symbol name
transmission rate \u03b2 beta
recovery rate \u03b3 gamma
birth/death rate \u03bc mu
rate of loss of natural immunity \u03c3 sigma
rate of loss of vaccine immunity \u03c3 v sigmaV
reduction of susceptibility by primary infection \u03b8 theta
reduction of infection period by primary infection \u03bd nu
reduction of susceptibility by vaccination \u03c4 tau
reduction of infection period by vaccination \u03b7 eta

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model is hosted on BioModels Database and identified by: BIOMD0000000294.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This a model from the article:
Isoform switching facilitates period control in the Neurospora crassa circadian clock.
Akman OE, Locke JC, Tang S, Carr\u00e9 I, Millar AJ, Rand DA Mol. Syst. Biol. 2008;Vol 4: 164 18277380 ,
Abstract:
A striking and defining feature of circadian clocks is the small variation in period over a physiological range of temperatures. This is referred to as temperature compensation, although recent work has suggested that the variation observed is a specific, adaptive control of period. Moreover, given that many biological rate constants have a Q(10) of around 2, it is remarkable that such clocks remain rhythmic under significant temperature changes. We introduce a new mathematical model for the Neurospora crassa circadian network incorporating experimental work showing that temperature alters the balance of translation between a short and long form of the FREQUENCY (FRQ) protein. This is used to discuss period control and functionality for the Neurospora system. The model reproduces a broad range of key experimental data on temperature dependence and rhythmicity, both in wild-type and mutant strains. We present a simple mechanism utilising the presence of the FRQ isoforms (isoform switching) by which period control could have evolved, and argue that this regulatory structure may also increase the temperature range where the clock is robustly rhythmic.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the reduced model described in the article:
A synthetic Escherichia coli predator\u2013prey ecosystem
Balagadd\u00e9 FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L.Mol Syst Biol. 2008;4:187. Epub 2008 Apr 15. PMID: 18414488; DOI:10.1038/msb.2008.24

Abstract:
We have constructed a synthetic ecosystem consisting of two Escherichia coli populations, which communicate bi-directionally through quorum sensing and regulate each other's gene expression and survival via engineered gene circuits. Our synthetic ecosystem resembles canonical predator\u2013prey systems in terms of logic and dynamics. The predator cells kill the prey by inducing expression of a killer protein in the prey, while the prey rescue the predators by eliciting expression of an antidote protein in the predator. Extinction, coexistence and oscillatory dynamics of the predator and prey populations are possible depending on the operating conditions as experimentally validated by long-term culturing of the system in microchemostats. A simple mathematical model is developed to capture these system dynamics. Coherent interplay between experiments and mathematical analysis enables exploration of the dynamics of interacting populations in a predictable manner.

In the article the cell density is given in per 103 cells per microlitre. To evade a conversion factor in the SBML implementation, the unit for the cell densities was just left the same as for the AHLs A and A2 (nM).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Mathematical model of the morphogenesis checkpoint in budding yeast.
Ciliberto A, Novak B, Tyson JJ J. Cell Biol. [2003 Dec; Volume: 163 (Issue: 6 )] Page info: 1243-54 14691135 ,
Abstract:
The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next.

The model reproduces Fig 3 of the paper.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.
Leloup JC, Gonze D, Goldbeter A. J Biol Rhythms.1999 Dec;14(6):433-48. 10643740,
Abstract:
We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.

This particular version of the model has been translated from equations 1a-1j (Drosophila).

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Leloup JC, Gonze D, Goldbeter A. (1999) - version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.
Leloup JC, Gonze D, Goldbeter A. J Biol Rhythms. 1999 Dec;14(6):433-48. 10643740 ,
Abstract:
We examine theoretical models for circadian oscillations based on transcriptional regulation in Drosophila and Neurospora. For Drosophila, the molecular model is based on the negative feedback exerted on the expression of the per and tim genes by the complex formed between the PER and TIM proteins. For Neurospora, similarly, the model relies on the feedback exerted on the expression of the frq gene by its protein product FRQ. In both models, sustained rhythmic variations in protein and mRNA levels occur in continuous darkness, in the form of limit cycle oscillations. The effect of light on circadian rhythms is taken into account in the models by considering that it triggers degradation of the TIM protein in Drosophila, and frq transcription in Neurospora. When incorporating the control exerted by light at the molecular level, we show that the models can account for the entrainment of circadian rhythms by light-dark cycles and for the damping of the oscillations in constant light, though such damping occurs more readily in the Drosophila model. The models account for the phase shifts induced by light pulses and allow the construction of phase response curves. These compare well with experimental results obtained in Drosophila. The model for Drosophila shows that when applied at the appropriate phase, light pulses of appropriate duration and magnitude can permanently or transiently suppress circadian rhythmicity. We investigate the effects of the magnitude of light-induced changes on oscillatory behavior. Finally, we discuss the common and distinctive features of circadian oscillations in the two organisms.

This particular version of the model has been translated from equations 4a-4c (Neurospora).

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Leloup JC, Gonze D, Goldbeter A. (1999) - version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Hypoxia-dependent sequestration of an oxygen sensor by a widespread structural motif can shape the hypoxic response - a predictive kinetic model
Bernhard Schmierer, B\u00e9la Nov\u00e1k1 and Christopher J Schofield BMC Systems Biology2010, 4:139 20955552,
Abstract:
Background
The activity of the heterodimeric transcription factor hypoxia inducible factor (HIF) is regulated by the post-translational, oxygen-dependent hydroxylation of its \u03b1-subunit by members of the prolyl hydroxylase domain (PHD or EGLN)-family and by factor inhibiting HIF (FIH). PHD-dependent hydroxylation targets HIF\u03b1 for rapid proteasomal degradation; FIH-catalysed asparaginyl-hydroxylation of the C-terminal transactivation domain (CAD) of HIF\u03b1 suppresses the CAD-dependent subset of the extensive transcriptional responses induced by HIF. FIH can also hydroxylate ankyrin-repeat domain (ARD) proteins, a large group of proteins which are functionally unrelated but share common structural features. Competition by ARD proteins for FIH is hypothesised to affect FIH activity towards HIF\u03b1; however the extent of this competition and its effect on the HIF-dependent hypoxic response are unknown.
Results
To analyse if and in which way the FIH/ARD protein interaction affects HIF-activity, we created a rate equation model. Our model predicts that an oxygen-regulated sequestration of FIH by ARD proteins significantly shapes the input/output characteristics of the HIF system. The FIH/ARD protein interaction is predicted to create an oxygen threshold for HIF\u03b1 CAD-hydroxylation and to significantly sharpen the signal/response curves, which not only focuses HIF\u03b1 CAD-hydroxylation into a defined range of oxygen tensions, but also makes the response ultrasensitive to varying oxygen tensions. Our model further suggests that the hydroxylation status of the ARD protein pool can encode the strength and the duration of a hypoxic episode, which may allow cells to memorise these features for a certain time period after reoxygenation.
Conclusions
The FIH/ARD protein interaction has the potential to contribute to oxygen-range finding, can sensitise the response to changes in oxygen levels, and can provide a memory of the strength and the duration of a hypoxic episode. These emergent properties are predicted to significantly shape the characteristics of HIF activity in animal cells. We argue that the FIH/ARD interaction should be taken into account in studies of the effect of pharmacological inhibition of the HIF-hydroxylases and propose that the interaction of a signalling sensor with a large group of proteins might be a general mechanism for the regulation of signalling pathways.

There are there models described in the paper. 1) Skeleton Model 1 (SKM1) - HIF\u03b1 CAD-hydroxylation in the absence of the FIH/AR-interaction. 2) Skeleton Model 2 (SKM2) - FIG sequestration by ARD proteins and oxygen-dependent FIH-release. 3) Full Model (Fusion of SKM1 and SKM2) - the effects of the FIH/ARD proteins interaction on HIF\u03b1 CAD-hydroxylation.

This model corresponds to the \"Full Model\" described in the paper. The model reproduces figure 5 of the publication.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This is the model of the RTC3 counter described in the article:
Synthetic gene networks that count.
\t Friedland AE, Lu TK, Wang X, Shi D, Church G, Collins JJ. Science. 2009 May 29;324(5931):1199-202. PMID:19478183, DOI:10.1126/science.1172005

\t Abstract:
\t Synthetic gene networks can be constructed to emulate digital circuits and devices, giving one the ability to program and design cells with some of the principles of modern computing, such as counting. A cellular counter would enable complex synthetic programming and a variety of biotechnology applications. Here, we report two complementary synthetic genetic counters in Escherichia coli that can count up to three induction events: the first, a riboregulated transcriptional cascade, and the second, a recombinase-based cascade of memory units. These modular devices permit counting of varied user-defined inputs over a range of frequencies and can be expanded to count higher numbers.\t

\t The 3 arabinose pulses are implemented using events, one for the start of pulses and one for the end. The variable pulse_flag changes arabinose consumption to fit behaviour during pulses and in between. To simulate two pulses only, set the pulse length of the third pulse to a negative value (though with an absolute value smaller than the pulse intervall length). \t

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is a model of one presynaptic and one postsynaptic cell, as described in the article:
Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model.
Wang XJ, Buzs\u00e1ki G. J Neurosci. 1996 Oct 15;16(20):6402-13. PMID:8815919;\t

Abstract:
Fast neuronal oscillations (gamma, 20-80 Hz) have been observed in the neocortex and hippocampus during behavioral arousal. Using computer simulations, we investigated the hypothesis that such rhythmic activity can emerge in a random network of interconnected GABAergic fast-spiking interneurons. Specific conditions for the population synchronization, on properties of single cells and the circuit, were identified. These include the following: (1) that the amplitude of spike afterhyperpolarization be above the GABAA synaptic reversal potential; (2) that the ratio between the synaptic decay time constant and the oscillation period be sufficiently large; (3) that the effects of heterogeneities be modest because of a steep frequency-current relationship of fast-spiking neurons. Furthermore, using a population coherence measure, based on coincident firings of neural pairs, it is demonstrated that large-scale network synchronization requires a critical (minimal) average number of synaptic contacts per cell, which is not sensitive to the network size. By changing the GABAA synaptic maximal conductance, synaptic decay time constant, or the mean external excitatory drive to the network, the neuronal firing frequencies were gradually and monotonically varied. By contrast, the network synchronization was found to be high only within a frequency band coinciding with the gamma (20-80 Hz) range. We conclude that the GABAA synaptic transmission provides a suitable mechanism for synchronized gamma oscillations in a sparsely connected network of fast-spiking interneurons. In turn, the interneuronal network can presumably maintain subthreshold oscillations in principal cell populations and serve to synchronize discharges of spatially distributed neurons.\t

\t The presynaptic and postsynaptic cell have identical parameters and the variables in each cell are identified by using _pre or _post as a postfix to their names. The presynaptic cell influences the postsynaptic one via the synapse (variables and parameters: I_syn, E_syn, g_syn, F, theta_syn, alpha, beta). The applied current to the presynaptic cell, I_app_pre, is set to 2 microA/cm2 for 10 ms as in figure 1C of the article. The dependence of the postsynaptic cell on directly applied current can be investigated in isolation by setting I_app_pre to 0 and altering I_app_post. \t

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This is the continuous deterministic (ODE) model of the complement system described in the article:
Computational and Experimental Study of the Regulatory Mechanisms of the Complement System.
Liu B, Zhang J, Tan PY, Hsu D, Blom AM, Leong B, Sethi S, Ho B, Ding JL and Thiagarajan PS. PLoS Comp. Bio. 2011 Jan. 7:1; doi:10.1371/journal.pcbi.1001059

Abstract:
The complement system is key to innate immunity and its activation is necessary for the clearance of bacteria and apoptotic cells. However, insufficient or excessive complement activation will lead to immune-related diseases. It is so far unknown how the complement activity is up- or down- regulated and what the associated pathophysiological mechanisms are. To quantitatively understand the modulatory mechanisms of the complement system, we built a computational model involving the enhancement and suppression mechanisms that regulate complement activity. Our model consists of a large system of Ordinary Differential Equations (ODEs) accompanied by a dynamic Bayesian network as a probabilistic approximation of the ODE dynamics. Applying Bayesian inference techniques, this approximation was used to perform parameter estimation and sensitivity analysis. Our combined computational and experimental study showed that the antimicrobial response is sensitive to changes in pH and calcium levels, which determines the strength of the crosstalk between CRP and L-ficolin. Our study also revealed differential regulatory effects of C4BP. While C4BP delays but does not decrease the classical complement activation, it attenuates but does not significantly delay the lectin pathway activation. We also found that the major inhibitory role of C4BP is to facilitate the decay of C3 convertase. In summary, the present work elucidates the regulatory mechanisms of the complement system and demonstrates how the bio-pathway machinery maintains the balance between activation and inhibition. The insights we have gained could contribute to the development of therapies targeting the complement system.

Comment:
Reproduction of figures in the article:
Figure 5: the effects of C4BP
Fig 5A: set initial concentrations PC=0.0327796, GlcNac=0, vary the initial concentration of C4BP from 2.6 to 2600 using parameter scan
Fig 5B: set initial concentrations PC=0, GlcNac=0.0327796, vary the initial concentration of C4BP from 2.6 to 2600 using parameter scan
Figure 6: knockout simulations
Set PC=0.0327796, GlcNac=0
Fig 6A: kf01=0, kf02=0
Fig 6B: kf04=0, kf06=0, kf07=0
Fig 6C: kf05=0
Fig 6D: kf03=0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Bifurcation and resonance in a model for bursting nerve cells.
Plant RE J Math Biol1981 Jan; 11(1): 15-32 7252375,
Abstract:
In this paper we consider a model for the phenomenon of bursting in nerve cells. Experimental evidence indicates that this phenomenon is due to the interaction of multiple conductances with very different kinetics, and the model incorporates this evidence. As a parameter is varied the model undergoes a transition between two oscillatory waveforms; a corresponding transition is observed experimentally. After establishing the periodicity of the subcritical oscillatory solution, the nature of the transition is studied. It is found to be a resonance bifurcation, with the solution branching at the critical point to another periodic solution of the same period. Using this result a comparison is made between the model and experimental observations. The model is found to predict and allow an interpretation of these observations.

Also, look athttp://www.scholarpedia.org/article/Plant_model

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the 2 state model of Myosin V movement described in the article:
A simple kinetic model describes the processivity of myosin-v.
Kolomeisky AB , Fisher ME Biophys. J. 84(3):1642-50 (2003); PubmedID: 12609867

Abstract:
Myosin-V is a motor protein responsible for organelle and vesicle transport in cells. Recent single-molecule experiments have shown that it is an efficient processive motor that walks along actin filaments taking steps of mean size close to 36 nm. A theoretical study of myosin-V motility is presented following an approach used successfully to analyze the dynamics of conventional kinesin but also taking some account of step-size variations. Much of the present experimental data for myosin-V can be well described by a two-state chemical kinetic model with three load-dependent rates. In addition, the analysis predicts the variation of the mean velocity and of the randomness-a quantitative measure of the stochastic deviations from uniform, constant-speed motion-with ATP concentration under both resisting and assisting loads, and indicates a substep of size d(0) approximately 13-14 nm (from the ATP-binding state) that appears to accord with independent observations.

The model differs slightly from the published version. The ATP and ADP bound forms of myosin are called S0 and S1. The state transition and binding constants are called k_1, k_2, k_3 and k_4 instead of k0 0, u0 1, k' 0 and w0 1. Similarly the state loading factors are named th_1, th_2, th_3 and th_4 instead of \u03b8+ 0, \u03b8+ 1, \u03b8- 0 and \u03b8- 1. The species fwd_step1, fwd_step2, back_step1 and back_step2 count the number of state changes of each kind the myosine molecules have taken over time.
The model can be evaluated in a deterministic continuous or stochastic discreet fashion. The parameter V holds the (forward) speed at each time point, the V_avg the overall way divided by the simulation time and the amount of myosine molecules.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is an SBML implementation the model of the activator inhibitor oscillator (figure 2b) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3553, + "tag": "BioModels:BIOMD0000000306" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:53.035872+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000306", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2597": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2597, + "name": "Tyson2003_Substrate_Depletion_Osc", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of the substrate depletion oscillator (figure 2c) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3555, + "tag": "BioModels:BIOMD0000000307" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:53.473104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000307", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2598": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2598, + "name": "Tyson2003_NegFB_Oscillator", + "repository_type": "biomodels", + "summary": "

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This is an SBML implementation the model of negative feedback oscillator (figure 2a) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3556, + "tag": "BioModels:BIOMD0000000308" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:53.917840+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000308", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2599": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2599, + "name": "Tyson2003_NegFB_Homeostasis", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of homeostastis by negative feedback (figure 1g) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3557, + "tag": "BioModels:BIOMD0000000309" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:54.358607+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000309", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2600": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2600, + "name": "Tyson2003_Mutual_Inhibition", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of mutual inhibition (figure 1f) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3558, + "tag": "BioModels:BIOMD0000000310" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:54.948491+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000310", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2601": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2601, + "name": "Tyson2003_Mutual_Activation", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of mutual activation (figure 1e) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

The article has a typo: the expression k2*X*R most likely should be k2*R

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3559, + "tag": "BioModels:BIOMD0000000311" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:55.415236+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000311", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2602": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2602, + "name": "Tyson2003_Perfect_Adaption", + "repository_type": "biomodels", + "summary": "

This is an SBML implementation the model of the perfect adaptor (figure 1d) described in the article:
Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell.
Tyson JJ, Chen KC, Novak B. Curr Opin Cell Biol. 2003 Apr;15(2):221-31. PubmedID:12648679; DOI:10.1016/S0955-0674(03)00017-6;

Abstract:
The physiological responses of cells to external and internal stimuli are governed by genes and proteins interacting in complex networks whose dynamical properties are impossible to understand by intuitive reasoning alone. Recent advances by theoretical biologists have demonstrated that molecular regulatory networks can be accurately modeled in mathematical terms. These models shed light on the design principles of biological control systems and make predictions that have been verified experimentally.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3560, + "tag": "BioModels:BIOMD0000000312" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:55.864368+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000312", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2603": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2603, + "name": "Raia2010 - IL13 Signalling MedB1", + "repository_type": "biomodels", + "summary": "

This is the model of IL13 induced signalling in MedB-1 cell described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, B\u00f6hm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, M\u00f6ller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingm\u00fcller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID:21127196; DOI:10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 () and the conversion factor 2.265 molecules IL13/cell = 1 ng/ml to be around 60 fl.

SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:

% PottersWheel model definition filefunction m = Raia2010_IL13_MedB1()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'Raia2010_IL13_MedB1';m.name        = 'Raia2010_IL13_MedB1';m.description = '';m.authors     = {'Raia et al'};m.dates       = {'2010'};m.type        = 'PW-2-0-47';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'Rec'         ,              1.3, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Rec_i'       , 113.193916718733, 'global',  0.001, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_Rec'    ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec'  ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec_i',                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'JAK2'        ,              2.8, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pJAK2'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SHP1'        ,               91, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'STAT5'       ,              165, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pSTAT5'      ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SOCS3mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'DecoyR'      ,             0.34, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_DecoyR' ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SOCS3'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'CD274mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {'Rec'         }, {'IL13_Rec'    }, {'IL13stimulation'   }, 'C' , [] , 'k1 * m1 * r1 * 2.265'        , {'Kon_IL13Rec'                             });m = pwAddR(m, {'Rec'         }, {'Rec_i'       }, {                    }, 'MA', [] , []                            , {'Rec_intern'                              });m = pwAddR(m, {'Rec_i'       }, {'Rec'         }, {                    }, 'MA', [] , []                            , {'Rec_recycle'                             });m = pwAddR(m, {'IL13_Rec'    }, {'p_IL13_Rec'  }, {'pJAK2'             }, 'E' , [] , []                            , {'Rec_phosphorylation'                     });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'IL13_Rec','SOCS3'  }, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'p_IL13_Rec','SOCS3'}, 'C' , [] , 'k1 * m1 * r1 / (1 + k2 * m2)', {'JAK2_phosphorylation','JAK2_p_inhibition'});m = pwAddR(m, {'p_IL13_Rec'  }, {'p_IL13_Rec_i'}, {                    }, 'MA', [] , []                            , {'pRec_intern'                             });m = pwAddR(m, {'p_IL13_Rec_i'}, {              }, {                    }, 'MA', [] , []                            , {'pRec_degradation'                        });m = pwAddR(m, {'pJAK2'       }, {'JAK2'        }, {'SHP1'              }, 'E' , [] , []                            , {'pJAK2_dephosphorylation'                 });m = pwAddR(m, {'STAT5'       }, {'pSTAT5'      }, {'pJAK2'             }, 'E' , [] , []                            , {'STAT5_phosphorylation'                   });m = pwAddR(m, {'pSTAT5'      }, {'STAT5'       }, {'SHP1'              }, 'E' , [] , []                            , {'pSTAT5_dephosphorylation'                });m = pwAddR(m, {'DecoyR'      }, {'IL13_DecoyR' }, {'IL13stimulation'   }, 'C' , [] , 'k1 * m1 * r1 * 2.265'        , {'DecoyR_binding'                          });m = pwAddR(m, {              }, {'SOCS3mRNA'   }, {'pSTAT5'            }, 'C' , [] , 'm1*k1'                       , {'SOCS3mRNA_production'                    });m = pwAddR(m, {              }, {'SOCS3'       }, {'SOCS3mRNA'         }, 'C' , [] , 'm1*k1/(k2+m1)'               , {'SOCS3_translation','SOCS3_accumulation'  });m = pwAddR(m, {'SOCS3'       }, {              }, {                    }, 'MA', [] , []                            , {'SOCS3_degradation'                       });m = pwAddR(m, {              }, {'CD274mRNA'   }, {'pSTAT5'            }, 'C' , [] , 'm1*k1'                       , {'CD274mRNA_production'                    });%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'Kon_IL13Rec'             , 0.00341992477561527  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_phosphorylation'     , 999.630699390459     , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_intern'             , 0.152540135862128    , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_degradation'        , 0.17292753960894     , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_intern'              , 0.103345784175639    , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_recycle'             , 0.00135598001330518  , 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_phosphorylation'    , 0.157057142470047    , 'global', 1e-009, 1000);m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.000621906059346898 , 'global', 1e-009, 1000);m = pwAddK(m, 'STAT5_phosphorylation'   , 0.0382596267705733   , 'global', 1e-009, 1000);m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.000343391620492938 , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3mRNA_production'    , 0.00215826062955433  , 'global', 1e-009, 1000);m = pwAddK(m, 'DecoyR_binding'          , 0.000124391087466499 , 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_p_inhibition'       , 0.0168267797836881   , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3_translation'       , 11.9086462945188     , 'global', 1e-009, 1000);m = pwAddK(m, 'SOCS3_accumulation'      , 3.70803336415341     , 'global', 1     , 1000);m = pwAddK(m, 'SOCS3_degradation'       , 0.0429185935645562   , 'global', 1e-009, 1000);m = pwAddK(m, 'CD274mRNA_production'    , 8.21752278733562e-005, 'global', 1e-009, 1000);%% U: Driving input% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0]  , [0 1]  , [], [], [], [], {}, [], 'protein.generic');%% Default sampling time pointsm.t = 0:1:120;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec'                       , 'RecSurf_obs'  , 'scale_RecSurf'  , '0.10 * y + 0.1 * max(y)');m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i + IL13_DecoyR', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i'                         , 'pIL4Ra_obs'   , 'scale_pIL4Ra'   , '0.1 * y + 0.15 * max(y)');m = pwAddY(m, 'pJAK2'                                             , 'pJAK2_obs'    , 'scale_pJAK2'    , '0.15 * y + 0.1 * max(y)');m = pwAddY(m, 'SOCS3mRNA'                                         , 'SOCS3mRNA_obs', 'scale_SOCS3mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'CD274mRNA'                                         , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'SOCS3'                                             , 'SOCS3_obs'    , 'scale_SOCS3'    , '0.1 * y + 0.15 * max(y)');m = pwAddY(m, 'pSTAT5'                                            , 'pSTAT5_obs'   , 'scale_pSTAT5'   , '0.15 * y + 0.1 * max(y)');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_pJAK2'    , 1.39039557075997, 'global', 0.001, 10000);m = pwAddS(m, 'scale_pIL4Ra'   , 1.88700484471494, 'global', 0.001, 10000);m = pwAddS(m, 'scale_RecSurf'  ,                1,    'fix', 0.001, 10000);m = pwAddS(m, 'scale_IL13-cell', 5.56750251420935, 'global', 0.001, 10000);m = pwAddS(m, 'scale_SOCS3mRNA', 17.6699101927908, 'global', 0.001, 10000);m = pwAddS(m, 'scale_CD274mRNA', 2.48547378765387, 'global', 0.001, 10000);m = pwAddS(m, 'scale_pSTAT5'   ,                1,    'fix', 0.001, 10000);m = pwAddS(m, 'scale_SOCS3'    ,                1,    'fix', 0.001, 10000);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\ufffd\ufffdre N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3561, + "tag": "BioModels:BIOMD0000000313" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3562, + "tag": "Regulation of JAK-STAT cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4608, + "tag": "Mature T-cell and NK-cell lymphoma" + } + ], + "timestamp_created": "2025-01-30 13:34:56.324178+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000313", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2604": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2604, + "name": "Raia2011 - IL13 L1236", + "repository_type": "biomodels", + "summary": "

This is the model of IL13 induced signalling in L1236 cells described in the article:
Dynamic Mathematical Modeling of IL13-Induced Signaling in Hodgkin and Primary Mediastinal B-Cell Lymphoma Allows Prediction of Therapeutic Targets.
Raia V, Schilling M, B\u00f6hm M, Hahn B, Kowarsch A, Raue A, Sticht C, Bohl S, Saile M, M\u00f6ller P, Gretz N, Timmer J, Theis F, Lehmann WD, Lichter P and Klingm\u00fcller U. Cancer Res. 2011 Feb 1;71(3):693-704. PubmedID: 21127196 ; DOI: 10.1158/0008-5472.CAN-10-2987
Abstract:
Primary mediastinal B-cell lymphoma (PMBL) and classical Hodgkin lymphoma (cHL) share a frequent constitutive activation of JAK (Janus kinase)/STAT signaling pathway. Because of complex, nonlinear relations within the pathway, key dynamic properties remained to be identified to predict possible strategies for intervention. We report the development of dynamic pathway models based on quantitative data collected on signaling components of JAK/STAT pathway in two lymphoma-derived cell lines, MedB-1 and L1236, representative of PMBL and cHL, respectively. We show that the amounts of STAT5 and STAT6 are higher whereas those of SHP1 are lower in the two lymphoma cell lines than in normal B cells. Distinctively, L1236 cells harbor more JAK2 and less SHP1 molecules per cell than MedB-1 or control cells. In both lymphoma cell lines, we observe interleukin-13 (IL13)-induced activation of IL4 receptor \u03b1, JAK2, and STAT5, but not of STAT6. Genome-wide, 11 early and 16 sustained genes are upregulated by IL13 in both lymphoma cell lines. Specifically, the known STAT-inducible negative regulators CISH and SOCS3 are upregulated within 2 hours in MedB-1 but not in L1236 cells. On the basis of this detailed quantitative information, we established two mathematical models, MedB-1 and L1236 model, able to describe the respective experimental data. Most of the model parameters are identifiable and therefore the models are predictive. Sensitivity analysis of the model identifies six possible therapeutic targets able to reduce gene expression levels in L1236 cells and three in MedB-1. We experimentally confirm reduction in target gene expression in response to inhibition of STAT5 phosphorylation, thereby validating one of the predicted targets.

All concentrations in the model, apart from IL13, are in molecules/cell. IL13 is given in ng/ml. As the cell volume is not explicitely given in the article, it is just approximately derived from the MW of IL13 (15.8 kDa) and the conversion factor 3.776 molecules IL13/cell = 1 ng/ml to be around 100 fl.

SBML model exported from PottersWheel on 2010-08-10 12:14:57.
Inline follows the original matlab code:

% PottersWheel model definition filefunction m = Raia2010_IL13_L1236()m             = pwGetEmptyModel();%% Meta informationm.ID          = 'Raia2010_IL13_L1236';m.name        = 'Raia2010_IL13_L1236';m.description = '';m.authors     = {'Raia et al'};m.dates       = {'2010'};m.type        = 'PW-2-0-47';%% X: Dynamic variables% m = pwAddX(m, ID, startValue, type, minValue, maxValue, unit, compartment, name, description, typeOfStartValue)m = pwAddX(m, 'Rec'         ,              1.8, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'Rec_i'       , 118.598421286338, 'global',  0.001, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'IL13_Rec'    ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec'  ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'p_IL13_Rec_i',                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'JAK2'        ,               24, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pJAK2'       ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'SHP1'        ,              9.4, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'STAT5'       ,              209, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'pSTAT5'      ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');m = pwAddX(m, 'CD274mRNA'   ,                0, 'fix'   , 1e-006, 10000, 'molecules/cell (x 1000)', 'cell', []  , []  , []             , []  , 'protein.generic');%% R: Reactions% m = pwAddR(m, reactants, products, modifiers, type, options, rateSignature, parameters, description, ID, name, fast, compartments, parameterTrunks, designerPropsR, stoichiometry, reversible)m = pwAddR(m, {'Rec'         }, {'IL13_Rec'    }, {'IL13stimulation'}, 'C' , [] , 'k1 * m1 * r1 * 3.776', {'Kon_IL13Rec'             });m = pwAddR(m, {'Rec'         }, {'Rec_i'       }, {                 }, 'MA', [] , []                    , {'Rec_intern'              });m = pwAddR(m, {'Rec_i'       }, {'Rec'         }, {                 }, 'MA', [] , []                    , {'Rec_recycle'             });m = pwAddR(m, {'IL13_Rec'    }, {'p_IL13_Rec'  }, {'pJAK2'          }, 'E' , [] , []                    , {'Rec_phosphorylation'     });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'IL13_Rec'       }, 'E' , [] , []                    , {'JAK2_phosphorylation'    });m = pwAddR(m, {'JAK2'        }, {'pJAK2'       }, {'p_IL13_Rec'     }, 'E' , [] , []                    , {'JAK2_phosphorylation'    });m = pwAddR(m, {'p_IL13_Rec'  }, {'p_IL13_Rec_i'}, {                 }, 'MA', [] , []                    , {'pRec_intern'             });m = pwAddR(m, {'p_IL13_Rec_i'}, {              }, {                 }, 'MA', [] , []                    , {'pRec_degradation'        });m = pwAddR(m, {'pJAK2'       }, {'JAK2'        }, {'SHP1'           }, 'E' , [] , []                    , {'pJAK2_dephosphorylation' });m = pwAddR(m, {'STAT5'       }, {'pSTAT5'      }, {'pJAK2'          }, 'E' , [] , []                    , {'STAT5_phosphorylation'   });m = pwAddR(m, {'pSTAT5'      }, {'STAT5'       }, {'SHP1'           }, 'E' , [] , []                    , {'pSTAT5_dephosphorylation'});m = pwAddR(m, {              }, {'CD274mRNA'   }, {'pSTAT5'         }, 'C' , [] , 'm1*k1'               , {'CD274mRNA_production'    });%% C: Compartments% m = pwAddC(m, ID, size,  outside, spatialDimensions, name, unit, constant)m = pwAddC(m, 'cell', 1);%% K: Dynamical parameters% m = pwAddK(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddK(m, 'Kon_IL13Rec'             , 0.00174086832237195, 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_phosphorylation'     , 9.07540737285078   , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_intern'             , 0.324132341358502  , 'global', 1e-009, 1000);m = pwAddK(m, 'pRec_degradation'        , 0.417538218767296  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_intern'              , 0.259685756311325  , 'global', 1e-009, 1000);m = pwAddK(m, 'Rec_recycle'             , 0.00392430355501153, 'global', 1e-009, 1000);m = pwAddK(m, 'JAK2_phosphorylation'    , 0.300019047540849  , 'global', 1e-009, 1000);m = pwAddK(m, 'pJAK2_dephosphorylation' , 0.0981610557569751 , 'global', 1e-009, 1000);m = pwAddK(m, 'STAT5_phosphorylation'   , 0.00426766529531612, 'global', 1e-009, 1000);m = pwAddK(m, 'pSTAT5_dephosphorylation', 0.0116388587580445 , 'global', 1e-009, 1000);m = pwAddK(m, 'CD274mRNA_production'    , 0.0115927572109515 , 'global', 1e-009, 1000);%% U: Driving input% m = pwAddU(m, ID, uType, uTimes, uValues, compartment, name, description, u2Values, alternativeIDs, designerProps)m = pwAddU(m, 'IL13stimulation', 'steps', [-100 0]  , [0 1]  , [], [], [], [], {}, [], 'protein.generic');%% Default sampling time pointsm.t = 0:1:120;%% Y: Observables% m = pwAddY(m, rhs, ID, scalingParameter, errorModel, noiseType, unit, name, description, alternativeIDs, designerProps)m = pwAddY(m, 'Rec + IL13_Rec + p_IL13_Rec'         , 'RecSurf_obs'  , 'scale_RecSurf'  , '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'IL13_Rec + p_IL13_Rec + p_IL13_Rec_i', 'IL13-cell_obs', 'scale_IL13-cell', '0.15 * y + 0.05 * max(y)');m = pwAddY(m, 'p_IL13_Rec + p_IL13_Rec_i'           , 'pIL4Ra_obs'   , 'scale_pIL4Ra'   , '0.10 * y + 0.15 * max(y)');m = pwAddY(m, 'pJAK2'                               , 'pJAK2_obs'    , 'scale_pJAK2'    , '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'CD274mRNA'                           , 'CD274mRNA_obs', 'scale_CD274mRNA', '0.1 * y + 0.1 * max(y)');m = pwAddY(m, 'pSTAT5'                              , 'pSTAT5_obs'   , 'scale_pSTAT5'   , '0.1 * y + 0.1 * max(y)');%% S: Scaling parameters% m = pwAddS(m, ID, value, type, minValue, maxValue, unit, name, description)m = pwAddS(m, 'scale_pJAK2'    , 0.469836894150194, 'global',  0.001, 10000);m = pwAddS(m, 'scale_pIL4Ra'   ,  1.80002942264669, 'global',  0.001, 10000);m = pwAddS(m, 'scale_RecSurf'  ,                 1,    'fix', 0.0001, 10000);m = pwAddS(m, 'scale_IL13-cell',  174.726805005048, 'global',  0.001, 10000);m = pwAddS(m, 'scale_CD274mRNA', 0.110568221201943, 'global',  0.001, 10000);m = pwAddS(m, 'scale_pSTAT5'   ,                 1,    'fix',  0.001, 10000);%% Designer properties (do not modify)m.designerPropsM = [1 1 1 0 0 0 400 250 600 400 1 1 1 0 0 0 0];
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3563, + "tag": "BioModels:BIOMD0000000314" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3564, + "tag": "JAK-STAT cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4609, + "tag": "Hodgkin's lymphoma" + } + ], + "timestamp_created": "2025-01-30 13:34:56.905757+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000314", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2605": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2605, + "name": "Montagne2011_Oligator_optimised", + "repository_type": "biomodels", + "summary": "

This is the model of the in vitro DNA oscillator called oligator with the optmized set of parameters described in the article:
Programming an in vitro DNA oscillator using a molecular networking strategy.
Montagne K, Plasson R, Sakai Y, Fujii T, Rondelez Y. Mol Syst Biol. 2011 Feb 1;7:466. PubmedID:21283142, Doi:10.1038/msb.2010.120

Abstract:
Living organisms perform and control complex behaviours by using webs of chemical reactions organized in precise networks. This powerful system concept, which is at the very core of biology, has recently become a new foundation for bioengineering. Remarkably, however, it is still extremely difficult to rationally create such network architectures in artificial, non-living and well-controlled settings. We introduce here a method for such a purpose, on the basis of standard DNA biochemistry. This approach is demonstrated by assembling de novo an efficient chemical oscillator: we encode the wiring of the corresponding network in the sequence of small DNA templates and obtain the predicted dynamics. Our results show that the rational cascading of standard elements opens the possibility to implement complex behaviours in vitro. Because of the simple and well-controlled environment, the corresponding chemical network is easily amenable to quantitative mathematical analysis. These synthetic systems may thus accelerate our understanding of the underlying principles of biological dynamic modules.

The model reproduces the time courses in fig 2B. The parameter identifiers of the reaction constants are not the same as in the supplemental material, but are just called kXd and kXr for the forward and backwards constant of reaction X respectively.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the coherent feed forward loop with an AND-gate like control of the response operon described in the article:
Network motifs in the transcriptional regulation network of Escherichia coli
Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon, Nat Genet 2002 31:64-68; PMID: 11967538 ; DOI: 10.1038/ng881 ;

Abstract:
Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

This model reproduces the timecourse presented in Figure 2a. All species and parameters in the model are dimensionless.

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This is the single input module, SIM, described in the article:
Network motifs in the transcriptional regulation network of Escherichia coli
Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan, Uri Alon, Nat Genet 2002 31:64-68; PMID:11967538; DOI:10.1038/ng881;

Abstract:
Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.

This model reproduces the SIM timecourse presented in Figure 2b. All species and parameters in the model are dimensionless.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3569, + "tag": "BioModels:BIOMD0000000317" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:58.327210+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000317", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2608": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2608, + "name": "Yao2008_Rb_E2F_Switch", + "repository_type": "biomodels", + "summary": "

This is the model described in the article:
A bistable Rb-E2F switch underlies the restriction point
Guang Yao, Tae Jun Lee, Seiichi Mori, Joseph R. Nevins, Lingchong You, Nat Cell Biol 2008 10:476-482; PMID: 18364697 ; DOI: 10.1038/ncb1711 .

Abstract:
The restriction point (R-point) marks the critical event when a mammalian cell commits to proliferation and becomes independent of growth stimulation. It is fundamental for normal differentiation and tissue homeostasis, and seems to be dysregulated in virtually all cancers. Although the R-point has been linked to various activities involved in the regulation of G1-S transition of the mammalian cell cycle, the underlying mechanism remains unclear. Using single-cell measurements, we show here that the Rb-E2F pathway functions as a bistable switch to convert graded serum inputs into all-or-none E2F responses. Once turned ON by sufficient serum stimulation, E2F can memorize and maintain this ON state independently of continuous serum stimulation. We further show that, at critical concentrations and duration of serum stimulation, bistable E2F activation correlates directly with the ability of a cell to traverse the R-point.

This model reproduces the serum-pulse stimulation-protocol in Figure 3(b).


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novere N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3570, + "tag": "BioModels:BIOMD0000000318" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:34:58.778858+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000318", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2609": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2609, + "name": "Decroly1982_Enzymatic_Oscillator", + "repository_type": "biomodels", + "summary": "

This is the scaled model described in the article:
Birhythmicity, chaos, and other patterns of temporal self-organization in a multiply regulated biochemical system
Olivier Decroly, Albert Goldbeter, Proc Natl Acad Sci USA 1982 79:6917-6921; PMID:6960354;

Abstract:
We analyze on a model biochemical system the effect of a coupling between two instability-generating mechanisms. The system considered is that of two allosteric enzymes coupled in series and activated by their respective products. In addition to simple periodic oscillations, the system can exhibit a variety of new modes of dynamic behavior; coexistence between two stable periodic regimes (birhythmicity), random oscillations (chaos), and coexistence of a stable periodic regime with a stable steady state (hard excitation) or with chaos. The relationship between these patterns of temporal self-organization is analyzed as a function of the control parameters of the model. Chaos and birhythmicity appear to be rare events in comparison with simple periodic behavior. We discuss the relevance of these results with respect to the regularity of most biological rhythms.

The parameters q1 = 50 and q2 = 0.02 are explicitely included as the stoichiometric coefficients of beta and gamma in the reactions r2 and r3, respectively. Parameter values and initial conditions [ks=1.99/sec, alpha(0)=29.19988, beta(0)=188.8, gamma(0)=0.3367] are for the chaotic regime presented in the upper-curve of Figure 3b.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3571, + "tag": "BioModels:BIOMD0000000319" + }, + { + "id": 3055, + "tag": "Rhythmic behavior" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:34:59.223593+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000319", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2610": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2610, + "name": "Grange2001 - PK interaction of L-dopa and benserazide", + "repository_type": "biomodels", + "summary": "
Grange2001 - PK interaction of L-dopa and benserazide

A pharmacokinetics of L-dopa in rats after administration of L-dopa alone (BIOMD0000000321) or L-dopa combined with a peripheral AADC (amino-acid-decarboxylase) inhibitor (this model: BIOMD0000000320) has been studied using noncompartmental analysis.

This model is described in the article:

Grange S, Holford NH, Guentert TW
Pharmaceutical Research [2001, 18(8):1174-1184]

Abstract:

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The volumes and variables in this model are taken for a rat with 0.25 kg. The inital dose for L_Dopa (L_Dopa_per_kg_rat) and Benserazide (Benserazide_per_kg_rat) are to be given in umole per kg. 80 mg/kg L-Dopa correspond to 404 umol/kg, 20 mg/kg benserazide to 78 umol/kg. To change the model to a different mass of rat the compartment volumes, and the parameters rat_body_mass and Q have to changed accordingly.

The model has three species (A-dopa, A_B, A_M) whose initial concentrations are calculated from a listOfInitialAssignments . While running for the first time the time-course (24hrs) for this model in COPASI (up to version 4.6, Build 33), the resulting graph displays only straight lines for all the species. Any subsequent runs should provide proper plots (i.e. without making any change to the model, just by clicking the \"run\" button again).

The above issue is caused by some initial assignments which are not calculated when COPASI imports the file. This issue should not be present in newer releases of COPASI.

This model is hosted on BioModels Database and identified by: MODEL0910130001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Grange2001 - L-dopa PK model

A pharmacokinetics of L-dopa in rats after administration of L-dopa alone (this model: BIOMD0000000321) or L-dopa combined with a peripheral AADC (amino-acid-decarboxylase) inhibitor (BIOMD0000000320) has been studied using noncompartmental analysis.

This model is described in the article:

Grange S, Holford NH, Guentert TW
Pharmaceutical Research [2001, 18(8):1174-1184]

Abstract:

PURPOSE: To study the PK interaction of L-dopa/benserazide in rats. METHODS: Male rats received a single oral dose of 80 mg/kg L-dopa or 20 mg/kg benserazide or 80/20 mg/kg L-dopa/benserazide. Based on plasma concentrations the kinetics of L-dopa, 3-O-methyldopa (3-OMD), benserazide, and its metabolite Ro 04-5127 were characterized by noncompartmental analysis and a compartmental model where total L-dopa clearance was the sum of the clearances mediated by amino-acid-decarboxylase (AADC), catechol-O-methyltransferase and other enzymes. In the model Ro 04-5127 inhibited competitively the L-dopa clearance by AADC.

RESULTS: The coadministration of L-dopa/benserazide resulted in a major increase in systemic exposure to L-dopa and 3-OMD and a decrease in L-dopa clearance. The compartmental model allowed an adequate description of the observed L-dopa and 3-OMD concentrations in the absence and presence of benserazide. It had an advantage over noncompartmental analysis because it could describe the temporal change of inhibition and recovery of AADC.

CONCLUSIONS: Our study is the first investigation where the kinetics of benserazide and Ro 04-5127 have been described by a compartmental model. The L-dopa/benserazide model allowed a mechanism-based view of the L-dopa/benserazide interaction and supports the hypothesis that Ro 04-5127 is the primary active metabolite of benserazide.

The model has a species (A-dopa) whose initial concentration is calculated from a listOfInitialAssignments . While running for the first time the time-course (24hrs) for this model in COPASI (up to version 4.6, Build 33), the resulting graph displays only straight lines for all the species. Any subsequent runs should provide proper plots (i.e. without making any change to the model, just by clicking the \"run\" button again).

The above issue is caused by some initial assignments which are not calculated when COPASI imports the file. This issue should not be present in newer releases of COPASI.

This model is hosted on BioModels Database and identified by: MODEL1103250000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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This a model from the article:
Synthetic in vitro transcriptional oscillators.
Kim J, Winfree E Mol. Syst. Biol. 2011 Feb 1;7:465. 21283141 ,
Abstract:
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplifiedanalog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platformfor the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally,a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.

Note:

The paper describes 7 models (MODEL1012090000-6) and all these are submitted by the authors. Thismodel (MODEL1012090000) corresponds to the Simple model for both mode I and II (Design I and II). The model reproduces timecourse figure plotted in the supplementary material (page 10 of Supplementary material) of the reference publication.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Synthetic in vitro transcriptional oscillators.
Kim J, Winfree E Mol. Syst. Biol. 2011 Feb 1;7:465. 21283141 ,
Abstract:
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells.

Notes:

The paper describes 7 models (MODEL1012090000-6) and all these are submitted by the authors. This model (MODEL1012090001) corresponds to the Simple model of the three-switch ring oscillator (Design III). The model reproduces figure 6 (central figures) of the reference publication. The time is rescaled by s=v_d/K_I*t where K_I=0.333 and v_d=1 (for alpha = 1) and v_d=0.5 (for alpha = 0.5). i.e. For alpha = 1, s = 0.003 * t (roughly 10 unitless time = 1hr; the time-course should be run for 60 timeunits (6hrs) to get figure 6a). For alpha = 2, s= 0.0015 * t (roughly 5 unitless time = 1hr; the time-course shoue be run for 100 timesunits (20hrs) to get figure 6b).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the full model (eq. 1 and 2) of the voltage oscillations in barnacle muscle fibers described in the article:
Voltage oscillations in the barnacle giant muscle fiber.
\tMorris C, Lecar H. Biophys J. 1981 Jul;35(1):193-213. PubmedID:7260316; DOI:10.1016/S0006-3495(81)84782-0
\tAbstract:
\tBarnacle muscle fibers subjected to constant current stimulation produce a variety of types of oscillatory behavior when the internal medium contains the Ca++ chelator EGTA. Oscillations are abolished if Ca++ is removed from the external medium, or if the K+ conductance is blocked. Available voltage-clamp data indicate that the cell's active conductance systems are exceptionally simple. Given the complexity of barnacle fiber voltage behavior, this seems paradoxical. This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior.

The model consists of the differential equations (1) and (2) given on pages 195 and 196 of the article. There is one typo in the equation for I in (1), gL(VL) should be gL(V - VL). This was changed in the SBML file. As there are no current values given, for reproducing the time courses in figure 6 an applied current of 50 uA was assumed. The legend for the broken and the full line in this figure seems to be confounded in the article.

Originally created by libAntimony v1.4 (using libSBML 3.4.1)

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This is the model of the minmal 2 feedback switch described in the article:
Synthetic conversion of a graded receptor signal into a tunable, reversible switch.
Santhosh Palani and Casim A. Sarkar, 2011, Molecular Systems Biology 7:480; doi: 10.1038/msb.2011.13

The ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network switches have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (nH420), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties.Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways.

This model is parametrised for a transcription factor and receptor feedback strength of 3, TFs = 3 and Rs = 3. To reproduce figure 1 E, the parameters TFs and Rs have to be varied accordingly.

Nomenclature for the model:
L : Ligand
R : Receptor
C : Ligand-Receptor Complex
I : Inactive Transcription Factor
X : C bound to I
A : Active Transcription Factor

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Network-level analysis of light adaptation in rod cells under normal and altered conditions.
Dell'Orco D, Schmidt H, Mariani S, Fanelli F Mol Biosyst2009 Oct; 5(10):1232-46 19756313,
Abstract:
Photoreceptor cells finely adjust their sensitivity and electrical response according to changes in light stimuli as a direct consequence of the feedback and regulation mechanisms in the phototransduction cascade. In this study, we employed a systems biology approach to develop a dynamic model of vertebrate rod phototransduction that accounts for the details of the underlying biochemistry. Following a bottom-up strategy, we first reproduced the results of a robust model developed by Hamer et al. (Vis. Neurosci., 2005, 22(4), 417), and then added a number of additional cascade reactions including: (a) explicit reactions to simulate the interaction between the activated effector and the regulator of G-protein signalling (RGS); (b) a reaction for the reformation of the G-protein from separate subunits; (c) a reaction for rhodopsin (R) reconstitution from the association of the opsin apoprotein with the 11-cis-retinal chromophore; (d) reactions for the slow activation of the cascade by opsin. The extended network structure successfully reproduced a number of experimental conditions that were inaccessible to prior models. With a single set of parameters the model was able to predict qualitative and quantitative features of rod photoresponses to light stimuli ranging over five orders of magnitude, in normal and altered conditions, including genetic manipulations of the cascade components. In particular, the model reproduced the salient dynamic features of the rod from Rpe65(-/-) animals, a well established model for Leber congenital amaurosis and vitamin A deficiency. The results of this study suggest that a systems-level approach can help to unravel the adaptation mechanisms in normal and in disease-associated conditions on a molecular basis.


Note:

Figure 7 of the reference is reproduced here. Each plot is obtained by increasing flash strength. More details about generating the plots can be obtained from the comments in the curation figure (go to curation tab).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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A mathematical model of the pancreatic duct cell generating high bicarbonate concentrations in pancreatic juice
David C Whitcomb, G Bard Ermentrout, Pancreas 2004 29:e30-40; PubMedID:15257112

Abstract:
OBJECTIVE:To develop a simple, physiologically based mathematical model of pancreatic duct cell secretion using experimentally derived parameters that generates pancreatic fluid bicarbonate concentrations of >140 mM after CFTR activation.
METHODS:A new mathematical model was developed simulating a duct cell within a proximal pancreatic duct and included a sodium-2-bicarbonate cotransporter (NBC) and sodium-potassium pump (NaK pump) on a chloride-impermeable basolateral membrane, CFTR on the luminal membrane with 0.2 to 1 bicarbonate to chloride permeability ratio. Chloride-bicarbonate antiporters (Cl/HCO3 AP) were added or subtracted from the basolateral (APb) and luminal (APl) membranes. The model was integrated over time using XPPAUT.
RESULTS:This model predicts robust, NaK pump-dependent bicarbonate secretion with opening of the CFTR, generates and maintains pancreatic fluid secretion with bicarbonate concentrations >140 mM, and returns to basal levels with CFTR closure. Limiting CFTR permeability to bicarbonate, as seen in some CFTR mutations, markedly inhibited pancreatic bicarbonate and fluid secretion.
CONCLUSIONS:A simple CFTR-dependent duct cell model can explain active, high-volume, high-concentration bicarbonate secretion in pancreatic juice that reproduces the experimental findings. This model may also provide insight into why CFTR mutations that predominantly affect bicarbonate permeability predispose to pancreatic dysfunction in humans.

This SBML version of the model was created directly from the XPPAUT code found in the appendix with the exception of the parameter vr, the ratio between the duct cell volume and the duct lumen, which is defined inversely to the main text in the XPPAUT code. vr was defined as the ratio of the duct cell volume to the duct lumen volume as in the main text. The model reproduces the figures found in the article. The model uses initial assignments for the lumen volume and events to trigger CFTR opening, so only tools supporting these features can be used to simulate it (eg. Copasi and SBW/Roadrunner).

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This is the model of atorvastatin metabolism in hepaitc cells described in the article:
A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes
Joachim Bucher , Stephan Riedmaier , Anke Schnabel , Katrin Marcus , Gabriele Vacun , Thomas S Weiss , Wolfgang E Thasler , Andreas K Nussler , Ulrich M Zanger and Matthias Reuss. BMC Systems Biology 2011, 5:66. DOI:10.1186/1752-0509-5-66

Abstract:
Background:
The individual character of pharmacokinetics is of great importance in the risk assessment of new drug leads in pharmacological research. Amongst others, it is severely influenced by the properties and inter-individual variability of the enzymes and transporters of the drug detoxification system of the liver. Predicting individual drug biotransformation capacity requires quantitative and detailed models.
Results:
In this contribution we present the de novo deterministic modeling of atorvastatin biotransformation based on comprehensive published knowledge on involved metabolic and transport pathways as well as physicochemical properties. The model was evaluated in primary human hepatocytes and parameter identifiability analysis was performed under multiple experimental constraints. Dynamic simulations of atorvastatin biotransformation considering the inter-individual variability of the two major involved enzymes CYP3A4 and UGT1A3 based on quantitative protein expression data in a large human liver bank (n=150) highlighted the variability in the individual biotransformation profiles and therefore also points to the individuality of pharmacokinetics.
Conclusions:
A dynamic model for the biotransformation of atorvastatin has been developed using quantitative metabolite measurements in primary human hepatocytes. The model comprises kinetics for transport processes and metabolic enzymes as well as population liver expression data allowing us to assess the impact of inter-individual variability of concentrations of key proteins. Application of computational tools for parameter sensitivity analysis enabled us to considerably improve the validity of the model and to create a consistent framework for precise computer-aided simulations in toxicology.

The model is parameterized for patient 1 and reproduces the time courses in figure 2 of the article.

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Kummer2000 - Oscillations in CalciumSignalling

Simplified (3-variable) calcium oscillation model Kummer et al. (2000) Biophys. J. 79, 1188-1195 This model is defined in a small compartment with low concentrations. You can run it first with the LSODA ODE solver and then with the Gillespie Monte Carlo method (in Time Course widget). This illustrates that at low particle numbers, as here, the stochastic simulation and the ODE approach produce different results (the stochastic approach is more correct in these circumstances). This file also demonstrates the use of several different plots to visualize results, including a histogram.

This model is described in the article:

Kummer U, Olsen LF, Dixon CJ, Green AK, Bornberg-Bauer E, Baier G.
Biophys. J. 2000 Sep; 79(3): 1188-1195

Abstract:

We present a new model for calcium oscillations based on experiments in hepatocytes. The model considers feedback inhibition on the initial agonist receptor complex by calcium and activated phospholipase C, as well as receptor type-dependent self-enhanced behavior of the activated G(alpha) subunit. It is able to show simple periodic oscillations and periodic bursting, and it is the first model to display chaotic bursting in response to agonist stimulations. Moreover, our model offers a possible explanation for the differences in dynamic behavior observed in response to different agonists in hepatocytes.

This model is hosted on BioModels Database and identified by: BIOMD0000000329.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model is from the article:
On the encoding and decoding of calcium signals in hepatocytes
Ann Zahle Larsen, Lars Folke Olsen and Ursula Kummera Biophysical ChemistryVolume 107, Issue 1, 1 January 2004, Pages 83-99 14871603,
Abstract:
Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.

Note:

This model corresponds to the 5 variable receptor-operated model, as described by Larsen et al., 2004. This model is a modified version of the model described in Kummer 2000 (PMID:10968983)

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This a model from the article:
On the encoding and decoding of calcium signals in hepatocytes
Ann Zahle Larsen, Lars Folke Olsen and Ursula Kummera Biophysical ChemistryVolume 107, Issue 1, 1 January 2004, Pages 83-99 14871603,
Abstract:
Many different agonists use calcium as a second messenger. Despite intensive research in intracellular calcium signalling it is an unsolved riddle how the different types of information represented by the different agonists, is encoded using the universal carrier calcium. It is also still not clear how the information encoded is decoded again into the intracellular specific information at the site of enzymes and genes. After the discovery of calcium oscillations, one likely mechanism is that information is encoded in the frequency, amplitude and waveform of the oscillations. This hypothesis has received some experimental support. However, the mechanism of decoding of oscillatory signals is still not known. Here, we study a mechanistic model of calcium oscillations, which is able to reproduce both spiking and bursting calcium oscillations. We use the model to study the decoding of calcium signals on the basis of co-operativity of calcium binding to various proteins. We show that this co-operativity offers a simple way to decode different calcium dynamics into different enzyme activities.

Note:

This model corresponds to the improved model eqn 1-7, as described by Larsen et al., 2004 implemented to investigate how the cell can decode different oscillations. This is done by introducing 2 more variables Enzyme and Product in addition to the 5 variables G-alpha, PLC, Ca_cyt, Ca_ER and Ca_mit receptor-operated model described in the first part of the paper. The receptor-operated model is itself a modified version of the model described in Kummer 2000 (PMID:10968983)

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This model is from the article:
Modelling thrombin generation in human ovarian follicular fluid
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Bulletin of Mathematical BiologyVolume 68, Issue 8, 12 July 2006, Pages 2283-302 16838084,
Abstract:
A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.

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This model is from the article:
Modelling thrombin generation in human ovarian follicular fluid
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Bulletin of Mathematical BiologyVolume 68, Issue 8, 12 July 2006, Pages 2283-302 16838084,
Abstract:
A mathematical model is constructed to study thrombin production in human ovarian follicular fluid. The model results show that the amount of thrombin that can be produced in ovarian follicular fluid is much lower than that in blood plasma, failing to reach the level required for fibrin formation, and thereby supporting the hypothesis that in follicular fluid thrombin functions to initiate cellular activities via intracellular signalling receptors. It is also concluded that the absence of the amplification pathway to thrombin production in follicular fluid is a major factor in restricting the amount of thrombin that can be produced. Titration of the initial concentrations of the various reactants in the model lead to predictions for the amount of tissue factor and phospholipid that is required to maintain thrombin production in the follicle, as well as to the conclusion that tissue factor pathway inhibitor has little effect on the time that thrombin generation is sustained. Numerical experiments to determine the effect of factor V, which is at a much reduced level in follicular fluid compared to plasma, and thrombomodulin, illustrate the importance for further experimental work to determine values for several parameters that have yet to be reported in the literature.

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This model is from the article:
A mathematical model of lipid-mediated thrombin generation
Bungay Sharene D., Gentry Patricia A., Gentry Rodney D. Mathematical Medicine and BiologyVolume 20, Issue 1, 1 March 2003, Pages 105-29 12974500,
Abstract:
Thrombin is an enzyme that is generated in both vascular and non-vascular systems. In blood coagulation, a fundamental process in all species, thrombin induces the formation of a fibrin clot. A dynamical model of thrombin generation in the presence of lipid surfaces is presented. This model also includes the self-regulating thrombin feedback reactions, the thrombomodulin-protein C-protein S inhibitory system, tissue factor pathway inhibitor (TFPI), and the inhibitor, antithrombin (AT). The dynamics of this complex system were found to be highly lipid dependent, as would be expected from experimental studies. Simulations of this model indicate that a threshold lipid level is required to generate physiologically relevant amounts of thrombin. The dependence of the onset, the peak levels, and the duration of thrombin generation on lipid was saturable. The lipid concentration affects the way in which the inhibitors modulate thrombin production. A novel feature of this model is the inclusion of the dynamical protein C pathway, initiated by thrombin feedback. This inhibitory system exerts its effects on the lipid surface, where its substrates are formed. The maximum impact of TFPI occurs at intermediate vesicle concentrations. Inhibition by AT is only indirectly affected by the lipid since AT irreversibly binds only to solution phase proteins. In a system with normal plasma concentrations of the proteins involved in thrombin formation, the combination of these three inhibitors is sufficient both to effectively stop thrombin generation prior to the exhaustion of its precursor, prothrombin, and to inhibit all thrombin formed. This model can be used to predict thrombin generation under extreme lipid conditions that are difficult to implement experimentally and to examine thrombin generation in non-vascular systems.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
A model for the stoichiometric regulation of blood coagulation.
Hockin MF, Jones KC, Everse SJ, Mann KG. Journal of Biological ChemistryVolume 277, Issue 21, 24 May 2002, Pages 18322 -18333 11893748,
Abstract:
We have developed a model of the extrinsic blood coagulation system that includes the stoichiometric anticoagulants. The model accounts for the formation, expression, and propagation of the vitamin K-dependent procoagulant complexes and extends our previous model by including: (a) the tissue factor pathway inhibitor (TFPI)-mediated inactivation of tissue factor (TF).VIIa and its product complexes; (b) the antithrombin-III (AT-III)-mediated inactivation of IIa, mIIa, factor VIIa, factor IXa, and factor Xa; (c) the initial activation of factor V and factor VIII by thrombin generated by factor Xa-membrane; (d) factor VIIIa dissociation/activity loss; (e) the binding competition and kinetic activation steps that exist between TF and factors VII and VIIa; and (f) the activation of factor VII by IIa, factor Xa, and factor IXa. These additions to our earlier model generate a model consisting of 34 differential equations with 42 rate constants that together describe the 27 independent equilibrium expressions, which describe the fates of 34 species. Simulations are initiated by \"exposing\" picomolar concentrations of TF to an electronic milieu consisting of factors II, IX, X, VII, VIIa, V, and VIIII, and the anticoagulants TFPI and AT-III at concentrations found in normal plasma or associated with coagulation pathology. The reaction followed in terms of thrombin generation, proceeds through phases that can be operationally defined as initiation, propagation, and termination. The generation of thrombin displays a nonlinear dependence upon TF, AT-III, and TFPI and the combination of these latter inhibitors displays kinetic thresholds. At subthreshold TF, thrombin production/expression is suppressed by the combination of TFPI and AT-III; for concentrations above the TF threshold, the bolus of thrombin produced is quantitatively equivalent. A comparison of the model with empirical laboratory data illustrates that most experimentally observable parameters are captured, and the pathology that results in enhanced or deficient thrombin generation is accurately described.

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Jones1994_BloodCoagulation
This model is built based on theexperimental findings described in Lawson et al., 1994(PMID:8083241)

This model is described in the article:

Jones KC, Mann KG.
J. Biol. Chem. 1994 Sep; 269(37): 23367-23373

Abstract:

A mathematical simulation of the tissue factor pathway to the generation of thrombin has been developed using a combination of empirical, estimated, and deduced rate constants for reactions involving the activation of factor IX, X, V, and VIII, in the formation of thrombin, as well as rate constants for the assembly of the coagulation enzyme complexes which involve factor VIIIa-factor IXa (intrinsic tenase) and factor Va-Xa (prothrombinase) assembled on phospholipid membrane. Differential equations describing the fate of each species in the reaction were developed and solved using an interactive procedure based upon the Runge-Kutta technique. In addition to the theoretical considerations involving the reactions of the tissue factor pathway, a physical constraint associated with the stability of the factor VIIIa-factor IXa complex has been incorporated into the model based upon the empirical observations associated with the stability of this complex. The model system provides a realistic accounting of the fates of each of the proteins in the coagulation reaction through a range of initiator (factor VIIa-tissue factor) concentrations ranging from 5 pM to 5 nM. The model is responsive to alterations in the concentrations of factor VIII, factor V, and their respective activated species, factor VIIIa and factor Va, and overall provides a reasonable approximation of empirical data. The computer model permits the assessment of the reaction over a broad range of conditions and provides a useful tool for the development and management of reaction studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000336.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This model is from the article:
Cooperation and Competition in the Evolution of ATP-Producing Pathways
Thomas Pfeiffer, Stefan Schuster, Sebastian Bonhoeffer Science 2001 Apr; Volume:292 (Issue:5516); Page info:504-7 11283355 ,
Abstract:
Heterotrophic organisms generally face a trade-off between rate and yield of adenosine triphosphate (ATP) production. This trade-off may result in an evolutionary dilemma, because cells with a higher rate but lower yield of ATP production may gain a selective advantage when competing for shared energy resources. Using an analysis of model simulations and biochemical observations, we show that ATP production with a low rate and high yield can be viewed as a form of cooperative resource use and may evolve in spatially structured environments. Furthermore, we argue that the high ATP yield of respiration may have facilitated the evolutionary transition from unicellular to undifferentiated multicellular organisms.


Note:

This model reproduces the competition and invasion described in Supplemental Figure 2.

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This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

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This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3601, + "tag": "BioModels:BIOMD0000000339" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4611, + "tag": "Hemophilia B" + } + ], + "timestamp_created": "2025-01-30 13:35:08.887908+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000339", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2630": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2630, + "name": "Wajima2009_BloodCoagulation_warfarin_heparin", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A comprehensive model for the humoral coagulation network in humans.
Wajima T, Isbister GK, Duffull SB. Clinical Pharmacology and therapeuticsVolume 86, Issue 3, 10 June 2009, EPub 19516255,
Abstract:
Coagulation is an important process in hemostasis and comprises a complicated interaction of multiple enzymes and proteins. We have developed a mechanistic quantitative model of the coagulation network. The model accurately describes the time courses of coagulation factors following in vivo activation as well as in vitro blood coagulation tests of prothrombin time (PT, often reported as international normalized ratio (INR)) and activated partial thromboplastin time (aPTT). The model predicts the concentration-time and time-effect profiles of warfarin, heparins, and vitamin K in humans. The model can be applied to predict the time courses of coagulation kinetics in clinical situations (e.g., hemophilia) and for biomarker identification during drug development. The model developed in this study is the first quantitative description of the comprehensive coagulation network.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3602, + "tag": "BioModels:BIOMD0000000340" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4611, + "tag": "Hemophilia B" + } + ], + "timestamp_created": "2025-01-30 13:35:09.330510+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000340", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2631": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2631, + "name": "Topp2000_BetaCellMass_Diabetes", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A model of beta-cell mass, insulin, and glucose kinetics: pathways to diabetes.
Topp B, Promislow K, deVries G, Miura RM, Finegood DT. J Theor Biol.2000 Oct 21;206(4):605-19. 11013117,
Abstract:
Diabetes is a disease of the glucose regulatory system that is associated with increased morbidity and early mortality. The primary variables of this system are beta-cell mass, plasma insulin concentrations, and plasma glucose concentrations. Existing mathematical models of glucose regulation incorporate only glucose and/or insulin dynamics. Here we develop a novel model of beta -cell mass, insulin, and glucose dynamics, which consists of a system of three nonlinear ordinary differential equations, where glucose and insulin dynamics are fast relative to beta-cell mass dynamics. For normal parameter values, the model has two stable fixed points (representing physiological and pathological steady states), separated on a slow manifold by a saddle point. Mild hyperglycemia leads to the growth of the beta -cell mass (negative feedback) while extreme hyperglycemia leads to the reduction of the beta-cell mass (positive feedback). The model predicts that there are three pathways in prolonged hyperglycemia: (1) the physiological fixed point can be shifted to a hyperglycemic level (regulated hyperglycemia), (2) the physiological and saddle points can be eliminated (bifurcation), and (3) progressive defects in glucose and/or insulin dynamics can drive glucose levels up at a rate faster than the adaptation of the beta -cell mass which can drive glucose levels down (dynamical hyperglycemia).

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3603, + "tag": "BioModels:BIOMD0000000341" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3411, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:35:09.766321+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000341", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2632": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2632, + "name": "Zi2011_TGF-beta_Pathway", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Quantitative analysis of transient and sustained transforming growth factor-\u03b2 signaling dynamics.
Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 ,
Abstract:
Mammalian cells can decode the concentration of extracellular transforming growth factor-\u03b2 (TGF-\u03b2) and transduce this cue into appropriate cell fate decisions. How variable TGF-\u03b2 ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-\u03b2 stimulation. The TGF-\u03b2 pathway elicits a transient signaling response to a single pulse of TGF-\u03b2 stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-\u03b2 pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-\u03b2 levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-\u03b2 pathway might be critical for cell fate determination.

Note:

Developer of the model: Zhike Zi

Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011

1. The global parameter that set the type of stimulation

(a) for sustained TGF-beta stimulation: set stimulation_type = 1.

(b) for single pulse of TGF-beta stimulation: set stimulation_type = 2.

parameter \"single_pulse_duration\" is for the duration of stimulation, for example,

single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation.

*Note: make sure that the time course cover the time point when the event is triggered.

(c) for single pulse of TGF-beta stimulation in COPASI

change the trigger of event \"single_pulse_TGF_beta_washout\"

from

\"and(eq(stimulation_type, 2), eq(time, single_pulse_duration))\" (for SBML-SAT)

to

\"and(eq(stimulation_type, 2), gt(time, single_pulse_duration))\" (for COPASI)

2. Notes for TGF-beta dose in terms of molecules per cell

(a) The following equation applies for conversion of TGF-beta dose in molecules per cell

TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23

(b) for standard experimental setup 1e6 cells in 2 mL medium

0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell

0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell

(c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed.

initial Vmed = 1e-8 (1e6 cells in 10 mL medium)

Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium)

3. Please note that this model contains events and the medium compartment size is varied.

4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.

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This model is from the article:
Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework.
Brannmark C, Palmer R, Glad ST, Cedersund G, Stralfors P. J Biol Chem.2010 Jun 25;285(26):20171-9. 20421297,
Abstract:
Insulin and other hormones control target cells through a network of signal-mediating molecules. Such networks are extremely complex due to multiple feedback loops in combination with redundancy, shared signal mediators, and cross-talk between signal pathways. We present a novel framework that integrates experimental work and mathematical modeling to quantitatively characterize the role and relation between co-existing submechanisms in complex signaling networks. The approach is independent of knowing or uniquely estimating model parameters because it only relies on (i) rejections and (ii) core predictions (uniquely identified properties in unidentifiable models). The power of our approach is demonstrated through numerous iterations between experiments, model-based data analyses, and theoretical predictions to characterize the relative role of co-existing feedbacks governing insulin signaling. We examined phosphorylation of the insulin receptor and insulin receptor substrate-1 and endocytosis of the receptor in response to various different experimental perturbations in primary human adipocytes. The analysis revealed that receptor endocytosis is necessary for two identified feedback mechanisms involving mass and information transfer, respectively. Experimental findings indicate that interfering with the feedback may substantially increase overall signaling strength, suggesting novel therapeutic targets for insulin resistance and type 2 diabetes. Because the central observations are present in other signaling networks, our results may indicate a general mechanism in hormonal control.

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This model is from the article:
Modelling the Role of the Hsp70/Hsp90 System in the Maintenance of Protein Homeostasis
Proctor CJ, Lorimer IAJ PLoS ONE2011; 6(7): e22038. doi:10.1371/journal.pone.0022038,
Abstract:
Neurodegeneration is an age-related disorder which is characterised by the accumulation of aggregated protein and neuronal cell death. There are many different neurodegenerative diseases which are classified according to the specific proteins involved and the regions of the brain which are affected. Despite individual differences, there are common mechanisms at the sub-cellular level leading to loss of protein homeostasis. The two central systems in protein homeostasis are the chaperone system, which promotes correct protein folding, and the cellular proteolytic system, which degrades misfolded or damaged proteins. Since these systems and their interactions are very complex, we use mathematical modelling to aid understanding of the processes involved. The model developed in this study focuses on the role of Hsp70 (IPR00103) and Hsp90 (IPR001404) chaperones in preventing both protein aggregation and cell death. Simulations were performed under three different conditions: no stress; transient stress due to an increase in reactive oxygen species; and high stress due to sustained increases in reactive oxygen species. The model predicts that protein homeostasis can be maintained during short periods of stress. However, under long periods of stress, the chaperone system becomes overwhelmed and the probability of cell death pathways being activated increases. Simulations were also run in which cell death mediated by the JNK (P45983) and p38 (Q16539) pathways was inhibited. The model predicts that inhibiting either or both of these pathways may delay cell death but does not stop the aggregation process and that eventually cells die due to aggregated protein inhibiting proteasomal function. This problem can be overcome if the sequestration of aggregated protein into inclusion bodies is enhanced. This model predicts responses to reactive oxygen species-mediated stress that are consistent with currently available experimental data. The model can be used to assess specific interventions to reduce cell death due to impaired protein homeostasis.

Note:

Simulations were performed under three different conditions: 1) normal condition (no stress), 2) moderate stress due to an increase in reactive oxygen species (ROS) i.e. ROS levels were increased by a factor of 4 at time=4hours for a period of 1 hour (not 2 hours as mentioned in the figure 5 legend of the reference publication. This is a typo in the paper and is clarified by the author) and 3) high stress due to sustained increase in reactive oxygen species (ROS) (here ROS increases with time).

The model that corresponds to the normal condition is submitted as a main model in the BioModels Database. The other two models, that corresponds to the moderate stress conditions and high stress conditions are available in SBML format as supporting files [go to Curation tab].

Supplementary figures S3 (normal condition), S4 (moderate stress condition) and S6 (high stress condition) are reproduced here.

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This model is from the article:
Mathematical modeling and analysis of insulin clearance in vivo.
Koschorreck M, Gilles ED. BMC Syst Biol. 2008 May 13;2:43. 18477391,
Abstract:
BACKGROUND:Analyzing the dynamics of insulin concentration in the blood is necessary for a comprehensive understanding of the effects of insulin in vivo. Insulin removal from the blood has been addressed in many studies. The results are highly variable with respect to insulin clearance and the relative contributions of hepatic and renal insulin degradation.RESULTS:We present a dynamic mathematical model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. The model describes renal and hepatic insulin degradation, pancreatic insulin secretion and nonspecific insulin binding in the liver. Hepatic insulin receptor activation by insulin binding, receptor internalization and autophosphorylation is explicitly included in the model. We present a detailed mathematical analysis of insulin degradation and insulin clearance. Stationary model analysis shows that degradation rates, relative contributions of the different tissues to total insulin degradation and insulin clearance highly depend on the insulin concentration.CONCLUSION:This study provides a detailed dynamic model of insulin concentration in the blood and of insulin receptor activation in hepatocytes. Experimental data sets from literature are used for the model validation. We show that essential dynamic and stationary characteristics of insulin degradation are nonlinear and depend on the actual insulin concentration.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the original model from Richard FitzHugh, which led the famous FitzHugh\u2013Nagumo model, still used for instance in computational neurosciences.
Impulses and Physiological States in Theoretical Models of Nerve Membrane
FitzHugh R Biophysical Journal, 1961 July:1(6):445-466 doi:10.1016/S0006-3495(61)86902-6 ,
Abstract:
Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting BVP model has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a physiological state diagram, with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range.
Bachmann J, Raue A, Schilling M, B\u00f6hm ME, Kreutz C, Kaschek D, Busch H, Gretz N, Lehmann WD, Timmer J, Klingm\u00fcller U. Mol Syst Biol. 2011 Jul 19;7:516. 21772264 ,
Abstract:
Cellular signal transduction is governed by multiple feedback mechanisms to elicit robust cellular decisions. The specific contributions of individual feedback regulators, however, remain unclear. Based on extensive time-resolved data sets in primary erythroid progenitor cells, we established a dynamic pathway model to dissect the roles of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK2/STAT5 signaling. Facilitated by the model, we calculated the STAT5 response for experimentally unobservable Epo concentrations and provide a quantitative link between cell survival and the integrated response of STAT5 in the nucleus. Model predictions show that the two feedbacks CIS and SOCS3 are most effective at different ligand concentration ranges due to their distinct inhibitory mechanisms. This divided function of dual feedback regulation enables control of STAT5 responses for Epo concentrations that can vary 1000-fold in vivo. Our modeling approach reveals dose-dependent feedback control as key property to regulate STAT5-mediated survival decisions over a broad range of ligand concentrations.

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This a model from the article:
Glucose sensing in the pancreatic beta cell: a computational systems analysis.
Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556,
Abstract:
BACKGROUND:Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.METHODS:We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.RESULTS:The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.CONCLUSION:This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.

This model was taken from the Vcell MathModel directory and was converted to SBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3616, + "tag": "BioModels:BIOMD0000000348" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3411, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3617, + "tag": "Type B pancreatic cell proliferation" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:35:13.303703+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000348", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2639": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2639, + "name": "Fridlyand2010_GlucoseSensitivity_B", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Glucose sensing in the pancreatic beta cell: a computational systems analysis.
Fridlyand LE, Philipson LH.Theor Biol Med Model.2010 May 24;7:15. 20497556,
Abstract:
BACKGROUND:Pancreatic beta-cells respond to rising blood glucose by increasing oxidative metabolism, leading to an increased ATP/ADP ratio in the cytoplasm. This leads to a closure of KATP channels, depolarization of the plasma membrane, influx of calcium and the eventual secretion of insulin. Such mechanism suggests that beta-cell metabolism should have a functional regulation specific to secretion, as opposed to coupling to contraction. The goal of this work is to uncover contributions of the cytoplasmic and mitochondrial processes in this secretory coupling mechanism using mathematical modeling in a systems biology approach.METHODS:We describe a mathematical model of beta-cell sensitivity to glucose. The cytoplasmic part of the model includes equations describing glucokinase, glycolysis, pyruvate reduction, NADH and ATP production and consumption. The mitochondrial part begins with production of NADH, which is regulated by pyruvate dehydrogenase. NADH is used in the electron transport chain to establish a proton motive force, driving the F1F0 ATPase. Redox shuttles and mitochondrial Ca2+ handling were also modeled.RESULTS:The model correctly predicts changes in the ATP/ADP ratio, Ca2+ and other metabolic parameters in response to changes in substrate delivery at steady-state and during cytoplasmic Ca2+ oscillations. Our analysis of the model simulations suggests that the mitochondrial membrane potential should be relatively lower in beta cells compared with other cell types to permit precise mitochondrial regulation of the cytoplasmic ATP/ADP ratio. This key difference may follow from a relative reduction in respiratory activity. The model demonstrates how activity of lactate dehydrogenase, uncoupling proteins and the redox shuttles can regulate beta-cell function in concert; that independent oscillations of cytoplasmic Ca2+ can lead to slow coupled metabolic oscillations; and that the relatively low production rate of reactive oxygen species in beta-cells under physiological conditions is a consequence of the relatively decreased mitochondrial membrane potential.CONCLUSION:This comprehensive model predicts a special role for mitochondrial control mechanisms in insulin secretion and ROS generation in the beta cell. The model can be used for testing and generating control hypotheses and will help to provide a more complete understanding of beta-cell glucose-sensing central to the physiology and pathology of pancreatic beta-cells.

This model was taken from the Vcell MathModel directory and was converted to SBML

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3618, + "tag": "BioModels:BIOMD0000000349" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3411, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3617, + "tag": "Type B pancreatic cell proliferation" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:35:13.811766+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000349", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2640": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2640, + "name": "Troein2011_ClockCircuit_OstreococcusTauri", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Multiple light inputs to a simple clock circuit allow complex biological rhythms
Troein C, Corellou F, Dixon LE, van Ooijen G, O'Neill JS, Bouget FY, Millar AJ. Plant J.2011 Apr;66(2):375-85. 21219507,
Abstract:
Circadian clocks are biological timekeepers that allow living cells to time their activity in anticipation of predictable environmental changes. Detailed understanding of the circadian network of higher plants, such as Arabidopsis thaliana, is hampered by the high number of partially redundant genes. However, the picoeukaryotic alga Ostreococcus tauri, which was recently shown to possess a small number of non-redundant clock genes, presents an attractive alternative target for detailed modelling of circadian clocks in the green lineage. Based on extensive time-series data from in vivo reporter gene assays, we developed a model of the Ostreococcus clock as a feedback loop between the genes TOC1 and CCA1. The model reproduces the dynamics of the transcriptional and translational reporters over a range of photoperiods. Surprisingly, the model is also able to predict the transient behaviour of the clock when the light conditions are altered. Despite the apparent simplicity of the clock circuit, it displays considerable complexity in its response to changing light conditions. Systematic screening of the effects of altered day length revealed a complex relationship between phase and photoperiod, which is also captured by the model. The complex light response is shown to stem from circadian gating of light-dependent mechanisms. This study provides insights into the contributions of light inputs to the Ostreococcus clock. The model suggests that a high number of light-dependent reactions are important for flexible timing in a circadian clock with only one feedback loop.

Note: Two-gene model of the Ostreococcus circadian clock

This is a model of the circadian clock of Ostreococcus tauri, with a negative feedback loop between TOC1 and CCA1 (a.k.a. LHY) and multiple light inputs. It was used and described in Troein et al., Plant Journal (2011).

The model incorporates luciferase reporters, and in this SBML model the four different versions of the model for transcriptional and translational reporter lines (pTOC1::LUC, pCCA1::LUC, TOC1-LUC and CCA1-LUC) are all accessible by setting one of the rep_X parameters to 1 and the others to 0. You can also set all four to 0 to only simulate the non-reporter core of the system.

Input to the system should be provided by modifying the \"light\" function. An implementation of LD 12:12 is provided as an example, but the model was also used with more complicated light regimes that vary between data sets and are not convenient to express directly in SBML.

The functions \"ox_cca1\" and \"ox_toc1\" can be altered to add overexpression of CCA1 and TOC1. Setting either to x gives additional, constitutive transcription at x times the maximal (and typically not realizable) transcription rate of the native gene. The overexpression mutant fits in Figure 7 of Troein et al. (2011) used ox_cca1 = 0.115 and oc_toc1 = 0.0584, respectively.

The functions \"copies_toc1\" and \"copies_cca1\" are normally 1 but can be lowered to simulate knockdown experiments. The functions \"transcription\", \"translation\" and \"proteasome\" can be modified to simulate the effects of altering the overall rate of transcription, translation and protein degradation.

The parameters were fitted specifically to data from transgenic reporter lines TOC8, pTOC3, LHY7 and pLHY7 (Corellou et al., Plant Cell 2009). Parameters that begin with \"effcopies\" describe the effective number of copies of CCA1 or TOC1 in the respective translational fusion lines, with anything above 1 due to the fusion proteins.

For the model fitting, the initial values were fitted to the data in the various time courses. The initial values given here correspond to the limit cycle of the system in LD 12:12. The system converges to the limit cycle in just a few days under most light conditions, so these initial values are biologically meaningful.

The species cca1luc_c and cca1luc_n have been merged into cca1luc (which corresponds to the observable luminescence signal), because Copasi refused to run the system otherwise. For TOC1-LUC, the predicted output signal is the sum of toc1luc_1 and toc1luc_2.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3619, + "tag": "BioModels:BIOMD0000000350" + }, + { + "id": 3370, + "tag": "Circadian rhythm" + }, + { + "id": 3620, + "tag": "Ostreococcus tauri" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:14.265478+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000350", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2641": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2641, + "name": "Vernoux2011_AuxinSignaling_AuxinSingleStepInput", + "repository_type": "biomodels", + "summary": "

This model is from the article:
The auxin signalling network translates dynamic input into robust patterning at the shoot apex.
Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Gu\u00e9don Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 ,
Abstract:
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong bufferingcapacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Note:

Figure 3 of the supplementary material of the reference article has been reproduced here. Time evolution of all the variables in the model are plotted, under the influence of a step input of auxin level (auxin=5, when time>1000; 0.11, otherwise). pi_A is varied between 0 and 2 by steps of 0.1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 3472, + "tag": "Auxin-activated signaling pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3621, + "tag": "BioModels:BIOMD0000000351" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:14.967113+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000351", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2642": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2642, + "name": "Vernoux2011_AuxinSignaling_AuxinFluctuating", + "repository_type": "biomodels", + "summary": "

This model is from the article:
The auxin signalling network translates dynamic input into robust patterning at the shoot apex.
Vernoux T, Brunoud G, Farcot E, Morin V, Van den Daele H, Legrand J, Oliva M, Das P, Larrieu A, Wells D, Gu\u00e9don Y, Armitage L, Picard F, Guyomarc'h S, Cellier C, Parry G, Koumproglou R, Doonan JH, Estelle M, Godin C, Kepinski S, Bennett M, De Veylder L, Traas J. Mol Syst Biol. 2011 Jul 5;7:508. 21734647 ,
Abstract:
The plant hormone auxin is thought to provide positional information for patterning during development. It is still unclear, however, precisely how auxin is distributed across tissues and how the hormone is sensed in space and time. The control of gene expression in response to auxin involves a complex network of over 50 potentially interacting transcriptional activators and repressors, the auxin response factors (ARFs) and Aux/IAAs. Here, we perform a large-scale analysis of the Aux/IAA-ARF pathway in the shoot apex of Arabidopsis, where dynamic auxin-based patterning controls organogenesis. A comprehensive expression map and full interactome uncovered an unexpectedly simple distribution and structure of this pathway in the shoot apex. A mathematical model of the Aux/IAA-ARF network predicted a strong buffering capacity along with spatial differences in auxin sensitivity. We then tested and confirmed these predictions using a novel auxin signalling sensor that reports input into the signalling pathway, in conjunction with the published DR5 transcriptional output reporter. Our results provide evidence that the auxin signalling network is essential to create robust patterns at the shoot apex.

Note:

Figure 4 of the supplementary material of the reference article has been reproduced here. In this model, the fluctuations of auxin level is represented using sinux function. Time evolution of the variables AUX/IAA (I) and mRNA (R) are plotted, under the influence of fluctuations of auxin level. pi_A is varied between 0 and 2 by steps of 0.1.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 3472, + "tag": "Auxin-activated signaling pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3622, + "tag": "BioModels:BIOMD0000000352" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:15.409353+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000352", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2643": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2643, + "name": "Nag2011_ChloroplasticStarchDegradation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Kinetic modeling and exploratory numerical simulation of chloroplastic starch degradation.
Nag A, Lunacek M, Graf PA, Chang CH. BMC Syst Biol.2011 Jun 18;5:94. 21682905,
Abstract:
BACKGROUND:Higher plants and algae are able to fix atmospheric carbon dioxide through photosynthesis and store this fixed carbon in large quantities as starch, which can be hydrolyzed into sugars serving as feedstock for fermentation to biofuels and precursors. Rational engineering of carbon flow in plant cells requires a greater understanding of how starch breakdown fluxes respond to variations in enzyme concentrations, kinetic parameters, and metabolite concentrations. We have therefore developed and simulated a detailed kinetic ordinary differential equation model of the degradation pathways for starch synthesized in plants and green algae, which to our knowledge is the most complete such model reported to date.RESULTS:Simulation with 9 internal metabolites and 8 external metabolites, the concentrations of the latter fixed at reasonable biochemical values, leads to a single reference solution showing \u03b2-amylase activity to be the rate-limiting step in carbon flow from starch degradation. Additionally, the response coefficients for stromal glucose to the glucose transporter kcat and KM are substantial, whereas those for cytosolic glucose are not, consistent with a kinetic bottleneck due to transport. Response coefficient norms show stromal maltopentaose and cytosolic glucosylated arabinogalactan to be the most and least globally sensitive metabolites, respectively, and \u03b2-amylase kcat and KM for starch to be the kinetic parameters with the largest aggregate effect on metabolite concentrations as a whole. The latter kinetic parameters, together with those for glucose transport, have the greatest effect on stromal glucose, which is a precursor for biofuel synthetic pathways. Exploration of the steady-state solution space with respect to concentrations of 6 external metabolites and 8 dynamic metabolite concentrations show that stromal metabolism is strongly coupled to starch levels, and that transport between compartments serves to lower coupling between metabolic subsystems in different compartments.CONCLUSIONS:We find that in the reference steady state, starch cleavage is the most significant determinant of carbon flux, with turnover of oligosaccharides playing a secondary role. Independence of stationary point with respect to initial dynamic variable values confirms a unique stationary point in the phase space of dynamically varying concentrations of the model network. Stromal maltooligosaccharide metabolism was highly coupled to the available starch concentration. From the most highly converged trajectories, distances between unique fixed points of phase spaces show that cytosolic maltose levels depend on the total concentrations of arabinogalactan and glucose present in the cytosol. In addition, cellular compartmentalization serves to dampen much, but not all, of the effects of one subnetwork on another, such that kinetic modeling of single compartments would likely capture most dynamics that are fast on the timescale of the transport reactions.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3623, + "tag": "BioModels:BIOMD0000000353" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3624, + "tag": "Starch catabolic process" + } + ], + "timestamp_created": "2025-01-30 13:35:15.875711+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000353", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2644": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2644, + "name": "Abell2011_CalciumSignaling_WithoutAdaptation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.
Abell E, Ahrends R, Bandara S, Park BO, Teruel MN. Proc Natl Acad Sci U S A. 2011 Aug 15. 21844332 ,
Abstract:
Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.

Note:

There are two models described in the paper to simulate basal and receptor stimulated Ca 2+ signaling. 1) No adaptive feedback (this model: MODEL1108050000) and 2) with three slow adaptive feedback loops (MODEL1108050001).

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3625, + "tag": "BioModels:BIOMD0000000354" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 132, + "tag": "Drosophila" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:16.370900+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000354", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2645": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2645, + "name": "Abell2011_CalciumSignaling_WithAdaptation", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Parallel adaptive feedback enhances reliability of the Ca2+ signaling system.
Abell E, Ahrends R, Bandara S, Park BO, Teruel MN. Proc Natl Acad Sci U S A. 2011 Aug 15. 21844332 ,
Abstract:
Despite large cell-to-cell variations in the concentrations of individual signaling proteins, cells transmit signals correctly. This phenomenon raises the question of what signaling systems do to prevent a predicted high failure rate. Here we combine quantitative modeling, RNA interference, and targeted selective reaction monitoring (SRM) mass spectrometry, and we show for the ubiquitous and fundamental calcium signaling system that cells monitor cytosolic and endoplasmic reticulum (ER) Ca(2+) levels and adjust in parallel the concentrations of the store-operated Ca(2+) influx mediator stromal interaction molecule (STIM), the plasma membrane Ca(2+) pump plasma membrane Ca-ATPase (PMCA), and the ER Ca(2+) pump sarco/ER Ca(2+)-ATPase (SERCA). Model calculations show that this combined parallel regulation in protein expression levels effectively stabilizes basal cytosolic and ER Ca(2+) levels and preserves receptor signaling. Our results demonstrate that, rather than directly controlling the relative level of signaling proteins in a forward regulation strategy, cells prevent transmission failure by sensing the state of the signaling pathway and using multiple parallel adaptive feedbacks.

Note:

There are two models described in the paper to simulate basal and receptor stimulated Ca 2+ signaling. 1) No adaptive feedback (MODEL1108050000) and 2) with three slow adaptive feedback loops (this model: MODEL1108050001).

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3626, + "tag": "BioModels:BIOMD0000000355" + }, + { + "id": 3063, + "tag": "Calcium-mediated signaling" + }, + { + "id": 132, + "tag": "Drosophila" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:16.879215+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000355", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2646": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2646, + "name": "Nyman2011_M3Hierarachical_InsulinGlucosedynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A Hierarchical Whole-body Modeling Approach Elucidates the Link between in Vitro Insulin Signaling and in Vivo Glucose Homeostasis.
Nyman E, Brannmark C, Palmer R, Brugard J, Nystrom FH, Stralfors P, Cedersund G.J Biol Chem.2011 Jul 22;286(29):26028-41. 21572040,
Abstract:
Type 2 diabetes is a metabolic disease that profoundly affects energy homeostasis. The disease involves failure at several levels and subsystems and is characterized by insulin resistance in target cells and tissues (i.e. by impaired intracellular insulin signaling). We have previously used an iterative experimental-theoretical approach to unravel the early insulin signaling events in primary human adipocytes. That study, like most insulin signaling studies, is based on in vitro experimental examination of cells, and the in vivo relevance of such studies for human beings has not been systematically examined. Herein, we develop a hierarchical model of the adipose tissue, which links intracellular insulin control of glucose transport in human primary adipocytes with whole-body glucose homeostasis. An iterative approach between experiments and minimal modeling allowed us to conclude that it is not possible to scale up the experimentally determined glucose uptake by the isolated adipocytes to match the glucose uptake profile of the adipose tissue in vivo. However, a model that additionally includes insulin effects on blood flow in the adipose tissue and GLUT4 translocation due to cell handling can explain all data, but neither of these additions is sufficient independently. We also extend the minimal model to include hierarchical dynamic links to more detailed models (both to our own models and to those by others), which act as submodules that can be turned on or off. The resulting multilevel hierarchical model can merge detailed results on different subsystems into a coherent understanding of whole-body glucose homeostasis. This hierarchical modeling can potentially create bridges between other experimental model systems and the in vivo human situation and offers a framework for systematic evaluation of the physiological relevance of in vitro obtained molecular/cellular experimental data.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3627, + "tag": "BioModels:BIOMD0000000356" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3411, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:35:17.338109+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000356", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2647": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2647, + "name": "Lee2010_ThrombinActivation_OneForm_reduced", + "repository_type": "biomodels", + "summary": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. A structural model is consistent with the data-derived finding that mechanisms that feature channeling or ratcheting are not necessary to describe thrombin production.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3628, + "tag": "BioModels:BIOMD0000000357" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3629, + "tag": "Serine-type endopeptidase activity" + } + ], + "timestamp_created": "2025-01-30 13:35:17.791139+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000357", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2648": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2648, + "name": "Stortelder1997 - Thrombin Generation Amidolytic Activity", + "repository_type": "biomodels", + "summary": "
Stortelder1997 - Thrombin Generation Amidolytic Activity

Mathematical modelling of a part of the blood coagulation mechanism.

This model is described in the article:

Stortelder W.J.H., Hemker P.W., Hemker, H.C.
CWI. Modelling, Analysis and Simulation, No. R 9720, p.1-11.

Abstract:

This paper describes the mathematical modelling of a part of the blood coagulation mechanism. The model includes the activation of factor X by a purified enzyme from Russel's Viper Venom (RVV), factor V and prothrombin, and also comprises the inactivation of the products formed. In this study we assume that in principle the mechanism of the process is known. However, the exact structure of the mechanism is unknown, and the process still can be described by different mathematical models. These models are put to test by measuring their capacity to explain the course of thrombin generation as observed in plasma after recalcification in presence of RVV. The mechanism studied is mathematically modelled as a system of differential-algebraic equations (DAEs). Each candidate model contains some freedom, which is expressed in the model equations by the presence of unknown parameters. For example, reaction constants or initial concentrations are unknown. The goal of parameter estimation is to determine these unknown parameters in such a way that the theoretical (i.e., computed) results fit the experimental data within measurement accuracy and to judge which modifications of the chemical reaction scheme allow the best fit. We present results on model discrimination and estimation of reaction constants, which are hard to obtain in another way.

This model is hosted on BioModels Database and identified by: BIOMD0000000358 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3630, + "tag": "BioModels:BIOMD0000000358" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:18.247622+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000358", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2649": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2649, + "name": "Panteleev2002_TFPImechanism_schmema3", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3631, + "tag": "BioModels:BIOMD0000000359" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:18.722449+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000359", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2650": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2650, + "name": "Panteleev2002_TFPImechanism_schmema2", + "repository_type": "biomodels", + "summary": "Mikhail A. Panteleev, Veronica I. Zarnitsina & Fazoil I. Ataullakhanov. Tissue factor pathway inhibitor: a possible mechanism of action. European Journal of Biochemistry 269, 8 (2002).

We have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3632, + "tag": "BioModels:BIOMD0000000360" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:19.173009+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000360", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2651": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2651, + "name": "Panteleev2002_TFPImechanism_schmema1", + "repository_type": "biomodels", + "summary": "Mikhail A. Panteleev, Veronica I. Zarnitsina & Fazoil I. Ataullakhanov. Tissue factor pathway inhibitor: a possible mechanism of action. European Journal of Biochemistry 269, 8 (2002).

We have analyzed several mathematical models that describe inhibition of the factor VIIa-tissue factor complex (VIIa-TF) by tissue factor pathway inhibitor (TFPI). At the core of these models is a common mechanism of TFPI action suggesting that only the Xa-TFPI complex is the inhibitor of the extrinsic tenase activity. However, the model based on this hypothesis could not explain well all the available experimental data. Here, we show that a good quantitative description of all experimental data could be achieved in a model that contains two more assumptions. The first assumption is based on the hypothesis originally proposed by Baugh et al. [Baugh, R.J., Broze, G.J. Jr & Krishnaswamy, S. (1998) J. Biol. Chem. 273, 4378-4386], which suggests that TFPI could inhibit the enzyme-product complex Xa-VIIa-TF. The second assumption proposes an interaction between the X-VIIa-TF complex and the factor Xa-TFPI complex. Experiments to test these hypotheses are suggested.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3633, + "tag": "BioModels:BIOMD0000000361" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:19.611056+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000361", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2652": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2652, + "name": "Butenas2004_BloodCoagulation", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3634, + "tag": "BioModels:BIOMD0000000362" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:20.048197+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000362", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2653": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2653, + "name": "Lee2010_ThrombinActivation_OneForm_minimal", + "repository_type": "biomodels", + "summary": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. A structural model is consistent with the data-derived finding that mechanisms that feature channeling or ratcheting are not necessary to describe thrombin production.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3635, + "tag": "BioModels:BIOMD0000000363" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:20.490595+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000363", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2654": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2654, + "name": "Lee2010_ThrombinActivation_OneForm", + "repository_type": "biomodels", + "summary": "Chang Jun Lee, Sangwook Wu, Changsun Eun & Lee G. Pedersen. A revisit to the one form kinetic model of prothrombinase. Biophysical Chemistry 149, 1-2 (2010).

Thrombin is generated enzymatically from prothrombin by two pathways with the intermediates of meizothrombin and prethrombin-2. Experimental concentration profiles from two independent groups for these two pathways have been re-analyzed. By rationally combining the independent data sets, a simple mechanism can be established and rate constants determined. A structural model is consistent with the data-derived finding that mechanisms that feature channeling or ratcheting are not necessary to describe thrombin production.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3636, + "tag": "BioModels:BIOMD0000000364" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:20.941117+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000364", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2655": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2655, + "name": "Hockin1999_BloodCoagulation_VaInactivation", + "repository_type": "biomodels", + "summary": "Matthew F. Hockin, Kevin M. Cawthern, Michael Kalafatis & Kenneth G. Mann. A model describing the inactivation of factor Va by APC: bond cleavage, fragment dissociation, and product inhibition. Biochemistry 38, 21 (1999).

The inactivation of factor Va is a complex process which includes bond cleavage (at three sites) and dissociation of the A2N.A2C peptides, with intermediate activity in each species. Quantitation of the functional consequences of each step in the reaction has allowed for understanding of the presentation of disease in individuals possessing the factor V polymorphism factor VLEIDEN. APC cleavage of membrane-bound bovine factor Va (Arg306, Arg505, Arg662) leads to the dissociation of fragments of the A2 domain, residues 307-713 (A2N.A2C + A2C-peptide), leaving behind the membrane-bound A1.LC species. Evaluation of the dissociation process by light scattering yields invariant mass loss estimates as a function of APC concentration. The rate constant for A2 fragment dissociation varies with [APC], reaching a maximal value of k = 0.028 s-1, the unimolecular rate constant for A2 domain fragment dissociation. The APC binding site resides in the factor Va light chain (LC) (Kd = 7 nM), suggesting that the membrane-bound LC.A1 product would act to sequester APC. This inhibitory interaction (LC.A1.APC) is demonstrated to exist with either purified factor Va LC or the products of factor Va inactivation. Utilizing these experimental data and the reported rates of bond cleavage, binding constants, and product activity values for factor Va partial inactivation products, a model is developed which describes factor Va inactivation and accounts for the defect in factor VLEIDEN. The model accurately predicts the rates of inactivation of factor Va and factor VaLEIDEN, and the effect of product inhibition. Modeled reaction progress diagrams and activity profiles (from either factor Va or factor VaLEIDEN) are coincident with experimentally derived data, providing a mechanistic and kinetic explanation for all steps in the inactivation of normal factor Va and the pathology associated with factor VLEIDEN.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3637, + "tag": "BioModels:BIOMD0000000365" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4612, + "tag": "Inherited blood coagulation disease" + } + ], + "timestamp_created": "2025-01-30 13:35:21.370475+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000365", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2656": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2656, + "name": "Orfao2008_ThrombinGeneration_AmidolyticActivity", + "repository_type": "biomodels", + "summary": "S. Cunha Orfao, G. Jank, K. Mottaghy, S. Walcher & E. Zerz. Qualitative properties and stabilizability of a model for blood thrombin formation. Journal of Mathematical Analysis and Applications 346, 1 (2008).

We perform a qualitative analysis of a differential equation that was originally introduced by Stortelder, Hemker and Hemker to model the formation of thrombin, and discuss issues of controllability and stabilizability. Results include a general proof of convergence to equilibrium, and of local exponential stabilizability.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3638, + "tag": "BioModels:BIOMD0000000366" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4604, + "tag": "Cardiovascular system disease" + } + ], + "timestamp_created": "2025-01-30 13:35:21.823167+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000366", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2657": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2657, + "name": "Mueller2008_ThrombinGeneration_minimal", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3639, + "tag": "BioModels:BIOMD0000000367" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4613, + "tag": "Coronary artery disease" + } + ], + "timestamp_created": "2025-01-30 13:35:22.257161+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000367", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2658": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2658, + "name": "Beltrami1995_ThrombinGeneration_C", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3640, + "tag": "BioModels:BIOMD0000000368" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:22.737104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000368", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2659": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2659, + "name": "Beltrami1995_ThrombinGeneration_D", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3641, + "tag": "BioModels:BIOMD0000000369" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:23.181244+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000369", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2660": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2660, + "name": "Vinod2011_MitoticExit", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Computational modelling of mitotic exit in budding yeast: the role of separase and Cdc14 endocycles
Vinod PK, Freire P, Rattani A, Ciliberto A, Uhlmann F, Novak B. J R Soc Interface. 2011 Aug 7;8(61):1128-41. Epub 2011 Feb 2. 21288956 ,
Abstract:
The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20-APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Channel sharing in pancreatic beta -cells revisited: enhancement of emergentbursting by noise.
De Vries G, Sherman A. J Theor Biol2000 Dec 21;207(4):513-30 11093836,
Abstract:
Secretion of insulin by electrically coupled populations of pancreatic beta-cells is governed by bursting electrical activity. Isolated beta -cells,however, exhibit atypical bursting or continuous spike activity. We studybursting as an emergent property of the population, focussing on interactionsamong the subclass of spiking cells. These are modelled by equipping the fastsubsystem with a saddle-node-loop bifurcation, which makes it monostable. Suchcells can only spike tonically or remain silent when isolated, but can beinduced to burst with weak diffusive coupling. With stronger coupling, the cellsrevert to tonic spiking. We demonstrate that the addition of noise dramaticallyincreases, via a phenomenon like stochastic resonance, the coupling range overwhich bursting is seen. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: De Vries G, Sherman A. (2000) - version01

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modeling the insulin-glucose feedback system: the significance of pulsatileinsulin secretion.
Tolic IM, Mosekilde E, Sturis J. J Theor Biol2000 Dec 7;207(3):361-75 11082306,
Abstract:
A mathematical model of the insulin-glucose feedback regulation in man is usedto examine the effects of an oscillatory supply of insulin compared to aconstant supply at the same average rate. We show that interactions between theoscillatory insulin supply and the receptor dynamics can be of minutesignificance only. It is possible, however, to interpret seemingly conflictingresults of clinical studies in terms of their different experimental conditionswith respect to the hepatic glucose release. If this release is operating nearan upper limit, an oscillatory insulin supply will be more efficient in loweringthe blood glucose level than a constant supply. If the insulin level is highenough for the hepatic release of glucose to nearly vanish, the opposite effectis observed. For insulin concentrations close to the point of inflection of theinsulin-glucose dose-response curve an oscillatory and a constant insulininfusion produce similar effects. Copyright 2000 Academic Press.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Tolic IM, Mosekilde E, Sturis J. (2000) - version=1.0

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Calcium and glycolysis mediate multiple bursting modes in pancreatic islets.
Bertram R, Satin L, Zhang M, Smolen P, Sherman A. Biophys J2004 Nov;87(5):3074-87 15347584,
Abstract:
Pancreatic islets of Langerhans produce bursts of electrical activity whenexposed to stimulatory glucose levels. These bursts often have a regularrepeating pattern, with a period of 10-60 s. In some cases, however, the burstsare episodic, clustered into bursts of bursts, which we call compound bursting.Consistent with this are recordings of free Ca2+ concentration, oxygenconsumption, mitochondrial membrane potential, and intraislet glucose levelsthat exhibit very slow oscillations, with faster oscillations superimposed. Wedescribe a new mathematical model of the pancreatic beta-cell that can accountfor these multimodal patterns. The model includes the feedback of cytosolic Ca2+onto ion channels that can account for bursting, and a metabolic subsystem thatis capable of producing slow oscillations driven by oscillations in glycolysis.This slow rhythm is responsible for the slow mode of compound bursting in themodel. We also show that it is possible for glycolytic oscillations alone todrive a very slow form of bursting, which we call \"glycolytic bursting.\"Finally, the model predicts that there is bistability between stationary andoscillatory glycolysis for a range of parameter values. We provide experimentalsupport for this model prediction. Overall, the model can account for adiversity of islet behaviors described in the literature over the past 20 years.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Satin L, Zhang M, Smolen P, Sherman A. (2004) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3647, + "tag": "BioModels:BIOMD0000000373" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3645, + "tag": "Regulation of type B pancreatic cell proliferation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3617, + "tag": "Type B pancreatic cell proliferation" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:35:25.180157+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000373", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2664": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2664, + "name": "Bertram1995_PancreaticBetaCell_CRAC", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A role for calcium release-activated current (CRAC) in cholinergic modulation ofelectrical activity in pancreatic beta-cells.
Bertram R, Smolen P, Sherman A, Mears D, Atwater I, Martin F, Soria B. Biophys J1995 Jun;68(6):2323-32 7647236,
Abstract:
S. Bordin and colleagues have proposed that the depolarizing effects ofacetylcholine and other muscarinic agonists on pancreatic beta-cells aremediated by a calcium release-activated current (CRAC). We support thishypothesis with additional data, and present a theoretical model which accountsfor most known data on muscarinic effects. Additional phenomena, such as thebiphasic responses of beta-cells to changes in glucose concentration and thedepolarizing effects of the sarco-endoplasmic reticulum calcium ATPase pumppoison thapsigargin, are also accounted for by our model. The ability of thissingle hypothesis, that CRAC is present in beta-cells, to explain so manyphenomena motivates a more complete characterization of this current.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Smolen P, Sherman A, Mears D, Atwater I, Martin F, Soria B. (1995) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3648, + "tag": "BioModels:BIOMD0000000374" + }, + { + "id": 3649, + "tag": "Calcium-release channel activity" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3645, + "tag": "Regulation of type B pancreatic cell proliferation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3617, + "tag": "Type B pancreatic cell proliferation" + } + ], + "timestamp_created": "2025-01-30 13:35:25.614252+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000374", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2665": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2665, + "name": "Mears1997_CRAC_PancreaticBetaCells", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Evidence that calcium release-activated current mediates the biphasic electricalactivity of mouse pancreatic beta-cells.
Mears D, Sheppard NF Jr, Atwater I, Rojas E, Bertram R, Sherman A. J Membr Biol1997 Jan 1;155(1):47-59 9002424,
Abstract:
The electrical response of pancreatic beta-cells to step increases in glucoseconcentration is biphasic, consisting of a prolonged depolarization with actionpotentials (Phase 1) followed by membrane potential oscillations known asbursts. We have proposed that the Phase 1 response results from the combineddepolarizing influences of potassium channel closure and an inward, nonselectivecation current (ICRAN) that activates as intracellular calcium stores emptyduring exposure to basal glucose (Bertram et al., 1995). The stores refillduring Phase 1, deactivating ICRAN and allowing steady-state bursting tocommence. We support this hypothesis with additional simulations andexperimental results indicating that Phase 1 duration is sensitive to thefilling state of intracellular calcium stores. First, the duration of the Phase1 transient increases with duration of prior exposure to basal (2.8 mM) glucose,reflecting the increased time required to fill calcium stores that have beenemptying for longer periods. Second, Phase 1 duration is reduced when islets areexposed to elevated K+ to refill calcium stores in the presence of basalglucose. Third, when extracellular calcium is removed during the basal glucoseexposure to reduce calcium influx into the stores, Phase 1 duration increases.Finally, no Phase 1 is observed following hyperpolarization of the beta-cellmembrane with diazoxide in the continued presence of 11 mm glucose, a conditionin which intracellular calcium stores remain full. Application of carbachol toempty calcium stores during basal glucose exposure did not increase Phase 1duration as the model predicts. Despite this discrepancy, the good agreementbetween most of the experimental results and the model predictions providesevidence that a calcium release-activated current mediates the Phase 1electrical response of the pancreatic beta-cell.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Mears D, Sheppard NF Jr, Atwater I, Rojas E, Bertram R, Sherman A. (1997) - version=1.0
The original CellML model was created by:
Tessa Paris
tpar054@aucklanduni.ac.uk
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the model described in the article:
Interaction of glycolysis and mitochondrial respiration in metabolic oscillations of pancreatic islets.
Bertram R, Satin LS, Pedersen MG, Luciani DS, Sherman A. Biophys J. 2007 Mar 1;92(5):1544-55. Pubmed ID: 17172305, doi: 10.1529/biophysj.106.097154.
Abstract:
Insulin secretion from pancreatic beta-cells is oscillatory, with a typical period of 2-7 min, reflecting oscillations in membrane potential and the cytosolic Ca(2+) concentration. Our central hypothesis is that the slow 2-7 min oscillations are due to glycolytic oscillations, whereas faster oscillations that are superimposed are due to Ca(2+) feedback onto metabolism or ion channels. We extend a previous mathematical model based on this hypothesis to include a more detailed description of mitochondrial metabolism. We demonstrate that this model can account for typical oscillatory patterns of membrane potential and Ca(2+) concentration in islets. It also accounts for temporal data on oxygen consumption in islets. A recent challenge to the notion that glycolytic oscillations drive slow Ca(2+) oscillations in islets are data showing that oscillations in Ca(2+), mitochondrial oxygen consumption, and NAD(P)H levels are all terminated by membrane hyperpolarization. We demonstrate that these data are in fact compatible with a model in which glycolytic oscillations are the key player in rhythmic islet activity. Finally, we use the model to address the recent finding that the activity of islets from some mice is uniformly fast, whereas that from islets of other mice is slow. We propose a mechanism for this dichotomy.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram, Satin, Pedersen, Luciani, Sherman, 2007 version 02
The original CellML model was created and curated by:
Catherine May Lloyd
c.lloyd(at)auckland.ac.nz
The University of Auckland, Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
The phantom burster model for pancreatic beta-cells.
Bertram R, Previte J, Sherman A, Kinard TA, Satin LS. Biophys J2000 Dec;79(6):2880-92 11106596,
Abstract:
Pancreatic beta-cells exhibit bursting oscillations with a wide range ofperiods. Whereas periods in isolated cells are generally either a few seconds ora few minutes, in intact islets of Langerhans they are intermediate (10-60 s).We develop a mathematical model for beta-cell electrical activity capable ofgenerating this wide range of bursting oscillations. Unlike previous models,bursting is driven by the interaction of two slow processes, one with arelatively small time constant (1-5 s) and the other with a much larger timeconstant (1-2 min). Bursting on the intermediate time scale is generated withoutneed for a slow process having an intermediate time constant, hence phantombursting. The model suggests that isolated cells exhibiting a fast pattern maynonetheless possess slower processes that can be brought out by injectingsuitable exogenous currents. Guided by this, we devise an experimental protocolusing the dynamic clamp technique that reliably elicits islet-like, mediumperiod oscillations from isolated cells. Finally, we show that strong electricalcoupling between a fast burster and a slow burster can produce synchronizedmedium bursting, suggesting that islets may be composed of cells that areintrinsically either fast or slow, with few or none that are intrinsicallymedium.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Bertram R, Previte J, Sherman A, Kinard TA, Satin LS. (2000) - version02

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Effects of extracellular calcium on electrical bursting and intracellular and luminal calcium oscillations in insulin secreting pancreatic beta-cells.
Chay TR Biophys J.1997 Sep;73(3):1673-88. 9284334,
Abstract:
The extracellular calcium concentration has interesting effects on bursting of pancreatic beta-cells. The mechanism underlying the extracellular Ca2+ effect is not well understood. By incorporating a low-threshold transient inward current to the store-operated bursting model of Chay, this paper elucidates the role of the extracellular Ca2+ concentration in influencing electrical activity, intracellular Ca2+ concentration, and the luminal Ca2+ concentration in the intracellular Ca2+ store. The possibility that this inward current is a carbachol-sensitive and TTX-insensitive Na+ current discovered by others is discussed. In addition, this paper explains how these three variables respond when various pharmacological agents are applied to the store-operated model.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Chay TR (1997) - version05
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Meal simulation model of the glucose-insulin system.
Dalla Man C, Rizza RA, Cobelli C.IEEE Trans Biomed Eng.2007 Oct;54(10):1740-9. 17926672,
Abstract:
A simulation model of the glucose-insulin system in the postprandial state can be useful in several circumstances, including testing of glucose sensors, insulin infusion algorithms and decision support systems for diabetes. Here, we present a new simulation model in normal humans that describes the physiological events that occur after a meal, by employing the quantitative knowledge that has become available in recent years. Model parameters were set to fit the mean data of a large normal subject database that underwent a triple tracer meal protocol which provided quasi-model-independent estimates of major glucose and insulin fluxes, e.g., meal rate of appearance, endogenous glucose production, utilization of glucose, insulin secretion. By decomposing the system into subsystems, we have developed parametric models of each subsystem by using a forcing function strategy. Model results are shown in describing both a single meal and normal daily life (breakfast, lunch, dinner) in normal. The same strategy is also applied on a smaller database for extending the model to type 2 diabetes

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Building a Kinetic Model of Trehalose Biosynthesis in Saccharomyces cerevisiae.
Smallbone K, Malys N, Messiha HL, Wishart JA, Simeonidis E. Methods Enzymol. 2011;500:355-70. 21943906 ,
Abstract:
In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway based on kinetic data from experimental measurements and literature review. Our methodology is presented by utilizing the example of trehalose metabolism in yeast. The biology of the trehalose cycle is briefly reviewed and discussed.

This SBML model is made available under the Creative Commons Attribution-Share Alike 3.0 Unported Licence (see www.creativecommons.org ).

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
Modelling the onset of Type 1 diabetes: can impaired macrophage phagocytosis make the difference between health and disease?
Maree AF, Kublik R, Finegood DT, Edelstein-Keshet L.Philos Transact A Math Phys Eng Sci.2006 May 15;364(1842):1267-82. 16608707,
Abstract:
A wave of apoptosis (programmed cell death) occurs normally in pancreatic beta-cells of newborn mice. We previously showed that macrophages from non-obese diabetic (NOD) mice become activated more slowly and engulf apoptotic cells at a lower rate than macrophages from control (Balb/c) mice. It has been hypothesized that this low clearance could result in secondary necrosis, escalating inflammation and self-antigen presentation that later triggers autoimmune, Type 1 diabetes (T1D). We here investigate whether this hypothesis could offer a reasonable and parsimonious explanation for onset of T1D in NOD mice. We quantify variants of the Copenhagen model (Freiesleben De Blasio et al. 1999 Diabetes 48, 1677), based on parameters from NOD and Balb/c experimental data. We show that the original Copenhagen model fails to explain observed phenomena within a reasonable range of parameter values, predicting an unrealistic all-or-none disease occurrence for both strains. However, if we take into account that, in general, activated macrophages produce harmful cytokines only when engulfing necrotic (but not apoptotic) cells, then the revised model becomes qualitatively and quantitatively reasonable. Further, we show that known differences between NOD and Balb/c mouse macrophage kinetics are large enough to account for the fact that an apoptotic wave can trigger escalating inflammatory response in NOD, but not Balb/c mice. In Balb/c mice, macrophages clear the apoptotic wave so efficiently, that chronic inflammation is prevented.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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\t This a model from the article:\t
Computer model for mechanisms underlying ultradian oscillations of insulin and glucose.\t
\t Sturis J, Polonsky KS, Mosekilde E, Van Cauter E.\t Am J Physiol.1991 May;260(5 Pt 1):E801-9.\t 2035636,\t
Abstract:
Oscillations in human insulin secretion have been observed in two distinct period ranges, 10-15 min (i.e. rapid) and 100-150 min (i.e., ultradian). The cause of the ultradian oscillations remains to be elucidated. To determine whether the oscillations could result from the feedback loops between insulin and glucose, a parsimonious mathematical model including the major mechanisms involved in glucose regulation was developed. This model comprises two major negative feedback loops describing the effects of insulin on glucose utilization and glucose production, respectively, and both loops include the stimulatory effect of glucose on insulin secretion. Model formulations and parameters are representative of results from published clinical investigations. The occurrence of sustained insulin and glucose oscillations was found to be dependent on two essential features: 1) a time delay of 30-45 min for the effect of insulin on glucose production and 2) a sluggish effect of insulin on glucose utilization, because insulin acts from a compartment remote from plasma. When these characteristics were incorporated in the model, numerical simulations mimicked all experimental findings so far observed for these ultradian oscillations, including 1) self-sustained oscillations during constant glucose infusion at various rates; 2) damped oscillations after meal or oral glucose ingestion; 3) increased amplitude of oscillation after increased stimulation of insulin secretion, without change in frequency; and 4) slight advance of the glucose oscillation compared with the insulin oscillation.(ABSTRACT TRUNCATED AT 250 WORDS)

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3663, + "tag": "BioModels:BIOMD0000000382" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:35:29.190751+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000382", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2673": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2673, + "name": "Arnold2011_Farquhar1980_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Farquhar et al. (1980, DOI:10.1007/BF00386231 ).

The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3664, + "tag": "BioModels:BIOMD0000000383" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:29.632895+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000383", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2674": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2674, + "name": "Arnold2011_Medlyn2002_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ).

The parameter values are widely taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713) . A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3665, + "tag": "BioModels:BIOMD0000000384" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:30.077727+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000384", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2675": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2675, + "name": "Arnold2011_Schultz2003_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Schultz (2003, DOI:10.1071/FP02146 ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Sharkey et al. (2007, DOI:10.1111/j.1365-3040.2007.01710.x ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Medlyn et al. (2002, DOI:10.1046/j.1365-3040.2002.00891.x ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3667, + "tag": "BioModels:BIOMD0000000386" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:30.954992+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000386", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2677": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2677, + "name": "Arnold2011_Damour2007_RuBisCO-CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle with focus on the RuBisCO reaction by Damour and Urban (2007, [for PDF click here] ).

The parameter values are partly taken from Farquhar et al. (1980, DOI:10.1007/BF00386231 ) and Urban et al. (2003, DOI:10.1093/treephys/23.5.289 ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3668, + "tag": "BioModels:BIOMD0000000387" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:31.399675+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000387", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2678": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2678, + "name": "Arnold2011_Zhu2009_CalvinCycle", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle by Zhu et al. (2009, DOI:10.1016/j.nonrwa.2008.01.021 ).

The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ).A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3669, + "tag": "BioModels:BIOMD0000000388" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:31.838838+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000388", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2679": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2679, + "name": "Arnold2011_Hahn1986_CalvinCycle_Starch_Sucrose", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science 2011 Oct 14. 22001849 ,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis by Hahn (1986, [click here for abstract] ).

The parameter values are taken from Hahn (1984, [click here for abstract] ). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713 ). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849 .
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle by Giersch et al. (1990, DOI:10.1007/BF00032595).

The parameter values are taken from Figure 4 and 5. The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3671, + "tag": "BioModels:BIOMD0000000390" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:32.751912+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000390", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2681": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2681, + "name": "Arnold2011_Poolman2000_CalvinCycle_Starch", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathway to starch synthesis by Poolman et al. (2000, DOI:10.1093/jexbot/51.suppl_1.319).

The parameter values are widely taken from Pettersson and Ryde-Pettersson (1988, DOI:10.1111/j.1432-1033.1988.tb14242.x) and Poolman (1999, [click here for PDF]). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis by Laisk et al. (2006, DOI:10.1007/s11120-006-9109-1) and the personally provided implementation to Laisk et al. (2009, DOI:10.1007/978-1-4020-9237-4_13).

A reduced version of the published model is implemented (light-dependent reactions are taken out). The parameter values are widely taken from Laisk et al. (1989, [click here for PDF]). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
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This model is from the article:
A quantitative comparison of Calvin\u2013Benson cycle models
Anne Arnold, Zoran Nikoloski Trends in Plant Science2011 Oct 14. 22001849,
Abstract:
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.

Note: Model of the Calvin cycle and the related end-product pathways to starch and sucrose synthesis and photorespiration by Zhu et al. (2007, DOI:10.1104/pp.107.103713) and the personally provided implementation.

The parameter values are partly taken from Pettersson and Ryde-Pettersson (1988, DOI:10.1111/j.1432-1033.1988.tb14242.x). The initial metabolite values are chosen from the data set of Zhu et al. (2007, DOI:10.1104/pp.107.103713). A detailed description of all modifications is given in the model described by Arnold and Nikoloski (2011, PMID:22001849.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3674, + "tag": "BioModels:BIOMD0000000393" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:35:34.139318+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000393", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2684": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2684, + "name": "Sivakumar2011 - EGF Receptor Signaling Pathway", + "repository_type": "biomodels", + "summary": " \t
\t Sivakumar2011 - EGF Receptor Signaling Pathway\t
\t
\t

EGFR belongs to the human epidermal receptor (HER) family of receptor tyrosine kinases, which consists of four closely related receptors (EGFR (HER1, erbB1), HER2 (neu, erbB2), HER3 (erbB3), and HER4 (erbB4)) that mediate cellular signaling pathways involved in growth and proliferation in response to the binding of a variety of growth factor ligands. There are currently six known endogenous ligands for EGFR: EGF, transforming growth factor- (TGF-), amphiregulin, betacellulin, heparin-binding EGF (HB-EGF), and epiregulin.Upon ligand binding, the EGFR forms homo- or heterodimeric complexes (usually with HER2), which leads to activation of the receptor tyrosine kinase, via autophosphorylation.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000394.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3675, + "tag": "BioModels:BIOMD0000000394" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:34.610863+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000394", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2685": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2685, + "name": "Sivakumar2011 - Hedgehog Signaling Pathway", + "repository_type": "biomodels", + "summary": " \t
\t Sivakumar2011 - Hedgehog Signaling Pathway\t
\t
\t

This is the current model for the Hedgehog signaling pathway. The best data for mechanism of signaling has been worked out in Drosophila, so this model is based largely on Drosophila data. Hedgehog target genes vary from tissue to tissue, so the identities of individual target genes have not been listed. The main difference between the Drosophila and mammalian Hedgehog signaling pathways is the fact that there are three mammalian homologs of Cubitus interruptus, Gli1 Gli2 and Gli3. Some or all of the mammalian homologs may be proteolytically processed, but the data are controversial. There are two mammalian Ptc genes and three mammalian Hedgehog genes as well. The pathway for Sonic Hedgehog appears to be most similar to the Drosophila hedgehog pathway.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000395.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3676, + "tag": "BioModels:BIOMD0000000395" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3677, + "tag": "Smoothened signaling pathway" + } + ], + "timestamp_created": "2025-01-30 13:35:35.057104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000395", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2686": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2686, + "name": "Sivakumar2011 - Notch Signaling Pathway", + "repository_type": "biomodels", + "summary": " \t
\t Sivakumar2011 - Notch Signaling Pathway\t
\t
\t

Notch is a transmembrane receptor that mediates local cell-cell communication and coordinates a signaling cascade. It plays a key role in modulating cell fate decisions throughout the development of invertebrate and vertebrate species and the misregulation leads to a number of human diseases.

References:

\t
\t
\t

This model is described in the article:

\t \t
Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
\t
Omics: a Journal of Integrative Biology. 2011; 15(10):729-737
\t

Abstract:

\t
\t

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

\t
\t
\t
\t

This model is hosted on BioModels Database and identified by: BIOMD0000000396.

\t

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

\t
\t
\t

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

\t
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3678, + "tag": "BioModels:BIOMD0000000396" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3679, + "tag": "Notch signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:35.522422+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000396", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2687": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2687, + "name": "Sivakumar2011_WntSignalingPathway", + "repository_type": "biomodels", + "summary": "
Sivakumar2011_WntSignalingPathway
The secreted protein Wnt activates theheptahelical receptor Frizzled on nieghboring cells. Activation ofFrizzled causes the recruitment of additional membrane proteinswhich in turn result in 1) the activation of the proteinDishevelled via phosphorylation and 2) the activation of aheterotrimeric G protein of unknown type. Activation of Dishevelledresults in the down-regulation of the Beta-Catenin destructioncomplex which causes ubiquitination of Beta-Catenin and itsultimate degradation via the proteasome. Inhibition of theBeta-Catenin destruction complex yields a higher cytosolicconcentration of Beta-Catenin, which enters the nucleus, bindsvarious transcriptional regulatory molecules including the TCF/LEFclass of proteins, and results in the transcription of TCF/LEFtarget genes. Activation of the heterotrimeric G-protein pathway inturn activates Phospholipase C which in turn catalyzes thecatalysis of PI(4,5)P2 into DAG and IP3.

Reference:

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
OMICS 2011 Oct; 15(10): 729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000397.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3680, + "tag": "BioModels:BIOMD0000000397" + }, + { + "id": 3681, + "tag": "Canonical Wnt signaling pathway" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:35.978851+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000397", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2688": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2688, + "name": "Sivakumar2011_NeuralStemCellDifferentiation_Crosstalk", + "repository_type": "biomodels", + "summary": "
Sivakumar2011_NeuralStemCellDifferentiation_Crosstalk
This model is generated by integrating BIOMD0000000394(EGFR), BIOMD0000000395(Hedgehog), BIOMD0000000396(Notch) and BIOMD0000000397(Wnt), to investigate the signalling crosstalk between the fourpathways.

This model is described in the article:

Sivakumar KC, Dhanesh SB, Shobana S, James J, Mundayoor S.
OMICS 2011 Oct; 15(10): 729-737

Abstract:

The Notch, Sonic Hedgehog (Shh), Wnt, and EGF pathways have long been known to influence cell fate specification in the developing nervous system. Here we attempted to evaluate the contemporary knowledge about neural stem cell differentiation promoted by various drug-based regulations through a systems biology approach. Our model showed the phenomenon of DAPT-mediated antagonism of Enhancer of split [E(spl)] genes and enhancement of Shh target genes by a SAG agonist that were effectively demonstrated computationally and were consistent with experimental studies. However, in the case of model simulation of Wnt and EGF pathways, the model network did not supply any concurrent results with experimental data despite the fact that drugs were added at the appropriate positions. This paves insight into the potential of crosstalks between pathways considered in our study. Therefore, we manually developed a map of signaling crosstalk, which included the species connected by representatives from Notch, Shh, Wnt, and EGF pathways and highlighted the regulation of a single target gene, Hes-1, based on drug-induced simulations. These simulations provided results that matched with experimental studies. Therefore, these signaling crosstalk models complement as a tool toward the discovery of novel regulatory processes involved in neural stem cell maintenance, proliferation, and differentiation during mammalian central nervous system development. To our knowledge, this is the first report of a simple crosstalk map that highlights the differential regulation of neural stem cell differentiation and underscores the flow of positive and negative regulatory signals modulated by drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000398.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3682, + "tag": "BioModels:BIOMD0000000398" + }, + { + "id": 3681, + "tag": "Canonical Wnt signaling pathway" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3679, + "tag": "Notch signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3677, + "tag": "Smoothened signaling pathway" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:35:36.444844+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000398", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2690": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2690, + "name": "Cooling2007_IP3transients_CardiacMyocyte", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling hypertrophic IP3 transients in the cardiac myocyte.
Cooling M, Hunter P, Crampin EJ. Biophys J2007 Nov 15;93(10):3421-33 17693463,
Abstract:
Cardiac hypertrophy is a known risk factor for heart disease, and at thecellular level is caused by a complex interaction of signal transductionpathways. The IP3-calcineurin pathway plays an important role in stimulating thetranscription factor NFAT which binds to DNA cooperatively with otherhypertrophic transcription factors. Using available kinetic data, we construct amathematical model of the IP3 signal production system after stimulation by ahypertrophic alpha-adrenergic agonist (endothelin-1) in the mouse atrial cardiacmyocyte. We use a global sensitivity analysis to identify key controllingparameters with respect to the resultant IP3 transient, including thephosphorylation of cell-membrane receptors, the ligand strength and bindingkinetics to precoupled (with G(alpha)GDP) receptor, and the kinetics associatedwith precoupling the receptors. We show that the kinetics associated with thereceptor system contribute to the behavior of the system to a great extent, withprecoupled receptors driving the response to extracellular ligand. Finally, byreparameterizing for a second hypertrophic alpha-adrenergic agonist,angiotensin-II, we show that differences in key receptor kinetic and membranedensity parameters are sufficient to explain different observed IP3 transientsin essentially the same pathway.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Cooling M, Hunter P, Crampin EJ. (2007) - version02
The original CellML model was created by:
Cooling, Mike,
m.cooling@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model without Tumour.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3687, + "tag": "BioModels:BIOMD0000000401" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3249, + "tag": "Regulation of bone remodeling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4614, + "tag": "Multiple myeloma" + } + ], + "timestamp_created": "2025-01-30 13:35:38.051061+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000401", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2692": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2692, + "name": "Ayati2010_BoneRemodelingDynamics_WithTumour", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3688, + "tag": "BioModels:BIOMD0000000402" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3249, + "tag": "Regulation of bone remodeling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4614, + "tag": "Multiple myeloma" + } + ], + "timestamp_created": "2025-01-30 13:35:38.516935+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000402", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2693": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2693, + "name": "Ayati2010_BoneRemodelingDynamics_WithTumour+DrugTreatment", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A mathematical model of bone remodeling dynamics for normal bone cell populations and myeloma bone disease
Bruce P Ayati, Claire M Edwards, Glenn F Webb and John P Wikswo. Biology Direct2010 Apr 20;5(28). 20406449,
Abstract:
BACKGROUND:Multiple myeloma is a hematologic malignancy associated with the development of a destructive osteolytic bone disease.RESULTS:Mathematical models are developed for normal bone remodeling and for the dysregulated bone remodeling that occurs in myeloma bone disease. The models examine the critical signaling between osteoclasts (bone resorption) and osteoblasts (bone formation). The interactions of osteoclasts and osteoblasts are modeled as a system of differential equations for these cell populations, which exhibit stable oscillations in the normal case and unstable oscillations in the myeloma case. In the case of untreated myeloma, osteoclasts increase and osteoblasts decrease, with net bone loss as the tumor grows. The therapeutic effects of targeting both myeloma cells and cells of the bone marrow microenvironment on these dynamics are examined.CONCLUSIONS:The current model accurately reflects myeloma bone disease and illustrates how treatment approaches may be investigated using such computational approaches.

Note:

The paper describes three models 1) Zero-dimensional Bone Model without Tumour, 2) Zero-dimensional Bone Model with Tumour and 3) Zero-dimensional Bone Model with Tumour and Drug Treatment. This model corresponds to the Zero-dimensional Bone Model with Tumour and Drug Treatment.

Typos in the publication:

Equation (4): The first term should be (\u03b21/\u03b11)^(g12/\u0393) and not (\u03b22/\u03b12)^(g12/\u0393)

Equation (14): The first term should be (\u03b21/\u03b11)^(((g12/(1+r12))/\u0393) and not (\u03b22/\u03b12)^(((g12/(1+r12))/\u0393)

Equation (13): The first term should be (\u03b21/\u03b11)^((1-g22+r22)/\u0393) and not (\u03b21/\u03b11)^((1-g22-r22)/\u0393)

All these corrections has been implemented in the model, with the authors agreement.

Beyond these, there are several mismatches between the equation numbers that are mentioned in for each equation and the reference that has been made to these equations in the figure legend.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3689, + "tag": "BioModels:BIOMD0000000403" + }, + { + "id": 3212, + "tag": "Chordata" + }, + { + "id": 3249, + "tag": "Regulation of bone remodeling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4614, + "tag": "Multiple myeloma" + } + ], + "timestamp_created": "2025-01-30 13:35:38.968688+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000403", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2694": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2694, + "name": "Bray1993_chemotaxis", + "repository_type": "biomodels", + "summary": "

This version of the model is very close to the version described in the paper with one exception: the binding of aspartate to the various receptor complexes, as well as the formation of the different complexes are modeled using chemical kinetics (mass action law), rather than instant equilibrium. The qualitative behaviour of the model is unchanged. Note that in order to quantitatively replicate the figure 8b, and in particular to have a basal bias of 0.7, we have to change the rate constant of the aspartate-triggered dephosphorylation of CheY from 59000 to 70000. The peaks have then slightly different values.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3690, + "tag": "Bacterial chemotaxis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3691, + "tag": "BioModels:BIOMD0000000404" + }, + { + "id": 3343, + "tag": "Chemotaxis" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:39.422110+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000404", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2695": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2695, + "name": "Cookson2011_EnzymaticQueueingCoupling", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Queueing up for enzymatic processing: correlated signaling through coupled degradation.
Natalie A Cookson, William H Mather, Tal Danino, Octavio Mondrag\u00f3n-Palomino, Ruth J Williams, Lev S Tsimring, & Jeff Hasty Molecular Systems Biology2011; 7:561; DOI:10.1038/msb.2011.94
Abstract:
High-throughput technologies have led to the generation of complex wiring diagrams as a post-sequencing paradigm for depicting the interactions between vast and diverse cellular species. While these diagrams are useful for analyzing biological systems on a large scale, a detailed understanding of the molecular mechanisms that underlie the observed network connections is critical for the further development of systems and synthetic biology. Here, we use queueing theory to investigate how \u2018waiting lines\u2019 can lead to correlations between protein \u2018customers\u2019 that are coupled solely through a downstream set of enzymatic \u2018servers\u2019. Using the E. coli ClpXP degradation machine as a model processing system, we observe significant cross-talk between two networks that are indirectly coupled through a common set of processors. We further illustrate the implications of enzymatic queueing using a synthetic biology application, in which two independent synthetic networks demonstrate synchronized behavior when common ClpXP machinery is overburdened. Our results demonstrate that such post-translational processes can lead to dynamic connections in cellular networks and may provide a mechanistic understanding of existing but currently inexplicable links.

Note:
Individual stochastic trajectories for a queueing system in three different conditions, 1) Underloaded, 2) Balanced and 3) Overloaded, demonstrate correlation resonance. The parameter values in this model correspond to the Balanced Condition.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3692, + "tag": "BioModels:BIOMD0000000405" + }, + { + "id": 3453, + "tag": "Regulation of catalytic activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:39.890666+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000405", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2696": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2696, + "name": "Moriya2011_CellCycle_FissionYeast", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Overexpression limits of fission yeast cell-cycle regulators in vivo and in silico.
Moriya H, Chino A, Kapuy O, Csik\u00e1sz-Nagy A, Nov\u00e1k B. Mol Syst Biol. 2011 Dec 6;7:556. 22146300 ,
Abstract:
Cellular systems are generally robust against fluctuations of intracellular parameters such as gene expression level. However, little is known about expression limits of genes required to halt cellular systems. In this study, using the fission yeast Schizosaccharomyces pombe, we developed a genetic 'tug-of-war' (gTOW) method to assess the overexpression limit of certain genes. Using gTOW, we determined copy number limits for 31 cell-cycle regulators; the limits varied from 1 to >100. Comparison with orthologs of the budding yeast Saccharomyces cerevisiae suggested the presence of a conserved fragile core in the eukaryotic cell cycle. Robustness profiles of networks regulating cytokinesis in both yeasts (septation-initiation network (SIN) and mitotic exit network (MEN)) were quite different, probably reflecting differences in their physiologic functions. Fragility in the regulation of GTPase spg1 was due to dosage imbalance against GTPase-activating protein (GAP) byr4. Using the gTOW data, we modified a mathematical model and successfully reproduced the robustness of the S. pombe cell cycle with the model.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3693, + "tag": "BioModels:BIOMD0000000406" + }, + { + "id": 3331, + "tag": "Regulation of cell cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2986, + "tag": "Schizosaccharomyces pombe" + } + ], + "timestamp_created": "2025-01-30 13:35:40.432407+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000406", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2697": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2697, + "name": "Schliemann2011_TNF_ProAntiApoptosis", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Heterogeneity Reduces Sensitivity of Cell Death for TNF-Stimuli
Schliemann M, Bullinger E, Borchers S, Allgower F, Findeisen R, Scheurich P. BMC Syst Biol. 2011 Dec 28;5(1):204. 22204418 ,
Abstract:
BACKGROUND:Apoptosis is a form of programmed cell death essential for the maintenance of homeostasis and the removal of potentially damaged cells in multicellular organisms. By binding its cognate membrane receptor, TNF receptor type 1 (TNF-R1), the proinflammatory cytokine Tumor Necrosis Factor (TNF) activates pro-apoptotic signaling via caspase activation, but at the same time also stimulates nuclear factor kappaB (NF-kappaB)-mediated survival pathways. Differential dose-response relationships of these two major TNF signaling pathways have been described experimentally and using mathematical modeling. However, the quantitative analysis of the complex interplay between pro- and anti-apoptotic signaling pathways is an open question as it is challenging for several reasons: the overall signaling network is complex, various time scales are present, and cells respond quantitatively and qualitatively in a heterogeneous manner.RESULTS:This study analyzes the complex interplay of the crosstalk of TNF-R1 induced pro- and anti-apoptotic signaling pathways based on an experimentally validated mathematical model. The mathematical model describes the temporal responses on both the single cell level as well as the level of a heterogeneous cell population, as observed in the respective quantitative experiments using TNF-R1 stimuli of different strengths and durations. Global sensitivity of the heterogeneous population was quantified by measuring the average gradient of time of death versus each population parameter. This global sensitivity analysis uncovers the concentrations of Caspase-8 and Caspase-3, and their respective inhibitors BAR and XIAP, as key elements for deciding the cell's fate. A simulated knockout of the NF-kappaB-mediated anti-apoptotic signaling reveals the importance of this pathway for delaying the time of death, reducing the death rate in the case of pulse stimulation and significantly increasing cell-to-cell variability.CONCLUSIONS:Cell ensemble modeling of a heterogeneous cell population including a global sensitivity analysis presented here allowed us to illuminate the role of the different elements and parameters on apoptotic signaling. The receptors serve to transmit the external stimulus; procaspases and their inhibitors control the switching from life to death, while NF-kappaB enhances the heterogeneity of the cell population. The global sensitivity analysis of the cell population model further revealed an unexpected impact of heterogeneity, i.e. the reduction of parametric sensitivity.

Note: SBML model generated from Matlab system description on 12-July-2011 21:08:15 by exportSBML Copyright Eric Bullinger 2007-2011

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3694, + "tag": "BioModels:BIOMD0000000407" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3695, + "tag": "rhabdomyosarcoma cell line" + } + ], + "timestamp_created": "2025-01-30 13:35:40.979406+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000407", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2698": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2698, + "name": "Hettling2011_CreatineKinase", + "repository_type": "biomodels", + "summary": "

This model is from the article:
\tAnalyzing the functional properties of the creatine kinase system with multiscale 'sloppy' modeling.
Hettling H, van Beek JH PLoS Comput Biol.2011 Aug;7(8):e1002130. PMEDID,
Abstract:
In this study the function of the two isoforms of creatine kinase (CK; EC 2.7.3.2) in myocardium is investigated. The 'phosphocreatine shuttle' hypothesis states that mitochondrial and cytosolic CK plays a pivotal role in the transport of high-energy phosphate (HEP) groups from mitochondria to myofibrils in contracting muscle. Temporal buffering of changes in ATP and ADP is another potential role of CK. With a mathematical model, we analyzed energy transport and damping of high peaks of ATP hydrolysis during the cardiac cycle. The analysis was based on multiscale data measured at the level of isolated enzymes, isolated mitochondria and on dynamic response times of oxidative phosphorylation measured at the whole heart level. Using 'sloppy modeling' ensemble simulations, we derived confidence intervals for predictions of the contributions by phosphocreatine (PCr) and ATP to the transfer of HEP from mitochondria to sites of ATP hydrolysis. Our calculations indicate that only 15\u00b18% (mean\u00b1SD) of transcytosolic energy transport is carried by PCr, contradicting the PCr shuttle hypothesis. We also predicted temporal buffering capabilities of the CK isoforms protecting against high peaks of ATP hydrolysis (3750 \u00b5M*s(-1)) in myofibrils. CK inhibition by 98% in silico leads to an increase in amplitude of mitochondrial ATP synthesis pulsation from 215\u00b123 to 566\u00b131 \u00b5M*s(-1), while amplitudes of oscillations in cytosolic ADP concentration double from 77\u00b111 to 146\u00b11 \u00b5M. Our findings indicate that CK acts as a large bandwidth high-capacity temporal energy buffer maintaining cellular ATP homeostasis and reducing oscillations in mitochondrial metabolism. However, the contribution of CK to the transport of high-energy phosphate groups appears limited. Mitochondrial CK activity lowers cytosolic inorganic phosphate levels while cytosolic CK has the opposite effect.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3056, + "tag": "ATP metabolic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3696, + "tag": "BioModels:BIOMD0000000408" + }, + { + "id": 3058, + "tag": "Creatine metabolic process" + }, + { + "id": 3059, + "tag": "Oryctolagus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:41.473026+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000408", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2699": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2699, + "name": "Queralt2006_MitoticExit_Cdc55DownregulationBySeparase", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Downregulation of PP2A(Cdc55) phosphatase by separase initiates mitotic exit in budding yeast.
Queralt E, Lehane C, Novak B, Uhlmann F. Cell. 2006 May 19;125(4):719-32. 16713564 ,
Abstract:
After anaphase, the high mitotic cyclin-dependent kinase (Cdk) activity is downregulated to promote exit from mitosis. To this end, in the budding yeast S. cerevisiae, the Cdk counteracting phosphatase Cdc14 is activated. In metaphase, Cdc14 is kept inactive in the nucleolus by its inhibitor Net1. During anaphase, Cdk- and Polo-dependent phosphorylation of Net1 is thought to release active Cdc14. How Net1 is phosphorylated specifically in anaphase, when mitotic kinase activity starts to decline, has remained unexplained. Here, we show that PP2A(Cdc55) phosphatase keeps Net1 underphosphorylated in metaphase. The sister chromatid-separating protease separase, activated at anaphase onset, interacts with and downregulates PP2A(Cdc55), thereby facilitating Cdk-dependent Net1 phosphorylation. PP2A(Cdc55) downregulation also promotes phosphorylation of Bfa1, contributing to activation of the \"mitotic exit network\" that sustains Cdc14 as Cdk activity declines. These findings allow us to present a new quantitative model for mitotic exit in budding yeast.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3697, + "tag": "BioModels:BIOMD0000000409" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:35:41.974551+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000409", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2700": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2700, + "name": "Wegner2012_TGFbetaSignalling_FeedbackLoops", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Dynamics and feedback loops in the transforming growth factor \u03b2 signaling pathway.
Wegner K, Bachmann A, Schad JU, Lucarelli P, Sahle S, Nickel P, Meyer C, Klingm\u00fcller U, Dooley S, Kummer U. Biophys Chem. 2012 Jan 5. 22284904 ,
Abstract:
Transforming growth factor \u03b2 (TGF-\u03b2) ligands activate a signaling cascade with multiple cell context dependent outcomes. Disruption or disturbance leads to variant clinical disorders. To develop strategies for disease intervention, delineation of the pathway in further detail is required. Current theoretical models of this pathway describe production and degradation of signal mediating proteins and signal transduction from the cell surface into the nucleus, whereas feedback loops have not exhaustively been included. In this study we present a mathematical model to determine the relevance of feedback regulators (Arkadia, Smad7, Smurf1, Smurf2, SnoN and Ski) on TGF-\u03b2 target gene expression and the potential to initiate stable oscillations within a realistic parameter space. We employed massive sampling of the parameters space to pinpoint crucial players for potential oscillations as well as transcriptional product levels. We identified Smad7 and Smurf2 with the highest impact on the dynamics. Based on these findings, we conducted preliminary time course experiments.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3698, + "tag": "BioModels:BIOMD0000000410" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3158, + "tag": "Transforming growth factor beta receptor signaling pathway" + }, + { + "id": 3471, + "tag": "hepatocyte" + } + ], + "timestamp_created": "2025-01-30 13:35:42.494835+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000410", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2701": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2701, + "name": "Heiland2012_CircadianClock_C.reinhardtii", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Modeling temperature entrainment of circadian clocks using the Arrhenius equation and a reconstructed model from Chlamydomonas reinhardtii
Ines Heiland, Christian Bodenstein, Thomas Hinze, Olga Weisheit, Oliver Ebenhoeh, Maria Mittag and Stefan Schuster Journal of Biological Physics 4 March 2012; pp 1-16; doi: 10.1007/s10867-012-9264-x ,
Abstract:
Endogenous circadian rhythms allow living organisms to anticipate daily variations in their natural environment. Temperature regulation and entrainment mechanisms of circadian clocks are still poorly understood. To better understand the molecular basis of these processes, we built a mathematical model based on experimental data examining temperature regulation of the circadian RNA-binding protein CHLAMY1 from the unicellular green alga Chlamydomonas reinhardtii , simulating the effect of temperature on the rates by applying the Arrhenius equation. Using numerical simulations, we demonstrate that our model is temperature-compensated and can be entrained to temperature cycles of various length and amplitude. The range of periods that allow entrainment of the model depends on the shape of the temperature cycles and is larger for sinusoidal compared to rectangular temperature curves. We show that the response to temperature of protein (de)phosphorylation rates play a key role in facilitating temperature entrainment of the oscillator in Chlamydomonas reinhardtii . We systematically investigated the response of our model to single temperature pulses to explain experimentally observed phase response curves.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3699, + "tag": "BioModels:BIOMD0000000411" + }, + { + "id": 3700, + "tag": "Chlamydomonas reinhardtii" + }, + { + "id": 3408, + "tag": "Circadian temperature homeostasis" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:42.963680+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000411", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2702": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2702, + "name": "Pokhilko2012_CircClock_RepressilatorFeedbackloop", + "repository_type": "biomodels", + "summary": "

This model is from the article:
The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops
Pokhilko A, Fern\u00e1ndez AP, Edwards KD, Southern MM, Halliday KJ, Millar AJ. Mol Syst Biol.2012 Mar 6;8:574. 22395476,
Abstract:
Circadian clocks synchronise biological processes with the day/night cycle, using molecular mechanisms that include interlocked, transcriptional feedback loops. Recent experiments identified the evening complex (EC) as a repressor that can be essential for gene expression rhythms in plants. Integrating the EC components in this role significantly alters our mechanistic, mathematical model of the clock gene circuit. Negative autoregulation of the EC genes constitutes the clock's evening loop, replacing the hypothetical component Y. The EC explains our earlier conjecture that the morning gene PSEUDO-RESPONSE REGULATOR 9 was repressed by an evening gene, previously identified with TIMING OF CAB EXPRESSION1 (TOC1). Our computational analysis suggests that TOC1 is a repressor of the morning genes LATE ELONGATED HYPOCOTYL and CIRCADIAN CLOCK ASSOCIATED1 rather than an activator as first conceived. This removes the necessity for the unknown component X (or TOC1mod) from previous clock models. As well as matching timeseries and phase-response data, the model provides a new conceptual framework for the plant clock that includes a three-component repressilator circuit in its complex structure.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3701, + "tag": "BioModels:BIOMD0000000412" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:43.469364+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000412", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2703": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2703, + "name": "Band2012_DII-Venus_FullModel", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.
Band LR, Wells DM, Larrieu A, Sun J, Middleton AM, French AP, Brunoud G, Sato EM, Wilson MH, P\u00e9ret B, Oliva M, Swarup R, Sairanen I, Parry G, Ljung K, Beeckman T, Garibaldi JM, Estelle M, Owen MR, Vissenberg K, Hodgman TC, Pridmore TP, King JR, Vernoux T, Bennett MJ. Proc Natl Acad Sci U S A.2012 Mar 20;109(12):4668-73 22393022,
Abstract:
Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

This model corresponds to the full model described in the article.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3702, + "tag": "BioModels:BIOMD0000000413" + }, + { + "id": 3703, + "tag": "Positive gravitropism" + }, + { + "id": 3704, + "tag": "Regulation of auxin metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:43.976248+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000413", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2704": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2704, + "name": "Band2012_DII-Venus_ReducedModel", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Root gravitropism is regulated by a transient lateral auxin gradient controlled by a tipping-point mechanism.
Band LR, Wells DM, Larrieu A, Sun J, Middleton AM, French AP, Brunoud G, Sato EM, Wilson MH, P\u00e9ret B, Oliva M, Swarup R, Sairanen I, Parry G, Ljung K, Beeckman T, Garibaldi JM, Estelle M, Owen MR, Vissenberg K, Hodgman TC, Pridmore TP, King JR, Vernoux T, Bennett MJ. Proc Natl Acad Sci U S A.2012 Mar 20;109(12):4668-73 22393022,
Abstract:
Gravity profoundly influences plant growth and development. Plants respond to changes in orientation by using gravitropic responses to modify their growth. Cholodny and Went hypothesized over 80 years ago that plants bend in response to a gravity stimulus by generating a lateral gradient of a growth regulator at an organ's apex, later found to be auxin. Auxin regulates root growth by targeting Aux/IAA repressor proteins for degradation. We used an Aux/IAA-based reporter, domain II (DII)-VENUS, in conjunction with a mathematical model to quantify auxin redistribution following a gravity stimulus. Our multidisciplinary approach revealed that auxin is rapidly redistributed to the lower side of the root within minutes of a 90\u00b0 gravity stimulus. Unexpectedly, auxin asymmetry was rapidly lost as bending root tips reached an angle of 40\u00b0 to the horizontal. We hypothesize roots use a \"tipping point\" mechanism that operates to reverse the asymmetric auxin flow at the midpoint of root bending. These mechanistic insights illustrate the scientific value of developing quantitative reporters such as DII-VENUS in conjunction with parameterized mathematical models to provide high-resolution kinetics of hormone redistribution.

This model corresponds to the simplified model described in the article. It is assumed that, on the timescale of DII-VENUS degradation, the concentrations of auxin, TIR1/AFB, and their complexes can be approximated by quasi-steady-state expressions. This reduced the full model to a single ODE that describes how the DII-VENUS dynamics depend on the auxin influx and four parameter groupings.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3705, + "tag": "BioModels:BIOMD0000000414" + }, + { + "id": 3703, + "tag": "Positive gravitropism" + }, + { + "id": 3704, + "tag": "Regulation of auxin metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:44.461170+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000414", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2705": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2705, + "name": "Mellor2012_LipooxygenasePathway", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Reduction of off-flavor generation in soybean homogenates: a mathematical model.
Mellor N , Bligh F , Chandler I , Hodgman C \tJ. Food Sci.2010 Sep; 75(7): R131-8; PMID: 2153556,
Abstract:
The generation of off-flavors in soybean homogenates such as n-hexanal via the lipoxygenase (LOX) pathway can be a problem in the processed food industry. Previous studies have examined the effect of using soybean varieties missing one or more of the 3 LOX isozymes on n-hexanal generation. A dynamic mathematical model of the soybean LOX pathway using ordinary differential equations was constructed using parameters estimated from existing data with the aim of predicting how n-hexanal generation could be reduced. Time-course simulations of LOX-null beans were run and compared with experimental results. Model L(2), L(3), and L(12) beans were within the range relative to the wild type found experimentally, with L(13) and L(23) beans close to the experimental range. Model L(1) beans produced much more n-hexanal relative to the wild type than those in experiments. Sensitivity analysis indicates that reducing the estimated K(m) parameter for LOX isozyme 3 (L-3) would improve the fit between model predictions and experimental results found in the literature. The model also predicts that increasing L-3 or reducing L-2 levels within beans may reduce n-hexanal generation. PRACTICAL APPLICATION: This work describes the use of mathematics to attempt to quantify the enzyme-catalyzed conversions of compounds in soybean homogenates into undesirable flavors, primarily from the compound n-hexanal. The effect of different soybean genotypes and enzyme kinetic constants was also studied, leading to recommendations on which combinations might minimize off-flavor levels and what further work might be carried out to substantiate these conclusions.

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This model is from the article:
The influence of cytokinin-auxin cross-regulation on cell-fate determination in Arabidopsis thaliana root development
Muraro D, Byrne H, King J, Voss U, Kieber J, Bennett M. J Theor Biol.2011 Aug 21;283(1):152-67.PMID: 21640126,
Abstract:
Root growth and development in Arabidopsis thaliana are sustained by a specialised zone termed the meristem, which contains a population of dividing and differentiating cells that are functionally analogous to a stem cell niche in animals. The hormones auxin and cytokinin control meristem size antagonistically. Local accumulation of auxin promotes cell division and the initiation of a lateral root primordium. By contrast, high cytokinin concentrations disrupt the regular pattern of divisions that characterises lateral root development, and promote differentiation. The way in which the hormones interact is controlled by a genetic regulatory network. In this paper, we propose a deterministic mathematical model to describe this network and present model simulations that reproduce the experimentally observed effects of cytokinin on the expression of auxin regulated genes. We show how auxin response genes and auxin efflux transporters may be affected by the presence of cytokinin. We also analyse and compare the responses of the hormones auxin and cytokinin to changes in their supply with the responses obtained by genetic mutations of SHY2, which encodes a protein that plays a key role in balancing cytokinin and auxin regulation of meristem size. We show that although shy2 mutations can qualitatively reproduce the effect of varying auxin and cytokinin supply on their response genes, some elements of the network respond differently to changes in hormonal supply and to genetic mutations, implying a different, general response of the network. We conclude that an analysis based on the ratio between these two hormones may be misleading and that a mathematical model can serve as a useful tool for stimulate further experimental work by predicting the response of the network to changes in hormone levels and to other genetic mutations.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model is from the article:
Asymmetric positive feedback loops reliably control biological responses
Alexander V Ratushny, Ramsey A Saleem, Katherine Sitko, Stephen A Ramsey & John D Aitchison Mol Syst Biol. 2012 Apr 24;8:577. 22531117 ,
Abstract:
Positive feedback is a common mechanism enabling biological systems to respond to stimuli in a switch-like manner. Such systems are often characterized by the requisite formation of a heterodimer where only one of the pair is subject to feedback. This ASymmetric Self-UpREgulation (ASSURE) motif is central to many biological systems, including cholesterol homeostasis (LXR\u03b1/RXR\u03b1), adipocyte differentiation (PPAR\u03b3/RXR\u03b1), development and differentiation (RAR/RXR), myogenesis (MyoD/E12) and cellular antiviral defense (IRF3/IRF7). To understand why this motif is so prevalent, we examined its properties in an evolutionarily conserved transcriptional regulatory network in yeast (Oaf1p/Pip2p). We demonstrate that the asymmetry in positive feedback confers a competitive advantage and allows the system to robustly increase its responsiveness while precisely tuning the response to a consistent level in the presence of varying stimuli. This study reveals evolutionary advantages for the ASSURE motif, and mechanisms for control, that are relevant to pharmacologic intervention and synthetic biology applications.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3715, + "tag": "BioModels:BIOMD0000000421" + }, + { + "id": 3711, + "tag": "Regulation of transcription, DNA-templated" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:35:48.019744+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000421", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2712": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2712, + "name": "Middleton2012_GibberellinSignalling", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Mathematical modeling elucidates the role of transcriptional feedback in gibberellin signaling.
Middleton AM , \u00dabeda-Tom\u00e1s S , Griffiths J , Holman T , Hedden P , Thomas SG , Phillips AL , Holdsworth MJ , Bennett MJ , King JR, Owen MR Proc. Natl. Acad. Sci. U.S.A. 2012 May; 109(19): 7571-6 22523240 ,
Abstract:
The hormone gibberellin (GA) is a key regulator of plant growth. Many of the components of the gibberellin signal transduction [e.g., GIBBERELLIN INSENSITIVE DWARF 1 (GID1) and DELLA], biosynthesis [e.g., GA 20-oxidase (GA20ox) and GA3ox], and deactivation pathways have been identified. Gibberellin binds its receptor, GID1, to form a complex that mediates the degradation of DELLA proteins. In this way, gibberellin relieves DELLA-dependent growth repression. However, gibberellin regulates expression of GID1, GA20ox, and GA3ox, and there is also evidence that it regulates DELLA expression. In this paper, we use integrated mathematical modeling and experiments to understand how these feedback loops interact to control gibberellin signaling. Model simulations are in good agreement with in vitro data on the signal transduction and biosynthesis pathways and in vivo data on the expression levels of gibberellin-responsive genes. We find that GA-GID1 interactions are characterized by two timescales (because of a lid on GID1 that can open and close slowly relative to GA-GID1 binding and dissociation). Furthermore, the model accurately predicts the response to exogenous gibberellin after a number of chemical and genetic perturbations. Finally, we investigate the role of the various feedback loops in gibberellin signaling. We find that regulation of GA20ox transcription plays a significant role in both modulating the level of endogenous gibberellin and generating overshoots after the removal of exogenous gibberellin. Moreover, although the contribution of other individual feedback loops seems relatively small, GID1 and DELLA transcriptional regulation acts synergistically with GA20ox feedback.

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This model is from the article:
Mechanistic explanations for counter-intuitive phosphorylation dynamics of the insulin receptor and insulin receptor substrate-1 in response to insulin in murine adipocytes.
Nyman E, Fagerholm S, Jullesson D, Str\u00e5lfors P, Cedersund G. FEBS J. 2012 Jan 16. 22248283 ,
Abstract:
Insulin signaling through insulin receptor (IR) and insulin receptor substrate-1 (IRS1) is important for insulin control of target cells. We have previously demonstrated a rapid and simultaneous overshoot behavior in the phosphorylation dynamics of IR and IRS1 in human adipocytes. Herein, we demonstrate that in murine adipocytes a similar overshoot behavior is not simultaneous for IR and IRS1. The peak of IRS1 phosphorylation, which is a direct consequence of the phosphorylation and the activation of IR, occurs earlier than the peak of IR phosphorylation. We used a conclusive modeling framework to unravel the mechanisms behind this counter-intuitive order of phosphorylation. Through a number of rejections, we demonstrate that two fundamentally different mechanisms may create the reversed order of peaks: (i) two pools of phosphorylated IR, where a large pool of internalized IR peaks late, but phosphorylation of IRS1 is governed by a small plasma membrane-localized pool of IR with an early peak, or (ii) inhibition of the IR-catalyzed phosphorylation of IRS1 by negative feedback. Although (i) may explain the reversed order, this two-pool hypothesis alone requires extensive internalization of IR, which is not supported by experimental data. However, with the additional assumption of limiting concentrations of IRS1, (i) can explain all data. Also, (ii) can explain all available data. Our findings illustrate how modeling can potentiate reasoning, to help draw nontrivial conclusions regarding competing mechanisms in signaling networks. Our work also reveals new differences between human and murine insulin signaling. Database The mathematical model described here has been submitted to the Online Cellular Systems Modelling Database and can be accessed at http://jjj.biochem.sun.ac.za/database/nyman/index.html free of charge.

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Faratian2009 - Role of PTEN in Trastuzumabresistance

This model is described in the article:

Faratian D, Goltsov A, Lebedeva G, Sorokin A, Moodie S, Mullen P, Kay C, Um IH, Langdon S, Goryanin I, Harrison DJ.
Cancer Res. 2009 Aug; 69(16): 6713-6720

Abstract:

Resistance to targeted cancer therapies such as trastuzumab is a frequent clinical problem not solely because of insufficient expression of HER2 receptor but also because of the overriding activation states of cell signaling pathways. Systems biology approaches lend themselves to rapid in silico testing of factors, which may confer resistance to targeted therapies. Inthis study, we aimed to develop a new kinetic model that could be interrogated to predict resistance to receptor tyrosine kinase (RTK) inhibitor therapies and directly test predictions in vitro and in clinical samples. The new mathematical model included RTK inhibitor antibody binding, HER2/HER3 dimerization and inhibition, AKT/mitogen-activated protein kinase cross-talk, and the regulatory properties of PTEN. The model was parameterized using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays. Quantitative PTEN protein expression was found to be the key determinant of resistance to anti-HER2 therapy in silico, which was predictive of unseen experiments in vitro using the PTEN inhibitor bp(V). When measured in cancer cell lines, PTEN expression predicts sensitivity to anti-HER2 therapy; furthermore, this quantitative measurement is more predictive of response (relative risk, 3.0; 95% confidence interval, 1.6-5.5; P < 0.0001) than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of 122 breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalized therapy in cancer and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision making in patients treated with anti-HER2 therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000424.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3721, + "tag": "BioModels:BIOMD0000000424" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3512, + "tag": "Phosphatidylinositol 3-kinase signaling" + }, + { + "id": 3722, + "tag": "Response to drug" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 13:35:49.575671+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000424", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2715": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2715, + "name": "Tan2012 - Antibiotic Treatment, Inoculum Effect", + "repository_type": "biomodels", + "summary": "
Tan2012 - Antibiotic Treatment, Inoculum Effect

The efficacy of many antibiotics decreases with increasing bacterial density, a phenomenon called the \u2018inoculum effect\u2019 (IE). This study reveals that, for ribosome-targeting antibiotics, IE is due to bistable inhibition of bacterial growth, which reduces the efficacy of certain treatment frequencies.

This model is described in the article:

Tan C, Phillip Smith R, Srimani JK, Riccione KA, Prasada S, Kuehn M, You L.
Mol Syst Biol. 2012 Oct 9; 8:617

Abstract:

The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome. We show that the IE can be explained by bistable inhibition of bacterial growth. A critical requirement for this bistability is sufficiently fast degradation of ribosomes, which can result from antibiotic-induced heat-shock response. Furthermore, antibiotics that elicit the IE can lead to 'band-pass' response of bacterial growth to periodic antibiotic treatment: the treatment efficacy drastically diminishes at intermediate frequencies of treatment. Our proposed mechanism for the IE may be generally applicable to other bacterial species treated with antibiotics targeting the ribosomes.

This model is hosted on BioModels Database and identified by: MODEL1208300000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3723, + "tag": "BioModels:BIOMD0000000425" + }, + { + "id": 3724, + "tag": "Cellular response to antibiotic" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3810, + "tag": "Bacterial infectious disease" + } + ], + "timestamp_created": "2025-01-30 13:35:50.068580+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000425", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2716": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2716, + "name": "Mosca2012 - Central Carbon Metabolism Regulated by AKT", + "repository_type": "biomodels", + "summary": "
Mosca2012 - Central Carbon Metabolism Regulated by AKT

The role of the PI3K/Akt/PKB signalling pathway in oncogenesis has been extensively investigated and altered expression or mutations of many components of this pathway have been implicated in human cancers. Indeed, expression of constitutively active forms of Akt/PKB can prevent cell death upon growth factor withdrawal. PI3K/Akt/mTOR-mediated survival relies on a profound metabolic adaptation, including aerobic glycolysis. Here, the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis has been modelled, considering two states - high and low PI3K/Akt/mTOR activity. The high PI3K/Akt/mTOR activity represents cancer cell line where PI3K/Akt/mTOR promotes a high rate of glucose metabolism (condition H) and the low PI3K/Akt/mTOR activity is characterised by a lower glycolytic rate due to a reduced PI3K/Akt/mTOR signal (condition L). This model corresponds to the high PI3K/Akt/mTOR signal (condition H).

This model is described in the article:

Mosca E, Alfieri R, Maj C, Bevilacqua A, Canti G, Milanesi L.
Frontiers in Systems Biology. 2012 Oct 13

Abstract:

Signal transduction pathways and gene regulation determine a major reorganization of metabolic activities in order to support cell proliferation. Protein Kinase B (PKB), also known as Akt, participates in the PI3K/Akt/mTOR pathway, a master regulator of aerobic glycolysis and cellular biosynthesis, two activities shown by both normal and cancer proliferating cells. Not surprisingly considering its relevance for cellular metabolism, Akt/PKB is often found hyperactive in cancer cells. In the last decade, many efforts have been made to improve the understanding of the control of glucose metabolism and the identification of a therapeutic window between proliferating cancer cells and proliferating normal cells. In this context, we have modelled the link between the PI3K/Akt/mTOR pathway, glycolysis, lactic acid production and nucleotide biosynthesis. We used a computational model in order to compare two metabolic states generated by the specific variation of the metabolic fluxes regulated by the activity of the PI3K/Akt/mTOR pathway. One of the two states represented the metabolism of a growing cancer cell characterised by aerobic glycolysis and cellular biosynthesis, while the other state represented the same metabolic network with a reduced glycolytic rate and a higher mitochondrial pyruvate metabolism, as reported in literature in relation to the activity of the PI3K/Akt/mTOR. Some steps that link glycolysis and pentose phosphate pathway revealed their importance for controlling the dynamics of cancer glucose metabolism.

This model is hosted on BioModels Database and identified by: MODEL1210150000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3725, + "tag": "BioModels:BIOMD0000000426" + }, + { + "id": 3451, + "tag": "HeLa cell" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3726, + "tag": "Protein kinase B signaling" + }, + { + "id": 3727, + "tag": "Regulation of metabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 13:35:50.687096+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000426", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2717": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2717, + "name": "Bianconi2012 - EGFR and IGF1R pathway in lung cancer", + "repository_type": "biomodels", + "summary": "
Bianconi2012 - EGFR and IGF1R pathway in lung cancer

EGFR and IGF1R pathways play a key role in various human cancers and are crucial for tumour transformation and survival of malignant cells. High EGFR and IGF1R expression and activity has been associated with multiple aspects of cancer progression including tumourigenesis, metastasis, resistance to chemotherapeutics and other molecularly targeted drugs. Here, the biological relationship between the proteins involved in EGFR and IGF1R pathways and the downstream MAPK and PIK3 networks has been modelled to study the time behaviour of the overall system, and the functional interdependencies among the receptors, the proteins and kinases involved.

This model is described in the article:

Bianconi F, Baldelli E, Ludovini V, Crin\u00f2 L, Flacco A, Valigi P.
Biotechnol Adv. 2012 Jan-Feb;30(1):142-53.

Abstract:

In this paper we propose a Systems Biology approach to understand the molecular biology of the Epidermal Growth Factor Receptor (EGFR, also known as ErbB1/HER1) and type 1 Insulin-like Growth Factor (IGF1R) pathways in non-small cell lung cancer (NSCLC). This approach, combined with Translational Oncology methodologies, is used to address the experimental evidence of a close relationship among EGFR and IGF1R protein expression, by immunohistochemistry (IHC) and gene amplification, by in situ hybridization (FISH) and the corresponding ability to develop a more aggressive behavior. We develop a detailed in silico model, based on ordinary differential equations, of the pathways and study the dynamic implications of receptor alterations on the time behavior of the MAPK cascade down to ERK, which in turn governs proliferation and cell migration. In addition, an extensive sensitivity analysis of the proposed model is carried out and a simplified model is proposed which allows us to infer a similar relationship among EGFR and IGF1R activities and disease outcome.

This model is hosted on BioModels Database and identified by: MODEL1209230000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3728, + "tag": "BioModels:BIOMD0000000427" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3729, + "tag": "Regulation of ERK1 and ERK2 cascade" + }, + { + "id": 3730, + "tag": "Regulation of phosphatidylinositol 3-kinase signaling" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4615, + "tag": "Non-small cell lung carcinoma" + } + ], + "timestamp_created": "2025-01-30 13:35:51.177657+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000427", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2718": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2718, + "name": "Achcar2012 - Glycolysis in bloodstream form T. brucei", + "repository_type": "biomodels", + "summary": "
Achcar2012 - Glycolysis in bloodstream form T. brucei

Kinetic models of metabolism require quantitative knowledge of detailed kinetic parameters. However, the knowledge about these parameters is often uncertain. An analysis of the effect of parameter uncertainties on a particularly well defined example of a quantitative metablic model, the model of glycolysis in bloodstream form Trypanosoma brucei , has been presented here.

This model is described in the article:

Achcar F, Kerkhoven EJ; SilicoTryp Consortium, Bakker BM, Barrett MP, Breitling R.
PLoS Comput Biol. 2012 Jan; 8(1):e1002352.

Abstract:

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

This model is hosted on BioModels Database and identified by: MODEL1209130000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3731, + "tag": "BioModels:BIOMD0000000428" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:35:51.745375+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000428", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2719": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2719, + "name": "Schaber2012 - Hog pathway in yeast", + "repository_type": "biomodels", + "summary": "
Schaber2012 - Hog pathway in yeast

The high osmolarity glycerol (HOG) pathway in the yeast Saccharomyces cerevisiae is one of the best-studied mitogen-activated protein kinase (MAPK) pathways and serves as a prototype signalling system for eukaryotes. This pathway is necessary and sufficient to adapt to high external osmolarity. A key component of this pathway is the stress-activated protein kinase (SAPK) Hog1, which is rapidly phosphorylated by the SAPK kinase Pbs2 upon hyper-osmotic shock, and which is the terminal kinase of two parallel signalling pathways, subsequently called the Sho1 branch and the Sln1 branch, respectively. Ensemble modelling (192 models) is used to study the yeast HOG pathway, a prototype for eukaryotic mitogen-activated kinase signalling systems. The best fit model (Model Nr.22: described here) provides new insights into the function of this system, some of which are then experimentally validated.

This model is described in the article:

Schaber J, Baltanas R, Bush A, Klipp E, Colman-Lerner A.
Mol Syst Biol. 2012 Nov 13;8:622.

Abstract:

The high osmolarity glycerol (HOG) pathway in yeast serves as a prototype signalling system for eukaryotes. We used an unprecedented amount of data to parameterise 192 models capturing different hypotheses about molecular mechanisms underlying osmo-adaptation and selected a best approximating model. This model implied novel mechanisms regulating osmo-adaptation in yeast. The model suggested that (i) the main mechanism for osmo-adaptation is a fast and transient non-transcriptional Hog1-mediated activation of glycerol production, (ii) the transcriptional response serves to maintain an increased steady-state glycerol production with low steady-state Hog1 activity, and (iii) fast negative feedbacks of activated Hog1 on upstream signalling branches serves to stabilise adaptation response. The best approximating model also indicated that homoeostatic adaptive systems with two parallel redundant signalling branches show a more robust and faster response than single-branch systems. We corroborated this notion to a large extent by dedicated measurements of volume recovery in single cells. Our study also demonstrates that systematically testing a model ensemble against data has the potential to achieve a better and unbiased understanding of molecular mechanisms.

This model is hosted on BioModels Database and identified by: MODEL1209110001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3732, + "tag": "BioModels:BIOMD0000000429" + }, + { + "id": 3189, + "tag": "MAPK cascade involved in osmosensory signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:35:52.314518+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000429", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2720": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2720, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_USEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-40). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280020 - M4_K2_USEQ] correspond to type M4 with mass-action kinetics K2, in USEQ (Unsequestrated ) condition.

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280020 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3733, + "tag": "BioModels:BIOMD0000000430" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:52.852792+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000430", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2721": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2721, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_PSEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280024 - M4_K2_PSEQ] correspond to type M4 with mass-action kinetics K2, in PSEQ (sequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280024 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3734, + "tag": "BioModels:BIOMD0000000431" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:53.334796+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000431", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2722": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2722, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_USEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280036 - M4_K2_QSS_USEQ] correspond to type M4 with mass-action kinetics K2, in QSS (quasi steady state) and USEQ (Unsequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280036 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3735, + "tag": "BioModels:BIOMD0000000432" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:53.860208+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000432", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2723": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2723, + "name": "Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Interaction topologies of MAPK cascade (M4_K2_QSS_PSEQ)

The paper presents the various interaction topologies between the kinases and phosphatases of MAPK cascade. They are represented as M1, M2, M3 and M4. The kinases of the cascades are MKKK, MKK and MK, and Phos1, Phos2 and Phos3 are phosphatases of the system. All three kinases in a M1 type network have specific phosphatases Phos1, Phos2 and Phos3 for the dephosphorylation process. In a M2 type system, kinases MKKK and MKK are dephosphorylated by Phos1 and MK is dephosphorylated by Phos2. The architecture of system like M3 is such that MKKK gets dephosphorylated by Phos1, whereas Phos2 dephosphorylates both MKK and MK. Finally, the MAPK cascade exhibiting more complex design of interaction such as M4 is such that MKKK and MKK are dephosphorylated by Phos1 whereas MKK and MK are dephosphorylated by Phos2. In addition, as it is plausible that the kinases can sequester their respective phosphatases by binding to them, this is considered in the design of the systems (PSEQ-sequestrated system; USEQ-Unsequestrated system). The robustness of different interaction designs of the systems is checked, considering both MichaelisMenten type kinetics (K1) and elementary mass action kinetics (K2). In the living systems, the MAPK cascade transmit both short and long duration signals where short duration signals trigger proliferation and long duration signals trigger cell differentiation. These signal variants are considered to interpret the systems behaviour. It is also tested how the robustness and signal response behaviour of K2 models are affected when K2 assumes quasi steady state (QSS). The combinations of the above variants resulted in 40 models (MODEL1204280001-MODEL1204280040). All these 40 models are available from BioModels Database .

Models that correspond to type M4 with mass-action kinetics K2, in four condition 1) USEQ [ MODEL1204280020 - M4_K2_USEQ], 2) PSEQ [ MODEL1204280024 - M4_K2_PSEQ], 3) QSS_USEQ [ MODEL1204280036 - M4_K2_QSS_USEQ] and 4) QSS_PSEQ [ MODEL1204280040 - M4_K2_QSS_PSEQ] are available from the curated branch. The remaining 36 models can be accessed from the non-curated branch.

This model [ MODEL1204280040 - M4_K2_QSS_PSEQ] correspond to type M4 with mass-action kinetics K2, in QSS (quasi steady state) and USEQ (Unsequestrated ) condition. .

This model is described in the article:

Sarma U, Ghosh I.
BMC Syst Biol. 2012 Jul 2;6(1):82.

Abstract:

BACKGROUND: The three layer mitogen activated protein kinase (MAPK) signaling cascade exhibits different designs of interactions between its kinases and phosphatases. While the sequential interactions between the three kinases of the cascade are tightly preserved, the phosphatases of the cascade, such as MKP3 and PP2A, exhibit relatively diverse interactions with their substrate kinases. Additionally, the kinases of the MAPK cascade can also sequester their phosphatases. Thus, each topologically distinct interaction design of kinases and phosphatases could exhibit unique signal processing characteristics, and the presence of phosphatase sequestration may lead to further fine tuning of the propagated signal.

RESULTS: We have built four models of the MAPK cascade, each model with identical kinase-kinase interactions but unique kinases-phosphatases interactions. Our simulations unravelled that MAPK cascade's robustness to external perturbations is a function of nature of interaction between its kinases and phosphatases. The cascade's output robustness was enhanced when phosphatases were sequestrated by their target kinases. We uncovered a novel implicit/hidden negative feedback loop from the phosphatase MKP3 to its upstream kinase Raf-1, in a cascade resembling the B cell MAPK cascade. Notably, strength of the feedback loop was reciprocal to the strength of phosphatases' sequestration and stronger sequestration abolished the feedback loop completely. An experimental method to verify the presence of the feedback loop is also proposed. We further showed, when the models were activated by transient signal, memory (total time taken by the cascade output to reach its unstimulated level after removal of signal) of a cascade was determined by the specific designs of interaction among its kinases and phosphatases.

CONCLUSIONS: Differences in interaction designs among the kinases and phosphatases can differentially shape the robustness and signal response behaviour of the MAPK cascade and phosphatase sequestration dramatically enhances the robustness to perturbations in each of the cascade. An implicit negative feedback loop was uncovered from our analysis and we found that strength of the negative feedback loop is reciprocally related to the strength of phosphatase sequestration. Duration of output phosphorylation in response to a transient signal was also found to be determined by the individual cascade's kinase-phosphatase interaction design.

This model is hosted on BioModels Database and identified by: MODEL1204280040 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3736, + "tag": "BioModels:BIOMD0000000433" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:54.352448+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000433", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2724": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2724, + "name": "McAuley2012 - Whole-body Cholesterol Metabolism", + "repository_type": "biomodels", + "summary": "
McAuley2012 - Whole-body Cholesterol Metabolism

Lipid metabolism has a key role to play in human longevity and healthy aging. A whole-body mathematical model of cholesterol metabolism that explores the changes in both the rate of intestinal cholesterol absorption and the hepatic rate of clearance of LDL-C from the plasma, has been presented here. The model showed that of these two mechanisms, changes to the rate of LDL-C removal from the plasma with age had the most significant effect on cholesterol metabolism.

The original SBML model file was generated using MathSBML 2.5.1.

This model is described in the article:

Mc Auley MM, Wilkinson DJ, Jones JJ, Kirkwood TT.
BMC Syst Biol. 2012 Oct 10;6(1):130.

Abstract:

BACKGROUND: Global demographic changes have stimulated marked interest in the process of ageing. There has been, and will continue to be, an unrelenting rise in the number of the oldest old ( >85 years of age). Together with an ageing population there comes an increase in the prevalence of age related disease. Of the diseases of ageing, cardiovascular disease (CVD) has by far the highest prevalence. It is regarded that a finely tuned lipid profile may help to prevent CVD as there is a long established relationship between alterations to lipid metabolism and CVD risk. In fact elevated plasma cholesterol, particularly Low Density Lipoprotein Cholesterol (LDL-C) has consistently stood out as a risk factor for having a cardiovascular event. Moreover it is widely acknowledged that LDL-C may rise with age in both sexes in a wide variety of groups. The aim of this work was to use a whole-body mathematical model to investigate why LDL-C rises with age, and to test the hypothesis that mechanistic changes to cholesterol absorption and LDL-C removal from the plasma are responsible for the rise. The whole-body mechanistic nature of the model differs from previous models of cholesterol metabolism which have either focused on intracellular cholesterol homeostasis or have concentrated on an isolated area of lipoprotein dynamics. The model integrates both current and previously published data relating to molecular biology, physiology, ageing and nutrition in an integrated fashion.

RESULTS: The model was used to test the hypothesis that alterations to the rate of cholesterol absorption and changes to the rate of removal of LDL-C from the plasma are integral to understanding why LDL-C rises with age. The model demonstrates that increasing the rate of intestinal cholesterol absorption from 50% to 80% by age 65 years can result in an increase of LDL-C by as much as 34mg/dL in a hypothetical male subject. The model also shows that decreasing the rate of hepatic clearance of LDL-C gradually to 50% by age 65 years can result in an increase of LDL-C by as much as 116mg/dL.

CONCLUSIONS: Our model clearly demonstrates that of the two putative mechanisms that have been implicated in the dysregulation of cholesterol metabolism with age, alterations to the removal rate of plasma LDL-C has the most significant impact on cholesterol metabolism and small changes to the number of hepatic LDL receptors can result in a significant rise in LDL-C. This first whole-body systems based model of cholesterol balance could potentially be used as a tool to further improve our understanding of whole-body cholesterol metabolism and its dysregulation with age. Furthermore, given further fine tuning the model may help to investigate potential dietary and lifestyle regimes that have the potential to mitigate the effects aging has on cholesterol metabolism.

This model is hosted on BioModels Database and identified by: MODEL1206010000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3737, + "tag": "Age-dependent general metabolic decline" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3738, + "tag": "BioModels:BIOMD0000000434" + }, + { + "id": 3739, + "tag": "Cholesterol metabolic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4616, + "tag": "Cholesterol ester storage disease" + } + ], + "timestamp_created": "2025-01-30 13:35:54.869368+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000434", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2725": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2725, + "name": "deBack2012 - Lineage Specification in Pancreas Development", + "repository_type": "biomodels", + "summary": "
deBack2012 - Lineage Specification in Pancreas Development

This model of two neighbouring pancreas precursor cells, describes the exocrine versus endocrine lineage specification process. To account for the tissue scale patterns, this couplet model has been extended to hundreds of coupled cells.

This model is described in the article:

de Back W., Zhou JX, Brusch L
J. R. Soc. Interface 6 February 2013 vol. 10 no. 79 20120766

Abstract:

The cell fate decision of multi-potent pancreatic progenitor cells between the exocrine and endocrine lineages is regulated by Notch signalling, mediated by cell\u2013cell interactions. However, canonical models of Notch-mediated lateral inhibition cannot explain the scattered spatial distribution of endocrine cells and the cell-type ratio in the developing pancreas. Based on evidence from acinar-to-islet cell transdifferentiation in vitro, we propose that lateral stabilization, i.e. positive feedback between adjacent progenitor cells, acts in parallel with lateral inhibition to regulate pattern formation in the pancreas. A simple mathematical model of transcriptional regulation and cell\u2013cell interaction reveals the existence of multi-stability of spatial patterns whose simultaneous occurrence causes scattering of endocrine cells in the presence of noise. The scattering pattern allows for control of the endocrine-to-exocrine cell-type ratio by modulation of lateral stabilization strength. These theoretical results suggest a previously unrecognized role for lateral stabilization in lineage specification, spatial patterning and cell-type ratio control in organ development.

This model is hosted on BioModels Database and identified by: MODEL1211010000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3740, + "tag": "BioModels:BIOMD0000000435" + }, + { + "id": 3741, + "tag": "Cell-cell signaling" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:55.345368+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000435", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2726": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2726, + "name": "Gupta2009 - Eicosanoid Metabolism", + "repository_type": "biomodels", + "summary": "
Gupta2009 - Eicosanoid Metabolism

Integrated model of eicosanoid metabolism and signaling based on lipidomics flux analysis.

This model is described in the article:

Gupta S, Maurya MR, Stephens DL, Dennis EA, Subramaniam S.
Biophys. J. 2009 Jun; 96(11):4542-51.

Abstract:

There is increasing evidence for a major and critical involvement of lipids in signal transduction and cellular trafficking, and this has motivated large-scale studies on lipid pathways. The Lipid Metabolites and Pathways Strategy consortium is actively investigating lipid metabolism in mammalian cells and has made available time-course data on various lipids in response to treatment with KDO(2)-lipid A (a lipopolysaccharide analog) of macrophage RAW 264.7 cells. The lipids known as eicosanoids play an important role in inflammation. We have reconstructed an integrated network of eicosanoid metabolism and signaling based on the KEGG pathway database and the literature and have developed a kinetic model. A matrix-based approach was used to estimate the rate constants from experimental data and these were further refined using generalized constrained nonlinear optimization. The resulting model fits the experimental data well for all species, and simulated enzyme activities were similar to their literature values. The quantitative model for eicosanoid metabolism that we have developed can be used to design experimental studies utilizing genetic and pharmacological perturbations to probe fluxes in lipid pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000436 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3742, + "tag": "BioModels:BIOMD0000000436" + }, + { + "id": 3743, + "tag": "Eicosanoid signaling pathway" + }, + { + "id": 3744, + "tag": "Icosanoid metabolic process" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:35:55.917188+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000436", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2727": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2727, + "name": "Tseng2012 - Circadian clock of N.crassa", + "repository_type": "biomodels", + "summary": "
Tseng2012 - Circadian clock of N.crassa

A comprehensive model of the circardian clock of fungal Neurospora crassa , which encompasses existing knowledge of the biochemistry of Neurospora clock, is described by Tseng et al. (2012). The model is validated against a wide range of experimental phenotypes and has been used to investigate possible molecular explanations of temperature compensation.

This model is described in the article:

Tseng YY, Hunt SM, Heintzen C, Crosthwaite SK, Schwartz JM
PLoS Comput. Biol. [2012 ; Volume: 8 (Issue: 3 )] Page info: e1002437

Abstract:

Circadian clocks provide an internal measure of external time allowing organisms to anticipate and exploit predictable daily changes in the environment. Rhythms driven by circadian clocks have a temperature compensated periodicity of approximately 24 hours that persists in constant conditions and can be reset by environmental time cues. Computational modelling has aided our understanding of the molecular mechanisms of circadian clocks, nevertheless it remains a major challenge to integrate the large number of clock components and their interactions into a single, comprehensive model that is able to account for the full breadth of clock phenotypes. Here we present a comprehensive dynamic model of the Neurospora crassa circadian clock that incorporates its key components and their transcriptional and post-transcriptional regulation. The model accounts for a wide range of clock characteristics including: a periodicity of 21.6 hours, persistent oscillation in constant conditions, arrhythmicity in constant light, resetting by brief light pulses, and entrainment to full photoperiods. Crucial components influencing the period and amplitude of oscillations were identified by control analysis. Furthermore, simulations enabled us to propose a mechanism for temperature compensation, which is achieved by simultaneously increasing the translation of frq RNA and decreasing the nuclear import of FRQ protein.

Figure 3 of the reference publication has been reproduced using Copasi 4.8 (Build 35).

This model is hosted on BioModels Database and identifiedby: MODEL1212150000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3745, + "tag": "BioModels:BIOMD0000000437" + }, + { + "id": 3366, + "tag": "Neurospora crassa" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3746, + "tag": "Temperature compensation of the circadian clock" + } + ], + "timestamp_created": "2025-01-30 13:35:56.388186+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000437", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2728": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2728, + "name": "Saeidi2012 - Quorum sensing device that produces GFP", + "repository_type": "biomodels", + "summary": "
Saeidi2012 - Quorum sensing device that produces GFP

Saeidi et al. (2012) has modelled a quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL).

This model is described in the article:

Nazanin Saeidi, Mohamed Arshath, Matthew Wook Chang, Chueh Loo Poh
Chemical Engineering Science. December 2012.

Abstract:

Modeling of biological parts is of crucial importance as it enables the in silico study of synthetic biological systems prior to the actual construction of genetic circuits, which can be time consuming and costly. Because standard biological parts are utilized to build the synthetic systems, it is important that each of these standard parts is well characterized and has a corresponding mathematical model that could simulate the characteristics of the part. These models could be used in computer aided design (CAD) tools during the design stage to facilitate the building of the model of biological systems. This paper describes the development of a mathematical model that is able to simulate both the dynamic and static performance of a biological device created using standard parts. We modeled an example quorum sensing device that produces green fluorescent protein (GFP) as reporter in the presence of Acyl Homoserine Lactone (AHL). The parameters of the model were estimated using experimental results. The simulation results show that the model was able to simulate behavior similar to experimental results. Since it is important that these models and the content in the models can be searchable and readable by machines, standard SBML (system biology markup language) format was used to store the models. All parts and reactions are fully annotated to enable easy searching, and the models follow the Minimum Information Requested In the Annotation of Models (MIRIAM) compliance as well as the Minimum Information About a Simulation Experiment (MIASE).

Figure 4a of the reference publication has been reproduced as curation figure. The plot shows the performance of the model at different concentrations of the inducer (3OC12HSL=5E-10, 5E-07, 5E-07).

This model is hosted on BioModels Database and identifiedby: MODEL1301110000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3747, + "tag": "BioModels:BIOMD0000000438" + }, + { + "id": 3748, + "tag": "Quorum sensing" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:56.883471+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000438", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2729": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2729, + "name": "Smith2009 - RGS mediated GTP hydrolysis", + "repository_type": "biomodels", + "summary": "
Smith2009 - RGS mediated GTP hydrolysis

This model is described in the article:

Smith B, Hill C, Godfrey EL, Rand D, van den Berg H, Thornton S, Hodgkin M, Davey J, Ladds G.
Cell Signal. 2009 Jul;21(7):1151-60.

Abstract:

G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increases the intrinsic GTPase activity of Galpha-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where Galpha(GTP) enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the Galpha produces this unimodal relationship.

To reproduce dose-response plots in the publication, the model is simulated with 12 different concentrations (see parameter Ligand_conc). For each concentration, a single value must be obtained from the integral of the trajectory of species z3 from time=0 to time=30. These values are then used to build a dose-response plot (authors used GraphPad Prism). Mutant strains are simulated with alternative parameter values or initial conditions in Table S3.

This model is hosted on BioModels Database and identified by: MODEL1212040001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3749, + "tag": "BioModels:BIOMD0000000439" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2986, + "tag": "Schizosaccharomyces pombe" + } + ], + "timestamp_created": "2025-01-30 13:35:57.357041+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000439", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2730": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2730, + "name": "Sarma2012 - Oscillations in MAPK cascade (S1)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S1)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S1 that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3750, + "tag": "BioModels:BIOMD0000000440" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:57.833370+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000440", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2731": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2731, + "name": "Sarma2012 - Oscillations in MAPK cascade (S2)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S2)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S2 that comprises negative feedback from MK-PP to MKK_PP layer coupled to positive feedback from MK-PP to MKKK-P layer.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190004 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3751, + "tag": "BioModels:BIOMD0000000441" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:58.300393+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000441", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2732": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2732, + "name": "Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling module", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S2), inclusion of external signalling module

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. This model corresponds to model S2 that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer. In addition, a positive feedback loop from S2 to the phosphorylation step of Kinase X has been introduced.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190005 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3752, + "tag": "BioModels:BIOMD0000000442" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:58.955439+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000442", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2733": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2733, + "name": "Sarma2012 - Oscillations in MAPK cascade (S1n)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S1n)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. Further these models were extended to S1n and S2n, to incorporate the nuclear-cytoplasmic translocation of the MK layer components of the cascade. This model corresponds to model S1n that comprises negative feedback from MK-PP to MKKK-P layer coupled to positive feedback from MK-PP to MKK-PP layer, with the inclusion of nuclear-cytoplasmic translocation.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190006 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3753, + "tag": "BioModels:BIOMD0000000443" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:59.494042+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000443", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2734": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2734, + "name": "Sarma2012 - Oscillations in MAPK cascade (S2n)", + "repository_type": "biomodels", + "summary": "
Sarma2012 - Oscillations in MAPK cascade (S2n)

Two plausible designs (S1 and S2) of coupled positive and negative feedback loops of MAPK cascade has been described in this paper. Further these models were extended to S1n and S2n, to incorporate the nuclear-cytoplasmic translocation of the MK layer components of the cascade. This model corresponds to model S2n that comprises negative feedback from MK-PP to MKK-PP layer coupled to positive feedback from MK-PP to MKKK-PP layer, with the inclusion of nuclear-cytoplasmic translocation.

This model is described in the article:

Sarma U, Ghosh I.
BMC Res Notes. 2012 Jun 13;5:287.

Abstract:

BACKGROUND:
Feedback loops, both positive and negative are embedded in the Mitogen Activated Protein Kinase (MAPK) cascade. In the three layer MAPK cascade, both feedback loops originate from the terminal layer and their sites of action are either of the two upstream layers. Recent studies have shown that the cascade uses coupled positive and negative feedback loops in generating oscillations. Two plausible designs of coupled positive and negative feedback loops can be elucidated from the literature; in one design the positive feedback precedes the negative feedback in the direction of signal flow and vice-versa in another. But it remains unexplored how the two designs contribute towards triggering oscillations in MAPK cascade. Thus it is also not known how amplitude, frequency, robustness or nature (analogous/digital) of the oscillations would be shaped by these two designs.

RESULTS:
We built two models of MAPK cascade that exhibited oscillations as function of two underlying designs of coupled positive and negative feedback loops. Frequency, amplitude and nature (digital/analogous) of oscillations were found to be differentially determined by each design. It was observed that the positive feedback emerging from an oscillating MAPK cascade and functional in an external signal processing module can trigger oscillations in the target module, provided that the target module satisfy certain parametric requirements. The augmentation of the two models was done to incorporate the nuclear-cytoplasmic shuttling of cascade components followed by induction of a nuclear phosphatase. It revealed that the fate of oscillations in the MAPK cascade is governed by the feedback designs. Oscillations were unaffected due to nuclear compartmentalization owing to one design but were completely abolished in the other case.

CONCLUSION:
The MAPK cascade can utilize two distinct designs of coupled positive and negative feedback loops to trigger oscillations. The amplitude, frequency and robustness of the oscillations in presence or absence of nuclear compartmentalization were differentially determined by two designs of coupled positive and negative feedback loops. A positive feedback from an oscillating MAPK cascade was shown to induce oscillations in an external signal processing module, uncovering a novel regulatory aspect of MAPK signal processing.

This model is hosted on BioModels Database and identifiedby: MODEL1112190007 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3754, + "tag": "BioModels:BIOMD0000000444" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:35:59.974831+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000444", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2735": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2735, + "name": "Pokhilko2013 - TOC1 signalling in Arabidopsis circadian clock", + "repository_type": "biomodels", + "summary": "
Pokhilko2013 - TOC1 signalling in Arabidopsiscircadian clock

In this model, Pokhilko et al. has incorporated the negative transcriptional regulations of the core clock genes by TOC1 and the up-regulation of TOC1 expression by ABA signalling, to their previous model BIOMD0000000412

This model is described in the article:

Pokhilko A, Mas P, Millar AJ.
BMC Syst Biol 2013; 7: 23

Abstract:

BACKGROUND: 24-hour biological clocks are intimately connected to the cellular signalling network, which complicates the analysis of clock mechanisms. The transcriptional regulator TOC1 (TIMING OF CAB EXPRESSION 1) is a founding component of the gene circuit in the plant circadian clock. Recent results show that TOC1 suppresses transcription of multiple target genes within the clock circuit, far beyond its previously-described regulation of the morning transcription factors LHY (LATE ELONGATED HYPOCOTYL) and CCA1 (CIRCADIAN CLOCK ASSOCIATED 1). It is unclear how this pervasive effect of TOC1 affects the dynamics of the clock and its outputs. TOC1 also appears to function in a nested feedback loop that includes signalling by the plant hormone Abscisic Acid (ABA), which is upregulated by abiotic stresses, such as drought. ABA treatments both alter TOC1 levels and affect the clock's timing behaviour. Conversely, the clock rhythmically modulates physiological processes induced by ABA, such as the closing of stomata in the leaf epidermis. In order to understand the dynamics of the clock and its outputs under changing environmental conditions, the reciprocal interactions between the clock and other signalling pathways must be integrated. RESULTS: We extended the mathematical model of the plant clock gene circuit by incorporating the repression of multiple clock genes by TOC1, observed experimentally. The revised model more accurately matches the data on the clock's molecular profiles and timing behaviour, explaining the clock's responses in TOC1 over-expression and toc1 mutant plants. A simplified representation of ABA signalling allowed us to investigate the interactions of ABA and circadian pathways. Increased ABA levels lengthen the free-running period of the clock, consistent with the experimental data. Adding stomatal closure to the model, as a key ABA- and clock-regulated downstream process allowed to describe TOC1 effects on the rhythmic gating of stomatal closure. CONCLUSIONS: The integrated model of the circadian clock circuit and ABA-regulated environmental sensing allowed us to explain multiple experimental observations on the timing and stomatal responses to genetic and environmental perturbations. These results crystallise a new role of TOC1 as an environmental sensor, which both affects the pace of the central oscillator and modulates the kinetics of downstream processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000445.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3755, + "tag": "BioModels:BIOMD0000000445" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:00.459329+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000445", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2736": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2736, + "name": "Erguler2013 - Unfolded protein stress response", + "repository_type": "biomodels", + "summary": "
Erguler2013 - Unfolded protein stress response

The model investigates the mechanism by which UPR (unfolded protein response) outcome switches between survival and death.

This model is described in the article:

Erguler K, Pieri M, Deltas C.
BMC Syst Biol. 2013 Feb 21;7(1):16.

Abstract:

BACKGROUND: The unfolded protein response (UPR) is a major signalling cascade acting in the quality control ofprotein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory rolein a range of genetic and environmental disorders including neurodegenerative and cardiovasculardiseases, diabetes and kidney diseases. The three major receptors of the ER stress involved withthe UPR, i.e. IRE1a, PERK and ATF6, signal through a complex web of pathways to convey anappropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on theseverity of unfolded protein accumulation in the ER; however, the decision mechanism for the switchand its timing have so far been poorly understood.

RESULTS:Here, we propose a mechanism by which the UPR outcome switches between survival and death.We compose a mathematical model integrating the three signalling branches, and perform a comprehensivebifurcation analysis to investigate possible responses to stimuli. The analysis reveals threedistinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance,and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understoodas a dynamic process where the balance between the stress and the folding capacity of the ER playsa pivotal role in managing the delivery of the most appropriate response. The model demonstratesfor the first time that the UPR is capable of generating oscillations in translation attenuation and theapoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set ofparameters controlling this behaviour.

CONCLUSIONS:This work contributes largely to the understanding of one of the most ubiquitous signalling pathwaysinvolved in protein folding quality control in the metazoan ER. The insights gained have direct consequenceson the management of many UPR-related diseases, revealing, in addition, an extended listof candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioningmight be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the wayto novel approaches for the treatment of many UPR-related conditions.

In the paper, PERKA refers to the amount of phosphorylated PERK monomer. However, it refers to the active complex in the model. The complex with the model parameterization is formed of 4 monomers (n=4). So, the value of PERKA should be multiplied by 4, in order to generate the figures in the paper (eg. Figure 12).

An additional parameter (tmr=10)) is used in the model. This parameter is not mentioned in the paper. The model values of kf(=10) and kr(=1) are not consistent with that of the paper (kf=100, kr=10, in the paper). However, this is corrected by the introduction of \"tmr\" in the model, which is multiplied with kf and kr to get the resulting values.

The term \"tmr\" was missing in the kinetic laws of the reactions reu7 and reu8, in the original model. This has been corrected as per the author's request.

This model is hosted on BioModels Database and identifiedby: MODEL1302180000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3756, + "tag": "BioModels:BIOMD0000000446" + }, + { + "id": 3757, + "tag": "Endoplasmic reticulum unfolded protein response" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:01.046864+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000446", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2737": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2737, + "name": "Venkatraman2012 - Interplay between PLS and TSP1 in TGF-\u03b21 activation", + "repository_type": "biomodels", + "summary": "
Venkatraman2012 - Interplay between PLS and TSP1 in TGF-\u03b21 activation

The interplay between PLS (Plasmin) and TSP1 (Thrombospondin-1) in TGF-\u03b21 (Transforming growth factor-\u03b21)is shown using mathematical modelling and in vitro experimentents.

This model is described in the article:

Venkatraman L, Chia SM, Narmada BC, White JK, Bhowmick SS, Forbes Dewey C Jr, So PT, Tucker-Kellogg L, Yu H.
Biophys J. 2012 Sep 5;103(5):1060-8.

Abstract:

Transforming growth factor-\u03b21 (TGF-\u03b21) is a potent regulator of extracellular matrix production, wound healing, differentiation, and immune response, and is implicated in the progression of fibrotic diseases and cancer. Extracellular activation of TGF-\u03b21 from its latent form provides spatiotemporal control over TGF-\u03b21 signaling, but the current understanding of TGF-\u03b21 activation does not emphasize cross talk between activators. Plasmin (PLS) and thrombospondin-1 (TSP1) have been studied individually as activators of TGF-\u03b21, and in this work we used a systems-level approach with mathematical modeling and in vitro experiments to study the interplay between PLS and TSP1 in TGF-\u03b21 activation. Simulations and steady-state analysis predicted a switch-like bistable transition between two levels of active TGF-\u03b21, with an inverse correlation between PLS and TSP1. In particular, the model predicted that increasing PLS breaks a TSP1-TGF-\u03b21 positive feedback loop and causes an unexpected net decrease in TGF-\u03b21 activation. To test these predictions in vitro, we treated rat hepatocytes and hepatic stellate cells with PLS, which caused proteolytic cleavage of TSP1 and decreased activation of TGF-\u03b21. The TGF-\u03b21 activation levels showed a cooperative dose response, and a test of hysteresis in the cocultured cells validated that TGF-\u03b21 activation is bistable. We conclude that switch-like behavior arises from natural competition between two distinct modes of TGF-\u03b21 activation: a TSP1-mediated mode of high activation and a PLS-mediated mode of low activation. This switch suggests an explanation for the unexpected effects of the plasminogen activation system on TGF-\u03b21 in fibrotic diseases in vivo, as well as novel prognostic and therapeutic approaches for diseases with TGF-\u03b2 dysregulation.

This model is hosted on BioModels Database and identifiedby: MODEL1303130000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3758, + "tag": "BioModels:BIOMD0000000447" + }, + { + "id": 3087, + "tag": "Rattus rattus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3759, + "tag": "Thrombospondin receptor activity" + }, + { + "id": 3760, + "tag": "Transforming growth factor beta activation" + } + ], + "timestamp_created": "2025-01-30 13:36:01.553168+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000447", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2738": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2738, + "name": "Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (normal condition)", + "repository_type": "biomodels", + "summary": "
Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (normal condition)

The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under normal condtion

This model is described in the article:

Br\u00e4nnmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Str\u00e5lfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.

Abstract:

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

This model is hosted on BioModels Database and identifiedby: MODEL1304190000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3761, + "tag": "BioModels:BIOMD0000000448" + }, + { + "id": 3762, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4052, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:36:02.059770+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000448", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2739": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2739, + "name": "Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (diabetic condition)", + "repository_type": "biomodels", + "summary": "
Br\u00e4nnmark2013 - Insulin signalling in human adipocytes (diabetic condition)

The paper describes insulin signalling in human adipocytes under normal and diabetic states using mathematical models based on experimental data. This model corresponds to insulin signalling under diabetic condtion

This model is described in the article:

Br\u00e4nnmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, Str\u00e5lfors P.
J Biol Chem. 2013 Apr 5;288(14):9867-80.

Abstract:

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

This model is hosted on BioModels Database and identifiedby: MODEL1304160000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3763, + "tag": "BioModels:BIOMD0000000449" + }, + { + "id": 3762, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4052, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:36:02.767464+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000449", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2740": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2740, + "name": "Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version2", + "repository_type": "biomodels", + "summary": "
Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version2

Mammalian polyamine metabolism consists of a bi-cycle with two required entrances, omithine and S-adenosyl methionine (SAM), and several alternative exists. The relevant regulatory roles of the short half-life enzymes ornithine decarboxylase (ODC), S-adenosyl methione decarboxylase (SAMDC) and spermindine/spermine acetyl transferase (SSAT) in polyamine metabolism are well studied, and has been modelled here.

This model is described in the article:

Reyes-Palomares A, Monta\u00f1ez R, S\u00e1nchez-Jim\u00e9nez F, Medina MA
Amino Acids, February 2012, Volume 42, Issue 2-3, pp 597-610

Abstract:

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

Notes by the author:

This model combines BIOMD0000000190 and BIOMD0000000268 from BioModels Database, both models include corrections respect to their originals publications.

To simulate a MATI/MATIII switch to MATII in proliferating liver:

We set to 0 the Vmax parameters of MATI and MATIII

We included MATII reaction equation.

We add a regulation factor dependent of SAM levels in ODC and SAMDCe rates of synthesis (66.5/[SAM]).

H2O2 was increased in a 50 % according to an initial state ofproliferating and regenerating liver.

This model is hosted on BioModels Database and identifiedby: MODEL1305060001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3764, + "tag": "BioModels:BIOMD0000000450" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:03.286166+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000450", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2741": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2741, + "name": "Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity", + "repository_type": "biomodels", + "summary": "
Carbo2013 - Cytokine driven CD4+ T Cell differentiation and phenotype plasticity

CD4+ T cells can differentiate into different phenotypes depending on the cytokine milieu. Here a computational and mathematical model with sixty ordinary differential equations representing a CD4+ T cell differentiating into either Th1, Th2, Th17 or iTreg cells, has been constructed. The model includes cytokines, nuclear receptors and transcription factors that define fate and function of CD4+ T cells. Computational simulations illustrate how a proinflammatory Th17 cell can undergo reprogramming into an anti-inflammatory iTreg phenotype following PPARc activation.

This model is described in the article:

Carbo A, Hontecillas R, Kronsteiner B, Viladomiu M, Pedragosa M, Lu P, Philipson CW, Hoops S, Marathe M, Eubank S, Bisset K, Wendelsdorf K, Jarrah A, Mei Y, Bassaganya-Riera J
PLoS Computational Biology [2013, 9(4):e1003027]

Abstract:

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPAR\u03b3) in modulating plasticity between Th17 and iTreg cells. PPAR\u03b3 regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPAR\u03b3 activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPAR\u03b3. Deletion of PPAR\u03b3 in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPAR\u03b3 in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

Author's comment: CD4+ T cell computational model (Version 1.4)Steady state corrected. There was a problem in the internalization of IL-17 in its mathematical function.

This model is hosted on BioModels Database and identifiedby: MODEL1304230001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3765, + "tag": "BioModels:BIOMD0000000451" + }, + { + "id": 3766, + "tag": "CD4 biosynthetic process" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3368, + "tag": "T cell differentiation" + }, + { + "id": 3810, + "tag": "Bacterial infectious disease" + } + ], + "timestamp_created": "2025-01-30 13:36:04.013099+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000451", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2742": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2742, + "name": "Bidkhori2012 - normal EGFR signalling", + "repository_type": "biomodels", + "summary": "
Bidkhori2012 - normal EGFR signalling

The paper describes and compares two models on EGFR signalling between normal and NSCLC cells. Moreover, it is shown that ERK (MAPK), STAT and Akt factor's activation pattern are different between normal and NSCLA models. This model corresponds to EGFR signalling in normal cells.

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

This model is described in the article:

Bidkhori G, Moeini A, Masoudi-Nejad A
PloS one [2012, 7(10):e48004]

Abstract:

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

This model is hosted on BioModels Database and identifiedby: MODEL1304020000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3767, + "tag": "BioModels:BIOMD0000000452" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:04.534407+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000452", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2743": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2743, + "name": "Bidkhori2012 - EGFR signalling in NSCLC", + "repository_type": "biomodels", + "summary": "
Bidkhori2012 - EGFR signalling in NSCLC

The paper describes and compares two models on EGFR signalling between normal and NSCLC cells. Moreover, it is shown that ERK (MAPK), STAT and Akt factor's activation pattern are different between normal and NSCLA models. This model corresponds to EGFR signalling in NSCLA cells.

Created by The MathWorks, Inc. SimBiology tool, Version 3.3

This model is described in the article:

Bidkhori G, Moeini A, Masoudi-Nejad A
PloS one [2012, 7(10):e48004]

Abstract:

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

This model is hosted on BioModels Database and identifiedby: MODEL1304020001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3768, + "tag": "BioModels:BIOMD0000000453" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + }, + { + "id": 4615, + "tag": "Non-small cell lung carcinoma" + } + ], + "timestamp_created": "2025-01-30 13:36:05.011778+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000453", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2744": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2744, + "name": "Smallbone2013 - Metabolic Control Analysis - Example 1", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Metabolic Control Analysis - Example 1

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3769, + "tag": "BioModels:BIOMD0000000454" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:05.480763+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000454", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2745": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2745, + "name": "Smallbone2013 - Metabolic Control Analysis - Example 2", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Metabolic Control Analysis - Example 2

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3770, + "tag": "BioModels:BIOMD0000000455" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:05.947683+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000455", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2746": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2746, + "name": "Smallbone2013 - Metabolic Control Analysis - Example 3", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Metabolic Control Analysis - Example 3

Metabolic control analysis (MCA) is a biochemical formalism, defining how variables, such as fluxes and concentrations, depend on network parameters. In this paper, owing to its limitations, it is shown with three example models (MODEL1305030000-2) that the algorithm with slight modification can be applied to all models.

This model is described in the article:

Kieran Smallbone
Quantitative Methods; Tue, 28 May 2013.

Abstract:

Metabolic control analysis is a biochemical formalism defined by Kacser and Burns in 1973, and given firm mathematical basis by Reder in 1988. The algorithm defined by Reder for calculating the control matrices is still used by software programs today, but is only valid for some biochemical models. We show that, with slight modification, the algorithm may be applied to all models.

This model is hosted on BioModels Database and identifiedby: MODEL1305030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3771, + "tag": "BioModels:BIOMD0000000456" + }, + { + "id": 3554, + "tag": "Regulation of binding" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:06.454372+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000456", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2747": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2747, + "name": "Firczuk2013 - Eukaryotic mRNA translation machinery", + "repository_type": "biomodels", + "summary": "
Firczuk2013 - Eukaryotic mRNA translation machinery

This is a model of Saccharomyces cerevisiae mRNA translation which includes the initiation, elongation and termination phases. The model is for 20 condon mRNAs. The building of a multi-factor complex in initiation and also the different processes in elongation and termination are modelled in detail. The model takes into account that ribosomes cover more than one codon of mRNA so that the movement of ribosomes are effectively blocked by other ribosomes several codons downstream. It is assumed that 15 codons are occupied by each ribosome. This blocking effect is considered in reaction R18 in initiation and also reaction R26, the reaction where translocation of ribosomes takes place in elongation. The kinetic functions of these two reactions are based on MacDonald et al. 1968 and Heinrich & Rapaport 1980. All other kinetic functions follow mass-action kinetics. The concentrations of transfer RNA species (Met-tRNA, aa-tRNA and tRNA in the model) are kept constant, while the other species' concentrations can change in the course of the simulation. The model describes the translation of a short mRNA with 20 codons. Therefore, all reactions in the elongation cycle (R22, R23, R25, R26, R28 and R29) and the corresponding species are replicated accordingly to model the species with ribosomes bound at different positions. In summary, the model contains 165 different species and 141 reactions.

The value of the 56 rate constant parameters were estimated by fitting the model against a series of experimental data consisting of modulation of the various translation factors (Figures 2, 3 and S3). Overall the parameter estimation was carried out over 212 different data points (steady states).

This model is described in the article:

Helena Firczuk, Shichina Kannambath, J\u00fcrgen Pahle, Amy Claydon, Robert Beynon, John Duncan, Hans Westerhoff, Pedro Mendes and John EG McCarthy
Molecular Systems Biology. 9:635

Abstract:

Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNAi to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5\u2032untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5\u2032UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.

This model is hosted on BioModels Database and identified by: BIOMD0000000457 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3772, + "tag": "BioModels:BIOMD0000000457" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + }, + { + "id": 3773, + "tag": "Translation" + } + ], + "timestamp_created": "2025-01-30 13:36:06.943950+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000457", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2748": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2748, + "name": "Smallbone2013 - Serine biosynthesis", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Serine biosynthesis

Kinetic modelling of metabolic pathways in application to Serine biosynthesis.

This model is described in the article:

Kieran Smallbone, Natalie J. Stanford
Methods in Molecular Biology. 2013; 985:113-121

Abstract:

In this chapter, we describe the steps needed to create a kinetic model of a metabolic pathway using kinetic data from both experimental measurements and literature review. Our methodology is presented by using the example of serine biosynthesis in E. coli.

As there are no plots to be reproduced as curation figure, table 6 and 7 that corresponds to steady state concentration of metabolite and steady state fluxes of reactions has been reproduced.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000458 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3774, + "tag": "BioModels:BIOMD0000000458" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 3775, + "tag": "L-serine biosynthetic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:07.416664+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000458", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2749": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2749, + "name": "Liebal2012 - B.subtilis post-transcriptional instability model", + "repository_type": "biomodels", + "summary": "
Liebal2012 - B.subtilis post-transcription instability model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the post-transcription instability model (MODEL1302080001).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1302080001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3412, + "tag": "Bacillus subtilis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3776, + "tag": "BioModels:BIOMD0000000459" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:07.957855+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000459", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2750": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2750, + "name": "Liebal2012 - B.subtilis sigB proteolysis model", + "repository_type": "biomodels", + "summary": "
Liebal2012 - B.subtilis sigB proteolysis model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models were constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the sigma B proteolysis model (MODEL1302080000).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1302080000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3412, + "tag": "Bacillus subtilis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3777, + "tag": "BioModels:BIOMD0000000460" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:08.414878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000460", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2751": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2751, + "name": "Liebal2012 - B.subtilis transcription inhibition model", + "repository_type": "biomodels", + "summary": "
Liebal2012 - B.subtilis transcription inhibition model

An important transcription factor of B.subsilis is sigma B . Liebal et al. (2012) have performed experiments in B.subtilis wild type and mutant straits to test and validate a mathematical model of the dynamics of sigma B activity. The following three models are constructed and their ability to fit the experimental data were tested. 1) Transcription inhibition model (MODEL1212180000), 2) sigma B proteolysis model (MODEL1302080000) and 3) Post-transcriptional instability model (MODEL1302080001). This model corresponds to the Transcription inhibition model (MODEL1212180000).

This model is described in the article:

Liebal UW, Sappa PK, Millat T, Steil L, Homuth G, V\u00f6lker U, Wolkenhauer O.
2012 Jun;8(6):1806-14.

Abstract:

In Bacillus subtilis the \u03c3(B) mediated general stress response provides protection against various environmental and energy related stress conditions. To better understand the general stress response, we need to explore the mechanism by which the components interact. Here, we performed experiments in B. subtilis wild type and mutant strains to test and validate a mathematical model of the dynamics of \u03c3(B) activity. In the mutant strain BSA115, \u03c3(B) transcription is inducible by the addition of IPTG and negative control of \u03c3(B) activity by the anti-sigma factor RsbW is absent. In contrast to our expectations of a continuous \u03b2-galactosidase activity from a ctc::lacZ fusion, we observed a transient activity in the mutant. To explain this experimental finding, we constructed mathematical models reflecting different hypotheses regarding the regulation of \u03c3(B) and \u03b2-galactosidase dynamics. Only the model assuming instability of either ctc::lacZ mRNA or \u03b2-galactosidase protein is able to reproduce the experiments in silico. Subsequent Northern blot experiments revealed stable high-level ctc::lacZ mRNA concentrations after the induction of the \u03c3(B) response. Therefore, we conclude that protein instability following \u03c3(B) activation is the most likely explanation for the experimental observations. Our results thus support the idea that B. subtilis increases the cytoplasmic proteolytic degradation to adapt the proteome in face of environmental challenges following activation of the general stress response. The findings also have practical implications for the analysis of stress response dynamics using lacZ reporter gene fusions, a frequently used strategy for the \u03c3(B) response.

Figure 3a of the reference article has been reproduced. beta-galactosidase (lacz in model) activity at different concentrations of IPTG (100M, 200M and 1000M) has been reproduced. SED-ML (Simulation Experiment Description Markup Language) file is available for this model (see curation tab).

This model is hosted on BioModels Database and identifiedby: MODEL1212180000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3412, + "tag": "Bacillus subtilis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3778, + "tag": "BioModels:BIOMD0000000461" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:08.894251+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000461", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2752": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2752, + "name": "Proctor2012 - Role of Amyloid-beta dimers in aggregation formation", + "repository_type": "biomodels", + "summary": "
Proctor2012 - Amyloid-beta aggregation

This model supports the current thinking that levels of dimers are important in initiating the aggregation process.

This model is described in the article:

Proctor CJ, Pienaar IS, Elson JL, Kirkwood TB
Molecular Neurodegeneration. 2012; 7:32

Abstract:

BACKGROUND: Alzheimer's disease (AD) is the most frequently diagnosed neurodegenerative disorder affecting humans, with advanced age being the most prominent risk factor for developing AD. Despite intense research efforts aimed at elucidating the precise molecular underpinnings of AD, a definitive answer is still lacking. In recent years, consensus has grown that dimerisation of the polypeptide amyloid-beta (A\u00df), particularly A\u00df\u2084\u2082, plays a crucial role in the neuropathology that characterise AD-affected post-mortem brains, including the large-scale accumulation of fibrils, also referred to as senile plaques. This has led to the realistic hope that targeting A\u00df\u2084\u2082 immunotherapeutically could drastically reduce plaque burden in the ageing brain, thus delaying AD onset or symptom progression. Stochastic modelling is a useful tool for increasing understanding of the processes underlying complex systems-affecting disorders such as AD, providing a rapid and inexpensive strategy for testing putative new therapies. In light of the tool's utility, we developed computer simulation models to examine A\u00df\u2084\u2082 turnover and its aggregation in detail and to test the effect of immunization against A\u00df dimers.

RESULTS: Our model demonstrates for the first time that even a slight decrease in the clearance rate of A\u00df\u2084\u2082 monomers is sufficient to increase the chance of dimers forming, which could act as instigators of protofibril and fibril formation, resulting in increased plaque levels. As the process is slow and levels of A\u03b2 are normally low, stochastic effects are important. Our model predicts that reducing the rate of dimerisation leads to a significant reduction in plaque levels and delays onset of plaque formation. The model was used to test the effect of an antibody mediated immunological response. Our results showed that plaque levels were reduced compared to conditions where antibodies are not present.

CONCLUSION: Our model supports the current thinking that levels of dimers are important in initiating the aggregation process. Although substantial knowledge exists regarding the process, no therapeutic intervention is on offer that reliably decreases disease burden in AD patients. Computer modelling could serve as one of a number of tools to examine both the validity of reliable biomarkers and aid the discovery of successful intervention strategies.

This model is hosted on BioModels Database and identified by: BIOMD0000000462 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3779, + "tag": "Amyloid-beta clearance" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3780, + "tag": "BioModels:BIOMD0000000462" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:09.370135+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000462", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2753": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2753, + "name": "Heldt2012 - Influenza Virus Replication", + "repository_type": "biomodels", + "summary": "
Heldt2012 - Influenza Virus Replication

The model describes the life cycle of influenza A virus in a mammalian cell including the following steps: attachment of parental virions to the cell membrane, receptor-mediated endocytosis, fusion of the virus envelope with the endosomal membrane, nuclear import of vRNPs, viral transcription and replication, translation of the structural viral proteins, nuclear export of progeny vRNPs and budding of new virions. It also explicitly accounts for the stabilization of cRNA by viral polymerases and NP and the inhibition of vRNP activity by M1 protein binding. In short, the model focuses on the molecular mechanism that controls viral transcription and replication.

This model is described in the article:

Heldt FS, Frensing T, Reichl U.
J Virol.

Abstract:

Influenza viruses transcribe and replicate their negative-sense RNA genome inside the nucleus of host cells via three viral RNA species. In the course of an infection, these RNAs show distinct dynamics, suggesting that differential regulation takes place. To investigate this regulation in a systematic way, we developed a mathematical model of influenza virus infection at the level of a single mammalian cell. It accounts for key steps of the viral life cycle, from virus entry to progeny virion release, while focusing in particular on the molecular mechanisms that control viral transcription and replication. We therefore explicitly consider the nuclear export of viral genome copies (vRNPs) and a recent hypothesis proposing that replicative intermediates (cRNA) are stabilized by the viral polymerase complex and the nucleoprotein (NP). Together, both mechanisms allow the model to capture a variety of published data sets at an unprecedented level of detail. Our findings provide theoretical support for an early regulation of replication by cRNA stabilization. However, they also suggest that the matrix protein 1 (M1) controls viral RNA levels in the late phase of infection as part of its role during the nuclear export of viral genome copies. Moreover, simulations show an accumulation of viral proteins and RNA toward the end of infection, indicating that transport processes or budding limits virion release. Thus, our mathematical model provides an ideal platform for a systematic and quantitative evaluation of influenza virus replication and its complex regulation.

With the current parameter set, the model reproduces an infection at a multiplicity of infection (MOI) of 10. Figure 2A of the paper is reproduced here, with parameters kDegRnp and kSynP changed to zeros.

Initial conditions and parameter changes that were used to obtain specific figures in the article can be found in Table A2.

The model has the correct value for kAttLo as 4.55e-04. The value of this parameter mentioned as 4.55e-02 in Table 1 of the paper is incorrect. This is checked with the author.

This model is hosted on BioModels Database and identifiedby: MODEL1307270000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

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Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3785, + "tag": "BioModels:BIOMD0000000464" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3786, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3787, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:10.733081+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000464", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2755": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2755, + "name": "Koo2013 - Shear stress induced AKT and eNOS phosphorylation - Model 2", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3788, + "tag": "BioModels:BIOMD0000000465" + }, + { + "id": 3789, + "tag": "ENOS synthesizes NO" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3786, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3787, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 3790, + "tag": "Response to fluid shear stress" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:11.246343+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000465", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2756": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2756, + "name": "Koo2013 - Shear stress induced eNOS expression - Model 3", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3791, + "tag": "BioModels:BIOMD0000000466" + }, + { + "id": 3789, + "tag": "ENOS synthesizes NO" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3786, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3787, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 3790, + "tag": "Response to fluid shear stress" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:11.792839+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000466", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2757": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2757, + "name": "Koo2013 - Shear stress induced NO production - Model 4", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3792, + "tag": "BioModels:BIOMD0000000467" + }, + { + "id": 3789, + "tag": "ENOS synthesizes NO" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3786, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3787, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:12.281790+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000467", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2758": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2758, + "name": "Koo2013 - Integrated shear stress induced NO production model", + "repository_type": "biomodels", + "summary": "Andrew Koo, David Nordsletten, Renato Umeton, Beracah Yankama, Shiva Ayyadurai, Guillermo Garc\u00eda-Carde\u00f1a & C.\u00a0Forbes Dewey. In silico modeling of shear-stress-induced nitric oxide production in endothelial cells through systems biology. Biophysical Journal 104, 10 (2013).

Nitric oxide (NO) produced by vascular endothelial cells is a potent vasodilator and an antiinflammatory mediator. Regulating production of endothelial-derived NO is a complex undertaking, involving multiple signaling and genetic pathways that are activated by diverse humoral and biomechanical stimuli. To gain a thorough understanding of the rich diversity of responses observed experimentally, it is necessary to account for an ensemble of these pathways acting simultaneously. In\u00a0this article, we have assembled four quantitative molecular pathways previously proposed for shear-stress-induced NO production. In these pathways, endothelial NO synthase is activated 1), via calcium release, 2), via phosphorylation reactions, and\u00a03), via enhanced protein expression. To these activation pathways, we have added a fourth, a pathway describing actual NO production from endothelial NO synthase and its various protein partners. These pathways were combined and simulated using CytoSolve, a computational environment for combining independent pathway calculations. The integrated model is able to\u00a0describe the experimentally observed change in NO production with time after the application of fluid shear stress. This model can also be used to predict the specific effects on the system after interventional pharmacological or genetic changes. Importantly, this model reflects the up-to-date understanding of the NO system, providing a platform upon which information can be aggregated in an additive way.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3793, + "tag": "BioModels:BIOMD0000000468" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3786, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 3787, + "tag": "Nitric oxide production involved in inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:12.744474+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000468", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2759": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2759, + "name": "Smallbone2013 - E.coli metabolic model with linlog rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3794, + "tag": "BioModels:BIOMD0000000469" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 3796, + "tag": "Organic substance biosynthetic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:13.210657+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000469", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2760": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2760, + "name": "Smallbone2013 - E.coli metabolic model with modular rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3797, + "tag": "BioModels:BIOMD0000000470" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:13.724313+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000470", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2761": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2761, + "name": "Smallbone2013 - Yeast metabolic model with linlog rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3798, + "tag": "BioModels:BIOMD0000000471" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3799, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-01-30 13:36:14.194619+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000471", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2762": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2762, + "name": "Smallbone2013 - Yeast metabolic model with modular rate law", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3800, + "tag": "BioModels:BIOMD0000000472" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3799, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-01-30 13:36:14.762443+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000472", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2763": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2763, + "name": "Smallbone2013 - Yeast metabolic model with modular rate law, merged with Pritchard 2002", + "repository_type": "biomodels", + "summary": "Kieran Smallbone & Pedro Mendes. Large-Scale Metabolic Models: From Reconstruction to Differential Equations. Industrial Biotechnology 9, 4 (2013).

Genome-scale kinetic models of metabolism are important for rational design of the metabolic engineering required for industrial biotechnology applications. They allow one to predict the alterations needed to optimize the flux or yield of the compounds of interest, while keeping the other functions of the host organism to a minimal, but essential, level. We define a pipeline for the generation of genome-scale kinetic models from reconstruction data. To build such a model, inputs of all concentrations, fluxes, rate laws, and kinetic parameters are required. However, we propose typical estimates for these numbers when experimental data are not available. While little data are required to produce the model, the pipeline ensures consistency with any known flux or concentration data, or any kinetic constants. We apply the method to create genome-scale models of Escherichia coli and Saccharomyces cerevisiae. We go on to show how these may be used to expand a detailed model of yeast glycolysis to the genome level.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3801, + "tag": "BioModels:BIOMD0000000473" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3799, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-01-30 13:36:15.253375+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000473", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2764": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2764, + "name": "Smith2013 - Regulation of Insulin Signalling by Oxidative Stress", + "repository_type": "biomodels", + "summary": "
Smith2013 - Regulation of Insulin Signalling by Oxidative Stress

The model describes insulin signalling (in rodent adipocytes), which includes in addition to the core pathway, the transcriptional feedback through the Forkhead box type O (FOXO) transcription factor and interaction with oxidative stress.

This model is described in the article:

Smith GR, Shanley DP.
BMC Syst Biol. 2013 May 24;7:41.

Abstract:

BACKGROUND: Existing models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO.

RESULTS: Insulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear.

CONCLUSION: Our model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level.A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000.Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012_insulin_signalling.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000474 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3802, + "tag": "BioModels:BIOMD0000000474" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:36:15.758775+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000474", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2765": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2765, + "name": "Amara2013 - PCNA ubiquitylation in the activation of PRR pathway", + "repository_type": "biomodels", + "summary": " Mechanistic model of the Post-Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA). This model has been defined according to the stochastic formulation of chemical kinetics [Gillespie DT, J Phys Chem 1977, 81(25):2340-2361], which requires to specify the set of molecular species occurring in the pathway and their respective interactions, formally described as a set of biochemical reactions. The volume considered for this system is 1.666667e-17L; this value can be used to convert the model into the deterministic formulation. ", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3803, + "tag": "BioModels:BIOMD0000000475" + }, + { + "id": 3804, + "tag": "Postreplication repair" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:16.211183+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000475", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2766": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2766, + "name": "Adams2012 - Locke2006 Circadian Rhythm model refined with Input Signal Light Function", + "repository_type": "biomodels", + "summary": "

As per BIO0000000089.xml but including a functional light.

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Created by The MathWorks, Inc. SimBiology tool, Version 3.3

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3806, + "tag": "BioModels:BIOMD0000000477" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3807, + "tag": "Immune response-regulating signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4618, + "tag": "Leishmaniasis" + } + ], + "timestamp_created": "2025-01-30 13:36:17.281119+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000477", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2768": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2768, + "name": "Besozzi2012 - Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiae", + "repository_type": "biomodels", + "summary": "
Besozzi2012 - Oscillatory regimes in the Ras/cAMP/PKA pathway in S.cerevisiae

Mechanistic model of the Ras/cAMP/PKA in yeast S.cerevisiae. The Ras/cAMP/PKA pathway plays a major role in the regulation of metabolism, stress resistance and cell cycle progress and is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA). This model investigates the dynamics of the second messenger cAMP on Ras/cAMP/PKA pathway, to determine the effects of the feedback mechanisms on establising stable oscillatory regimes.

The model has been defined according to the stochastic formulation of chemical kinetics [Gillespie DT, 1977] , which requires to specify the set of molecular species occurring in the pathway and their respective interactions, formally described as a set of biochemical reactions.

The volume considered for this system is 30fL; this value can be used to convert the model into the deterministic formulation.

This model is described in the article:

Besozzi D, Cazzaniga P, Pescini D, Mauri G, Colombo S, Martegani E.
EURASIP J Bioinform Syst Biol. 2012 Jul 20;2012(1):10.

Abstract:

In the yeast Saccharomyces cerevisiae, the Ras/cAMP/PKA pathway is involved in the regulation of cell growth and proliferation in response to nutritional sensing and stress conditions. The pathway is tightly regulated by multiple feedback loops, exerted by the protein kinase A (PKA) on a few pivotal components of the pathway. In this article, we investigate the dynamics of the second messenger cAMP by performing stochastic simulations and parameter sweep analysis of a mechanistic model of the Ras/cAMP/PKA pathway, to determine the effects that the modulation of these feedback mechanisms has on the establishment of stable oscillatory regimes. In particular, we start by studying the role of phosphodiesterases, the enzymes that catalyze the degradation of cAMP, which represent the major negative feedback in this pathway. Then, we show the results on cAMP oscillations when perturbing the amount of protein Cdc25 coupled with the alteration of the intracellular ratio of the guanine nucleotides (GTP/GDP), which are known to regulate the switch of the GTPase Ras protein. This multi-level regulation of the amplitude and frequency of oscillations in the Ras/cAMP/PKA pathway might act as a fine tuning mechanism for the downstream targets of PKA, as also recently evidenced by some experimental investigations on the nucleocytoplasmic shuttling of the transcription factor Msn2 in yeast cells.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000478 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3808, + "tag": "BioModels:BIOMD0000000478" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:17.742125+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000478", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2769": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2769, + "name": "Croft2013 - GPCR-RGS interaction that compartmentalizes RGS activity", + "repository_type": "biomodels", + "summary": "
Croft2013 - GPCR-RGS interaction that compartmentalizes RGS activity

Through modelling studies, the classic quaternary complex (ligand-GPCR-G-RGS) has been extended to include an additional layer of regulation through GPCR-RGS interactions, which facilitate the compartmentalization of RGS activity into the plasma membrane and non-plasma compartments.

This model is described in the article:

Croft W, Hill C, McCann E, Bond M, Esparza-Franco M, Bennett J, Rand D, Davey J, Ladds G.
J Biol Chem. 2013 Sep 20;288(38):27327-42.

Abstract:

G protein-coupled receptors (GPCRs) can interact with regulator of G protein signaling (RGS) proteins. However, the effects of such interactions on signal transduction and their physiological relevance have been largely undetermined. Ligand-bound GPCRs initiate by promoting exchange of GDP for GTP on the G\u03b1 subunit of heterotrimeric G proteins. Signaling is terminated by hydrolysis of GTP to GDP through intrinsic GTPase activity of the G\u03b1 subunit, a reaction catalyzed by RGS proteins. Using yeast as a tool to study GPCR signaling in isolation, we define an interaction between the cognate GPCR (Mam2) and RGS (Rgs1), mapping the interaction domains. This reaction tethers Rgs1 at the plasma membrane and is essential for physiological signaling response. In vivo quantitative data inform the development of a kinetic model of the GTPase cycle, which extends previous attempts by including GPCR-RGS interactions. In vivo and in silico data confirm that GPCR-RGS interactions can impose an additional layer of regulation through mediating RGS subcellular localization to compartmentalize RGS activity within a cell, thus highlighting their importance as potential targets to modulate GPCR signaling pathways.

Author's comment on reproducing the plots:
To reproduce dose-response plots in the publication, the model is simulated with 12 different ligand concentrations (see parameter Ligand_conc).
For each ligand concentration, a single value corresponding to total amount of output must be obtained, by calculating the area under the curve of the trajectory of species z3, from time=0 to time=30.
These total output values are then used to build a dose-response plot (authors used GraphPad Prism).
Mutant strains are simulated with alternative parameter values or initial conditions specified in the Supplementary Material.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000479 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3809, + "tag": "BioModels:BIOMD0000000479" + }, + { + "id": 3287, + "tag": "Regulation of GTPase activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:18.204084+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000479", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2770": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2770, + "name": "Carbo2013 - Mucosal Immune Response during H.pylori Infection", + "repository_type": "biomodels", + "summary": " ", + "tags": [ + { + "id": 3810, + "tag": "Bacterial infectious disease" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3811, + "tag": "BioModels:BIOMD0000000480" + }, + { + "id": 3812, + "tag": "Defense response to Gram-negative bacterium" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3813, + "tag": "T cell mediated immunity" + } + ], + "timestamp_created": "2025-01-30 13:36:18.760643+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000480", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2771": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2771, + "name": "St\u00f6tzel2012 - Bovine estrous cycle, synchronization with prostaglandin F2\u03b1", + "repository_type": "biomodels", + "summary": "C. St\u00f6tzel, J. Pl\u00f6ntzke, W. Heuwieser & S. R\u00f6blitz. Advances in modeling of the bovine estrous cycle: synchronization with PGF2\u03b1. Theriogenology 78, 7 (2012).

Our model of the bovine estrous cycle is a set of ordinary differential equations which generates hormone profiles of successive estrous cycles with several follicular waves per cycle. It describes the growth and decay of the follicles and the corpus luteum, as well as the change of the key reproductive hormones, enzymes and processes over time. In this work we describe recent developments of this model towards the administration of prostaglandin F2\u03b1. We validate our model by showing that the simulations agree with observations from synchronization studies and with measured progesterone data after single dose administrations of synthetic prostaglandin F2\u03b1.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3814, + "tag": "BioModels:BIOMD0000000481" + }, + { + "id": 3337, + "tag": "Bos taurus" + }, + { + "id": 3815, + "tag": "Ovulation cycle" + }, + { + "id": 3816, + "tag": "Response to prostaglandin F" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4619, + "tag": "Ovarian disease" + } + ], + "timestamp_created": "2025-01-30 13:36:19.253895+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000481", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2772": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2772, + "name": "Noguchi2013 - Insulin dependent glucose metabolism", + "repository_type": "biomodels", + "summary": "Rei Noguchi, Hiroyuki Kubota, Katsuyuki Yugi, Yu Toyoshima, Yasunori Komori, Tomoyoshi Soga & Shinya Kuroda. The selective control of glycolysis, gluconeogenesis and glycogenesis by temporal insulin patterns. Molecular Systems Biology 9 (2013).

Insulin governs systemic glucose metabolism, including glycolysis, gluconeogenesis and glycogenesis, through temporal change and absolute concentration. However, how insulin-signalling pathway selectively regulates glycolysis, gluconeogenesis and glycogenesis remains to be elucidated. To address this issue, we experimentally measured metabolites in glucose metabolism in response to insulin. Step stimulation of insulin induced transient response of glycolysis and glycogenesis, and sustained response of gluconeogenesis and extracellular glucose concentration (GLC(ex)). Based on the experimental results, we constructed a simple computational model that characterises response of insulin-signalling-dependent glucose metabolism. The model revealed that the network motifs of glycolysis and glycogenesis pathways constitute a feedforward (FF) with substrate depletion and incoherent feedforward loop (iFFL), respectively, enabling glycolysis and glycogenesis responsive to temporal changes of insulin rather than its absolute concentration. In contrast, the network motifs of gluconeogenesis pathway constituted a FF inhibition, enabling gluconeogenesis responsive to absolute concentration of insulin regardless of its temporal patterns. GLC(ex) was regulated by gluconeogenesis and glycolysis. These results demonstrate the selective control mechanism of glucose metabolism by temporal patterns of insulin.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3817, + "tag": "BioModels:BIOMD0000000482" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 3411, + "tag": "Regulation of insulin secretion involved in cellular response to glucose stimulus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4364, + "tag": "Diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 13:36:19.729373+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000482", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2773": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2773, + "name": "Cao2008 - Network of a toggle switch", + "repository_type": "biomodels", + "summary": "Youfang Cao & Jie Liang. Optimal enumeration of state space of finitely buffered stochastic molecular networks and exact computation of steady state landscape probability. BMC Systems Biology 2 (2008).

Stochasticity plays important roles in many molecular networks when molecular concentrations are in the range of 0.1 muM to 10nM (about 100 to 10 copies in a cell). The chemical master equation provides a fundamental framework for studying these networks, and the time-varying landscape probability distribution over the full microstates, i.e., the combination of copy numbers of molecular species, provide a full characterization of the network dynamics. A complete characterization of the space of the microstates is a prerequisite for obtaining the full landscape probability distribution of a network. However, there are neither closed-form solutions nor algorithms fully describing all microstates for a given molecular network.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3818, + "tag": "BioModels:BIOMD0000000483" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:20.207814+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000483", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2774": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2774, + "name": "Cao2013 - Application of ABSIS method in birth-death process", + "repository_type": "biomodels", + "summary": "Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3819, + "tag": "BioModels:BIOMD0000000484" + }, + { + "id": 3820, + "tag": "Regulation of growth" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:20.703316+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000484", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2775": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2775, + "name": "Cao2013 - Application of ABSIS method in the bistable Schl\u00f6gl model", + "repository_type": "biomodels", + "summary": "F. Schl\ufffdgl. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift f\ufffdr Physik 253, 2 (1972).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3821, + "tag": "BioModels:BIOMD0000000485" + }, + { + "id": 3822, + "tag": "Catalytic activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:21.188135+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000485", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2776": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2776, + "name": "Cao2013 - Application of ABSIS method in the reversible isomerization model", + "repository_type": "biomodels", + "summary": "Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3823, + "tag": "BioModels:BIOMD0000000486" + }, + { + "id": 3824, + "tag": "Macromolecule modification" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:21.714191+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000486", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2777": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2777, + "name": "Cao2013 - Application of ABSIS in the the enzymatic futile cycle", + "repository_type": "biomodels", + "summary": "Michael Samoilov, Sergey Plyasunov & Adam P. Arkin. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proceedings of the National Academy of Sciences 102, 7 (2005).

Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schl\u00f6gl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3825, + "tag": "BioModels:BIOMD0000000487" + }, + { + "id": 3826, + "tag": "Heat generation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:22.224924+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000487", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2778": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2778, + "name": "Proctor2013 - Effect of A\u03b2 immunisation in Alzheimer's disease (deterministic version)", + "repository_type": "biomodels", + "summary": "
Proctor2013 - Effect of A\u03b2 immunisationin Alzheimer's disease (deterministic version)

Extension of a previously published stochastic model (designed to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau ( BIOMD0000000286, BIOMD0000000462)) to include the main processes involved in passive and active immunisation against A\u03b2 and then to demonstrate the effects of this intervention on soluble A\u03b2. This is the deterministic version of the model, the stochastic version is BIOMD0000000634.

This model is described in the article:

Proctor CJ, Boche D, Gray DA, Nicoll JA.
PLoS ONE 2013; 8(9): e73631

Abstract:

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000488.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3827, + "tag": "BioModels:BIOMD0000000488" + }, + { + "id": 3320, + "tag": "DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:22.740598+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000488", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2779": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2779, + "name": "Sharp2013 - Lipopolysaccharide induced NFkB activation", + "repository_type": "biomodels", + "summary": "Markus W. Covert, Thomas H. Leung, Jahlionais E. Gaston & David Baltimore. Achieving stability of lipopolysaccharide-induced NF-kappaB activation. Science 309, 5742 (2005).

Preterm birth is the single biggest cause of significant neonatal morbidity and mortality, and the incidence is rising. Development of new therapies to treat and prevent preterm labour is seriously hampered by incomplete understanding of the molecular mechanisms that initiate labour at term and preterm. Computational modelling provides a new opportunity to improve this understanding. It is a useful tool in (i) identifying gaps in knowledge and informing future research, and (ii) providing the basis for an in silico model of parturition in which novel drugs to prevent or treat preterm labour can be \"tested\". Despite their merits, computational models are rarely used to study the molecular events initiating labour. Here, we present the first attempt to generate a dynamic kinetic model that has relevance to the molecular mechanisms of preterm labour. Using published data, we model an important candidate signalling pathway in infection-induced preterm labour: that of lipopolysaccharide (LPS) -induced activation of Nuclear Factor kappa B. This is the first model of this pathway to explicitly include molecular interactions upstream of Nuclear Factor kappa B activation. We produced a formalised graphical depiction of the pathway and built a kinetic model based on ordinary differential equations. The kinetic model accurately reproduced published in vitro time course plots of Lipopolysaccharide-induced Nuclear Factor kappa B activation in mouse embryo fibroblasts. In this preliminary work we have provided proof of concept that it is possible to build computational models of signalling pathways that are relevant to the regulation of labour, and suggest that models that are validated with wet-lab experiments have the potential to greatly benefit the field.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3828, + "tag": "BioModels:BIOMD0000000489" + }, + { + "id": 3829, + "tag": "Lipopolysaccharide metabolic process" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3830, + "tag": "Release of cytoplasmic sequestered NF-kappaB" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:23.217793+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000489", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2780": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2780, + "name": "Demin2013 - PKPD behaviour - 5-Lipoxygenase inhibitors", + "repository_type": "biomodels", + "summary": "
Demin2013 - PKPD behaviour - 5-Lipoxygenaseinhibitors

This model is described in the article:

Demin O, Karelina T, Svetlichniy D, Metelkin E, Speshilov G, Demin O Jr, Fairman D, van der Graaf PH, Agoram BM.
CPT Pharmacometrics Syst Pharmacol 2013; 2: e74

Abstract:

Zileuton, a 5-lipoxygenase (5LO) inhibitor, displays complex pharmaokinetic (PK)-pharmacodynamic (PD) behavior. Available clinical data indicate a lack of dose-bronchodilatory response during initial treatment, with a dose response developing after ~1-2 weeks. We developed a quantitative systems pharmacology (QSP) model to understand the mechanism behind this phenomenon. The model described the release, maturation, and trafficking of eosinophils into the airways, leukotriene synthesis by the 5LO enzyme, leukotriene signaling and bronchodilation, and the PK of zileuton. The model provided a plausible explanation for the two-phase bronchodilatory effect of zileuton-the short-term bronchodilation was due to leukotriene inhibition and the long-term bronchodilation was due to inflammatory cell infiltration blockade. The model also indicated that the theoretical maximum bronchodilation of both 5LO inhibition and leukotriene receptor blockade is likely similar. QSP modeling provided interesting insights into the effects of leukotriene modulation.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e74; doi:10.1038/psp.2013.49; advance online publication 11 September 2013.

This model is hosted on BioModels Database and identified by: BIOMD0000000490.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3831, + "tag": "BioModels:BIOMD0000000490" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3832, + "tag": "Inflammatory response" + }, + { + "id": 3833, + "tag": "Response to bronchodilator" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4620, + "tag": "Asthma" + } + ], + "timestamp_created": "2025-01-30 13:36:23.694937+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000490", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2781": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2781, + "name": "Pathak2013 - MAPK activation in response to various abiotic stresses", + "repository_type": "biomodels", + "summary": "
Pathak2013 - MAPK activation in response to various abiotic stresses

MAPK activation mechanism in response to various abiotic stress conditions, such as cold, salt, drought, H2O2, heavy metal and ethylene, in plants

This model is described in the article:

Pathak RK, Taj G, Pandey D, Arora S, Kumar A.
Bioinformation 2013; 9(9): 443-449

Abstract:

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000491 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3834, + "tag": "BioModels:BIOMD0000000491" + }, + { + "id": 3835, + "tag": "Cellular response to abiotic stimulus" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:36:24.184016+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000491", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2782": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2782, + "name": "Pathak2013 - MAPK activation in response to various biotic stresses", + "repository_type": "biomodels", + "summary": "
Pathak2013 - MAPK activation in response to various biotic stresses

MAPK activation mechanism in response to various biotic (fungal and bacterial pathogens) stress conditions in plants

This model is described in the article:

Pathak RK, Taj G, Pandey D, Arora S, Kumar A.
Bioinformation 2013; 9(9): 443-449

Abstract:

Mitogen-Activated Protein Kinases (MAPKs) cascade plays an important role in regulating plant growth and development, generating cellular responses to the extracellular stimuli. MAPKs cascade mainly consist of three sub-families i.e. mitogen-activated protein kinase kinase kinase (MAPKKK), mitogen-activated protein kinase kinase (MAPKK) and mitogen activated protein kinase (MAPK), several cascades of which are activated by various abiotic and biotic stresses. In this work we have modeled the holistic molecular mechanisms essential to MAPKs activation in response to several abiotic and biotic stresses through a system biology approach and performed its simulation studies. As extent of abiotic and biotic stresses goes on increasing, the process of cell division, cell growth and cell differentiation slow down in time dependent manner. The models developed depict the combinatorial and multicomponent signaling triggered in response to several abiotic and biotic factors. These models can be used to predict behavior of cells in event of various stresses depending on their time and exposure through activation of complex signaling cascades.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000492 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3836, + "tag": "BioModels:BIOMD0000000492" + }, + { + "id": 3837, + "tag": "Cellular response to biotic stimulus" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3523, + "tag": "Viridiplantae" + } + ], + "timestamp_created": "2025-01-30 13:36:24.707691+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000492", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2783": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2783, + "name": "Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes", + "repository_type": "biomodels", + "summary": "
Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes

Mathematical model describing the mechanism of differentiation of mesenchymal stem cells to bone (osteoblasts) or cartilage (chondrocytes) cells.

This model is described in the article:

Schittler D, Hasenauer J, Allg\u00f6wer F, Waldherr S.
Chaos 2010 Dec; 20(4): 045121

Abstract:

Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000493 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3838, + "tag": "BioModels:BIOMD0000000493" + }, + { + "id": 3839, + "tag": "Mesenchymal cell differentiation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:25.256153+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000493", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2784": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2784, + "name": "Roblitz2013 - Menstrual Cycle following GnRH analogue administration", + "repository_type": "biomodels", + "summary": "
Roblitz2013 - Menstrual Cycle following GnRH analogue administration

The model describes the menstrual cycle feedback mechanisms. GnRH, FSH, LH, E2, P4, inbibins A and B, and follicular development are modelled. The model predicts hormonal changes following GnRH analogue administration. Simulation results agree with measurements of hormone blood concentrations. The model gives insight into mechanisms underlying gonadotropin supression.

This model is described in the article:

R\u00f6blitz S, St\u00f6tzel C, Deuflhard P, Jones HM, Azulay DO, van der Graaf PH, Martin SW.
J. Theor. Biol. 2013 Mar; 321: 8-27

Abstract:

The paper presents a differential equation model for the feedback mechanisms between gonadotropin-releasing hormone (GnRH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), development of follicles and corpus luteum, and the production of estradiol (E2), progesterone (P4), inhibin A (IhA), and inhibin B (IhB) during the female menstrual cycle. Compared to earlier human cycle models, there are three important differences: The model presented here (a) does not involve any delay equations, (b) is based on a deterministic modeling of the GnRH pulse pattern, and (c) contains less differential equations and less parameters. These differences allow for a faster simulation and parameter identification. The focus is on modeling GnRH-receptor binding, in particular, by inclusion of a pharmacokinetic/pharmacodynamic (PK/PD) model for a GnRH agonist, Nafarelin, and a GnRH antagonist, Cetrorelix, into the menstrual cycle model. The final mathematical model describes the hormone profiles (LH, FSH, P4, E2) throughout the menstrual cycle of 12 healthy women. It correctly predicts hormonal changes following single and multiple dose administration of Nafarelin or Cetrorelix at different stages in the cycle.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000494 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3840, + "tag": "BioModels:BIOMD0000000494" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 3815, + "tag": "Ovulation cycle" + }, + { + "id": 3120, + "tag": "Response to gonadotropin-releasing hormone" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3841, + "tag": "female" + }, + { + "id": 4619, + "tag": "Ovarian disease" + } + ], + "timestamp_created": "2025-01-30 13:36:25.749971+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000494", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2785": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2785, + "name": "Sen2013 - Phospholipid Synthesis in P.knowlesi", + "repository_type": "biomodels", + "summary": "
Sen2013 - Phospholipid Synthesis in P.knowlesi

The model describes the multiple phospholipid synthetic pathways in Plasmodium knowlesi.

This model is described in the article:

Sen P, Vial HJ, Radulescu O.
BMC Syst Biol 2013 Nov; 7(1): 123

Abstract:

BACKGROUND: Plasmodium is the causal parasite of malaria, infectious disease responsible for the death of up to one million people each year. Glycerophospholipid and consequently membrane biosynthesis are essential for the survival of the parasite and are targeted by a new class of antimalarial drugs developed in our lab. In order to understand the highly redundant phospholipid synthethic pathways and eventual mechanism of resistance to various drugs, an organism specific kinetic model of these metabolic pathways need to be developed in Plasmodium species. RESULTS: Fluxomic data were used to build a quantitative kinetic model of glycerophospholipid pathways in Plasmodium knowlesi. In vitro incorporation dynamics of phospholipids unravels multiple synthetic pathways. A detailed metabolic network with values of the kinetic parameters (maximum rates and Michaelis constants) has been built. In order to obtain a global search in the parameter space, we have designed a hybrid, discrete and continuous, optimization method. Discrete parameters were used to sample the cone of admissible fluxes, whereas the continuous Michaelis and maximum rates constants were obtained by local minimization of an objective function.The model was used to predict the distribution of fluxes within the network of various metabolic precursors.The quantitative analysis was used to understand eventual links between different pathways. The major source of phosphatidylcholine (PC) is the CDP-choline Kennedy pathway.In silico knock-out experiments showed comparable importance of phosphoethanolamine-N-methyltransferase (PMT) and phosphatidylethanolamine-N-methyltransferase (PEMT) for PC synthesis.The flux values indicate that, major part of serine derived phosphatidylethanolamine (PE) is formed via serine decarboxylation, whereas major part of phosphatidylserine (PS) is formed by base-exchange reactions.Sensitivity analysis of CDP-choline pathway shows that the carrier-mediated choline entry into the parasite and the phosphocholine cytidylyltransferase reaction have the largest sensitivity coefficients in this pathway, but does not distinguish a reaction as an unique rate-limiting step.CONCLUSION: We provide a fully parametrized kinetic model for the multiple phospholipid synthetic pathways in P. knowlesi. This model has been used to clarify the relative importance of the various reactions in these metabolic pathways. Future work extensions of this modelling strategy will serve to elucidate the regulatory mechanisms governing the development of Plasmodium during its blood stages, as well as the mechanisms of action of drugs on membrane biosynthetic pathways and eventual mechanisms of resistance.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000495 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3842, + "tag": "BioModels:BIOMD0000000495" + }, + { + "id": 3843, + "tag": "Phospholipid biosynthetic process" + }, + { + "id": 3844, + "tag": "Plasmodium knowlesi" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:26.331104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000495", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2786": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2786, + "name": "Stanford2013 - Kinetic model of yeast metabolic network (standard)", + "repository_type": "biomodels", + "summary": "
Stanford2013 - Kinetic model of yeast metabolic network (standard)

Large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants.

This model is described in the article:

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W.
PLoS ONE 2013; 8(11): e79195

Abstract:

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

[corrections made to the model compared to the paper, optional]

This model is hosted on BioModels Database and identifiedby: BIOMD0000000496 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3845, + "tag": "BioModels:BIOMD0000000496" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 3796, + "tag": "Organic substance biosynthetic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:26.869466+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000496", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2787": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2787, + "name": "Stanford2013 - Kinetic model of yeast metabolic network (regulation)", + "repository_type": "biomodels", + "summary": "
Stanford2013 - Kinetic model of yeast metabolic network (standard)

Large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. This model is built with regulatory information.

This model is described in the article:

Stanford NJ, Lubitz T, Smallbone K, Klipp E, Mendes P, Liebermeister W.
PLoS ONE 2013; 8(11): e79195

Abstract:

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000497 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3846, + "tag": "BioModels:BIOMD0000000497" + }, + { + "id": 3795, + "tag": "Metabolic process" + }, + { + "id": 3796, + "tag": "Organic substance biosynthetic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:27.450900+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000497", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2788": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2788, + "name": "Mitchell2013 - Liver Iron Metabolism", + "repository_type": "biomodels", + "summary": "
Mitchell2013 - Liver Iron Metabolism

The model includes the core regulatory components of human liver iron metabolism.

This model is described in the article:

Mitchell S, Mendes P.
PLoS Comput. Biol. 2013 Nov; 9(11): e1003299

Abstract:

Iron is essential for all known life due to its redox properties; however, these same properties can also lead to its toxicity in overload through the production of reactive oxygen species. Robust systemic and cellular control are required to maintain safe levels of iron, and the liver seems to be where this regulation is mainly located. Iron misregulation is implicated in many diseases, and as our understanding of iron metabolism improves, the list of iron-related disorders grows. Recent developments have resulted in greater knowledge of the fate of iron in the body and have led to a detailed map of its metabolism; however, a quantitative understanding at the systems level of how its components interact to produce tight regulation remains elusive. A mechanistic computational model of human liver iron metabolism, which includes the core regulatory components, is presented here. It was constructed based on known mechanisms of regulation and on their kinetic properties, obtained from several publications. The model was then quantitatively validated by comparing its results with previously published physiological data, and it is able to reproduce multiple experimental findings. A time course simulation following an oral dose of iron was compared to a clinical time course study and the simulation was found to recreate the dynamics and time scale of the systems response to iron challenge. A disease state simulation of haemochromatosis was created by altering a single reaction parameter that mimics a human haemochromatosis gene (HFE) mutation. The simulation provides a quantitative understanding of the liver iron overload that arises in this disease. This model supports and supplements understanding of the role of the liver as an iron sensor and provides a framework for further modelling, including simulations to identify valuable drug targets and design of experiments to improve further our knowledge of this system.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000498 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3847, + "tag": "BioModels:BIOMD0000000498" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4621, + "tag": "Hemochromatosis" + } + ], + "timestamp_created": "2025-01-30 13:36:27.952317+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000498", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2791": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2791, + "name": "Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer model", + "repository_type": "biomodels", + "summary": "
Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer model

The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely \"single GAS polymer model\" and \"double GAS polymer model\" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the \"double GAS polymer model\".

This model is described in the article:

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U
Nat Immunol. 2014 Feb;15(2):168-76.

Abstract:

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000501 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3852, + "tag": "BioModels:BIOMD0000000501" + }, + { + "id": 3851, + "tag": "Interferon-gamma-mediated signaling pathway" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:29.645214+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000501", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2792": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2792, + "name": "Messiha2013 - Pentose phosphate pathway model", + "repository_type": "biomodels", + "summary": "
Messiha2013 - Pentose phosphate pathway model

This model describes the dynamic behaviour of the pentose phosphate pathway with the inclusion of various enzymes involved in the pathway. The model's predictions are compared with experimental observations of transient metabolite concentrations following a glucose pulse.

This model is described in the article:

Hanan L. Messiha, Edward Kent, Naglis Malys, Kathleen M. Carroll, Pedro Mendes, Kieran Smallbone
PeerJ PrePrints 1:e146v2

Abstract:

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000502 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3853, + "tag": "BioModels:BIOMD0000000502" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:30.210227+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000502", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2793": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2793, + "name": "Messiha2013 - combined glycolysis and pentose phosphate pathway model", + "repository_type": "biomodels", + "summary": "
Messiha2013 - combined glycolysis and pentose phosphate pathway model

BIOMD0000000502 and MODEL1303260018 are combined to examine the response to oxidative stress.

This model is described in the article:

Hanan L. Messiha, Edward Kent, Naglis Malys, Kathleen M. Carroll, Pedro Mendes, Kieran Smallbone
PeerJ PrePrints 1:e146v2

Abstract:

We present the quantification and kinetic characterisation of the enzymes of the pentose phosphate pathway in Saccharomyces cerevisiae. The data are combined into a mathematical model that describes the dynamics of this system and allows for the predicting changes in metabolite concentrations and fluxes in response to perturbations. We use the model to study the response of yeast to a glucose pulse. We then combine the model with an existing glycolysis one to study the effect of oxidative stress on carbohydrate metabolism. The combination of these two models was made possible by the standardized enzyme kinetic experiments carried out in both studies. This work demonstrates the feasibility of constructing larger network models by merging smaller pathway models.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000503 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3854, + "tag": "BioModels:BIOMD0000000503" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:36:30.722241+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000503", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2794": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2794, + "name": "Proctor2013 - Cartilage breakdown, interventions to reduce collagen release", + "repository_type": "biomodels", + "summary": "
Proctor2013 - Cartilage breakdown, interventions to reduce collagen release

The molecular pathways involved in cartilage breakdown is studied using this model to examine possible interventions to reduce cartilage collagen release. The model contains three separate submodels, one which describes the IL-1/JNK signalling pathway, secondly the OSM/STAT3 signalling pathway, and lastly a module which includes proMMP (Matrix matalloproteinase) activation, and aggrecan and collagen release.

This model is described in the article:

Proctor CJ, Macdonald C, Milner JM, Rowan AD, Cawston TE.
Arthritis Rheum. 2013 Nov 27.

Abstract:

Objective. To use a novel computational approach to examine the molecular pathways involved in cartilage breakdown and to use computer simulation to test possible interventions to reduce collagen release. Methods. We constructed a computational model of the relevant molecular pathways using the Systems Biology Markup Language (SBML), a computer-readable format of a biochemical network. The model was constructed using our experimental data showing that interleukin-1 (IL-1) and oncostatin M (OSM) act synergistically to up-regulate collagenase protein and activity and initiate cartilage collagen breakdown. Simulations were performed in the COPASI software package. Results. The model predicted that simulated inhibition of c-Jun N-terminal kinase (JNK) or p38 mitogen-activated protein kinase, and over-expression of tissue inhibitor of metalloproteinases 3 (TIMP-3) led to a reduction in collagen release. Over-expression of TIMP-1 was much less effective than TIMP-3 and led to a delay, rather than a reduction, in collagen release. Simulated interventions of receptor antagonists and inhibition of Janus kinase 1 (JAK1), the first kinase in the OSM pathway, were ineffective. So, importantly, the model predicts that it is more effective to intervene at targets which are downstream, such as the JNK pathway, rather than close to the cytokine signal. In vitro experiments confirmed the effectiveness of JNK inhibition. Conclusion. Our study shows the value of computer modelling as a tool for examining possible interventions to reduce cartilage collagen breakdown. The model predicts interventions that either prevent transcription or inhibit activity of collagenases are promising strategies and should be investigated further in an experimental setting. \u00a9 2013 American College of Rheumatology.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000504 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3855, + "tag": "BioModels:BIOMD0000000504" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3856, + "tag": "Regulation of collagen catabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4625, + "tag": "Cartilage disease" + } + ], + "timestamp_created": "2025-01-30 13:36:31.203465+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000504", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2795": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2795, + "name": "vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (steady-state model)", + "repository_type": "biomodels", + "summary": "
vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (steady-state model)

Lipid metabolism plays an important role in the development of metabolic syndrome, a major risk factor for cardiovascular disease and diabetes. This model gives insights into the response of lipid oxidation to dietart and medical interventions. The model predicts the rate of lipid oxidation and the time course of most acyl carnitines. There are two models described in the paper, (i) steady-state model [ BIOMD0000000505 ], (ii) time-course model [ BIOMD0000000506 ]. This model corresponds to the steady-state model.

This model is described in the article:

van Eunen K, Simons SM, Gerding A, Bleeker A, den Besten G, Touw CM, Houten SM, Groen BK, Krab K, Reijngoud DJ, Bakker BM.
PLoS Comput Biol. 2013;9(8):e1003186.

Abstract:

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000505 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3857, + "tag": "BioModels:BIOMD0000000505" + }, + { + "id": 3858, + "tag": "Fatty acid oxidation" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:31.781735+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000505", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2796": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2796, + "name": "vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (time-course model)", + "repository_type": "biomodels", + "summary": "
vanEunen2013 - Network dynamics of fatty acid \u03b2-oxidation (time-course model)

Lipid metabolism plays an important role in the development of metabolic syndrome, a major risk factor for cardiovascular disease and diabetes. This model gives insights into the response of lipid oxidation to dietart and medical interventions. The model predicts the rate of lipid oxidation and the time course of most acyl carnitines. There are two models described in the paper, (i) steady-state model [ BIOMD0000000505 ], (ii) time-course model [ BIOMD0000000506 ]. This model corresponds to the time-course model.

This model is described in the article:

van Eunen K, Simons SM, Gerding A, Bleeker A, den Besten G, Touw CM, Houten SM, Groen BK, Krab K, Reijngoud DJ, Bakker BM.
PLoS Comput Biol. 2013;9(8):e1003186.

Abstract:

Fatty-acid metabolism plays a key role in acquired and inborn metabolic diseases. To obtain insight into the network dynamics of fatty-acid \u03b2-oxidation, we constructed a detailed computational model of the pathway and subjected it to a fat overload condition. The model contains reversible and saturable enzyme-kinetic equations and experimentally determined parameters for rat-liver enzymes. It was validated by adding palmitoyl CoA or palmitoyl carnitine to isolated rat-liver mitochondria: without refitting of measured parameters, the model correctly predicted the \u03b2-oxidation flux as well as the time profiles of most acyl-carnitine concentrations. Subsequently, we simulated the condition of obesity by increasing the palmitoyl-CoA concentration. At a high concentration of palmitoyl CoA the \u03b2-oxidation became overloaded: the flux dropped and metabolites accumulated. This behavior originated from the competition between acyl CoAs of different chain lengths for a set of acyl-CoA dehydrogenases with overlapping substrate specificity. This effectively induced competitive feedforward inhibition and thereby led to accumulation of CoA-ester intermediates and depletion of free CoA (CoASH). The mitochondrial [NAD\u207a]/[NADH] ratio modulated the sensitivity to substrate overload, revealing a tight interplay between regulation of \u03b2-oxidation and mitochondrial respiration.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000506 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3859, + "tag": "BioModels:BIOMD0000000506" + }, + { + "id": 3858, + "tag": "Fatty acid oxidation" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:32.362027+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000506", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2797": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2797, + "name": "Gardner2000 - genetic toggle switch in E.coli", + "repository_type": "biomodels", + "summary": "
Gardner2000 - genetic toggle switch in E.coli

The behaviour of the genetic toggle switch and the conditions for bistability has been studies using a synthetic, bistable gene circuit.

This model is described in the article:

Gardner TS, Cantor CR, Collins JJ
Nature. 2000 Jan 20;403(6767):339-42.

Abstract:

It has been proposed' that gene-regulatory circuits with virtually any desired property can be constructed from networks of simple regulatory elements. These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000507 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3860, + "tag": "BioModels:BIOMD0000000507" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:32.878467+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000507", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2798": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2798, + "name": "Barrack2014 - Calcium/cell cycle coupling - Cyclin D dependent ATP release", + "repository_type": "biomodels", + "summary": "
Barrack2014 - Calcium/cell cycle coupling - Cyclin D dependent ATP release

This model is designed based on the hypothesis that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescents cells onto the cell cycle. The model describes the ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell.

This model is described in the article:

Barrack DS, Thul R, Owen MR.
J Theor Biol. 2014 Jan 13;347C:17-32.

Abstract:

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000508 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3861, + "tag": "BioModels:BIOMD0000000508" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:33.360024+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000508", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2799": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2799, + "name": "Barrack2014 - Calcium/cell cycle coupling - Rs dependent ATP release", + "repository_type": "biomodels", + "summary": "
Barrack2014 - Calcium/cell cycle coupling - Rs dependent ATP release

This model is designed based on the hypothesis that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescents cells onto the cell cycle. The model describes the ATP mediated calcium-cell cycle coupling via Rs (retinoblastoma tumour suppressor protein bound to the E2F transcription factor) in a single radial glial cell.

This model is described in the article:

Barrack DS, Thul R, Owen MR.
J Theor Biol. 2014 Jan 13;347C:17-32.

Abstract:

Most neocortical neurons formed during embryonic brain development arise from radial glial cells which communicate, in part, via ATP mediated calcium signals. Although the intercellular signalling mechanisms that regulate radial glia proliferation are not well understood, it has recently been demonstrated that ATP dependent intracellular calcium release leads to an increase of nearly 100% in overall cellular proliferation. It has been hypothesised that cytoplasmic calcium accelerates entry into S phase of the cell cycle and/or acts to recruit otherwise quiescent cells onto the cell cycle. In this paper we study this cell cycle acceleration and recruitment by forming a differential equation model for ATP mediated calcium-cell cycle coupling via Cyclin D in a single radial glial cell. Bifurcation analysis and numerical simulations suggest that the cell cycle period depends only weakly on cytoplasmic calcium. Therefore, the accelerative impact of calcium on the cell cycle can only account for a small fraction of the large increase in proliferation observed experimentally. Crucially however, our bifurcation analysis reveals that stable fixed point and stable limit cycle solutions can coexist, and that calcium dependent Cyclin D dynamics extend the oscillatory region to lower Cyclin D synthesis rates, thus rendering cells more susceptible to cycling. This supports the hypothesis that cycling glial cells recruit quiescent cells (in G0 phase) onto the cell cycle, via a calcium signalling mechanism, and that this may be the primary means by which calcium augments proliferation rates at the population scale. Numerical simulations of two coupled cells demonstrate that such a scenario is indeed feasible.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000509 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3862, + "tag": "BioModels:BIOMD0000000509" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:33.861554+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000509", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2800": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2800, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C (with glucosomal ribokinase)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei -MODEL C (with glucosomal ribokinase)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model C of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000510 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3863, + "tag": "BioModels:BIOMD0000000510" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:36:34.320605+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000510", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2801": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2801, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D (with ATP:ADP antiporter)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D (with ATP:ADP antiporter)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model D of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000511 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3864, + "tag": "BioModels:BIOMD0000000511" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:36:34.808632+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000511", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2802": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2802, + "name": "Benson2014 - FAAH inhibitors for the treatment of osteoarthritic pain", + "repository_type": "biomodels", + "summary": "
Benson2014 - FAAH inhibitors for the treatment of osteoarthritic pain

Evaluation of fatty acid amide hydrolase (FAAH) as a target for osteoarthritic pain in humans, using an integrated systems pharmacology model.

The SBML version of the model is obtained from the supplementary material of the corresponding paper (see below).

This model is described in the article:

Benson N, Metelkin E, Demin O, Li GL, Nichols D, van der Graaf PH.
CPT Pharmacometrics Syst Pharmacol. 2014 Jan 15;3:e91.

Abstract:

The level of the endocannabinoid anandamide is controlled by fatty acid amide hydrolase (FAAH). In 2011, PF-04457845, an irreversible inhibitor of FAAH, was progressed to phase II clinical trials for osteoarthritic pain. This article discusses a prospective, integrated systems pharmacology model evaluation of FAAH as a target for pain in humans, using physiologically based pharmacokinetic and systems biology approaches. The model integrated physiological compartments; endocannabinoid production, degradation, and disposition data; PF-04457845 pharmacokinetics and pharmacodynamics, and cannabinoid receptor CB1-binding kinetics. The modeling identified clear gaps in our understanding and highlighted key risks going forward, in particular relating to whether methods are in place to demonstrate target engagement and pharmacological effect. The value of this modeling exercise will be discussed in detail and in the context of the clinical phase II data, together with recommendations to enable optimal future evaluation of FAAH inhibitors.

This model is hosted on BioModels Database and identifiedby: MODEL1402030000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3865, + "tag": "BioModels:BIOMD0000000512" + }, + { + "id": 3866, + "tag": "Endocannabinoid signaling pathway" + }, + { + "id": 3867, + "tag": "Fatty acid amide hydrolase activity" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4595, + "tag": "Osteoarthritis" + } + ], + "timestamp_created": "2025-01-30 13:36:35.338189+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000512", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2803": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2803, + "name": "Kerkhoven2013 - Glycolysis in T.brucei - MODEL A", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis in T.brucei - MODEL A

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model A of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000513 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3868, + "tag": "BioModels:BIOMD0000000513" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:36:35.944147+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000513", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2804": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2804, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL B", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL B

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model B of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000514 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3869, + "tag": "BioModels:BIOMD0000000514" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:36:36.467621+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000514", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2805": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2805, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C in fructose medium (with glucosomal ribokinase)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL C in fructose medium (with glucosomal ribokinase)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model C-fruc of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000515 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3870, + "tag": "BioModels:BIOMD0000000515" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:36:36.946438+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000515", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2806": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2806, + "name": "Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D in fructose medium (with ATP:ADP antiporter)", + "repository_type": "biomodels", + "summary": "
Kerkhoven2013 - Glycolysis and Pentose Phosphate Pathway in T.brucei - MODEL D in fructose medium (with ATP:ADP antiporter)

There are six models (Model A, B, C, C-fruc, D, D-fruc) described in the paper. Model A ( BIOMD0000000513 ) is the model developed originally by Achar et al. (2012) ( BIOMD0000000428 ), which describes glycolysis in T.brucei. This glycolysis model is extended to include pentose phosphate pathway (PPP), which is Model B (( BIOMD0000000514 ). Model B is further extended to include glycosomal ribokinase, leading to Model C ( BIOMD0000000510 ). Model D ( BIOMD0000000511 ) is again an extension of Model B, which includes an ATP:ADP antiporter. Model C-fruc ( BIOMD0000000515 ) and Model D-fruc ( BIOMD0000000516 ) are extensions of Model C and D, respectively, which includes fructose transporter and its subsequent utilizing reactions. This model correspond to Model D-fruc of the paper.

This model is described in the article:

Kerkhoven EJ, Achcar F, Alibu VP, Burchmore RJ, Gilbert IH, Trybi\u0142o M, Driessen NN, Gilbert D, Breitling R, Bakker BM, Barrett MP.
PLoS Comput Biol. 2013 Dec;9(12):e1003371.

Abstract:

Dynamic models of metabolism can be useful in identifying potential drug targets, especially in unicellular organisms. A model of glycolysis in the causative agent of human African trypanosomiasis, Trypanosoma brucei, has already shown the utility of this approach. Here we add the pentose phosphate pathway (PPP) of T. brucei to the glycolytic model. The PPP is localized to both the cytosol and the glycosome and adding it to the glycolytic model without further adjustments leads to a draining of the essential bound-phosphate moiety within the glycosome. This phosphate \"leak\" must be resolved for the model to be a reasonable representation of parasite physiology. Two main types of theoretical solution to the problem could be identified: (i) including additional enzymatic reactions in the glycosome, or (ii) adding a mechanism to transfer bound phosphates between cytosol and glycosome. One example of the first type of solution would be the presence of a glycosomal ribokinase to regenerate ATP from ribose 5-phosphate and ADP. Experimental characterization of ribokinase in T. brucei showed that very low enzyme levels are sufficient for parasite survival, indicating that other mechanisms are required in controlling the phosphate leak. Examples of the second type would involve the presence of an ATP:ADP exchanger or recently described permeability pores in the glycosomal membrane, although the current absence of identified genes encoding such molecules impedes experimental testing by genetic manipulation. Confronted with this uncertainty, we present a modeling strategy that identifies robust predictions in the context of incomplete system characterization. We illustrate this strategy by exploring the mechanism underlying the essential function of one of the PPP enzymes, and validate it by confirming the model predictions experimentally.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000516 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3871, + "tag": "BioModels:BIOMD0000000516" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3110, + "tag": "Trypanosoma brucei" + } + ], + "timestamp_created": "2025-01-30 13:36:37.450272+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000516", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2807": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2807, + "name": "Smallbone2013 - Colon Crypt cycle - Version 3", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 3

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190003 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3872, + "tag": "BioModels:BIOMD0000000517" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:36:37.920275+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000517", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2808": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2808, + "name": "Smallbone2013 - Colon Crypt cycle - Version 2", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 2

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190002 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3873, + "tag": "BioModels:BIOMD0000000518" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:36:38.395919+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000518", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2809": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2809, + "name": "Smallbone2013 - Colon Crypt cycle - Version 1", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 1

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190001 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3874, + "tag": "BioModels:BIOMD0000000519" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:36:38.916208+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000519", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2810": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2810, + "name": "Smallbone2013 - Colon Crypt cycle - Version 0", + "repository_type": "biomodels", + "summary": "
Smallbone2013 - Colon Crypt cycle - Version 0

This model is described in the article:

Kieran Smallbone, Bernard M. Corfe
Int J Exp Pathol. 2014 Feb;95(1):1-7.

Abstract:

Models of the development and early progression of colorectal cancer are based upon understanding the cycle of stem cell turnover, proliferation, differentiation and death. Existing crypt compartmental models feature a linear pathway of cell types, with little regulatory mechanism. Previous work has shown that there are perturbations in the enteroendocrine cell population of macroscopically normal crypts, a compartment not included in existing models. We show that existing models do not adequately recapitulate the dynamics of cell fate pathways in the crypt. We report the progressive development, iterative testing and fitting of a developed compartmental model with additional cell types, and which includes feedback mechanisms and cross-regulatory mechanisms between cell types. The fitting of the model to existing data sets suggests a need to invoke cross-talk between cell types as a feature of colon crypt cycle models.

This model is hosted on BioModels Database and identifiedby: MODEL1306190000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3875, + "tag": "BioModels:BIOMD0000000520" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3350, + "tag": "Stem cell differentiation" + } + ], + "timestamp_created": "2025-01-30 13:36:39.391164+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000520", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2811": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2811, + "name": "Ribba2012 - Low-grade gliomas, tumour growth inhibition model", + "repository_type": "biomodels", + "summary": "
Ribba2012 - Low-grade gliomas, tumour growth inhibition model

Using longitudinal mean tumour diameter (MTD) data, this model describe the size evolution of low-grade glioma (LGG) in patients treated with chemotherapy or radiotherapy.

This model is described in the article:

Ribba B, Kaloshi G, Peyre M, Ricard D, Calvez V, Tod M, Cajavec-Bernard B, Idbaih A, Psimaras D, Dainese L, Pallud J, Cartalat-Carel S, Delattre JY, Honnorat J, Grenier E, Ducray F.
Clin. Cancer Res. 2012 Sep; 18(18): 5071-5080

Abstract:

PURPOSE: To develop a tumor growth inhibition model for adult diffuse low-grade gliomas (LGG) able to describe tumor size evolution in patients treated with chemotherapy or radiotherapy.

EXPERIMENTAL DESIGN: Using longitudinal mean tumor diameter (MTD) data from 21 patients treated with first-line procarbazine, 1-(2-chloroethyl)-3-cyclohexyl-l-nitrosourea, and vincristine (PCV) chemotherapy, we formulated a model consisting of a system of differential equations, incorporating tumor-specific and treatment-related parameters that reflect the response of proliferative and quiescent tumor tissue to treatment. The model was then applied to the analysis of longitudinal tumor size data in 24 patients treated with first-line temozolomide (TMZ) chemotherapy and in 25 patients treated with first-line radiotherapy.

RESULTS: The model successfully described the MTD dynamics of LGG before, during, and after PCV chemotherapy. Using the same model structure, we were also able to successfully describe the MTD dynamics in LGG patients treated with TMZ chemotherapy or radiotherapy. Tumor-specific parameters were found to be consistent across the three treatment modalities. The model is robust to sensitivity analysis, and preliminary results suggest that it can predict treatment response on the basis of pretreatment tumor size data.

CONCLUSIONS: Using MTD data, we propose a tumor growth inhibition model able to describe LGG tumor size evolution in patients treated with chemotherapy or radiotherapy. In the future, this model might be used to predict treatment efficacy in LGG patients and could constitute a rational tool to conceive more effective chemotherapy schedules.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000521 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3876, + "tag": "Benign glioma" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3877, + "tag": "BioModels:BIOMD0000000521" + }, + { + "id": 3401, + "tag": "Defense response to tumor cell" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:39.878946+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000521", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2812": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2812, + "name": "Muraro2014 - Vascular patterning in Arabidopsis roots", + "repository_type": "biomodels", + "summary": "
Muraro2014 - Vascular patterning in Arabidopsis roots

Using a multicellular model, maintanence of vascular patterning in Arabidopsis roots has been studied. The model that is provided here is the single-cell version of the model. The two-cell and multicellular models described in the paper can be downloaded as python scripts (follow the curation tab to get these files).

This model is described in the article:

Muraro D, Mellor N, Pound MP, Help H, Lucas M, Chopard J, Byrne HM, Godin C, Hodgman TC, King JR, Pridmore TP, Helariutta Y, Bennett MJ, Bishopp A.
Proc Natl Acad Sci U S A. 2014 Jan 14;111(2):857-62.

Abstract:

As multicellular organisms grow, positional information is continually needed to regulate the pattern in which cells are arranged. In the Arabidopsis root, most cell types are organized in a radially symmetric pattern; however, a symmetry-breaking event generates bisymmetric auxin and cytokinin signaling domains in the stele. Bidirectional cross-talk between the stele and the surrounding tissues involving a mobile transcription factor, SHORT ROOT (SHR), and mobile microRNA species also determines vascular pattern, but it is currently unclear how these signals integrate. We use a multicellular model to determine a minimal set of components necessary for maintaining a stable vascular pattern. Simulations perturbing the signaling network show that, in addition to the mutually inhibitory interaction between auxin and cytokinin, signaling through SHR, microRNA165/6, and PHABULOSA is required to maintain a stable bisymmetric pattern. We have verified this prediction by observing loss of bisymmetry in shr mutants. The model reveals the importance of several features of the network, namely the mutual degradation of microRNA165/6 and PHABULOSA and the existence of an additional negative regulator of cytokinin signaling. These components form a plausible mechanism capable of patterning vascular tissues in the absence of positional inputs provided by the transport of hormones from the shoot.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000522 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3878, + "tag": "BioModels:BIOMD0000000522" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3879, + "tag": "Xylem and phloem pattern formation" + } + ], + "timestamp_created": "2025-01-30 13:36:40.366746+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000522", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2813": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2813, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000523 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3880, + "tag": "BioModels:BIOMD0000000523" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:40.885423+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000523", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2814": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2814, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000524 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3881, + "tag": "BioModels:BIOMD0000000524" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:41.386373+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000524", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2815": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2815, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans-cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, CD95 HeLa cells (cis/trans-cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000525 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3882, + "tag": "BioModels:BIOMD0000000525" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:42.403570+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000525", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2816": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2816, + "name": "Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans-cis/trans variant)", + "repository_type": "biomodels", + "summary": "
Kallenberger2014 - CD95L induced apoptosis initiated by caspase-8, wild-type HeLa cells (cis/trans-cis/trans variant)

The paper describes a new approach that combines single cell and population data in the same model. The model consists of a large number of single cell models, which are fitted to single cell data. Simultaneously, ensemble averages are fitted to population data. It is assumed that the kinetics in each cell can be described with the same kinetic parameters. Therefore, cell-to-cell variability is explained by variable initial protein concentrations.

There are four variants of the model (with [CD95L]=500ng/ml = 16.6nM), i) cistrans (in CD95-HeLa cells) [ MODEL1403050000 ], ii) cistrans (in wild-type HeLa cells) [ MODEL1403050001 ], iii) cistrans-cistrans (in CD95-HeLa cells) [ MODEL1403050002 ], and iv) cistrans-cistrans (in wild-type HeLa cells) [ MODEL1403050003 ].

These model contain the equations for one \"average cell\" with median initial concentrations for CD95, FADD, p55, BID, PrNES_mCherry and PrER_mGFP. By integrating the model, it should be possible to obtain trajectories for PrER_mGFP, PrNES_mCherry, p43 and p18 similar as in Figure 4A (CD95-HeLa cells) and Figure 4B (wild-type HeLa cells).

This model is described in the article:

Stefan M. Kallenberger, Jo\u00ebl Beaudouin, Juliane Claus, Carmen Fischer, Peter K. Sorger, Stefan Legewie, and Roland Eils
11 March 2014: Vol. 7, Issue 316, p. ra23

Abstract:

Apoptosis in response to the ligand CD95L (also known as Fas ligand) is initiated by caspase-8, which is activated by dimerization and self-cleavage at death-inducing signaling complexes (DISCs). Previous work indicated that the degree of substrate cleavage by caspase-8 determines whether a cell dies or survives in response to a death stimulus. To determine how a death ligand stimulus is effectively translated into caspase-8 activity, we assessed this activity over time in single cells with compartmentalized probes that are cleaved by caspase-8 and used multiscale modeling to simultaneously describe single-cell and population data with an ensemble of single-cell models. We derived and experimentally validated a minimal model in which cleavage of caspase-8 in the enzymatic domain occurs in an interdimeric manner through interaction between DISCs, whereas prodomain cleavage sites are cleaved in an intradimeric manner within DISCs. Modeling indicated that sustained membrane-bound caspase-8 activity is followed by transient cytosolic activity, which can be interpreted as a molecular timer mechanism reflected by a limited lifetime of active caspase-8. The activation of caspase-8 by combined intra- and interdimeric cleavage ensures weak signaling at low concentrations of CD95L and strongly accelerated activation at higher ligand concentrations, thereby contributing to precise control of apoptosis.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000526 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 3159, + "tag": "Apoptotic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3883, + "tag": "BioModels:BIOMD0000000526" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:42.902211+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000526", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2817": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2817, + "name": "Kaiser2014 - Salmonella persistence after ciprofloxacin treatment", + "repository_type": "biomodels", + "summary": "
Kaiser2014 - Salmonella persistence after ciprofloxacin treatment

The model describes the bacterial tolerance to antibiotics. Using a mouse model for Salmonella diarrhea, the authors have found that bacterial persistence occurs in the presence of the antibiotic ciprofloxacin because Salmonella can exist in two different states. One, the fast-growing population that spreads in the host's tissues and the other, slow-growing \"persister\" population that hide out inside dendritic cells of the host's immune system and cannot be attacked by the antibiotics. However, this can be killed by adding agents that directly stimulate the host's immune defense.

This model is described in the article:

Kaiser P, Regoes RR, Dolowschiak T, Wotzka SY, Lengefeld J, Slack E, Grant AJ, Ackermann M, Hardt WD.
PLoS Biol. 2014 Feb 18;12(2):e1001793.

Abstract:

In vivo, antibiotics are often much less efficient than ex vivo and relapses can occur. The reasons for poor in vivo activity are still not completely understood. We have studied the fluoroquinolone antibiotic ciprofloxacin in an animal model for complicated Salmonellosis. High-dose ciprofloxacin treatment efficiently reduced pathogen loads in feces and most organs. However, the cecum draining lymph node (cLN), the gut tissue, and the spleen retained surviving bacteria. In cLN, approximately 10%-20% of the bacteria remained viable. These phenotypically tolerant bacteria lodged mostly within CD103\u207aCX\u2083CR1\u207bCD11c\u207a dendritic cells, remained genetically susceptible to ciprofloxacin, were sufficient to reinitiate infection after the end of the therapy, and displayed an extremely slow growth rate, as shown by mathematical analysis of infections with mixed inocula and segregative plasmid experiments. The slow growth was sufficient to explain recalcitrance to antibiotics treatment. Therefore, slow-growing antibiotic-tolerant bacteria lodged within dendritic cells can explain poor in vivo antibiotic activity and relapse. Administration of LPS or CpG, known elicitors of innate immune defense, reduced the loads of tolerant bacteria. Thus, manipulating innate immunity may augment the in vivo activity of antibiotics.

This model is hosted on BioModels Database and identifiedby: MODEL1312170001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3884, + "tag": "BioModels:BIOMD0000000527" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3885, + "tag": "Response to antibiotic" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3886, + "tag": "Salmonella enterica subsp. enterica serovar Typhimurium str. DT104" + }, + { + "id": 4626, + "tag": "Primary bacterial infectious disease" + } + ], + "timestamp_created": "2025-01-30 13:36:43.369447+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000527", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2818": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2818, + "name": "Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - Cal/09)", + "repository_type": "biomodels", + "summary": "
Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - Cal/09)

The dynamics of the interplay between the viral antagonism and the innate immune response has been studied using modelling approaches. The responses of human monocyte-derived dendritic cells infected by two influenza A H1N1 strains (the pandemic swine-origin A/California/4/2009 (Cal/09) and the seasonal A/New Caledonia/20/1999 (NC/99)) that have different clinical outcomes have been modelled. From the time course gene expression measurements of a set of selected genes, the dynamic features of viral antagonism and innate immune response are extracted. It is found that the strength and the time scale of action of viral antagonism is significantly different between the two viruses. This model describes the viral infection by seasonal Cal/09.

This model is described in the article:

Fribourg M, Hartmann B, Schmolke M, Marjanovic N, Albrecht RA, Garc\u00eda-Sastre A, Sealfon SC, Jayaprakash C, Hayot F.
J Theor Biol. 2014 Mar 2;351C:47-57.

Abstract:

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

This model is hosted on BioModels Database and identifiedby: MODEL1403310002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3887, + "tag": "BioModels:BIOMD0000000528" + }, + { + "id": 3438, + "tag": "Defense response, incompatible interaction" + }, + { + "id": 3888, + "tag": "H1N1 swine influenza virus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4627, + "tag": "Swine influenza" + } + ], + "timestamp_created": "2025-01-30 13:36:43.930363+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000528", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2819": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2819, + "name": "Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - NC/99)", + "repository_type": "biomodels", + "summary": "
Fribourg2014 - Dynamics of viral antagonism and innate immune response (H1N1 influenza A virus - NC/99)

The dynamics of the interplay between the viral antagonism and the innate immune response has been studied using modelling approaches. The responses of human monocyte-derived dendritic cells infected by two influenza A H1N1 strains (the pandemic swine-origin A/California/4/2009 (Cal/09) and the seasonal A/New Caledonia/20/1999 (NC/99)) that have different clinical outcomes have been modelled. From the time course gene expression measurements of a set of selected genes, the dynamic features of viral antagonism and innate immune response are extracted. It is found that the strength and the time scale of action of viral antagonism is significantly different between the two viruses. This model describes the viral infection by seasonal NC/99.

This model is described in the article:

Fribourg M, Hartmann B, Schmolke M, Marjanovic N, Albrecht RA, Garc\u00eda-Sastre A, Sealfon SC, Jayaprakash C, Hayot F.
J Theor Biol. 2014 Mar 2;351C:47-57.

Abstract:

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

This model is hosted on BioModels Database and identifiedby: MODEL1403310001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models.

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3889, + "tag": "BioModels:BIOMD0000000529" + }, + { + "id": 3438, + "tag": "Defense response, incompatible interaction" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3890, + "tag": "Influenza A virus (A/New Caledonia/20/1999(H1N1))" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4617, + "tag": "Influenza" + } + ], + "timestamp_created": "2025-01-30 13:36:44.399878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000529", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2820": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2820, + "name": "Schmitz2014 - RNA triplex formation", + "repository_type": "biomodels", + "summary": "
Schmitz2014 - RNA triplex formation
The model is parameterized using theparameters for gene CCDC3 from Supplementary Table S1. The twomiRNAs which form the triplex together with CCDC3 are miR-551b andmiR-138.

This model is described in the article:

Schmitz U, Lai X, Winter F, Wolkenhauer O, Vera J, Gupta SK.
Nucleic Acids Res. 2014 Jul; 42(12): 7539-7552

Abstract:

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.

This model is hosted on BioModels Database and identified by: BIOMD0000000530.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3891, + "tag": "BioModels:BIOMD0000000530" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:36:44.889270+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000530", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2821": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2821, + "name": "Crespo2012 - Kinetics of Amyloid Fibril Formation", + "repository_type": "biomodels", + "summary": "
Crespo2012 - Kinetics of Amyloid FibrilFormation

This model is described in the article:

Crespo R, Rocha FA, Damas AM, Martins PM.
J. Biol. Chem. 2012 Aug; 287(36): 30585-30594

Abstract:

Associated with neurodegenerative disorders such as Alzheimer, Parkinson, or prion diseases, the conversion of soluble proteins into amyloid fibrils remains poorly understood. Extensive \"in vitro\" measurements of protein aggregation kinetics have been reported, but no consensus mechanism has emerged until now. This contribution aims at overcoming this gap by proposing a theoretically consistent crystallization-like model (CLM) that is able to describe the classic types of amyloid fibrillization kinetics identified in our literature survey. Amyloid conversion represented as a function of time is shown to follow different curve shapes, ranging from sigmoidal to hyperbolic, according to the relative importance of the nucleation and growth steps. Using the CLM, apparently unrelated data are deconvoluted into generic mechanistic information integrating the combined influence of seeding, nucleation, growth, and fibril breakage events. It is notable that this complex assembly of interdependent events is ultimately reduced to a mathematically simple model, whose two parameters can be determined by little more than visual inspection. The good fitting results obtained for all cases confirm the CLM as a good approximation to the generalized underlying principle governing amyloid fibrillization. A perspective is presented on possible applications of the CLM during the development of new targets for amyloid disease therapeutics.

This model is hosted on BioModels Database and identified by: BIOMD0000000531.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3893, + "tag": "BioModels:BIOMD0000000531" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:45.356746+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000531", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2822": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2822, + "name": "Vazquez2014 - Chemical inhibition from amyloid protein aggregation kinetics", + "repository_type": "biomodels", + "summary": "
Vazquez2014 - Chemical inhibition fromamyloid protein aggregation kinetics

This model is described in the article:

V\u00e1zquez JA.
BMC Pharmacol Toxicol 2014; 15(1): 9

Abstract:

BACKGROUNDS: The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. RESULTS: In this manuscript, experimental data of insulin, A?42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH?=?7.4/T?=?37\u00b0C) and taiwaniaflavone for A?42 were the compounds studied that shown the greatest inhibition capacity. CONCLUSIONS: An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them.

This model is hosted on BioModels Database and identified by: BIOMD0000000532.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3894, + "tag": "BioModels:BIOMD0000000532" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:45.845946+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000532", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2823": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2823, + "name": "Steckmann2012 - Amyloid beta-protein fibrillogenesis (kinetics of secondary structure conversion)", + "repository_type": "biomodels", + "summary": "
Steckmann2012 - Amyloid beta-proteinfibrillogenesis (kinetics of secondary structure conversion)

This model is described in the article:

Steckmann T, Awan Z, Gerstman BS, Chapagain PP.
J. Theor. Biol. 2012 May; 301: 95-102

Abstract:

Amyloid fibrils are a common component in many debilitating human neurological diseases such as Alzheimer's (AD), Parkinson's, and Creutzfeldt-Jakob, and in animal diseases such as BSE. The role of fibrillar ?? proteins in AD has stimulated interest in the kinetics of ?? fibril formation. Kinetic models that include reaction pathways and rate parameters for the various stages of the process can be helpful towards understanding the dynamics on a molecular level. Based upon experimental data, we have developed a mathematical model for the reaction pathways and determined rate parameters for peptide secondary structural conversion and aggregation during the entire fibrillogenesis process from random coil to mature fibrils, including the molecular species that accelerate the conversions. The model and the rate parameters include different molecular structural stages in the nucleation and polymerization processes and the numerical solutions yield graphs of concentrations of different molecular species versus time that are in close agreement with experimental results. The model also allows for the calculation of the time-dependent increase in aggregate size. The calculated results agree well with experimental results, and allow differences in experimental conditions to be included in the calculations. The specific steps of the model and the rate constants that are determined by fitting to experimental data provide insight on the molecular species involved in the fibril formation process.

This model is hosted on BioModels Database and identified by: BIOMD0000000533.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3895, + "tag": "Amyloid-beta formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3896, + "tag": "BioModels:BIOMD0000000533" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:46.436607+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000533", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2824": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2824, + "name": "Dwivedi2014 - Healthy Volunteer IL6 Model", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Healthy Volunteer IL6Model
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Healthy Volunteer model are 2c and2d.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000534.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3897, + "tag": "BioModels:BIOMD0000000534" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3898, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4628, + "tag": "Crohn's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:46.954369+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000534", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2825": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2825, + "name": "Dwivedi2014 - Crohns IL6 Disease model - Anti-IL6 Antibody", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Crohns IL6 Disease model -Anti-IL6 Antibody
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Crohn's Disease model are 4a, 4b,4c and 5a.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000535.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3899, + "tag": "BioModels:BIOMD0000000535" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3898, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4628, + "tag": "Crohn's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:47.454753+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000535", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2826": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2826, + "name": "Dwivedi2014 - Crohns IL6 Disease model - sgp130 activity", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Crohns IL6 Disease model -sgp130 activity
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
The figure referring to this Crohn's Disease model is 6b.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000536.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3900, + "tag": "BioModels:BIOMD0000000536" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3898, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4628, + "tag": "Crohn's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:47.952252+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000536", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2827": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2827, + "name": "Dwivedi2014 - Crohns IL6 Disease model - Anti-IL6R Antibody", + "repository_type": "biomodels", + "summary": "
Dwivedi2014 - Crohns IL6 Disease model -Anti-IL6R Antibody
This model is comprised of four models:
Possible avenues for Interleukin-6 (IL-6) inhibition intreating Crohn's disease are compared here. Each model refers toseparate ligands. The system simulates differential activity of theligands on the signalling of IL-6. This affects Signal Transducer and Activator ofTranscription 3 (STAT3) activity on the production ofbiomarker C-Reactive Protein (CRP) expression.
Figures referring to this Crohn's Disease model are 3a, 4d,4e, 4f and 5b.

This model is described in the article:

Dwivedi G, Fitz L, Hegen M, Martin SW, Harrold J, Heatherington A, Li C.
CPT Pharmacometrics Syst Pharmacol 2014; 3: e89

Abstract:

In this study, we have developed a multiscale systems model of interleukin (IL)-6-mediated immune regulation in Crohn's disease, by integrating intracellular signaling with organ-level dynamics of pharmacological markers underlying the disease. This model was linked to a general pharmacokinetic model for therapeutic monoclonal antibodies and used to comparatively study various biotherapeutic strategies targeting IL-6-mediated signaling in Crohn's disease. Our work illustrates techniques to develop mechanistic models of disease biology to study drug-system interaction. Despite a sparse training data set, predictions of the model were qualitatively validated by clinical biomarker data from a pilot trial with tocilizumab. Model-based analysis suggests that strategies targeting IL-6, IL-6R?, or the IL-6/sIL-6R? complex are less effective at suppressing pharmacological markers of Crohn's than dual targeting the IL-6/sIL-6R? complex in addition to IL-6 or IL-6R?. The potential value of multiscale system pharmacology modeling in drug discovery and development is also discussed.CPT: Pharmacometrics & Systems Pharmacology (2014) 3, e89; doi:10.1038/psp.2013.64; advance online publication 8 January 2014.

This model is hosted on BioModels Database and identified by: BIOMD0000000537.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3901, + "tag": "BioModels:BIOMD0000000537" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3898, + "tag": "Interleukin-6 receptor activity" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4628, + "tag": "Crohn's disease" + } + ], + "timestamp_created": "2025-01-30 13:36:48.430368+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000537", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2828": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2828, + "name": "Clarke2000 - One-hit model of cell death in neuronal degenerations", + "repository_type": "biomodels", + "summary": "
Clarke2000 - One-hit model of cell death inneuronal degenerations
This one-hit model fits differentneuronal-death associated diseases for different animalmodels.\u00a0

This model is described in the article:

Clarke G, Collins RA, Leavitt BR, Andrews DF, Hayden MR, Lumsden CJ, McInnes RR.
Nature 2000 Jul; 406(6792): 195-199

Abstract:

In genetic disorders associated with premature neuronal death, symptoms may not appear for years or decades. This delay in clinical onset is often assumed to reflect the occurrence of age-dependent cumulative damage. For example, it has been suggested that oxidative stress disrupts metabolism in neurological degenerative disorders by the cumulative damage of essential macromolecules. A prediction of the cumulative damage hypothesis is that the probability of cell death will increase over time. Here we show in contrast that the kinetics of neuronal death in 12 models of photoreceptor degeneration, hippocampal neurons undergoing excitotoxic cell death, a mouse model of cerebellar degeneration and Parkinson's and Huntington's diseases are all exponential and better explained by mathematical models in which the risk of cell death remains constant or decreases exponentially with age. These kinetics argue against the cumulative damage hypothesis; instead, the time of death of any neuron is random. Our findings are most simply accommodated by a 'one-hit' biochemical model in which mutation imposes a mutant steady state on the neuron and a single event randomly initiates cell death. This model appears to be common to many forms of neurodegeneration and has implications for therapeutic strategies.

This model is hosted on BioModels Database and identified by: BIOMD0000000538.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3902, + "tag": "BioModels:BIOMD0000000538" + }, + { + "id": 3903, + "tag": "Canis lupus familiaris" + }, + { + "id": 3904, + "tag": "Felis catus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3905, + "tag": "Neuron apoptotic process" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4629, + "tag": "Cerebral degeneration" + }, + { + "id": 4630, + "tag": "Huntington's disease" + }, + { + "id": 4631, + "tag": "Ocular albinism" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + }, + { + "id": 4632, + "tag": "Retinal degeneration" + }, + { + "id": 4633, + "tag": "Retinal detachment" + }, + { + "id": 4634, + "tag": "Retinal disease" + } + ], + "timestamp_created": "2025-01-30 13:36:48.923370+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000538", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2829": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2829, + "name": "Fran\u00e7ois2005 - Mixed Feedback Loop (two-gene network)", + "repository_type": "biomodels", + "summary": "Paul Fran\u00e7ois & Vincent Hakim. Core genetic module: the mixed feedback loop. Physical Review E 72, 3 Pt 1 (2005).

The so-called mixed feedback loop (MFL) is a small two-gene network where protein A regulates the transcription of protein B and the two proteins form a heterodimer. It has been found to be statistically over-represented in statistical analyses of gene and protein interaction databases and to lie at the core of several computer-generated genetic networks. Here, we propose and mathematically study a model of the MFL and show that, by itself, it can serve both as a bistable switch and as a clock (an oscillator) depending on kinetic parameters. The MFL phase diagram as well as a detailed description of the nonlinear oscillation regime are presented and some biological examples are discussed. The results emphasize the role of protein interactions in the function of genetic modules and the usefulness of modeling RNA dynamics explicitly.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3906, + "tag": "BioModels:BIOMD0000000539" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:49.438316+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000539", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2830": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2830, + "name": "Yugi2014 - Insulin induced signalling (PFKL phosphorylation) - model 1", + "repository_type": "biomodels", + "summary": "
Yugi2014 - Insulin induced signalling (PFKLphosphorylation) - model 1

Insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, which thereby controls a key reaction in glycolysis. This mechanism is revealed using the mathematical model. In this model, the PFKL phosphorylation time courses are obtained from experimental data.

Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio University, Japan) for his kind advice in converting the model from MATLAB to SBML.

This model is described in the article:

Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4): 1171-1183

Abstract:

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

This model is hosted on BioModels Database and identified by: BIOMD0000000540.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3907, + "tag": "BioModels:BIOMD0000000540" + }, + { + "id": 3908, + "tag": "Regulation of glycolytic process" + }, + { + "id": 3909, + "tag": "Response to insulin" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:49.924692+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000540", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2831": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2831, + "name": "Yugi2014 - Insulin induced signalling (PFKL phosphorylation) - model 2", + "repository_type": "biomodels", + "summary": "
Yugi2014 - Insulin induced signalling (PFKLphosphorylation) - model 2

Insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, which thereby controls a key reaction in glycolysis. This mechanism is revealed using the mathematical model. In this model, the PFKL phosphorylation time courses are calculation from the signalling pathway model developed by Kubata et al. (2012) ( MODEL1204060000 - Kubota2012_InsulinAction_AKTpathway).

Author's Note: Katsuyuki Yugi thank Akira Funahashi (Keio University, Japan) for his kind advice in converting the model from MATLAB to SBML.

This model is described in the article:

Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, Uda S, Kunida K, Tomizawa Y, Funato Y, Miki H, Matsumoto M, Nakayama KI, Kashikura K, Endo K, Ikeda K, Soga T, Kuroda S.
Cell Rep 2014 Aug; 8(4): 1171-1183

Abstract:

Cellular homeostasis is regulated by signals through multiple molecular networks that include protein phosphorylation and metabolites. However, where and when the signal flows through a network and regulates homeostasis has not been explored. We have developed a reconstruction method for the signal flow based on time-course phosphoproteome and metabolome data, using multiple databases, and have applied it to acute action of insulin, an important hormone for metabolic homeostasis. An insulin signal flows through a network, through signaling pathways that involve 13 protein kinases, 26 phosphorylated metabolic enzymes, and 35 allosteric effectors, resulting in quantitative changes in 44 metabolites. Analysis of the network reveals that insulin induces phosphorylation and activation of liver-type phosphofructokinase 1, thereby controlling a key reaction in glycolysis. We thus provide a versatile method of reconstruction of signal flow through the network using phosphoproteome and metabolome data.

This model is hosted on BioModels Database and identified by: BIOMD0000000541.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3910, + "tag": "BioModels:BIOMD0000000541" + }, + { + "id": 3908, + "tag": "Regulation of glycolytic process" + }, + { + "id": 3909, + "tag": "Response to insulin" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:50.421146+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000541", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2832": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2832, + "name": "Yuraszeck2010 - Vulnerabilities in the Tau Network in Tau Pathophysiology", + "repository_type": "biomodels", + "summary": "
Yuraszeck2010 - Vulnerabilities in the TauNetwork in Tau Pathophysiology

This model is described in the article:

Yuraszeck TM, Neveu P, Rodriguez-Fernandez M, Robinson A, Kosik KS, Doyle FJ 3rd.
PLoS Comput. Biol. 2010; 6(11): e1000997

Abstract:

The multifactorial nature of disease motivates the use of systems-level analyses to understand their pathology. We used a systems biology approach to study tau aggregation, one of the hallmark features of Alzheimer's disease. A mathematical model was constructed to capture the current state of knowledge concerning tau's behavior and interactions in cells. The model was implemented in silico in the form of ordinary differential equations. The identifiability of the model was assessed and parameters were estimated to generate two cellular states: a population of solutions that corresponds to normal tau homeostasis and a population of solutions that displays aggregation-prone behavior. The model of normal tau homeostasis was robust to perturbations, and disturbances in multiple processes were required to achieve an aggregation-prone state. The aggregation-prone state was ultrasensitive to perturbations in diverse subsets of networks. Tau aggregation requires that multiple cellular parameters are set coordinately to a set of values that drive pathological assembly of tau. This model provides a foundation on which to build and increase our understanding of the series of events that lead to tau aggregation and may ultimately be used to identify critical intervention points that can direct the cell away from tau aggregation to aid in the treatment of tau-mediated (or related) aggregation diseases including Alzheimer's.

This model is hosted on BioModels Database and identified by: BIOMD0000000542.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3911, + "tag": "BioModels:BIOMD0000000542" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4635, + "tag": "Tauopathy" + } + ], + "timestamp_created": "2025-01-30 13:36:50.913823+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000542", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2833": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2833, + "name": "Qi2013 - IL-6 and IFN crosstalk model (non-competitive)", + "repository_type": "biomodels", + "summary": "
Qi2013 - IL-6 and IFN crosstalk model(non-competitive)
This model [BIOMD0000000543]describes the crosstalk between IFN-gamma and IL-6 inducedsignalling; it aims to outline mechanisms and factors that maycontrol the interaction between both signalling pathways,discussing a role of heterodimer formation in signallingdysfunction.

To account for the possibility of different IFNR and gp130binding sites for STAT1 and STAT3, model 1 [BIOMD0000000543]assumes that there is no competition between STAT1 and STAT3 forthe receptor complexes (includes two extra reactions).
The reverse of this is true in model 2 [BIOMD0000000544]where it generally is assumed that there is competition betweenSTAT1 and STAT3 for the receptor complexes.

This model is described in the article:

Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX.
BMC Bioinformatics 2013; 14: 41

Abstract:

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000543.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3912, + "tag": "BioModels:BIOMD0000000543" + }, + { + "id": 3913, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:36:51.406087+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000543", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2834": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2834, + "name": "Qi2013 - IL-6 and IFN crosstalk model", + "repository_type": "biomodels", + "summary": "
Qi2013 - IL-6 and IFN crosstalk model
This model [BIOMD0000000544]describes the crosstalk between IFN-gamma and IL-6 inducedsignalling; it aims to outline mechanisms and factors that maycontrol the interaction between both signalling pathways,discussing a role of heterodimer formation in signallingdysfunction.

To account for the possibility of different IFNR and gp130binding sites for STAT1 and STAT3, model 1
[BIOMD0000000543]assumes that there is no competition between STAT1 and STAT3 forthe receptor complexes (includes two extra reactions).
The reverse of this is true in model 2 [BIOMD0000000544]where it generally is assumed that there is competition betweenSTAT1 and STAT3 for the receptor complexes.

This model is described in the article:

Qi YF, Huang YX, Wang HY, Zhang Y, Bao YL, Sun LG, Wu Y, Yu CL, Song ZB, Zheng LH, Sun Y, Wang GN, Li YX.
BMC Bioinformatics 2013; 14: 41

Abstract:

BACKGROUND: Interferon-gamma (IFN-gamma) and interleukin-6 (IL-6) are multifunctional cytokines that regulate immune responses, cell proliferation, and tumour development and progression, which frequently have functionally opposing roles. The cellular responses to both cytokines are activated via the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. During the past 10 years, the crosstalk mechanism between the IFN-gamma and IL-6 pathways has been studied widely and several biological hypotheses have been proposed, but the kinetics and detailed crosstalk mechanism remain unclear. RESULTS: Using established mathematical models and new experimental observations of the crosstalk between the IFN-gamma and IL-6 pathways, we constructed a new crosstalk model that considers three possible crosstalk levels: (1) the competition between STAT1 and STAT3 for common receptor docking sites; (2) the mutual negative regulation between SOCS1 and SOCS3; and (3) the negative regulatory effects of the formation of STAT1/3 heterodimers. A number of simulations were tested to explore the consequences of cross-regulation between the two pathways. The simulation results agreed well with the experimental data, thereby demonstrating the effectiveness and correctness of the model. CONCLUSION: In this study, we developed a crosstalk model of the IFN-gamma and IL-6 pathways to theoretically investigate their cross-regulation mechanism. The simulation experiments showed the importance of the three crosstalk levels between the two pathways. In particular, the unbalanced competition between STAT1 and STAT3 for IFNR and gp130 led to preferential activation of IFN-gamma and IL-6, while at the same time the formation of STAT1/3 heterodimers enhanced preferential signal transduction by sequestering a fraction of the activated STATs. The model provided a good explanation of the experimental observations and provided insights that may inform further research to facilitate a better understanding of the cross-regulation mechanism between the two pathways.

This model is hosted on BioModels Database and identified by: BIOMD0000000544.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3914, + "tag": "BioModels:BIOMD0000000544" + }, + { + "id": 3913, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 13:53:36.866065+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000544", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2835": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2835, + "name": "Ouyang2014 - photomorphogenic UV-B signalling network", + "repository_type": "biomodels", + "summary": "
Ouyang2014 - photomorphogenic UV-B signallingnetwork

This model is described in the article:

Ouyang X, Huang X, Jin X, Chen Z, Yang P, Ge H, Li S, Deng XW.
Proc. Natl. Acad. Sci. U.S.A. 2014 Aug; 111(31): 11539-11544

Abstract:

Long-wavelength and low-fluence UV-B light is an informational signal known to induce photomorphogenic development in plants. Using the model plant Arabidopsis thaliana, a variety of factors involved in UV-B-specific signaling have been experimentally characterized over the past decade, including the UV-B light receptor UV resistance locus 8; the positive regulators constitutive photomorphogenesis 1 and elongated hypocotyl 5; and the negative regulators cullin4, repressor of UV-B photomorphogenesis 1 (RUP1), and RUP2. Individual genetic and molecular studies have revealed that these proteins function in either positive or negative regulatory capacities for the sufficient and balanced transduction of photomorphogenic UV-B signal. Less is known, however, regarding how these signaling events are systematically linked. In our study, we use a systems biology approach to investigate the dynamic behaviors and correlations of multiple signaling components involved in Arabidopsis UV-B-induced photomorphogenesis. We define a mathematical representation of photomorphogenic UV-B signaling at a temporal scale. Supplemented with experimental validation, our computational modeling demonstrates the functional interaction that occurs among different protein complexes in early and prolonged response to photomorphogenic UV-B.

This model is hosted on BioModels Database and identified by: BIOMD0000000545.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3915, + "tag": "BioModels:BIOMD0000000545" + }, + { + "id": 3916, + "tag": "Photomorphogenesis" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:37.398067+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000545", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2836": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2836, + "name": "Miao2010 - Innate and adaptive immune responses to primary Influenza A Virus infection_1_1", + "repository_type": "biomodels", + "summary": "
Miao2010 - Innate and adaptive immuneresponses to primary Influenza A Virus infection

This model is described in the article:

Miao H, Hollenbaugh JA, Zand MS, Holden-Wiltse J, Mosmann TR, Perelson AS, Wu H, Topham DJ.
J. Virol. 2010 Jul; 84(13): 6687-6698

Abstract:

Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is approximately 0.5 days, and the average half-life of free infectious virus is approximately 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity.

This model is hosted on BioModels Database and identified by: BIOMD0000000546.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3917, + "tag": "Adaptive immune response" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3918, + "tag": "BioModels:BIOMD0000000546" + }, + { + "id": 3919, + "tag": "Influenza A virus (strain A/X-31 H3N2)" + }, + { + "id": 3920, + "tag": "Innate immune response" + }, + { + "id": 3422, + "tag": "Murinae" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4617, + "tag": "Influenza" + } + ], + "timestamp_created": "2025-01-30 13:53:37.924495+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000546", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2837": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2837, + "name": "Talemi2014 - Arsenic toxicity and detoxification mechanisms in yeast", + "repository_type": "biomodels", + "summary": "
Talemi2014 - Arsenic toxicity anddetoxification mechanisms in yeast
The model implements arsenite (AsIII)transport regulation, its distribution within main cellular AsIIIpools and detoxification. The intracellular As pools considered arefree AsIII (AsIIIin), protein-bound AsIII (AsIIIprot), glutathioneconjugated AsIII (AsGS3) and vacuolar sequestered AsIII (vAsGS3).

This model is described in the article:

Talemi SR, Jacobson T, Garla V, Navarrete C, Wagner A, Tam\u00e1s MJ, Schaber J.
Mol. Microbiol. 2014 Jun; 92(6): 1343-1356

Abstract:

Arsenic has a dual role as causative and curative agent of human disease. Therefore, there is considerable interest in elucidating arsenic toxicity and detoxification mechanisms. By an ensemble modelling approach, we identified a best parsimonious mathematical model which recapitulates and predicts intracellular arsenic dynamics for different conditions and mutants, thereby providing novel insights into arsenic toxicity and detoxification mechanisms in yeast, which could partly be confirmed experimentally by dedicated experiments. Specifically, our analyses suggest that: (i) arsenic is mainly protein-bound during short-term (acute) exposure, whereas glutathione-conjugated arsenic dominates during long-term (chronic) exposure, (ii) arsenic is not stably retained, but can leave the vacuole via an export mechanism, and (iii) Fps1 is controlled by Hog1-dependent and Hog1-independent mechanisms during arsenite stress. Our results challenge glutathione depletion as a key mechanism for arsenic toxicity and instead suggest that (iv) increased glutathione biosynthesis protects the proteome against the damaging effects of arsenic and that (v) widespread protein inactivation contributes to the toxicity of this metalloid. Our work in yeast may prove useful to elucidate similar mechanisms in higher eukaryotes and have implications for the use of arsenic in medical therapy.

This model is hosted on BioModels Database and identified by: BIOMD0000000547.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3921, + "tag": "BioModels:BIOMD0000000547" + }, + { + "id": 3922, + "tag": "Response to arsenic-containing substance" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:53:38.494657+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000547", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2838": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2838, + "name": "Sneppen2009 - Modeling proteasome dynamics in Parkinson's disease", + "repository_type": "biomodels", + "summary": "
Sneppen2009 - Modeling proteasome dynamics inParkinson's disease

This model is described in the article:

Sneppen K, Lizana L, Jensen MH, Pigolotti S, Otzen D.
Phys Biol 2009; 6(3): 036005

Abstract:

In Parkinson's disease (PD), there is evidence that alpha-synuclein (alphaSN) aggregation is coupled to dysfunctional or overburdened protein quality control systems, in particular the ubiquitin-proteasome system. Here, we develop a simple dynamical model for the on-going conflict between alphaSN aggregation and the maintenance of a functional proteasome in the healthy cell, based on the premise that proteasomal activity can be titrated out by mature alphaSN fibrils and their protofilament precursors. In the presence of excess proteasomes the cell easily maintains homeostasis. However, when the ratio between the available proteasome and the alphaSN protofilaments is reduced below a threshold level, we predict a collapse of homeostasis and onset of oscillations in the proteasome concentration. Depleted proteasome opens for accumulation of oligomers. Our analysis suggests that the onset of PD is associated with a proteasome population that becomes occupied in periodic degradation of aggregates. This behavior is found to be the general state of a proteasome/chaperone system under pressure, and suggests new interpretations of other diseases where protein aggregation could stress elements of the protein quality control system.

This model is hosted on BioModels Database and identified by: BIOMD0000000548.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3923, + "tag": "BioModels:BIOMD0000000548" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 3166, + "tag": "Proteasome-mediated ubiquitin-dependent protein catabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:39.011606+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000548", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2839": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2839, + "name": "Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis - Age Dependent", + "repository_type": "biomodels", + "summary": "
Baker2013 - Cytokine Mediated Inflammation inRheumatoid Arthritis - Age Dependant
This model by Baker M. 2013, describesthe interaction between pro and anti-inflammatory cytokinesignalling in rheumatoid arthritis.

Using two ordinary differential equations, the first model [BIOMD0000000550]analyses bifurcation and describes different pathological states byaltering inflammatory regulation parameters.
The second model [BIOMD0000000549]includes the effect that ageing has on pro-inflammatory signalling,allowing for time-dependant properties and disease progression tobe observed. The author also describes potential dosing forreversal of the disease state.

This model is described in the article:

Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR.
Math Med Biol 2013 Dec; 30(4): 311-337

Abstract:

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.

This model is hosted on BioModels Database and identified by: BIOMD0000000549.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3924, + "tag": "BioModels:BIOMD0000000549" + }, + { + "id": 3913, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3925, + "tag": "Regulation of inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4636, + "tag": "Rheumatoid arthritis" + } + ], + "timestamp_created": "2025-01-30 13:53:39.518192+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000549", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2840": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2840, + "name": "Baker2013 - Cytokine Mediated Inflammation in Rheumatoid Arthritis", + "repository_type": "biomodels", + "summary": "
Baker2013 - Cytokine Mediated Inflammation inRheumatoid Arthritis
This model by Baker M. 2013, describesthe interaction between pro and anti-inflammatory cytokinesignalling in rheumatoid arthritis.

Using two ordinary differential equations, the first model [BIOMD0000000550]analyses bifurcation and describes different pathological states byaltering inflammatory regulation parameters.
The second model [BIOMD0000000549]includes the effect that ageing has on pro-inflammatory signalling,allowing for time-dependant properties and disease progression tobe observed. The author also describes potential dosing forreversal of the disease state.

This model is described in the article:

Baker M, Denman-Johnson S, Brook BS, Gaywood I, Owen MR.
Math Med Biol 2013 Dec; 30(4): 311-337

Abstract:

Rheumatoid arthritis (RA) is a chronic inflammatory disease preferentially affecting the joints and leading, if untreated, to progressive joint damage and disability. Cytokines, a group of small inducible proteins, which act as intercellular messengers, are key regulators of the inflammation that characterizes RA. They can be classified into pro-inflammatory and anti-inflammatory groups. Numerous cytokines have been implicated in the regulation of RA with complex up and down regulatory interactions. This paper considers a two-variable model for the interactions between pro-inflammatory and anti-inflammatory cytokines, and demonstrates that mathematical modelling may be used to investigate the involvement of cytokines in the disease process. The model displays a range of possible behaviours, such as bistability and oscillations, which are strongly reminiscent of the behaviour of RA e.g. genetic susceptibility and remitting-relapsing disease. We also show that the dose regimen as well as the dose level are important factors in RA treatments.

This model is hosted on BioModels Database and identified by: BIOMD0000000550.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3926, + "tag": "BioModels:BIOMD0000000550" + }, + { + "id": 3913, + "tag": "Cytokine-mediated signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3925, + "tag": "Regulation of inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4636, + "tag": "Rheumatoid arthritis" + } + ], + "timestamp_created": "2025-01-30 13:53:40.032672+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000550", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2841": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2841, + "name": "Das2010 - Effect of a gamma-secretase inhibitor on Amyloid-beta dynamics", + "repository_type": "biomodels", + "summary": "
Das2010 - Effect of a gamma-secretaseinhibitor on Amyloid-beta dynamics

This model is described in the article:

Das R, Nachbar RB, Edelstein-Keshet L, Saltzman JS, Wiener MC, Bagchi A, Bailey J, Coombs D, Simon AJ, Hargreaves RJ, Cook JJ.
Bull. Math. Biol. 2011 Jan; 73(1): 230-247

Abstract:

Aggregation of the small peptide amyloid beta (A?) into oligomers and fibrils in the brain is believed to be a precursor to Alzheimer's disease. A? is produced via multiple proteolytic cleavages of amyloid precursor protein (APP), mediated by the enzymes ?- and ?-secretase. In this study, we examine the temporal dynamics of soluble (unaggregated) A? in the plasma and cerebral-spinal fluid (CSF) of rhesus monkeys treated with different oral doses of a ?-secretase inhibitor. A dose-dependent reduction of A? concentration was observed within hours of drug ingestion, for all doses tested. A? concentration in the CSF returned to its predrug level over the monitoring period. In contrast, A? concentration in the plasma exhibited an unexpected overshoot to as high as 200% of the predrug concentration, and this overshoot persisted as late as 72 hours post-drug ingestion. To account for these observations, we proposed and analyzed a minimal physiological model for A? dynamics that could fit the data. Our analysis suggests that the overshoot arises from the attenuation of an A? clearance mechanism, possibly due to the inhibitor. Our model predicts that the efficacy of A? clearance recovers to its basal (pretreatment) value with a characteristic time of >48 hours, matching the time-scale of the overshoot. These results point to the need for a more detailed investigation of soluble A? clearance mechanisms and their interaction with A?-reducing drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000551.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3779, + "tag": "Amyloid-beta clearance" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3927, + "tag": "BioModels:BIOMD0000000551" + }, + { + "id": 3928, + "tag": "Macaca mulatta" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:40.752530+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000551", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2842": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2842, + "name": "Ehrenstein2000 - Positive-Feedback model for the loss of acetylcholine in Alzheimer's disease", + "repository_type": "biomodels", + "summary": "
Ehrenstein2000 - Positive-Feedback model forthe loss of acetylcholine in Alzheimer's disease
Curated model derived from

BIOMD0000000553

This model is described in the article:

Ehrenstein G, Galdzicki Z, Lange GD.
Ann. N. Y. Acad. Sci. 2000; 899: 283-291

Abstract:

We describe a two-component positive-feedback system that could account for the large reduction of acetylcholine that is characteristic of patients with Alzheimer's disease (AD). One component is beta-amyloid-induced apoptosis of cholinergic cells, leading to a decrease in acetylcholine. The other component is an increase in the concentration of beta-amyloid in response to a decrease in acetylcholine. We describe each mechanism with a differential equation, and then solve the two equations numerically. The solution provides a description of the time course of the reduction of acetylcholine in AD patients that is consistent with epidemiological data. This model may also provide an explanation for the significant, but lesser, decrease of other neurotransmitters that is characteristic of AD.

This model is hosted on BioModels Database and identified by: BIOMD0000000552.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3929, + "tag": "Acetylcholine biosynthetic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3930, + "tag": "BioModels:BIOMD0000000552" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:41.230072+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000552", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2843": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2843, + "name": "Ehrenstein1997 - The choline-leakage hypothesis in Alzheimer's disease", + "repository_type": "biomodels", + "summary": "
Ehrenstein1997 - The choline-leakagehypothesis in Alzheimer's disease

This model is described in the article:

Ehrenstein G, Galdzicki Z, Lange GD.
Biophys. J. 1997 Sep; 73(3): 1276-1280

Abstract:

We present a hypothesis for the loss of acetylcholine in Alzheimer's disease that is based on two recent experimental results: that beta-amyloid causes leakage of choline across cell membranes and that decreased production of acetylcholine increases the production of beta-amyloid. According to the hypothesis, an increase in beta-amyloid concentration caused by proteolysis of the amyloid precursor protein results in an increase in the leakage of choline out of cells. This leads to a reduction in intracellular choline concentration and hence a reduction in acetylcholine production. The reduction in acetylcholine production, in turn, causes an increase in the concentration of beta-amyloid. The resultant positive feedback between decreased acetylcholine and increased beta-amyloid accelerates the loss of acetylcholine. We compare the predictions of the choline-leakage hypothesis with a number of experimental observations. We also approximate it with a pair of ordinary differential equations. The solutions of these equations indicate that the loss of acetylcholine is very sensitive to the initial rate of beta-amyloid production.

This model is hosted on BioModels Database and identified by: BIOMD0000000553.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3929, + "tag": "Acetylcholine biosynthetic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3931, + "tag": "BioModels:BIOMD0000000553" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:41.742796+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000553", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2844": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2844, + "name": "Cloutier2009 - Brain Energy Metabolism", + "repository_type": "biomodels", + "summary": "
Cloutier2009 - Brain Energy Metabolism


This model was taken from the\u00a0 CellMLrepository \u00a0andautomatically converted to SBML.\u00a0\u00a0Following thesubmission the parameters are manually encoded and annotated asspices and global quantities by BioModels curators.\u00a0
\u00a0
Theoriginal model was:\u00a0 Cloutier M, Bolger FB, Lowry JP, Wellstead P. (2009) -version=1.0\u00a0
Theoriginal CellML model was created by:\u00a0
CatherineLloyd \u00a0
c.lloyd@auckland.ac.nz\u00a0
TheUniversity of Auckland\u00a0

This model is described in the article:

Cloutier M, Bolger FB, Lowry JP, Wellstead P.
J Comput Neurosci 2009 Dec; 27(3): 391-414

Abstract:

An integrative, systems approach to the modelling of brain energy metabolism is presented. Mechanisms such as glutamate cycling between neurons and astrocytes and glycogen storage in astrocytes have been implemented. A unique feature of the model is its calibration using in vivo data of brain glucose and lactate from freely moving rats under various stimuli. The model has been used to perform simulated perturbation experiments that show that glycogen breakdown in astrocytes is significantly activated during sensory (tail pinch) stimulation. This mechanism provides an additional input of energy substrate during high consumption phases. By way of validation, data from the perfusion of 50 microM propranolol in the rat brain was compared with the model outputs. Propranolol affects the glucose dynamics during stimulation, and this was accurately reproduced in the model by a reduction in the glycogen breakdown in astrocytes. The model's predictive capacity was verified by using data from a sensory stimulation (restraint) that was not used for model calibration. Finally, a sensitivity analysis was conducted on the model parameters, this showed that the control of energy metabolism and transport processes are critical in the metabolic behaviour of cerebral tissue.

This model is hosted on BioModels Database and identified by: BIOMD0000000554.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3932, + "tag": "BioModels:BIOMD0000000554" + }, + { + "id": 3933, + "tag": "Generation of precursor metabolites and energy" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:42.270092+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000554", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2845": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2845, + "name": "Auer2010 - Correlation between lag time and aggregation rate in protein aggregation", + "repository_type": "biomodels", + "summary": "
Auer2010 - Correlation between lag time andaggregation rate in protein aggregation

This model is described in the article:

Auer S, Kashchiev D.
Proteins 2010 Aug; 78(11): 2412-2416

Abstract:

Under favorable conditions, many proteins can assemble into macroscopically large aggregates such as the amyloid fibrils that are associated with Alzheimer's, Parkinson's, and other neurological and systemic diseases. The overall process of protein aggregation is characterized by initial lag time during which no detectable aggregation occurs in the solution and by maximal aggregation rate at which the dissolved protein converts into aggregates. In this study, the correlation between the lag time and the maximal rate of protein aggregation is analyzed. It is found that the product of these two quantities depends on a single numerical parameter, the kinetic index of the curve quantifying the time evolution of the fraction of protein aggregated. As this index depends relatively little on the conditions and/or system studied, our finding provides insight into why for many experiments the values of the product of the lag time and the maximal aggregation rate are often equal or quite close to each other. It is shown how the kinetic index is related to a basic kinetic parameter of a recently proposed theory of protein aggregation.

This model is hosted on BioModels Database and identified by: BIOMD0000000555.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3934, + "tag": "BioModels:BIOMD0000000555" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:42.793488+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000555", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2846": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2846, + "name": "Ortega2013 - Interplay between secretases determines biphasic amyloid-beta level", + "repository_type": "biomodels", + "summary": "
Ortega2013 - Interplay between secretasesdetermines biphasic amyloid-beta level

This model is described in the article:

Ortega F, Stott J, Visser SA, Bendtsen C.
J. Biol. Chem. 2013 Jan; 288(2): 785-792

Abstract:

Amyloid-? (A?) is produced by the consecutive cleavage of amyloid precursor protein (APP) first by ?-secretase, generating C99, and then by ?-secretase. APP is also cleaved by ?-secretase. It is hypothesized that reducing the production of A? in the brain may slow the progression of Alzheimer disease. Therefore, different ?-secretase inhibitors have been developed to reduce A? production. Paradoxically, it has been shown that low to moderate inhibitor concentrations cause a rise in A? production in different cell lines, in different animal models, and also in humans. A mechanistic understanding of the A? rise remains elusive. Here, a minimal mathematical model has been developed that quantitatively describes the A? dynamics in cell lines that exhibit the rise as well as in cell lines that do not. The model includes steps of APP processing through both the so-called amyloidogenic pathway and the so-called non-amyloidogenic pathway. It is shown that the cross-talk between these two pathways accounts for the increase in A? production in response to inhibitor, i.e. an increase in C99 will inhibit the non-amyloidogenic pathway, redirecting APP to be cleaved by ?-secretase, leading to an additional increase in C99 that overcomes the loss in ?-secretase activity. With a minor extension, the model also describes plasma A? profiles observed in humans upon dosing with a ?-secretase inhibitor. In conclusion, this mechanistic model rationalizes a series of experimental results that spans from in vitro to in vivo and to humans. This has important implications for the development of drugs targeting A? production in Alzheimer disease.

This model is hosted on BioModels Database and identified by: BIOMD0000000556.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3935, + "tag": "Amyloid precursor protein catabolic process" + }, + { + "id": 3895, + "tag": "Amyloid-beta formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3936, + "tag": "BioModels:BIOMD0000000556" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:43.312743+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000556", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2847": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2847, + "name": "Reiterer2013 - pseudophosphatase STYX role in ERK signalling", + "repository_type": "biomodels", + "summary": "
Reiterer2013 - pseudophosphatase STYX role inERK signalling

This model is described in the article:

Reiterer V, Fey D, Kolch W, Kholodenko BN, Farhan H.
Proc. Natl. Acad. Sci. U.S.A. 2013 Jul; 110(31): E2934-43

Abstract:

Serine/threonine/tyrosine-interacting protein (STYX) is a catalytically inactive member of the dual-specificity phosphatases (DUSPs) family. Whereas the role of DUSPs in cellular signaling is well explored, the function of STYX is still unknown. Here, we identify STYX as a spatial regulator of ERK signaling. We used predictive-model simulation to test several hypotheses for possible modes of STYX action. We show that STYX localizes to the nucleus, competes with nuclear DUSP4 for binding to ERK, and acts as a nuclear anchor that regulates ERK nuclear export. Depletion of STYX increases ERK activity in both cytosol and nucleus. Importantly, depletion of STYX causes an ERK-dependent fragmentation of the Golgi apparatus and inhibits Golgi polarization and directional cell migration. Finally, we show that overexpression of STYX reduces ERK1/2 activation, thereby blocking PC12 cell differentiation. Overall, our results identify STYX as an important regulator of ERK1/2 signaling critical for cell migration and PC12 cell differentiation.

This model is hosted on BioModels Database and identified by: BIOMD0000000557.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3937, + "tag": "BioModels:BIOMD0000000557" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3729, + "tag": "Regulation of ERK1 and ERK2 cascade" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:43.853741+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000557", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2848": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2848, + "name": "Cloutier2012 - Feedback motif for Parkinson's disease", + "repository_type": "biomodels", + "summary": "
Cloutier2012 - Feedback motif for Parkinson'sdisease

This model is described in the article:

Cloutier M, Middleton R, Wellstead P.
IET Syst Biol 2012 Jun; 6(3): 86-93

Abstract:

Previous article on the integrative modelling of Parkinson's disease (PD) described a mathematical model with properties suggesting that PD pathogenesis is associated with a feedback-induced biochemical bistability. In this article, the authors show that the dynamics of the mathematical model can be extracted and distilled into an equivalent two-state feedback motif whose stability properties are controlled by multi-factorial combinations of risk factors and genetic mutations associated with PD. Based on this finding, the authors propose a principle for PD pathogenesis in the form of the switch-like transition of a bistable feedback process from 'healthy' homeostatic levels of reactive oxygen species and the protein ?-synuclein, to an alternative 'disease' state in which concentrations of both molecules are stable at the damagingly high-levels associated with PD. The bistability is analysed using the rate curves and steady-state response characteristics of the feedback motif. In particular, the authors show how a bifurcation in the feedback motif marks the pathogenic moment at which the 'healthy' state is lost and the 'disease' state is initiated. Further analysis shows how known risks (such as: age, toxins and genetic predisposition) modify the stability characteristics of the feedback motif in a way that is compatible with known features of PD, and which explain properties such as: multi-factorial causality, variability in susceptibility and severity, multi-timescale progression and the special cases of familial Parkinson's and Parkinsonian symptoms induced purely by toxic stress.

Stress of 2.6 obtained by optimization(parameter S1) was imposed between days 10 and 150 in order toreproduce the Figure.

This model is hosted on BioModels Database and identified by: BIOMD0000000558.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3938, + "tag": "BioModels:BIOMD0000000558" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 3939, + "tag": "Response to reactive oxygen species" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:44.347386+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000558", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2849": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2849, + "name": "Ouzounoglou2014 - Modeling of alpha-synuclein effects on neuronal homeostasis", + "repository_type": "biomodels", + "summary": "
Ouzounoglou2014 - Modeling of alpha-synucleineffects on neuronal homeostasis

This model is described in the article:

Ouzounoglou E, Kalamatianos D, Emmanouilidou E, Xilouri M, Stefanis L, Vekrellis K, Manolakos ES.
BMC Syst Biol 2014; 8: 54

Abstract:

BACKGROUND: Alpha-synuclein (ASYN) is central in Parkinson's disease (PD) pathogenesis. Converging pieces of evidence suggest that the levels of ASYN expression play a critical role in both familial and sporadic Parkinson's disease. ASYN fibrils are the main component of inclusions called Lewy Bodies (LBs) which are found mainly in the surviving neurons of the substantia nigra. Despite the accumulated knowledge regarding the involvement of ASYN in molecular mechanisms underlying the development of PD, there is much information missing which prevents understanding the causes of the disease and how to stop its progression. RESULTS: Using a Systems Biology approach, we develop a biomolecular reactions model that describes the intracellular ASYN dynamics in relation to overexpression, post-translational modification, oligomerization and degradation of the protein. Especially for the proteolysis of ASYN, the model takes into account the biological knowledge regarding the contribution of Chaperone Mediated Autophagy (CMA), macro-autophagic and proteasome pathways in the protein's degradation. Importantly, inhibitory phenomena, caused by ASYN, concerning CMA (more specifically the lysosomal-associated membrane protein 2a, abbreviated as Lamp2a receptor, which is the rate limiting step of CMA) and the proteasome are carefully modeled. The model is validated by simulation studies of known experimental overexpression data from SH-SY5Y cells and the unknown model parameters are estimated either computationally or by experimental fitting. The calibrated model is then tested under three hypothetical intervention scenarios and in all cases predicts increased cell viability that agrees with experimental evidence. The biomodel has been annotated and is made available in SBML format. CONCLUSIONS: The mathematical model presented here successfully simulates the dynamic phenomena of ASYN overexpression and oligomerization and predicts the biological system's behavior in a number of scenarios not used for model calibration. It allows, for the first time, to qualitatively estimate the protein levels that are capable of deregulating proteolytic homeostasis. In addition, it can help form new hypotheses for intervention that could be tested experimentally.


Note: The model contains reactions of species located in different compartments. If the model is applied using volume sizes unequal to one, an extension of the model might be reasonable to guarantee mass conservation.


This model is hosted on BioModels Database and identified by: BIOMD0000000559.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3940, + "tag": "Autophagy" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3941, + "tag": "BioModels:BIOMD0000000559" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 3942, + "tag": "Protein catabolic process" + }, + { + "id": 3943, + "tag": "Regulation of dopamine metabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:44.866863+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000559", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2850": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2850, + "name": "Hui2016 - Age-related changes in articular cartilage", + "repository_type": "biomodels", + "summary": "
Hui2014 - Age-related changes in articularcartilage

This model is described in the article:

Wang Hui1, David A Young1, Andrew D Rowan1, Xin Xu2, Tim E Cawston1, Carole J Proctor1,3
Annals of the Rheumatic Diseases

Abstract:

Objective: To use a computational approach to investigate the cellular and extracellular matrix changes that occur with age in the knee joints of mice. Methods: Knee joints from an inbred C57/BL1/6 (ICRFa) mouse colony were harvested at 3\u201330?months of age. Sections were stained with H&E, Safranin-O, Picro-sirius red and antibodies to matrix metalloproteinase-13 (MMP-13), nitrotyrosine, LC-3B, Bcl-2, and cleaved type II collagen used for immunohistochemistry. Based on this and other data from the literature, a computer simulation model was built using the Systems Biology Markup Language using an iterative approach of data analysis and modelling. Individual parameters were subsequently altered to assess their effect on the model. Results: A progressive loss of cartilage matrix occurred with age. Nitrotyrosine, MMP-13 and anaplastic lymphoma kinase (ALK1) staining in cartilage increased with age with a concomitant decrease in LC-3B and Bcl-2. Stochastic simulations from the computational model showed a good agreement with these data, once transforming growth factor-? signalling via ALK1/ALK5 receptors was included. Oxidative stress and the interleukin 1 pathway were identified as key factors in driving the cartilage breakdown associated with ageing. Conclusions: A progressive loss of cartilage matrix and cellularity occurs with age. This is accompanied with increased levels of oxidative stress, apoptosis and MMP-13 and a decrease in chondrocyte autophagy. These changes explain the marked predisposition of joints to develop osteoarthritis with age. Computational modelling provides useful insights into the underlying mechanisms involved in age-related changes in musculoskeletal tissues.

This model is hosted on BioModels Database and identified by: BIOMD0000000560.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3944, + "tag": "Age-dependent response to oxidative stress involved in replicative cell aging" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3945, + "tag": "BioModels:BIOMD0000000560" + }, + { + "id": 3946, + "tag": "Cartilage development" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4595, + "tag": "Osteoarthritis" + } + ], + "timestamp_created": "2025-01-30 13:53:45.372248+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000560", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2851": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2851, + "name": "Martins2013 - True and apparent inhibition of amyloid fribril formation", + "repository_type": "biomodels", + "summary": "
Martins2013 - True and apparent inhibition ofamyloid fribril formation

This model is described in the article:

Martins PM.
Prion 2013 Mar-Apr; 7(2): 136-139

Abstract:

A possible therapeutic strategy for amyloid diseases involves the use of small molecule compounds to inhibit protein assembly into insoluble aggregates. According to the recently proposed Crystallization-Like Model, the kinetics of amyloid fibrillization can be retarded by decreasing the frequency of new fibril formation or by decreasing the elongation rate of existing fibrils. To the compounds that affect the nucleation and/or the growth steps we call true inhibitors. An apparent inhibition mechanism may however result from the alteration of thermodynamic properties such as the solubility of the amyloidogenic protein. Apparent inhibitors markedly influence protein aggregation kinetics measured in vitro, yet they are likely to lead to disappointing results when tested in vivo. This is because cells and tissues media are in general much more buffered against small variations in composition than the solutions prepared in lab. Here we show how to discriminate between true and apparent inhibition mechanisms from experimental data on protein aggregation kinetics. The goal is to be able to identify false positives much earlier during the drug development process.

This model is hosted on BioModels Database and identified by: BIOMD0000000561.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3947, + "tag": "BioModels:BIOMD0000000561" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + }, + { + "id": 4637, + "tag": "Prion disease" + } + ], + "timestamp_created": "2025-01-30 13:53:45.885819+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000561", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2852": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2852, + "name": "Chaouiya2013 - EGF and TNFalpha mediated signalling pathway", + "repository_type": "biomodels", + "summary": "
Chaouiya2013 - EGF and TNFalpha mediated signalling pathway

This model is described in the article:

Chaouiya C, B\u00e9renguier D, Keating SM, Naldi A, van Iersel MP, Rodriguez N, Dr\u00e4ger A, B\u00fcchel F, Cokelaer T, Kowal B, Wicks B, Gon\u00e7alves E, Dorier J, Page M, Monteiro PT, von Kamp A, Xenarios I, de Jong H, Hucka M, Klamt S, Thieffry D, Le Nov\u00e8re N, Saez-Rodriguez J, Helikar T.
BMC Syst Biol 2013; 7: 135

Abstract:

BACKGROUND: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. RESULTS: We present the Systems Biology Markup Language (SBML) Qualitative Models Package (\"qual\"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models. CONCLUSIONS: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.

This model is hosted on BioModels Database and identified by: BIOMD0000000562.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3948, + "tag": "BioModels:BIOMD0000000562" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:46.412453+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000562", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2853": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2853, + "name": "Pritchard2014 - plant-microbe interaction", + "repository_type": "biomodels", + "summary": "
Pritchard2014 - plant-microbeinteraction
Thismodel is an abstraction of a generic interaction between microbes,and a plant host. The reactions are generally intended to berepresentative of processes, not specific molecular mechanisms(except where indicated, eg. for activation of receptors). Themodel is intended to be of a similar level of abstraction as theZig-Zag model proposed in Jones and Dangl (2006) [PMID: 17108957], but to represent a dynamic system. Jones and Dangl (2006) model is used here to illustrate the advantages of dynamic representations of systems over expository models such as the Zig-Zag model.

This model is described in the article:

Pritchard L, Birch PR.
Mol. Plant Pathol. 2014 Dec; 15(9): 865-870

Abstract:


This model is hosted on BioModels Database and identified by: BIOMD0000000563.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3083, + "tag": "Arabidopsis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3949, + "tag": "BioModels:BIOMD0000000563" + }, + { + "id": 3950, + "tag": "Immune system process" + }, + { + "id": 3951, + "tag": "Pseudomonas syringae" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:46.911897+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000563", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2854": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2854, + "name": "Gould2013 - Temperature Sensitive Circadian Clock", + "repository_type": "biomodels", + "summary": "
Gould2011 - Temperature Sensitive CircadianClock
This model is a temperature sensitiveversion of Pokhilko et al.\u00a0 2010 (PMID:20865009),which is BIOMD0000000273in BioModels.

This model is described in the article:

Gould PD, Ugarte N, Domijan M, Costa M, Foreman J, Macgregor D, Rose K, Griffiths J, Millar AJ, Finkenst\u00e4dt B, Penfield S, Rand DA, Halliday KJ, Hall AJ.
Mol. Syst. Biol. 2013; 9: 650

Abstract:

Circadian clocks exhibit 'temperature compensation', meaning that they show only small changes in period over a broad temperature range. Several clock genes have been implicated in the temperature-dependent control of period in Arabidopsis. We show that blue light is essential for this, suggesting that the effects of light and temperature interact or converge upon common targets in the circadian clock. Our data demonstrate that two cryptochrome photoreceptors differentially control circadian period and sustain rhythmicity across the physiological temperature range. In order to test the hypothesis that the targets of light regulation are sufficient to mediate temperature compensation, we constructed a temperature-compensated clock model by adding passive temperature effects into only the light-sensitive processes in the model. Remarkably, this model was not only capable of full temperature compensation and consistent with mRNA profiles across a temperature range, but also predicted the temperature-dependent change in the level of LATE ELONGATED HYPOCOTYL, a key clock protein. Our analysis provides a systems-level understanding of period control in the plant circadian oscillator.

This model is hosted on BioModels Database and identified by: BIOMD0000000564.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3952, + "tag": "BioModels:BIOMD0000000564" + }, + { + "id": 3370, + "tag": "Circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:47.414850+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000564", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2855": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2855, + "name": "Machado2014 - Curcumin production pathway in Escherichia coli", + "repository_type": "biomodels", + "summary": "
Machado2014 - Curcumin production pathway inEscherichia coli

This model is described in the article:

Machado D, Rodrigues LR, Rocha I.
BioSystems 2014 Nov; 125: 16-21

Abstract:

Curcumin is a natural compound obtained from turmeric, and is well known for its pharmacological effects. In this work, we design a heterologous pathway for industrial production of curcumin in Escherichia coli. A kinetic model of the pathway is then developed and connected to a kinetic model of the central carbon metabolism of E. coli. This model is used for optimization of the mutant strain through a rational design approach, and two manipulation targets are identified for overexpression. Dynamic simulations are then performed to compare the curcumin production profiles of the different mutant strains. Our results show that it is possible to obtain a significant improvement in the curcumin production rates with the proposed mutants. The kinetic model here developed can be an important framework to optimize curcumin production at an industrial scale and add value to its biomedical potential.

This model is hosted on BioModels Database and identified by: BIOMD0000000565.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3953, + "tag": "BioModels:BIOMD0000000565" + }, + { + "id": 3954, + "tag": "Curcumin metabolic process" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:47.902630+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000565", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2856": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2856, + "name": "Morris2009 - \u03b1-Synuclein aggregation variable temperature and pH", + "repository_type": "biomodels", + "summary": "
Morris2009 - \u03b1-Synuclein aggregationvariable temperature and pH

This model is described in the article:

Morris AM, Finke RG.
Biophys. Chem. 2009 Mar; 140(1-3): 9-15

Abstract:

The aggregation of proteins is believed to be intimately connected to many neurodegenerative disorders. We recently reported an \"Ockham's razor\"/minimalistic approach to analyze the kinetic data of protein aggregation using the Finke-Watzky (F-W) 2-step model of nucleation (A-->B, rate constant k(1)) and autocatalytic growth (A+B-->2B, rate constant k(2)). With that kinetic model we have analyzed 41 representative protein aggregation data sets in two recent publications, including amyloid beta, alpha-synuclein, polyglutamine, and prion proteins (Morris, A. M., et al. (2008) Biochemistry 47, 2413-2427; Watzky, M. A., et al. (2008) Biochemistry 47, 10790-10800). Herein we use the F-W model to reanalyze protein aggregation kinetic data obtained under the experimental conditions of variable temperature or pH 2.0 to 8.5. We provide the average nucleation (k(1)) and growth (k(2)) rate constants and correlations with variable temperature or varying pH for the protein alpha-synuclein. From the variable temperature data, activation parameters DeltaG(double dagger), DeltaH(double dagger), and DeltaS(double dagger) are provided for nucleation and growth, and those values are compared to the available parameters reported in the previous literature determined using an empirical method. Our activation parameters suggest that nucleation and growth are energetically similar for alpha-synuclein aggregation (DeltaG(double dagger)(nucleation)=23(3) kcal/mol; DeltaG(double dagger)(growth)=22(1) kcal/mol at 37 degrees C). From the variable pH data, the F-W analyses show a maximal k(1) value at pH approximately 3, as well as minimal k(1) near the isoelectric point (pI) of alpha-synuclein. Since solubility and net charge are minimized at the pI, either or both of these factors may be important in determining the kinetics of the nucleation step. On the other hand, the k(2) values increase with decreasing pH (i.e., do not appear to have a minimum or maximum near the pI) which, when combined with the k(1) vs. pH (and pI) data, suggest that solubility and charge are less important factors for growth, and that charge is important in the k(1), nucleation step of alpha-synuclein. The chemically well-defined nucleation (k(1)) rate constants obtained from the F-W analysis are, as expected, different than the 1/lag-time empirical constants previously obtained. However, k(2)x[A](0) (where k(2) is the rate constant for autocatalytic growth and [A](0) is the initial protein concentration) is related to the empirical constant, k(app) obtained previously. Overall, the average nucleation and average growth rate constants for alpha-synuclein aggregation as a function of pH and variable temperature have been quantitated. Those values support the previously suggested formation of a partially folded intermediate that promotes aggregation under high temperature or acidic conditions.

This model is hosted on BioModels Database and identified by: BIOMD0000000566.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3955, + "tag": "BioModels:BIOMD0000000566" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:48.390086+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000566", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2857": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2857, + "name": "Morris2008 - Fitting protein aggregation data via F-W 2-step mechanism", + "repository_type": "biomodels", + "summary": "
Morris2008 - Fitting protein aggregation datavia F-W 2-step mechanism

This model is described in the article:

Morris AM, Watzky MA, Agar JN, Finke RG.
Biochemistry 2008 Feb; 47(8): 2413-2427

Abstract:

The aggregation of proteins has been hypothesized to be an underlying cause of many neurological disorders including Alzheimer's, Parkinson's, and Huntington's diseases; protein aggregation is also important to normal life function in cases such as G to F-actin, glutamate dehydrogenase, and tubulin and flagella formation. For this reason, the underlying mechanism of protein aggregation, and accompanying kinetic models for protein nucleation and growth (growth also being called elongation, polymerization, or fibrillation in the literature), have been investigated for more than 50 years. As a way to concisely present the key prior literature in the protein aggregation area, Table 1 in the main text summarizes 23 papers by 10 groups of authors that provide 5 basic classes of mechanisms for protein aggregation over the period from 1959 to 2007. However, and despite this major prior effort, still lacking are both (i) anything approaching a consensus mechanism (or mechanisms), and (ii) a generally useful, and thus widely used, simplest/\"Ockham's razor\" kinetic model and associated equations that can be routinely employed to analyze a broader range of protein aggregation kinetic data. Herein we demonstrate that the 1997 Finke-Watzky (F-W) 2-step mechanism of slow continuous nucleation, A --> B (rate constant k1), followed by typically fast, autocatalytic surface growth, A + B --> 2B (rate constant k2), is able to quantitatively account for the kinetic curves from all 14 representative data sets of neurological protein aggregation found by a literature search (the prion literature was largely excluded for the purposes of this study in order provide some limit to the resultant literature that was covered). The F-W model is able to deconvolute the desired nucleation, k1, and growth, k2, rate constants from those 14 data sets obtained by four different physical methods, for three different proteins, and in nine different labs. The fits are generally good, and in many cases excellent, with R2 values >or=0.98 in all cases. As such, this contribution is the current record of the widest set of protein aggregation data best fit by what is also the simplest model offered to date. Also provided is the mathematical connection between the 1997 F-W 2-step mechanism and the 2000 3-step mechanism proposed by Sait\u00f4 and co-workers. In particular, the kinetic equation for Sait\u00f4's 3-step mechanism is shown to be mathematically identical to the earlier, 1997 2-step F-W mechanism under the 3 simplifying assumptions Sait\u00f4 and co-workers used to derive their kinetic equation. A list of the 3 main caveats/limitations of the F-W kinetic model is provided, followed by the main conclusions from this study as well as some needed future experiments.

This model is hosted on BioModels Database and identified by: BIOMD0000000567.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3956, + "tag": "BioModels:BIOMD0000000567" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4630, + "tag": "Huntington's disease" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:48.928547+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000567", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2858": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2858, + "name": "Mueller2015 - Hepatocyte proliferation, T160 phosphorylation of CDK2", + "repository_type": "biomodels", + "summary": "
Mueller2015 - Hepatocyte proliferation, T160phosphorylation of CDK2

This model is described in the article:

Mueller S, Huard J, Waldow K, Huang X, D'Alessandro LA, Bohl S, B\u00f6rner K, Grimm D, Klamt S, Klingm\u00fcller U, Schilling M.
Mol. Syst. Biol. 2015; 11(3): 795

Abstract:

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

This model is hosted on BioModels Database and identified by: BIOMD0000000568.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3957, + "tag": "BioModels:BIOMD0000000568" + }, + { + "id": 3958, + "tag": "Hepatocyte proliferation" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4607, + "tag": "Liver disease" + } + ], + "timestamp_created": "2025-01-30 13:53:49.460365+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000568", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2859": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2859, + "name": "Dutta-Roy2015 - Opening of the multiple AMPA receptor conductance states", + "repository_type": "biomodels", + "summary": "
Dutta-Roy2015 - Opening of the multiple AMPAreceptor conductance states

This model is described in the article:

Dutta-Roy R, Rosenmund C, Edelstein SJ, Le Nov\u00e8re N.
PLoS ONE 2015; 10(1): e0116616

Abstract:

Modulation of the properties of AMPA receptors at the post-synaptic membrane is one of the main suggested mechanisms underlying fast synaptic transmission in the central nervous system of vertebrates. Electrophysiological recordings of single channels stimulated with agonists showed that both recombinant and native AMPA receptors visit multiple conductance states in an agonist concentration dependent manner. We propose an allosteric model of the multiple conductance states based on concerted conformational transitions of the four subunits, as an iris diaphragm. Our model predicts that the thermodynamic behaviour of the conductance states upon full and partial agonist stimulations can be described with increased affinity of receptors as they progress to higher conductance states. The model also predicts the existence of AMPA receptors in non-liganded conductive substates. However, the probability of spontaneous openings decreases with increasing conductances. Finally, we predict that the large conductance states are stabilized within the rise phase of a whole-cell EPSC in glutamatergic hippocampal neurons. Our model provides a mechanistic link between ligand concentration and conductance states that can explain thermodynamic and kinetic features of AMPA receptor gating.

This model is hosted on BioModels Database and identified by: BIOMD0000000569.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3959, + "tag": "AMPA glutamate receptor activity" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3960, + "tag": "BioModels:BIOMD0000000569" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:49.987225+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000569", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2860": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2860, + "name": "Aubert2002 - Coupling between Brain electrical activity, Metabolism and Hemodynamics", + "repository_type": "biomodels", + "summary": "
Aubert2002 - Coupling between Brainelectrical activity, Metabolism and Hemodynamics
Felix Winter encoded this model in SBMLas part of his work at ASD GmbH

This model is described in the article:

Aubert A, Costalat R.
Neuroimage 2002 Nov; 17(3): 1162-1181

Abstract:

In order to improve the interpretation of functional neuroimaging data, we implemented a mathematical model of the coupling between membrane ionic currents, energy metabolism (i.e., ATP regeneration via phosphocreatine buffer effect, glycolysis, and mitochondrial respiration), blood-brain barrier exchanges, and hemodynamics. Various hypotheses were tested for the variation of the cerebral metabolic rate of oxygen (CMRO(2)): (H1) the CMRO(2) remains at its baseline level; (H2) the CMRO(2) is enhanced as soon as the cerebral blood flow (CBF) increases; (H3) the CMRO(2) increase depends on intracellular oxygen and pyruvate concentrations, and intracellular ATP/ADP ratio; (H4) in addition to hypothesis H3, the CMRO(2) progressively increases, due to the action of a second messenger. A good agreement with experimental data from magnetic resonance imaging and spectroscopy (MRI and MRS) was obtained when we simulated sustained and repetitive activation protocols using hypotheses (H3) or (H4), rather than hypotheses (H1) or (H2). Furthermore, by studying the effect of the variation of some physiologically important parameters on the time course of the modeled blood-oxygenation-level-dependent (BOLD) signal, we were able to formulate hypotheses about the physiological or biochemical significance of functional magnetic resonance data, especially the poststimulus undershoot and the baseline drift.

This model is hosted on BioModels Database and identified by: BIOMD0000000570.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3961, + "tag": "BioModels:BIOMD0000000570" + }, + { + "id": 3933, + "tag": "Generation of precursor metabolites and energy" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:50.479053+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000570", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2861": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2861, + "name": "Nishio2008 - Design of the phosphotransferase system for enhanced glucose uptake in E. coli.", + "repository_type": "biomodels", + "summary": "
Nishio2008 - Design of the phosphotransferasesystem for enhanced glucose uptake in E. coli.

This model is described in the article:

Nishio Y, Usuda Y, Matsui K, Kurata H.
Mol. Syst. Biol. 2008; 4: 160

Abstract:

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate.

This model is hosted on BioModels Database and identified by: BIOMD0000000571.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3962, + "tag": "BioModels:BIOMD0000000571" + }, + { + "id": 3101, + "tag": "Escherichia coli (strain K12)" + }, + { + "id": 3963, + "tag": "Protein-N(PI)-phosphohistidine-sugar phosphotransferase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:50.993802+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000571", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2862": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2862, + "name": "Costa2014 - Computational Model of L. lactis Metabolism", + "repository_type": "biomodels", + "summary": "
Costa2014 - Computational Model of L. lactisMetabolism

This model is described in the article:

Costa RS, Hartmann A, Gaspar P, Neves AR, Vinga S.
Mol Biosyst 2014 Mar; 10(3): 628-639

Abstract:

Biomedical research and biotechnological production are greatly benefiting from the results provided by the development of dynamic models of microbial metabolism. Although several kinetic models of Lactococcus lactis (a Lactic Acid Bacterium (LAB) commonly used in the dairy industry) have been developed so far, most of them are simplified and focus only on specific metabolic pathways. Therefore, the application of mathematical models in the design of an engineering strategy for the production of industrially important products by L. lactis has been very limited. In this work, we extend the existing kinetic model of L. lactis central metabolism to include industrially relevant production pathways such as mannitol and 2,3-butanediol. In this way, we expect to study the dynamics of metabolite production and make predictive simulations in L. lactis. We used a system of ordinary differential equations (ODEs) with approximate Michaelis-Menten-like kinetics for each reaction, where the parameters were estimated from multivariate time-series metabolite concentrations obtained by our team through in vivo Nuclear Magnetic Resonance (NMR). The results show that the model captures observed transient dynamics when validated under a wide range of experimental conditions. Furthermore, we analyzed the model using global perturbations, which corroborate experimental evidence about metabolic responses upon enzymatic changes. These include that mannitol production is very sensitive to lactate dehydrogenase (LDH) in the wild type (W.T.) strain, and to mannitol phosphoenolpyruvate: a phosphotransferase system (PTS(Mtl)) in a LDH mutant strain. LDH reduction has also a positive control on 2,3-butanediol levels. Furthermore, it was found that overproduction of mannitol-1-phosphate dehydrogenase (MPD) in a LDH/PTS(Mtl) deficient strain can increase the mannitol levels. The results show that this model has prediction capability over new experimental conditions and offers promising possibilities to elucidate the effect of alterations in the main metabolism of L. lactis, with application in strain optimization.

This model is hosted on BioModels Database and identified by: BIOMD0000000572.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3964, + "tag": "BioModels:BIOMD0000000572" + }, + { + "id": 3965, + "tag": "Butanediol biosynthetic process" + }, + { + "id": 3009, + "tag": "Lactococcus lactis" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:51.498750+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000572", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2863": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2863, + "name": "Aguilera 2014 - HIV latency. Interaction between HIV proteins and immune response", + "repository_type": "biomodels", + "summary": "
Aguilera 2014 - HIV latency. Interactionbetween HIV proteins and immune response

This model is described in the article:

Aguilera LU, Rodr\u00edguez-Gonz\u00e1lez J.
J. Theor. Biol. 2014 Nov; 360: 67-77

Abstract:

HIV infection leads to two cell fates, the viral productive state or viral latency (a reversible non-productive state). HIV latency is relevant because infected active CD4+ T-lymphocytes can reach a resting memory state in which the provirus remains silent for long periods of time. Despite experimental and theoretical efforts, the causal molecular mechanisms responsible for HIV latency are only partially understood. Studies have determined that HIV latency is influenced by the innate immune response carried out by cell restriction factors that inhibit the postintegration steps in the virus replication cycle. In this study, we present a mathematical study that combines deterministic and stochastic approaches to analyze the interactions between HIV proteins and the innate immune response. Using wide ranges of parameter values, we observed the following: (1) a phenomenological description of the viral productive and latent cell phenotypes is obtained by bistable and bimodal dynamics, (2) biochemical noise reduces the probability that an infected cell adopts the latent state, (3) the effects of the innate immune response enhance the HIV latency state, (4) the conditions of the cell before infection affect the latent phenotype, i.e., the existing expression of cell restriction factors propitiates HIV latency, and existing expression of HIV proteins reduces HIV latency.

This model is hosted on BioModels Database and identified by: BIOMD0000000573.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3966, + "tag": "BioModels:BIOMD0000000573" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3920, + "tag": "Innate immune response" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4360, + "tag": "Human immunodeficiency virus infectious disease" + } + ], + "timestamp_created": "2025-01-30 13:53:52.063978+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000573", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2864": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2864, + "name": "Lai2014 - Hemiconcerted MWC model of intact calmodulin with two targets", + "repository_type": "biomodels", + "summary": "
Lai2014 - Hemiconcerted MWC model of intactcalmodulin with two targets

This model is described in the article:

Lai M, Brun D, Edelstein SJ, Le Nov\u00e8re N.
PLoS Comput. Biol. 2015 Jan; 11(1): e1004063

Abstract:

Calmodulin is a calcium-binding protein ubiquitous in eukaryotic cells, involved in numerous calcium-regulated biological phenomena, such as synaptic plasticity, muscle contraction, cell cycle, and circadian rhythms. It exibits a characteristic dumbell shape, with two globular domains (N- and C-terminal lobe) joined by a linker region. Each lobe can take alternative conformations, affected by the binding of calcium and target proteins. Calmodulin displays considerable functional flexibility due to its capability to bind different targets, often in a tissue-specific fashion. In various specific physiological environments (e.g. skeletal muscle, neuron dendritic spines) several targets compete for the same calmodulin pool, regulating its availability and affinity for calcium. In this work, we sought to understand the general principles underlying calmodulin modulation by different target proteins, and to account for simultaneous effects of multiple competing targets, thus enabling a more realistic simulation of calmodulin-dependent pathways. We built a mechanistic allosteric model of calmodulin, based on an hemiconcerted framework: each calmodulin lobe can exist in two conformations in thermodynamic equilibrium, with different affinities for calcium and different affinities for each target. Each lobe was allowed to switch conformation on its own. The model was parameterised and validated against experimental data from the literature. In spite of its simplicity, a two-state allosteric model was able to satisfactorily represent several sets of experiments, in particular the binding of calcium on intact and truncated calmodulin and the effect of different skMLCK peptides on calmodulin's saturation curve. The model can also be readily extended to include multiple targets. We show that some targets stabilise the low calcium affinity T state while others stabilise the high affinity R state. Most of the effects produced by calmodulin targets can be explained as modulation of a pre-existing dynamic equilibrium between different conformations of calmodulin's lobes, in agreement with linkage theory and MWC-type models.

This model is hosted on BioModels Database and identified by: BIOMD0000000574.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3967, + "tag": "BioModels:BIOMD0000000574" + }, + { + "id": 3305, + "tag": "Calmodulin-dependent protein kinase activity" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:52.631178+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000574", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2865": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2865, + "name": "Sass2009 - Approach to an \u03b1-synuclein-based BST model of Parkinson's disease", + "repository_type": "biomodels", + "summary": "
Sass2009 - Approach to an\u03b1-synuclein-based BST model of Parkinson's disease

This model is described in the article:

Sass MB, Lorenz AN, Green RL, Coleman RA.
J. Neurosci. Methods 2009 Apr; 178(2): 366-377

Abstract:

This paper presents a detailed systems model of Parkinson's disease (PD), developed utilizing a pragmatic application of biochemical systems theory (BST) intended to assist experimentalists in the study of system behavior. This approach utilizes relative values as a reasonable initial estimate for BST and provides a theoretical means of applying numerical solutions to qualitative and semi-quantitative understandings of cellular pathways and mechanisms. The approach allows for the simulation of human disease through its ability to organize and integrate existing information about metabolic pathways without having a full quantitative description of those pathways, so that hypotheses about individual processes may be tested in a systems environment. Incorporating this method, the PD model describes alpha-synuclein aggregation as mediated by dopamine metabolism, the ubiquitin-proteasome system, and lysosomal degradation, allowing for the examination of dynamic pathway interactions and the evaluation of possible toxic mechanisms in the aggregation process. Four system perturbations: elevated alpha-synuclein aggregation, impaired dopamine packaging, increased neurotoxins, and alpha-synuclein overexpression, were analyzed for correlation to qualitative PD system hypotheses present in the literature, with the model demonstrating a high level of agreement with these hypotheses. Additionally, various PD treatment methods, including levadopa and monoamine oxidase inhibition (MAOI) therapy, were applied to the disease models to examine their effects on the system. Future additions and refinements to the model may further the understanding of the emergent behaviors of the disease, helping in the identification of system sensitivities and possible therapeutic targets.

This model is hosted on BioModels Database and identified by: BIOMD0000000575.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3968, + "tag": "BioModels:BIOMD0000000575" + }, + { + "id": 3969, + "tag": "Chaperone-mediated autophagy" + }, + { + "id": 3970, + "tag": "Dopamine metabolic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 3971, + "tag": "Macroautophagy" + }, + { + "id": 3166, + "tag": "Proteasome-mediated ubiquitin-dependent protein catabolic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 13:53:53.167538+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000575", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2866": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2866, + "name": "Kolodkin2013 - Nuclear receptor-mediated cortisol signalling network", + "repository_type": "biomodels", + "summary": "
Kolodkin2013 - Nuclear receptor-mediatedcortisol signalling network

This model is described in the article:

Kolodkin A, Sahin N, Phillips A, Hood SR, Bruggeman FJ, Westerhoff HV, Plant N.
Nat Commun 2013; 4: 1792

Abstract:

It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body's response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress.

This model is hosted on BioModels Database and identified by: BIOMD0000000576.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3972, + "tag": "BioModels:BIOMD0000000576" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3973, + "tag": "Response to cortisol" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:53.668222+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000576", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2867": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2867, + "name": "Zhou2015 - Circadian clock with immune regulator NPR1", + "repository_type": "biomodels", + "summary": "
Zhou2015 - Circadian clock with immuneregulator NPR1
Arabidopsis clock model modified fromP2012 (Pokhilko et al., 2013 - BIOMD0000000445)model to include the master immune regulator NPR1 coupling to LHY,TOC1 and PRR7.
Triggers: The Global Quantities contain triggers that allowone to change coupling settings, Salicyclic acid (SA) treatment andnpr1 mutants.
LHY_on: true->NPR1 couples to LHY
PRR7_on: true->NPR1 couples to PRR7
WT: true->WT plants, false->npr1 mutant plants
SA: true->SA treated plants, false->no treatment
This model has L=1, i.e. operates only under constant lightconditions and is not aiming to make preditions under diurnalconditions. Due to period overshoot only time points after 28h arerelevant.

This model is described in the article:

Zhou M, Wang W, Karapetyan S, Mwimba M, Marqu\u00e9s J, Buchler NE, Dong X.
Nature 2015 Jun;

Abstract:

Recent studies have shown that in addition to the transcriptional circadian clock, many organisms, including Arabidopsis, have a circadian redox rhythm driven by the organism's metabolic activities. It has been hypothesized that the redox rhythm is linked to the circadian clock, but the mechanism and the biological significance of this link have only begun to be investigated. Here we report that the master immune regulator NPR1 (non-expressor of pathogenesis-related gene 1) of Arabidopsis is a sensor of the plant's redox state and regulates transcription of core circadian clock genes even in the absence of pathogen challenge. Surprisingly, acute perturbation in the redox status triggered by the immune signal salicylic acid does not compromise the circadian clock but rather leads to its reinforcement. Mathematical modelling and subsequent experiments show that NPR1 reinforces the circadian clock without changing the period by regulating both the morning and the evening clock genes. This balanced network architecture helps plants gate their immune responses towards the morning and minimize costs on growth at night. Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.

This model is hosted on BioModels Database and identified by: BIOMD0000000577.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3974, + "tag": "BioModels:BIOMD0000000577" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:54.199207+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000577", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2868": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2868, + "name": "Invergo2014 - Phototransduction cascade in mouse rod cells", + "repository_type": "biomodels", + "summary": "
Invergo2014 - Phototransduction cascade inmouse rod cells

This model is described in the article:

Invergo BM, Dell'Orco D, Montanucci L, Koch KW, Bertranpetit J.
Mol Biosyst 2014 Jun; 10(6): 1481-1489

Abstract:

Vertebrate visual phototransduction is perhaps the most well-studied G-protein signaling pathway. A wealth of available biochemical and electrophysiological data has resulted in a rich history of mathematical modeling of the system. However, while the most comprehensive models have relied upon amphibian biochemical and electrophysiological data, modern research typically employs mammalian species, particularly mice, which exhibit significantly faster signaling dynamics. In this work, we present an adaptation of a previously published, comprehensive model of amphibian phototransduction that can produce quantitatively accurate simulations of the murine photoresponse. We demonstrate the ability of the model to predict responses to a wide range of stimuli and under a variety of mutant conditions. Finally, we employ the model to highlight a likely unknown mechanism related to the interaction between rhodopsin and rhodopsin kinase.

This model is hosted on BioModels Database and identified by: BIOMD0000000578.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3975, + "tag": "BioModels:BIOMD0000000578" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3580, + "tag": "Phototransduction" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:54.751104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000578", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2869": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2869, + "name": "Sengupta2015 - Knowledge base model of human energy pool network (HEPNet)", + "repository_type": "biomodels", + "summary": "
Sengupta2015 - Knowledge base model of humanenergy pool network (HEPNet)

This model is described in the article:

Sengupta A, Grover M, Chakraborty A, Saxena S.
PLoS ONE 2015; 10(6): e0127918

Abstract:

HEPNet is an electronic representation of metabolic reactions occurring within human cellular organization focusing on inflow and outflow of the energy currency ATP, GTP and other energy associated moieties. The backbone of HEPNet consists of primary bio-molecules such as carbohydrates, proteins and fats which ultimately constitute the chief source for the synthesis and obliteration of energy currencies in a cell. A series of biochemical pathways and reactions constituting the catabolism and anabolism of various metabolites are portrayed through cellular compartmentalization. The depicted pathways function synchronously toward an overarching goal of producing ATP and other energy associated moieties to bring into play a variety of cellular functions. HEPNet is manually curated with raw data from experiments and is also connected to KEGG and Reactome databases. This model has been validated by simulating it with physiological states like fasting, starvation, exercise and disease conditions like glycaemia, uremia and dihydrolipoamide dehydrogenase deficiency (DLDD). The results clearly indicate that ATP is the master regulator under different metabolic conditions and physiological states. The results also highlight that energy currencies play a minor role. However, the moiety creatine phosphate has a unique character, since it is a ready-made source of phosphoryl groups for the rapid synthesis of ATP from ADP. HEPNet provides a framework for further expanding the network diverse age groups of both the sexes, followed by the understanding of energetics in more complex metabolic pathways that are related to human disorders.

This model is hosted on BioModels Database and identified by: BIOMD0000000579.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3976, + "tag": "BioModels:BIOMD0000000579" + }, + { + "id": 3933, + "tag": "Generation of precursor metabolites and energy" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:55.284107+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000579", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2870": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2870, + "name": "Sonntag2012 - mTOR model - IRS dependent regulation of AMPK by insulin", + "repository_type": "biomodels", + "summary": "
Sonntag2012 - mTOR model - IRS dependent regulation of AMPK by insulin
TSC1-TSC2 complex has two states: 1)active (TSC1_TSC2_pS1387), regulated by AMPK_pT172; 2) inactive(TSC1_TSC2_pT1462) regulated by Akt_pT308. Particularly, mTORC1 isinhibited by TSC1_TSC2 in active state. AMPK is activated at T172by the species IRS1_p activated by the insulin receptor uponinsulin stimulation. Consequently, AMPK_pT172 is inhibited bymTORC1_pS2448 indirectly by the p70-S6K-negative feedback loop.

This model is described in the article:

Sonntag AG, Dalle Pezze P, Shanley DP, Thedieck K.
FEBS J. 2012 Sep; 279(18): 3314-3328

Abstract:

Mammalian target of rapamycin (mTOR) kinase responds to growth factors, nutrients and cellular energy status and is a central controller of cellular growth. mTOR exists in two multiprotein complexes that are embedded into a complex signalling network. Adenosine monophosphate-dependent kinase (AMPK) is activated by energy deprivation and shuts off adenosine 5'-triphosphate (ATP)-consuming anabolic processes, in part via the inactivation of mTORC1. Surprisingly, we observed that AMPK not only responds to energy deprivation but can also be activated by insulin, and is further induced in mTORC1-deficient cells. We have recently modelled the mTOR network, covering both mTOR complexes and their insulin and nutrient inputs. In the present study we extended the network by an AMPK module to generate the to date most comprehensive data-driven dynamic AMPK-mTOR network model. In order to define the intersection via which AMPK is activated by the insulin network, we compared simulations for six different hypothetical model structures to our observed AMPK dynamics. Hypotheses ranking suggested that the most probable intersection between insulin and AMPK was the insulin receptor substrate (IRS) and that the effects of canonical IRS downstream cues on AMPK would be mediated via an mTORC1-driven negative-feedback loop. We tested these predictions experimentally in multiple set-ups, where we inhibited or induced players along the insulin-mTORC1 signalling axis and observed AMPK induction or inhibition. We confirmed the identified model and therefore report a novel connection within the insulin-mTOR-AMPK network: we conclude that AMPK is positively regulated by IRS and can be inhibited via the negative-feedback loop.

This model is hosted on BioModels Database and identified by: BIOMD0000000580.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3977, + "tag": "BioModels:BIOMD0000000580" + }, + { + "id": 3762, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3978, + "tag": "Regulation of cAMP-dependent protein kinase activity" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:55.771953+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000580", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2871": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2871, + "name": "DallePezze2012 - TSC-independent mTORC2 regulation", + "repository_type": "biomodels", + "summary": "
DallePezze2012 - TSC-independent mTORC2regulation

This model is described in the article:

Dalle Pezze P, Sonntag AG, Thien A, Prentzell MT, G\u00f6del M, Fischer S, Neumann-Haefelin E, Huber TB, Baumeister R, Shanley DP, Thedieck K.
Sci Signal 2012 Mar; 5(217): ra25

Abstract:

The kinase mammalian target of rapamycin (mTOR) exists in two multiprotein complexes (mTORC1 and mTORC2) and is a central regulator of growth and metabolism. Insulin activation of mTORC1, mediated by phosphoinositide 3-kinase (PI3K), Akt, and the inhibitory tuberous sclerosis complex 1/2 (TSC1-TSC2), initiates a negative feedback loop that ultimately inhibits PI3K. We present a data-driven dynamic insulin-mTOR network model that integrates the entire core network and used this model to investigate the less well understood mechanisms by which insulin regulates mTORC2. By analyzing the effects of perturbations targeting several levels within the network in silico and experimentally, we found that, in contrast to current hypotheses, the TSC1-TSC2 complex was not a direct or indirect (acting through the negative feedback loop) regulator of mTORC2. Although mTORC2 activation required active PI3K, this was not affected by the negative feedback loop. Therefore, we propose an mTORC2 activation pathway through a PI3K variant that is insensitive to the negative feedback loop that regulates mTORC1. This putative pathway predicts that mTORC2 would be refractory to Akt, which inhibits TSC1-TSC2, and, indeed, we found that mTORC2 was insensitive to constitutive Akt activation in several cell types. Our results suggest that a previously unknown network structure connects mTORC2 to its upstream cues and clarifies which molecular connectors contribute to mTORC2 activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000581.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3979, + "tag": "BioModels:BIOMD0000000581" + }, + { + "id": 3762, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:56.260776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000581", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2872": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2872, + "name": "DallePezze2014 - Cellular senescene-induced mitochondrial dysfunction", + "repository_type": "biomodels", + "summary": "
DallePazze2014 - Cellular senescene-inducedmitochondrial dysfunction

This model is described in the article:

Dalle Pezze P, Nelson G, Otten EG, Korolchuk VI, Kirkwood TB, von Zglinicki T, Shanley DP.
PLoS Comput. Biol. 2014 Aug; 10(8): e1003728

Abstract:

Cellular senescence, a state of irreversible cell cycle arrest, is thought to help protect an organism from cancer, yet also contributes to ageing. The changes which occur in senescence are controlled by networks of multiple signalling and feedback pathways at the cellular level, and the interplay between these is difficult to predict and understand. To unravel the intrinsic challenges of understanding such a highly networked system, we have taken a systems biology approach to cellular senescence. We report a detailed analysis of senescence signalling via DNA damage, insulin-TOR, FoxO3a transcription factors, oxidative stress response, mitochondrial regulation and mitophagy. We show in silico and in vitro that inhibition of reactive oxygen species can prevent loss of mitochondrial membrane potential, whilst inhibition of mTOR shows a partial rescue of mitochondrial mass changes during establishment of senescence. Dual inhibition of ROS and mTOR in vitro confirmed computational model predictions that it was possible to further reduce senescence-induced mitochondrial dysfunction and DNA double-strand breaks. However, these interventions were unable to abrogate the senescence-induced mitochondrial dysfunction completely, and we identified decreased mitochondrial fission as the potential driving force for increased mitochondrial mass via prevention of mitophagy. Dynamic sensitivity analysis of the model showed the network stabilised at a new late state of cellular senescence. This was characterised by poor network sensitivity, high signalling noise, low cellular energy, high inflammation and permanent cell cycle arrest suggesting an unsatisfactory outcome for treatments aiming to delay or reverse cellular senescence at late time points. Combinatorial targeted interventions are therefore possible for intervening in the cellular pathway to senescence, but in the cases identified here, are only capable of delaying senescence onset.

This model is hosted on BioModels Database and identified by: BIOMD0000000582.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3980, + "tag": "BioModels:BIOMD0000000582" + }, + { + "id": 3762, + "tag": "Cellular response to insulin stimulus" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3981, + "tag": "Regulation of cellular senescence" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:53:56.769118+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000582", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2873": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2873, + "name": "Leber2015 - Mucosal immunity and gut microbiome interaction during C. difficile infection", + "repository_type": "biomodels", + "summary": "
Leber2015 - Mucosal immunity and gutmicrobiome interaction during C. difficile infection

This model is described in the article:

Leber A, Viladomiu M, Hontecillas R, Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera J.
PLoS ONE 2015; 10(7): e0134849

Abstract:

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000583.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3982, + "tag": "BioModels:BIOMD0000000583" + }, + { + "id": 3983, + "tag": "Clostridioides difficile" + }, + { + "id": 3984, + "tag": "Immune response" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3985, + "tag": "Obsolete host-pathogen interaction" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4638, + "tag": "Clostridium difficile colitis" + } + ], + "timestamp_created": "2025-01-30 13:53:57.248869+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000583", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2874": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2874, + "name": "Mandlik2015 - Tristable genetic circuit of Leishmania", + "repository_type": "biomodels", + "summary": "Vineetha Mandlik, Mayuri Gurav & Shailza Singh. Regulatory dynamics of network architecture and function in tristable genetic circuit of Leishmania: a mathematical biology approach. Journal of Biomolecular Structure and Dynamics 33, 12 (2015).

The emerging field of synthetic biology has led to the design of tailor-made synthetic circuits for several therapeutic applications. Biological networks can be reprogramed by designing synthetic circuits that modulate the expression of target proteins. IPCS (inositol phosphorylceramide synthase) has been an attractive target in the sphingolipid metabolism of the parasite Leishmania. In this study, we have constructed a tristable circuit for the IPCS protein. The circuit has been validated and its long-term behavior has been assessed. The robustness and evolvability of the circuit has been estimated using evolutionary algorithms. The tristable synthetic circuit has been specifically designed to improve the rate of production of phosphatidylcholine: ceramide cholinephosphotransferase 4 (SLS4 protein). Site-specific delivery of the circuit into the parasite-infected macrophages could serve as a possible therapeutic intervention of the infectious disease 'Leishmaniasis'.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3986, + "tag": "BioModels:BIOMD0000000584" + }, + { + "id": 3987, + "tag": "Leishmania" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3988, + "tag": "Sphingolipid metabolic process" + }, + { + "id": 4618, + "tag": "Leishmaniasis" + } + ], + "timestamp_created": "2025-01-30 13:53:57.750157+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000584", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2875": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2875, + "name": "Rateitschak2012 - Interferon-gamma (IFN\u03b3) induced STAT1 signalling (PC_IFNg100)", + "repository_type": "biomodels", + "summary": "
Rateitschak2012 - Interferon-gamma (IFN\u03b3) induced STAT1 signalling (PC_IFNg100)

This model is described in the article:

Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O.
PLoS Comput. Biol. 2012; 8(12): e1002815

Abstract:

The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFN\u03b3) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFN\u03b3 inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFN\u03b3 induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types.

This model is hosted on BioModels Database and identified by: BIOMD0000000585.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3989, + "tag": "BioModels:BIOMD0000000585" + }, + { + "id": 3564, + "tag": "JAK-STAT cascade" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 3990, + "tag": "Response to interferon-gamma" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4639, + "tag": "Pancreatic cancer" + } + ], + "timestamp_created": "2025-01-30 13:53:58.254806+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000585", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2876": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2876, + "name": "Karapetyan2016 - Genetic oscillatory network - Activator Titration Circuit (ATC)", + "repository_type": "biomodels", + "summary": "
Karapetyan2016 - Genetic oscillatory network - Activator Titration Circuit (ATC)

This model is described in the article:

Karapetyan S, Buchler NE.
Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712

Abstract:

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

This model is hosted on BioModels Database and identified by: BIOMD0000000586.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3991, + "tag": "BioModels:BIOMD0000000586" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:53:58.816205+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000586", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2877": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2877, + "name": "Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC)", + "repository_type": "biomodels", + "summary": "
Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC)

This model is described in the article:

Karapetyan S, Buchler NE.
Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712

Abstract:

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators.

This model is hosted on BioModels Database and identified by: BIOMD0000000587.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3992, + "tag": "BioModels:BIOMD0000000587" + }, + { + "id": 3163, + "tag": "Regulation of gene expression" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 13:53:59.472865+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000587", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2878": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2878, + "name": "Benson2013 - Identification of key drug targets in nerve growth factor pathway", + "repository_type": "biomodels", + "summary": "
Benson2013 - Identification of key drug targets in nerve growth factor pathway

This model is described in the article:

Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P, van der Graaf PH.
Interface Focus 2013 Apr; 3(2): 20120071

Abstract:

The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.

This model is hosted on BioModels Database and identified by: BIOMD0000000588.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3993, + "tag": "BioModels:BIOMD0000000588" + }, + { + "id": 3994, + "tag": "Negative regulation of neurotrophin TRK receptor signaling pathway" + }, + { + "id": 3995, + "tag": "Nerve growth factor signaling pathway" + }, + { + "id": 3065, + "tag": "Rattus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:00.206890+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000588", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2879": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2879, + "name": "Valero2016 - Ascorbate-Glutathione cycle in chloroplasts under light/dark conditions", + "repository_type": "biomodels", + "summary": "
Valero2016 - Ascorbate-Glutathione cycle in chloroplasts under light/dark conditions

This model is described in the article:

Valero E, Maci\u00e0 H, De la Fuente IM, Hern\u00e1ndez JA, Gonz\u00e1lez-S\u00e1nchez MI, Garc\u00eda-Carmona F.
BMC Syst Biol 2016; 10(1): 11

Abstract:

Light/dark cycles are probably the most important environmental signals that regulate plant development. Light is essential for photosynthesis, but an excess, in combination with the unavoidable presence of atmospheric oxygen inside the chloroplast, leads to excessive reactive oxygen species production. Among the defense mechanisms that activate plants to cope with environmental stress situations, it is worth noting the ascorbate-glutathione cycle, a complex metabolic pathway in which a variety of photochemical, chemical and enzymatic steps are involved.We herein studied the dynamic behavior of this pathway under light/dark conditions and for several consecutive days. For this purpose, a mathematical model was developed including a variable electron source with a rate law proportional to the intensity of solar irradiance during the photoperiod, and which is continuously turned off at night and on again the next day. The model is defined by a nonlinear system of ordinary differential equations with an on/off time-dependent input, including a parameter to simulate the fact that the photoperiod length is not constant throughout the year, and which takes into account the particular experimental kinetics of each enzyme involved in the pathway. Unlike previous models, which have only provided steady-state solutions, the present model is able to simulate diurnal fluctuations in the metabolite concentrations, fluxes and enzymatic rates involved in the network.The obtained results are broadly consistent with experimental observations and highlight the key role played by ascorbate recycling for plants to adapt to their surrounding environment. This approach provides a new strategy to in vivo studies to analyze plant defense mechanisms against oxidative stress induced by external changes, which can also be extrapolated to other complex metabolic pathways to constitute a useful tool to the scientific community in general.

This model is hosted on BioModels Database and identified by: BIOMD0000000580.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3996, + "tag": "Ascorbate glutathione cycle" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3997, + "tag": "BioModels:BIOMD0000000589" + }, + { + "id": 3998, + "tag": "Embryophyta" + }, + { + "id": 3999, + "tag": "Response to abiotic stimulus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:00.951412+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000589", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2880": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2880, + "name": "Hermansen2015 - denovo biosynthesis of pyrimidines in yeast", + "repository_type": "biomodels", + "summary": "
Hermansen2015 - denovo biosynthesis of pyrimidines in yeast

This model is described in the article:

Hermansen RA , Mannakee BK , Knecht W , Liberles DA , Gutenkunst RN
BMC Evolutionary Biology. 2015, 15:232

Abstract:

Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes.Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint.We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

This model is hosted on BioModels Database and identified by: MODEL1512160000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4000, + "tag": "BioModels:BIOMD0000000590" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:01.759522+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000590", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2881": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2881, + "name": "Boehm2014 - isoform-specific dimerization of pSTAT5A and pSTAT5B", + "repository_type": "biomodels", + "summary": "
Boehm2014 - isoform-specific dimerization of pSTAT5A and pSTAT5B
To study STAT5 activation, the authors build a dynamic model of pSTAT5 isoform dimerization. Combinatorial binding of pSTAT5A and pSTAT5B is analysed using model hypotheses and concurrent experiments. Model parameters are derived from the experiments on Ba/F3 cells. Results show that pSTAT5 heterodimerization hypothesis for STAT5 activation favours experimental results.

This model is described in the article:

Boehm ME, Adlung L, Schilling M, Roth S, Klingm\u00fcller U, Lehmann WD
J Proteome Res. 2014 Dec 5;13(12):5685-94

Abstract:

STAT5A and STAT5B are important transcription factors that dimerize and transduce activation signals of cytokine receptors directly to the nucleus. A typical cytokine that mediates STAT5 activation is erythropoietin (Epo). Differential functions of STAT5A and STAT5B have been reported. However, the extent to which phosphorylated STAT5A and STAT5B (pSTAT5A, pSTAT5B) form homo- or heterodimers is not understood, nor is how this might influence the signal transmission to the nucleus. To study this, we designed a concept to investigate the isoform-specific dimerization behavior of pSTAT5A and pSTAT5B that comprises isoform-specific immunoprecipitation (IP), measurement of the degree of phosphorylation, and isoform ratio determination between STAT5A and STAT5B. For the main analytical method, we employed quantitative label-free and -based mass spectrometry. For the cellular model system, we used Epo receptor (EpoR)-expressing BaF3 cells (BaF3-EpoR) stimulated with Epo. Three hypotheses of dimer formation between pSTAT5A and pSTAT5B were used to explain the analytical results by a static mathematical model: formation of (i) homodimers only, (ii) heterodimers only, and (iii) random formation of homo- and heterodimers. The best agreement between experimental data and model simulations was found for the last case. Dynamics of cytoplasmic STAT5 dimerization could be explained by distinct nuclear import rates and individual nuclear retention for homo- and heterodimers of phosphorylated STAT5.

This model is hosted on BioModels Database and identified by: BIOMD0000000591.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality, and Use..

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4001, + "tag": "BioModels:BIOMD0000000591" + }, + { + "id": 4002, + "tag": "Erythropoietin-mediated signaling pathway" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3277, + "tag": "Regulation of tyrosine phosphorylation of STAT protein" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:02.244782+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000591", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2882": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2882, + "name": "Martinez-Sanchez2015 - T CD4+ lymphocyte transcriptional regulatory network", + "repository_type": "biomodels", + "summary": "Mariana Esther Martinez-Sanchez, Luis Mendoza, Carlos Villarreal & Elena R. Alvarez-Buylla. A Minimal Regulatory Network of Extrinsic and Intrinsic Factors Recovers Observed Patterns of CD4+ T Cell Differentiation and Plasticity. PLOS Computational Biology 11, 6 (2015).

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-\u03b2 and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4003, + "tag": "BioModels:BIOMD0000000592" + }, + { + "id": 4004, + "tag": "Cell differentiation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3487, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-01-30 13:54:02.835504+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000592", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2883": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2883, + "name": "Martinez-Sanchez2015 - T CD4+ lymphocyte transcriptional-signaling regulatory network", + "repository_type": "biomodels", + "summary": "
Martinez-Sanchez2015 - T CD4+ lymphocytetranscriptional-signaling regulatory network

This model is described in the article:

Martinez-Sanchez ME, Mendoza L, Villarreal C, Alvarez-Buylla ER.
PLoS Comput. Biol. 2015 Jun; 11(6): e1004324

Abstract:

CD4+ T cells orchestrate the adaptive immune response in vertebrates. While both experimental and modeling work has been conducted to understand the molecular genetic mechanisms involved in CD4+ T cell responses and fate attainment, the dynamic role of intrinsic (produced by CD4+ T lymphocytes) versus extrinsic (produced by other cells) components remains unclear, and the mechanistic and dynamic understanding of the plastic responses of these cells remains incomplete. In this work, we studied a regulatory network for the core transcription factors involved in CD4+ T cell-fate attainment. We first show that this core is not sufficient to recover common CD4+ T phenotypes. We thus postulate a minimal Boolean regulatory network model derived from a larger and more comprehensive network that is based on experimental data. The minimal network integrates transcriptional regulation, signaling pathways and the micro-environment. This network model recovers reported configurations of most of the characterized cell types (Th0, Th1, Th2, Th17, Tfh, Th9, iTreg, and Foxp3-independent T regulatory cells). This transcriptional-signaling regulatory network is robust and recovers mutant configurations that have been reported experimentally. Additionally, this model recovers many of the plasticity patterns documented for different T CD4+ cell types, as summarized in a cell-fate map. We tested the effects of various micro-environments and transient perturbations on such transitions among CD4+ T cell types. Interestingly, most cell-fate transitions were induced by transient activations, with the opposite behavior associated with transient inhibitions. Finally, we used a novel methodology was used to establish that T-bet, TGF-? and suppressors of cytokine signaling proteins are keys to recovering observed CD4+ T cell plastic responses. In conclusion, the observed CD4+ T cell-types and transition patterns emerge from the feedback between the intrinsic or intracellular regulatory core and the micro-environment. We discuss the broader use of this approach for other plastic systems and possible therapeutic interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000593.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4005, + "tag": "BioModels:BIOMD0000000593" + }, + { + "id": 4004, + "tag": "Cell differentiation" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3487, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-01-30 13:54:03.353894+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000593", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2884": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2884, + "name": "Capuani2015 - Binding of Cbl and Gbr2 to EGFR (Multisite Phosphorylation Model - MPM)", + "repository_type": "biomodels", + "summary": "Fabrizio Capuani, Alexia Conte, Elisabetta Argenzio, Luca Marchetti, Corrado Priami, Simona Polo, Pier Paolo Di Fiore, Sara Sigismund & Andrea Ciliberto. Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells. Nature Communications 6 (2015).

Ubiquitination of the epidermal growth factor receptor (EGFR) that occurs when Cbl and Grb2 bind to three phosphotyrosine residues (pY1045, pY1068 and pY1086) on the receptor displays a sharp threshold effect as a function of EGF concentration. Here we use a simple modelling approach together with experiments to show that the establishment of the threshold requires both the multiplicity of binding sites and cooperative binding of Cbl and Grb2 to the EGFR. While the threshold is remarkably robust, a more sophisticated model predicted that it could be modulated as a function of EGFR levels on the cell surface. We confirmed experimentally that the system has evolved to perform optimally at physiological levels of EGFR. As a consequence, this system displays an intrinsic weakness that causes--at the supraphysiological levels of receptor and/or ligand associated with cancer--uncoupling of the mechanisms leading to signalling through phosphorylation and attenuation through ubiquitination.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4006, + "tag": "BioModels:BIOMD0000000594" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 13:54:03.962206+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000594", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2885": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2885, + "name": "Capuani2015 - Binding of Cbl and Grb2 to EGFR (Early Activation Model - EAM)", + "repository_type": "biomodels", + "summary": "Fabrizio Capuani, Alexia Conte, Elisabetta Argenzio, Luca Marchetti, Corrado Priami, Simona Polo, Pier Paolo Di Fiore, Sara Sigismund & Andrea Ciliberto. Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells. Nature Communications 6 (2015).

Ubiquitination of the epidermal growth factor receptor (EGFR) that occurs when Cbl and Grb2 bind to three phosphotyrosine residues (pY1045, pY1068 and pY1086) on the receptor displays a sharp threshold effect as a function of EGF concentration. Here we use a simple modelling approach together with experiments to show that the establishment of the threshold requires both the multiplicity of binding sites and cooperative binding of Cbl and Grb2 to the EGFR. While the threshold is remarkably robust, a more sophisticated model predicted that it could be modulated as a function of EGFR levels on the cell surface. We confirmed experimentally that the system has evolved to perform optimally at physiological levels of EGFR. As a consequence, this system displays an intrinsic weakness that causes--at the supraphysiological levels of receptor and/or ligand associated with cancer--uncoupling of the mechanisms leading to signalling through phosphorylation and attenuation through ubiquitination.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4007, + "tag": "BioModels:BIOMD0000000595" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 13:54:04.462553+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000595", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2886": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 2886, + "name": "Philipson2015 - Innate immune response modulated by NLRX1", + "repository_type": "biomodels", + "summary": "Casandra W. Philipson, Josep Bassaganya-Riera, Monica Viladomiu, Barbara Kronsteiner, Vida Abedi, Stefan Hoops, Pawel Michalak, Lin Kang, Stephen E. Girardin & Raquel Hontecillas. Modeling the Regulatory Mechanisms by Which NLRX1 Modulates Innate Immune Responses to Helicobacter pylori Infection. PLOS ONE 10, 9 (2015).

Helicobacter pylori colonizes half of the world's population as the dominant member of the gastric microbiota resulting in a lifelong chronic infection. Host responses toward the bacterium can result in asymptomatic, pathogenic or even favorable health outcomes; however, mechanisms underlying the dual role of H. pylori as a commensal versus pathogenic organism are not well characterized. Recent evidence suggests mononuclear phagocytes are largely involved in shaping dominant immunity during infection mediating the balance between host tolerance and succumbing to overt disease. We combined computational modeling, bioinformatics and experimental validation in order to investigate interactions between macrophages and intracellular H. pylori. Global transcriptomic analysis on bone marrow-derived macrophages (BMDM) in a gentamycin protection assay at six time points unveiled the presence of three sequential host response waves: an early transient regulatory gene module followed by sustained and late effector responses. Kinetic behaviors of pattern recognition receptors (PRRs) are linked to differential expression of spatiotemporal response waves and function to induce effector immunity through extracellular and intracellular detection of H. pylori. We report that bacterial interaction with the host intracellular environment caused significant suppression of regulatory NLRC3 and NLRX1 in a pattern inverse to early regulatory responses. To further delineate complex immune responses and pathway crosstalk between effector and regulatory PRRs, we built a computational model calibrated using time-series RNAseq data. Our validated computational hypotheses are that: 1) NLRX1 expression regulates bacterial burden in macrophages; and 2) early host response cytokines down-regulate NLRX1 expression through a negative feedback circuit. This paper applies modeling approaches to characterize the regulatory role of NLRX1 in mechanisms of host tolerance employed by macrophages to respond to and/or to co-exist with intracellular H. pylori.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4008, + "tag": "BioModels:BIOMD0000000596" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:04.995982+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000596", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2887": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2887, + "name": "Flis2015 - Plant clock gene circuit (P2011.1.2 PLM_71 ver 1)", + "repository_type": "biomodels", + "summary": "
Flis2015 - Plant clock gene circuit(P2011.1.2 PLM_71 ver 1)

This model is described in the article:

Flis A, Fern\u00e1ndez AP, Zielinski T, Mengin V, Sulpice R, Stratford K, Hume A, Pokhilko A, Southern MM, Seaton DD, McWatters HG, Stitt M, Halliday KJ, Millar AJ.
Open Biol 2015 Oct; 5(10):

Abstract:

Our understanding of the complex, transcriptional feedback loops in the circadian clock mechanism has depended upon quantitative, timeseries data from disparate sources. We measure clock gene RNA profiles in Arabidopsis thaliana seedlings, grown with or without exogenous sucrose, or in soil-grown plants and in wild-type and mutant backgrounds. The RNA profiles were strikingly robust across the experimental conditions, so current mathematical models are likely to be broadly applicable in leaf tissue. In addition to providing reference data, unexpected behaviours included co-expression of PRR9 and ELF4, and regulation of PRR5 by GI. Absolute RNA quantification revealed low levels of PRR9 transcripts (peak approx. 50 copies cell(-1)) compared with other clock genes, and threefold higher levels of LHY RNA (more than 1500 copies cell(-1)) than of its close relative CCA1. The data are disseminated from BioDare, an online repository for focused timeseries data, which is expected to benefit mechanistic modelling. One data subset successfully constrained clock gene expression in a complex model, using publicly available software on parallel computers, without expert tuning or programming. We outline the empirical and mathematical justification for data aggregation in understanding highly interconnected, dynamic networks such as the clock, and the observed design constraints on the resources required to make this approach widely accessible.


 cL_m_degr, param m1, modified to ensure light rate > dark rate.  

This model is hosted on BioModels Database and identified by: MODEL1510190000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4009, + "tag": "BioModels:BIOMD0000000597" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:05.494529+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000597", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2888": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2888, + "name": "Flis2015 - Plant clock gene circuit (P2011.2.1 PLM_71 ver 2)", + "repository_type": "biomodels", + "summary": "
 cL_m_degr, param m1, modified to ensure light rate > dark rate. Parameter set from PLM_67v2_LDLLLDs_newFFT_1, with modification to m1 (= old_m1 - m2).
", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4010, + "tag": "BioModels:BIOMD0000000598" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:06.047744+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000598", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2889": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2889, + "name": "Coggins2014 - CXCL12 dependent recruitment of beta arrestin", + "repository_type": "biomodels", + "summary": "Nathaniel L. Coggins, Danielle Trakimas, S. Laura Chang, Anna Ehrlich, Paramita Ray, Kathryn E. Luker, Jennifer J. Linderman & Gary D. Luker. CXCR7 controls competition for recruitment of \u03b2-arrestin 2 in cells expressing both CXCR4 and CXCR7. PLoS ONE 9, 6 (2014).

Chemokine CXCL12 promotes growth and metastasis of more than 20 different human cancers, as well as pathogenesis of other common diseases. CXCL12 binds two different receptors, CXCR4 and CXCR7, both of which recruit and signal through the cytosolic adapter protein \u03b2-arrestin 2. Differences in CXCL12-dependent recruitment of \u03b2-arrestin 2 in cells expressing one or both receptors remain poorly defined. To quantitatively investigate parameters controlling association of \u03b2-arrestin 2 with CXCR4 or CXCR7 in cells co-expressing both receptors, we used a systems biology approach combining real-time, multi-spectral luciferase complementation imaging with computational modeling. Cells expressing only CXCR4 maintain low basal association with \u03b2-arrestin 2, and CXCL12 induces a rapid, transient increase in this interaction. In contrast, cells expressing only CXCR7 have higher basal association with \u03b2-arrestin 2 and exhibit more gradual, prolonged recruitment of \u03b2-arrestin 2 in response to CXCL12. We developed and fit a data-driven computational model for association of either CXCR4 or CXCR7 with \u03b2-arrestin 2 in cells expressing only one type of receptor. We then experimentally validated model predictions that co-expression of CXCR4 and CXCR7 on the same cell substantially decreases both the magnitude and duration of CXCL12-regulated recruitment of \u03b2-arrestin 2 to CXCR4. Co-expression of both receptors on the same cell only minimally alters recruitment of \u03b2-arrestin 2 to CXCR7. In silico experiments also identified \u03b2-arrestin 2 as a limiting factor in cells expressing both receptors, establishing that CXCR7 wins the \"competition\" with CXCR4 for CXCL12 and recruitment of \u03b2-arrestin 2. These results reveal how competition for \u03b2-arrestin 2 controls integrated responses to CXCL12 in cells expressing both CXCR4 and CXCR7. These results advance understanding of normal and pathologic functions of CXCL12, which is critical for developing effective strategies to target these pathways therapeutically.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4011, + "tag": "BioModels:BIOMD0000000599" + }, + { + "id": 4012, + "tag": "CXCL12-activated CXCR4 signaling pathway" + }, + { + "id": 4013, + "tag": "Chemokine (C-X-C motif) ligand 12 signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 13:54:06.593126+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000599", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2890": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2890, + "name": "Celli\u00e8re2011 - Plasticity of TGF-\u03b2 Signalling", + "repository_type": "biomodels", + "summary": "
Celli\u00e8re2011 - Plasticity of TGF-\u03b2 Signalling

Transforming growth factor beta (TGF-\u03b2) signalling has been implicated as an important regulator of almost all major cell behaviours, including proliferation, differentiation, cell death, and motility. It remains unclear that how the TGF-\u03b2 signalling pathway accomplishes the flexibility in its responses. What and how many parameters have to be altered for cells to respond differently to perform complex tasks? This canonical response has been explored in this model, by considering the core signalling architecture of TGF-\u03b2 pathway.

This model is described in the article:

Celli\u00e8re G, Fengos G, Herv\u00e9 M, Iber D.
BMC Syst Biol. 2011 Nov 3;5:184.

Abstract:

The family of TGF-\u03b2 ligands is large and its members are involved in many different signaling processes. These signaling processes strongly differ in type with TGF-\u03b2 ligands eliciting both sustained or transient responses. Members of the TGF-\u03b2 family can also act as morphogen and cellular responses would then be expected to provide a direct read-out of the extracellular ligand concentration. A number of different models have been proposed to reconcile these different behaviours. We were interested to define the set of minimal modifications that are required to change the type of signal processing in the TGF-\u03b2 signaling network.\t\tRESULTS:To define the key aspects for signaling plasticity we focused on the core of the TGF-\u03b2 signaling network. With the help of a parameter screen we identified ranges of kinetic parameters and protein concentrations that give rise to transient, sustained, or oscillatory responses to constant stimuli, as well as those parameter ranges that enable a proportional response to time-varying ligand concentrations (as expected in the read-out of morphogens). A combination of a strong negative feedback and fast shuttling to the nucleus biases signaling to a transient rather than a sustained response, while oscillations were obtained if ligand binding to the receptor is weak and the turn-over of the I-Smad is fast. A proportional read-out required inefficient receptor activation in addition to a low affinity of receptor-ligand binding. We find that targeted modification of single parameters suffices to alter the response type. The intensity of a constant signal (i.e. the ligand concentration), on the other hand, affected only the strength but not the type of the response.CONCLUSIONS:The architecture of the TGF-\u03b2 pathway enables the observed signaling plasticity. The observed range of signaling outputs to TGF-\u03b2 ligand in different cell types and under different conditions can be explained with differences in cellular protein concentrations and with changes in effective rate constants due to cross-talk with other signaling pathways. It will be interesting to uncover the exact cellular differences as well as the details of the cross-talks in future work.

This model is hosted on BioModels Database and identified by: MODEL1208280000 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. PMID: 20587024 .

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to [CC0 Public Domain Dedication>http://creativecommons.org/publicdomain/zero/1.0/] for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4014, + "tag": "BioModels:BIOMD0000000600" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3158, + "tag": "Transforming growth factor beta receptor signaling pathway" + } + ], + "timestamp_created": "2025-01-30 13:54:07.102107+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000600", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2891": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2891, + "name": "Rosas2015 - Caffeine-induced luminal SR calcium changes", + "repository_type": "biomodels", + "summary": "
This SBML model reproduced the calcium release from SR by application of 20 mM or 2mM caffeine, described in the paper. * Ca_i_Total and Ca_SR_Total respectively represent the total calcium concentration in the sarcoplasm and in the sarcoplasmic reticulum. * Ca_i  and Ca_SR respectively represent the free calcium concentration in the sarcoplasm and  the sarcoplasmic reticulum.* J1 is the calcium  flux due to all mechanisms (except SERCA pumps) that remove the excess of calcium from the sarcoplasm.* J2 is the calcium flux from the reticulum to the sarcoplasm via the ryanodine receptors (RyR) present in the reticulum membrane.* J3 is the calcium flux from the sarcoplasm to the reticulum by the SERCA pumps located in the reticulum membrane.*The parameters are a,  b, B c, Ca_i_basal, Ca_SR_basal, caff, csq,  gamma, KC, kf, KR, Ks, nf, ns and nv.* The value of KC for the model were calculated for J2=J3, after substituting Ca_i=Ca_i_basal, Ca_SR=Ca_SR_basal and caff=0. * Po represents the RyR open probability based on CICR. * Caffeine (caff)** increases the calcium affinity of smooth muscle's RyR so they open even when calcium is at basal level.** Due to caffeine-induced calcium release, a 5 seconds  pulse of caffeine (20 mM) was applied (event called Caff_ON) at 10 seconds after the simulation starts. The event called Caff_OFF starts when the pulse of caffeine finished (caff=0).* PE denotes the concentration of calcium binding sites. * Xi=Ca_SR_Total+PE+KR*In order to reproduce the dynamics of calcium following the application of 2 mM of caffeine, the value of some parameters needs to be change: b=35, Ca_i_basal=9.257e-6, gamma=7.45, caff=0.002 and the initial condition for Ca_i_Total=9.257e-6.*The unit of the calcium concentration is mol/L.* The unit of time is second. *The original SBML code was exported from COPASI 4.12 (Build 81).
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4015, + "tag": "BioModels:BIOMD0000000601" + }, + { + "id": 3198, + "tag": "Cavia porcellus" + }, + { + "id": 4016, + "tag": "Release of sequestered calcium ion into cytosol by sarcoplasmic reticulum" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:26.177222+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000601", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2892": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2892, + "name": "Stavrum2013 - Tryptophan Metabolism in Liver", + "repository_type": "biomodels", + "summary": "
Stavrum2013 - Tryptophan Metabolism inLiver

This model is described in the article:

Stavrum AK, Heiland I, Schuster S, Puntervoll P, Ziegler M.
J. Biol. Chem. 2013 Nov; 288(48): 34555-34566

Abstract:

Tryptophan is utilized in various metabolic routes including protein synthesis, serotonin, and melatonin synthesis and the kynurenine pathway. Perturbations in these pathways have been associated with neurodegenerative diseases and cancer. Here we present a comprehensive kinetic model of the complex network of human tryptophan metabolism based upon existing kinetic data for all enzymatic conversions and transporters. By integrating tissue-specific expression data, modeling tryptophan metabolism in liver and brain returned intermediate metabolite concentrations in the physiological range. Sensitivity and metabolic control analyses identified expected key enzymes to govern fluxes in the branches of the network. Combining tissue-specific models revealed a considerable impact of the kynurenine pathway in liver on the concentrations of neuroactive derivatives in the brain. Moreover, using expression data from a cancer study predicted metabolite changes that resembled the experimental observations. We conclude that the combination of the kinetic model with expression data represents a powerful diagnostic tool to predict alterations in tryptophan metabolism. The model is readily scalable to include more tissues, thereby enabling assessment of organismal tryptophan metabolism in health and disease.

This model is hosted on BioModels Database and identified by: BIOMD0000000602.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4017, + "tag": "BioModels:BIOMD0000000602" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4018, + "tag": "Serotonin biosynthetic process from tryptophan" + }, + { + "id": 4019, + "tag": "Tryptophan metabolic process" + } + ], + "timestamp_created": "2025-01-30 14:02:26.697499+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000602", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2893": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2893, + "name": "PetelenzKuehn_osmoadaptation_WT", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4020, + "tag": "BioModels:BIOMD0000000603" + }, + { + "id": 4021, + "tag": "Canonical glycolysis" + }, + { + "id": 4022, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4023, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4024, + "tag": "Response to osmotic stress" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:27.279212+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000603", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2894": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2894, + "name": "PetelenzKuehn_osmoadaptation_pfk2627D", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4025, + "tag": "BioModels:BIOMD0000000604" + }, + { + "id": 4021, + "tag": "Canonical glycolysis" + }, + { + "id": 4022, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4023, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4024, + "tag": "Response to osmotic stress" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:27.853388+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000604", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2895": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2895, + "name": "PetelenzKuehn_osmoadaptation_HOG1att", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4026, + "tag": "BioModels:BIOMD0000000605" + }, + { + "id": 4021, + "tag": "Canonical glycolysis" + }, + { + "id": 4022, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4023, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4024, + "tag": "Response to osmotic stress" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:28.559254+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000605", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2896": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2896, + "name": "PetelenzKuehn_osmoadaptation_hog1D", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4027, + "tag": "BioModels:BIOMD0000000606" + }, + { + "id": 4021, + "tag": "Canonical glycolysis" + }, + { + "id": 4022, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4023, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4024, + "tag": "Response to osmotic stress" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:29.361228+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000606", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2897": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2897, + "name": "PetelenzKuehn_osmoadaptation_fps1D1", + "repository_type": "biomodels", + "summary": "J\u00f6rg Schaber & Edda Klipp. Short-term volume and turgor regulation in yeast. Essays in Biochemistry 45 (2008).

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4028, + "tag": "BioModels:BIOMD0000000607" + }, + { + "id": 4021, + "tag": "Canonical glycolysis" + }, + { + "id": 4022, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 4023, + "tag": "Intracellular accumulation of glycerol" + }, + { + "id": 4024, + "tag": "Response to osmotic stress" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:29.985245+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000607", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2898": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2898, + "name": "Palsson2013 - Fully-integrated immune response model (FIRM)", + "repository_type": "biomodels", + "summary": "
Palsson2013 - Fully-integration immune response model (FIRM)

FIRM (The Fully-integrated Immune Response Modeling) is a hybrid construct incorporating multiple existing models of the immune system [De Boer et al., (1985);Bell, (1970); Marino and Kirschner, (2004) ]. FIRM used a pharmacokinetic / pharmacodynamic modelling approach to combine previously published individual models of humoral and cellular response with antigen exposure. This integrated model has a potential to simulate a range of responses under a variety of conditions, for example, the immune response against tuberculosis infection, blood borne pathogen infection, Spontaneous tumour rejection and influence of regulatory T cells (Treg) on tumour rejection.

The SBML model provided here was generated from the matlab code (provided by the authors). The matlab to SBML conversion was done using MOCCASIN version 1.1.0. This model describes the immune response against tuberculosis (TB) infection and reproduces figure 7 of the reference publication.

Note:The following minor edit to the original matlab code was done during the conversion to SBML: The model had two parameters named k3 and K3. To avoid case-insensitive issues during the conversion, K3 was changed to K3s in the original matlap code before using the conversion software. The matlab code of the model provided by the authors (with the above change) can be obtained from the curation tab.

This model is described in the article:

Palsson S, Hickling TP, Bradshaw-Pierce EL, Zager M, Jooss K, O'Brien PJ, Spilker ME, Palsson BO, Vicini P.
BMC Syst Biol. 2013 Sep 28;7:95.

Abstract:

BACKGROUND: The complexity and multiscale nature of the mammalian immune response provides an excellent test bed for the potential of mathematical modeling and simulation to facilitate mechanistic understanding. Historically, mathematical models of the immune response focused on subsets of the immune system and/or specific aspects of the response. Mathematical models have been developed for the humoral side of the immune response, or for the cellular side, or for cytokine kinetics, but rarely have they been proposed to encompass the overall system complexity. We propose here a framework for integration of subset models, based on a system biology approach. RESULTS: A dynamic simulator, the Fully-integrated Immune Response Model (FIRM), was built in a stepwise fashion by integrating published subset models and adding novel features. The approach used to build the model includes the formulation of the network of interacting species and the subsequent introduction of rate laws to describe each biological process. The resulting model represents a multi-organ structure, comprised of the target organ where the immune response takes place, circulating blood, lymphoid T, and lymphoid B tissue. The cell types accounted for include macrophages, a few T-cell lineages (cytotoxic, regulatory, helper 1, and helper 2), and B-cell activation to plasma cells. Four different cytokines were accounted for: IFN-\u03b3, IL-4, IL-10 and IL-12. In addition, generic inflammatory signals are used to represent the kinetics of IL-1, IL-2, and TGF-\u03b2. Cell recruitment, differentiation, replication, apoptosis and migration are described as appropriate for the different cell types. The model is a hybrid structure containing information from several mammalian species. The structure of the network was built to be physiologically and biochemically consistent. Rate laws for all the cellular fate processes, growth factor production rates and half-lives, together with antibody production rates and half-lives, are provided. The results demonstrate how this framework can be used to integrate mathematical models of the immune response from several published sources and describe qualitative predictions of global immune system response arising from the integrated, hybrid model. In addition, we show how the model can be expanded to include novel biological findings. Case studies were carried out to simulate TB infection, tumor rejection, response to a blood borne pathogen and the consequences of accounting for regulatory T-cells. CONCLUSIONS: The final result of this work is a postulated and increasingly comprehensive representation of the mammalian immune system, based on physiological knowledge and susceptible to further experimental testing and validation. We believe that the integrated nature of FIRM has the potential to simulate a range of responses under a variety of conditions, from modeling of immune responses after tuberculosis (TB) infection to tumor formation in tissues. FIRM also has the flexibility to be expanded to include both complex and novel immunological response features as our knowledge of the immune system advances.

This model is hosted on BioModels Database and identified by: MODEL1603310000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4029, + "tag": "BioModels:BIOMD0000000608" + }, + { + "id": 3984, + "tag": "Immune response" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4640, + "tag": "Tuberculosis" + } + ], + "timestamp_created": "2025-01-30 14:02:30.660180+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000608", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2899": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2899, + "name": "Reddyhoff2015 - Acetaminophen metabolism and toxicity", + "repository_type": "biomodels", + "summary": "
Reddyhoff2015 - Acetaminophen metabolism and toxicity
This model examines acetaminophen metabolism and related hepatotoxicity. Multiple pathways associated with APAP metabolism has been included in the model. Using numerical, sensitivity and timescale analysis, key parameters involved in the toxicity has been identified. The model analysis highlights a critical acetaminophen dose in terms of the model parameters.

This model is described in the article:

Reddyhoff D, Ward J, Williams D, Regan S, Webb S
J Theor Biol. 2015 Dec 7;386:132-46.

Abstract:

Acetaminophen is a widespread and commonly used painkiller all over the world. However, it can cause liver damage when taken in large doses or at repeated chronic doses. Current models of acetaminophen metabolism are complex, and limited to numerical investigation though provide results that represent clinical investigation well. We derive a mathematical model based on mass action laws aimed at capturing the main dynamics of acetaminophen metabolism, in particular the contrast between normal and overdose cases, whilst remaining simple enough for detailed mathematical analysis that can identify key parameters and quantify their role in liver toxicity. We use singular perturbation analysis to separate the different timescales describing the sequence of events in acetaminophen metabolism, systematically identifying which parameters dominate during each of the successive stages. Using this approach we determined, in terms of the model parameters, the critical dose between safe and overdose cases, timescales for exhaustion and regeneration of important cofactors for acetaminophen metabolism and total toxin accumulation as a fraction of initial dose.

This model is hosted on BioModels Database and identified by: MODEL1603080000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4030, + "tag": "BioModels:BIOMD0000000609" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4031, + "tag": "Response to paracetamol" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:31.351335+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000609", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2900": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2900, + "name": "PetelenzKuehn_osmoadaptation_gpd1D", + "repository_type": "biomodels", + "summary": "
Petelenz-kurdzeil2013 - Osmo adaptationgpd1D

This model is described in the article:

Petelenz-Kurdziel E, Kuehn C, Nordlander B, Klein D, Hong KK, Jacobson T, Dahl P, Schaber J, Nielsen J, Hohmann S, Klipp E.
PLoS Comput. Biol. 2013; 9(6): e1003084

Abstract:

We provide an integrated dynamic view on a eukaryotic osmolyte system, linking signaling with regulation of gene expression, metabolic control and growth. Adaptation to osmotic changes enables cells to adjust cellular activity and turgor pressure to an altered environment. The yeast Saccharomyces cerevisiae adapts to hyperosmotic stress by activating the HOG signaling cascade, which controls glycerol accumulation. The Hog1 kinase stimulates transcription of genes encoding enzymes required for glycerol production (Gpd1, Gpp2) and glycerol import (Stl1) and activates a regulatory enzyme in glycolysis (Pfk26/27). In addition, glycerol outflow is prevented by closure of the Fps1 glycerol facilitator. In order to better understand the contributions to glycerol accumulation of these different mechanisms and how redox and energy metabolism as well as biomass production are maintained under such conditions we collected an extensive dataset. Over a period of 180 min after hyperosmotic shock we monitored in wild type and different mutant cells the concentrations of key metabolites and proteins relevant for osmoadaptation. The dataset was used to parameterize an ODE model that reproduces the generated data very well. A detailed computational analysis using time-dependent response coefficients showed that Pfk26/27 contributes to rerouting glycolytic flux towards lower glycolysis. The transient growth arrest following hyperosmotic shock further adds to redirecting almost all glycolytic flux from biomass towards glycerol production. Osmoadaptation is robust to loss of individual adaptation pathways because of the existence and upregulation of alternative routes of glycerol accumulation. For instance, the Stl1 glycerol importer contributes to glycerol accumulation in a mutant with diminished glycerol production capacity. In addition, our observations suggest a role for trehalose accumulation in osmoadaptation and that Hog1 probably directly contributes to the regulation of the Fps1 glycerol facilitator. Taken together, we elucidated how different metabolic adaptation mechanisms cooperate and provide hypotheses for further experimental studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000610.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4032, + "tag": "BioModels:BIOMD0000000610" + }, + { + "id": 4022, + "tag": "Cellular response to osmotic stress" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:32.013150+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000610", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2901": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2901, + "name": "Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers", + "repository_type": "biomodels", + "summary": "
Nayak2015 - Blood Coagulation Network - Predicting the Effects of Various Therapies on Biomarkers
Note:
The SBML model is generated from SimBiology. The SimBiology (.sbproj) file is available for download from the curation tab.

This model is described in the article:

Nayak S, Lee D, Patel-Hett S, Pittman DD, Martin SW, Heatherington AC, Vicini P, Hua F.
CPT Pharmacometrics Syst Pharmacol. 2015 Jul;4(7):396-405.

Abstract:

A number of therapeutics have been developed or are under development aiming to modulate the coagulation network to treat various diseases. We used a systems model to better understand the effect of modulating various components on blood coagulation. A computational model of the coagulation network was built to match in-house in vitro thrombin generation and activated Partial Thromboplastin Time (aPTT) data with various concentrations of recombinant factor VIIa (FVIIa) or factor Xa added to normal human plasma or factor VIII-deficient plasma. Sensitivity analysis applied to the model revealed that lag time, peak thrombin concentration, area under the curve (AUC) of the thrombin generation profile, and aPTT show different sensitivity to changes in coagulation factors' concentrations and type of plasma used (normal or factor VIII-deficient). We also used the model to explore how variability in concentrations of the proteins in coagulation network can impact the response to FVIIa treatment.

This model is hosted on BioModels Database and identified by: MODEL1511160000.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4033, + "tag": "BioModels:BIOMD0000000611" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4641, + "tag": "Blood coagulation disease" + } + ], + "timestamp_created": "2025-01-30 14:02:32.570258+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000611", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2902": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2902, + "name": "Proctor2016 - Circadian rhythm of PTH and the dynamics of signaling molecules on bone remodeling", + "repository_type": "biomodels", + "summary": "
Proctor2016 - Circadian rhythm of PTH and thedynamics of signaling molecules on bone remodeling

This model is described in the article:

Proctor CJ, Gartland A.
Front Endocrinol (Lausanne) 2016; 7: 61

Abstract:

Bone remodeling is the continuous process of bone resorption by osteoclasts and bone formation by osteoblasts, in order to maintain homeostasis. The activity of osteoclasts and osteoblasts is regulated by a network of signaling pathways, including Wnt, parathyroid hormone (PTH), RANK ligand/osteoprotegrin, and TGF-?, in response to stimuli, such as mechanical loading. During aging there is a gradual loss of bone mass due to dysregulation of signaling pathways. This may be due to a decline in physical activity with age and/or changes in hormones and other signaling molecules. In particular, hormones, such as PTH, have a circadian rhythm, which may be disrupted in aging. Due to the complexity of the molecular and cellular networks involved in bone remodeling, several mathematical models have been proposed to aid understanding of the processes involved. However, to date, there are no models, which explicitly consider the effects of mechanical loading, the circadian rhythm of PTH, and the dynamics of signaling molecules on bone remodeling. Therefore, we have constructed a network model of the system using a modular approach, which will allow further modifications as required in future research. The model was used to simulate the effects of mechanical loading and also the effects of different interventions, such as continuous or intermittent administration of PTH. Our model predicts that the absence of regular mechanical loading and/or an impaired PTH circadian rhythm leads to a gradual decrease in bone mass over time, which can be restored by simulated interventions and that the effectiveness of some interventions may depend on their timing.

This model is hosted on BioModels Database and identified by: BIOMD0000000612.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4034, + "tag": "BioModels:BIOMD0000000612" + }, + { + "id": 4035, + "tag": "Bone remodeling" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:33.211662+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000612", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2903": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2903, + "name": "Peterson2010 - Integrated calcium homeostasis and bone remodelling", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level3/version1/core"> <body xmlns="http://www.w3.org/1999/xhtml"> <div class="dc:title">Peterson2010 - integrated calcium homeostasisand bone remodelling</div><div class="dc:description"> </div><div class="dc:bibliographicCitation"> <p>This model is described in the article:</p> <div class="bibo:title"> <a href="http://identifiers.org/pubmed/19732857" title="Access to this publication">A physiologically based mathematical model of integrated calcium homeostasis and bone remodeling.</a> </div> <div class="bibo:authorList">Peterson MC, Riggs MM.</div> <div class="bibo:Journal">Bone 2010 Jan; 46(1): 49-63</div> <p>Abstract:</p> <div class="bibo:abstract"> <p>Bone biology is physiologically complex and intimately linked to calcium homeostasis. The literature provides a wealth of qualitative and/or quantitative descriptions of cellular mechanisms, bone dynamics, associated organ dynamics, related disease sequela, and results of therapeutic interventions. We present a physiologically based mathematical model of integrated calcium homeostasis and bone biology constructed from literature data. The model includes relevant cellular aspects with major controlling mechanisms for bone remodeling and calcium homeostasis and appropriately describes a broad range of clinical and therapeutic conditions. These include changes in plasma parathyroid hormone (PTH), calcitriol, calcium and phosphate (PO4), and bone-remodeling markers as manifested by hypoparathyroidism and hyperparathyroidism, renal insufficiency, daily PTH 1-34 administration, and receptor activator of NF-kappaB ligand (RANKL) inhibition. This model highlights the utility of systems approaches to physiologic modeling in the bone field. The presented bone and calcium homeostasis model provides an integrated mathematical construct to conduct hypothesis testing of influential system aspects, to visualize elements of this complex endocrine system, and to continue to build upon iteratively with the results of ongoing scientific research.</p> </div></div><div class="dc:publisher"> <p>This model is hosted on <a href="http://www.ebi.ac.uk/biomodels/">BioModels Database</a> and identified by: <a href="http://identifiers.org/biomodels.db/BIOMD0000000613">BIOMD0000000613</a>.</p> <p>To cite BioModels Database, please use: <a href="http://identifiers.org/pubmed/20587024" title="Latest BioModels Database publication">BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models</a>.</p></div><div class="dc:license"> <p>To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to <a href="http://creativecommons.org/publicdomain/zero/1.0/" title="Access to: CC0 1.0 Universal (CC0 1.0), Public Domain Dedication">CC0 Public Domain Dedication</a> for more information.</p></div></body> </notes>", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4036, + "tag": "BioModels:BIOMD0000000613" + }, + { + "id": 4035, + "tag": "Bone remodeling" + }, + { + "id": 4037, + "tag": "Calcium ion homeostasis" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4038, + "tag": "Regulation of parathyroid hormone secretion" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4642, + "tag": "Hyperparathyroidism" + }, + { + "id": 4643, + "tag": "Hypoparathyroidism" + } + ], + "timestamp_created": "2025-01-30 14:02:33.851933+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000613", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2904": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2904, + "name": "Kamihira2000 - calcitonin fibrillation kinetics", + "repository_type": "biomodels", + "summary": "
Kamihira2000 - calcitonin fibrillation kinetics

This model studies the kinetics of human calcitonin fibrillation described as a two-step process. Empirical data is used to determine the parameter values. Results show that the first step in fibrillation is a slow homogenous reaction and the second step is a fast autocatalytic heterogenous reaction.

This model is described in the article:

Kamihira M, Naito A, Tuzi S, Nosaka AY, Sait\u00f4 H.
Protein Sci. 2000 May; 9(5): 867-877

Abstract:

Conformational transitions of human calcitonin (hCT) during fibril formation in the acidic and neutral conditions were investigated by high-resolution solid-state 13C NMR spectroscopy. In aqueous acetic acid solution (pH 3.3), a local alpha-helical form is present around Gly10 whereas a random coil form is dominant as viewed from Phe22, Ala26, and Ala31 in the monomer form on the basis of the 13C chemical shifts. On the other hand, a local beta-sheet form as viewed from Gly10 and Phe22, and both beta-sheet and random coil as viewed from Ala26 and Ala31 were detected in the fibril at pH 3.3. The results indicate that conformational transitions from alpha-helix to beta-sheet, and from random coil to beta-sheet forms occurred in the central and C-terminus regions, respectively, during the fibril formation. The increased 13C resonance intensities of fibrils after a certain delay time suggests that the fibrillation can be explained by a two-step reaction mechanism in which the first step is a homogeneous association to form a nucleus, and the second step is an autocatalytic heterogeneous fibrillation. In contrast to the fibril at pH 3.3, the fibril at pH 7.5 formed a local beta-sheet conformation at the central region and exhibited a random coil at the C-terminus region. Not only a hydrophobic interaction among the amphiphilic alpha-helices, but also an electrostatic interaction between charged side chains can play an important role for the fibril formation at pH 7.5 and 3.3 acting as electrostatically favorable and unfavorable interactions, respectively. These results suggest that hCT fibrils are formed by stacking antiparallel beta-sheets at pH 7.5 and a mixture of antiparallel and parallel beta-sheets at pH 3.3.

This model is hosted on BioModels Database and identified by: BIOMD0000000614.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4039, + "tag": "BioModels:BIOMD0000000614" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4040, + "tag": "Regulation of supramolecular fiber organization" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:34.363959+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000614", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2905": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2905, + "name": "Kuznetsov2016(II) - \u03b1-syn aggregation kinetics in Parkinson's Disease", + "repository_type": "biomodels", + "summary": "
Kuznetsov2016(II) - \u03b1-syn aggregationkinetics in Parkinson's
This theoretical model uses 2-step Finke-Watzky (FW) kineticstodescribe the production, misfolding, aggregation, transport anddegradation of \u03b1-syn that may lead to Parkinson's Disease(PD). Deregulated \u03b1-syn degradation is predicted to becrucialfor PD pathogenesis.   

This model is described in the article:

Kuznetsov IA, Kuznetsov AV.
Math Biosci 2016 Aug; 278: 22-29

Abstract:

The aim of this paper is to develop a minimal model describing events leading to the onset of Parkinson's disease (PD). The model accounts for \u03b1-synuclein (\u03b1-syn) production in the soma, transport toward the synapse, misfolding, and aggregation. The production and aggregation of polymeric \u03b1-syn is simulated using a minimalistic 2-step Finke-Watzky model. We utilized the developed model to analyze what changes in a healthy neuron are likely to lead to the onset of \u03b1-syn aggregation. We checked the effects of interruption of \u03b1-syn transport toward the synapse, entry of misfolded (infectious) \u03b1-syn into the somatic and synaptic compartments, increasing the rate of \u03b1-syn synthesis in the soma, and failure of \u03b1-syn degradation machinery. Our model suggests that failure of \u03b1-syn degradation machinery is probably the most likely cause for the onset of \u03b1-syn aggregation leading to PD.

This model is hosted on BioModels Database and identified by: BIOMD0000000615.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4041, + "tag": "BioModels:BIOMD0000000615" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4590, + "tag": "Parkinson's disease" + } + ], + "timestamp_created": "2025-01-30 14:02:34.878249+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000615", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2906": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2906, + "name": "Dunster2014 - WBC Interactions (Model1)", + "repository_type": "biomodels", + "summary": "
Dunster2014 - WBC Interactions (Model1)
This is a sub-model of a three-stepinflammatory response modelling study. The model includes distinctpopulations of white blood cells namely, macrophages and active andapoptotic neutrophil populations. Neutrophil apoptosis rate ispredicted to be crucial for the qualitative nature of thesystem.

This model is described in the article:

Dunster JL, Byrne HM, King JR.
Bull. Math. Biol. 2014 Aug; 76(8): 1953-1980

Abstract:

There is growing interest in inflammation due to its involvement in many diverse medical conditions, including Alzheimer's disease, cancer, arthritis and asthma. The traditional view that resolution of inflammation is a passive process is now being superceded by an alternative hypothesis whereby its resolution is an active, anti-inflammatory process that can be manipulated therapeutically. This shift in mindset has stimulated a resurgence of interest in the biological mechanisms by which inflammation resolves. The anti-inflammatory processes central to the resolution of inflammation revolve around macrophages and are closely related to pro-inflammatory processes mediated by neutrophils and their ability to damage healthy tissue. We develop a spatially averaged model of inflammation centring on its resolution, accounting for populations of neutrophils and macrophages and incorporating both pro- and anti-inflammatory processes. Our ordinary differential equation model exhibits two outcomes that we relate to healthy and unhealthy states. We use bifurcation analysis to investigate how variation in the system parameters affects its outcome. We find that therapeutic manipulation of the rate of macrophage phagocytosis can aid in resolving inflammation but success is critically dependent on the rate of neutrophil apoptosis. Indeed our model predicts that an effective treatment protocol would take a dual approach, targeting macrophage phagocytosis alongside neutrophil apoptosis.

This model is hosted on BioModels Database and identified by: BIOMD0000000616.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4042, + "tag": "BioModels:BIOMD0000000616" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3832, + "tag": "Inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:35.398433+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000616", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2907": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2907, + "name": "Walsh2014 - Inhibition kinetics of DAPT on APP Cleavage", + "repository_type": "biomodels", + "summary": "
Walsh2014 - Inhibition kinetics of DAPT onAPP Cleavage

This model is described in the article:

Walsh R.
PeerJ 2014; 2: e649

Abstract:

Reproducibility of biological data is a significant problem in research today. One potential contributor to this, which has received little attention, is the over complication of enzyme kinetic inhibition models. The over complication of inhibitory models stems from the common use of the inhibitory term (1 + [I]/Ki ), an equilibrium binding term that does not distinguish between inhibitor binding and inhibitory effect. Since its initial appearance in the literature, around a century ago, the perceived mechanistic methods used in its production have spurred countless inhibitory equations. These equations are overly complex and are seldom compared to each other, which has destroyed their usefulness resulting in the proliferation and regulatory acceptance of simpler models such as IC50s for drug characterization. However, empirical analysis of inhibitory data recognizing the clear distinctions between inhibitor binding and inhibitory effect can produce simple logical inhibition models. In contrast to the common divergent practice of generating new inhibitory models for every inhibitory situation that presents itself. The empirical approach to inhibition modeling presented here is broadly applicable allowing easy comparison and rational analysis of drug interactions. To demonstrate this, a simple kinetic model of DAPT, a compound that both activates and inhibits ?-secretase is examined using excel. The empirical kinetic method described here provides an improved way of probing disease mechanisms, expanding the investigation of possible therapeutic interventions.

This model is hosted on BioModels Database and identified by: BIOMD0000000617.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3935, + "tag": "Amyloid precursor protein catabolic process" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4043, + "tag": "BioModels:BIOMD0000000617" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 14:02:35.917802+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000617", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2908": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2908, + "name": "Krohn2011 - Cerebral amyloid-\u03b2 proteostasis regulated by membrane transport protein ABCC1", + "repository_type": "biomodels", + "summary": "
Krohn2011 - Cerebral amyloid-\u03b2proteostasis regulated by membrane transport protein ABCC1

This model is described in the article:

Krohn M, Lange C, Hofrichter J, Scheffler K, Stenzel J, Steffen J, Schumacher T, Br\u00fcning T, Plath AS, Alfen F, Schmidt A, Winter F, Rateitschak K, Wree A, Gsponer J, Walker LC, Pahnke J.
J. Clin. Invest. 2011 Oct; 121(10): 3924-3931

Abstract:

In Alzheimer disease (AD), the intracerebral accumulation of amyloid-\u03b2 (A\u03b2) peptides is a critical yet poorly understood process. A\u03b2 clearance via the blood-brain barrier is reduced by approximately 30% in AD patients, but the underlying mechanisms remain elusive. ABC transporters have been implicated in the regulation of A\u03b2 levels in the brain. Using a mouse model of AD in which the animals were further genetically modified to lack specific ABC transporters, here we have shown that the transporter ABCC1 has an important role in cerebral A\u03b2 clearance and accumulation. Deficiency of ABCC1 substantially increased cerebral A\u03b2 levels without altering the expression of most enzymes that would favor the production of A\u03b2 from the A\u03b2 precursor protein. In contrast, activation of ABCC1 using thiethylperazine (a drug approved by the FDA to relieve nausea and vomiting) markedly reduced A\u03b2 load in a mouse model of AD expressing ABCC1 but not in such mice lacking ABCC1. Thus, by altering the temporal aggregation profile of A\u03b2, pharmacological activation of ABC transporters could impede the neurodegenerative cascade that culminates in the dementia of AD.

This model is hosted on BioModels Database and identified by: BIOMD0000000618.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3892, + "tag": "Amyloid fibril formation" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4044, + "tag": "BioModels:BIOMD0000000618" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4045, + "tag": "Transport" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + }, + { + "id": 4644, + "tag": "Non-spatial continuous framework" + } + ], + "timestamp_created": "2025-01-30 14:02:36.409488+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000618", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2909": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2909, + "name": "Sluka2016 - Acetaminophen PBPK", + "repository_type": "biomodels", + "summary": "

Basic PBPK (Physiologically Based PharmacoKinetic) model of Acetaminophen.

This is a basic model of Acetaminophen (APAP, Paracetamol) pharmacokinetics in humans. Many of the model parameters (compartment volumes, volumetric flow rates, etc.) are scaled allometrically based on the body weight (BW) raised to the\t\t\t3/4 power. Because of that, the assigned values of many of the parameters are recalculated at run time and are different \t\t\tthan the default values for the particular entity (e.g., the volume of a compartment and the volumetric flow rate between compartments).

APAP dose is initially given in grams (APAP_Dose_gram), which is converted to moles via the APAP molecular weight (APAP_MW).\t\t\tAPAP quantities throughout the rest of the models are given in moles.

The base parameters are for a 70Kg human and a pharmacological oral dose of 1.4 gram of APAP. Metabolism is modelled as a single ODE in the liver compartment and the metabolite does not leave that compartment.

This model is loosely based on the model of Wambaugh and Shaw (PLoS Comput Biol. 2010 Apr 22;6(4):e1000756. doi: 10.1371/journal.pcbi.1000756. Pubmed ID: PMID- 20421935) with the following changes:

  1. The lung lumen compartment was omitted.
  2. A kidney compartment was added.
  3. Glomerular Filtration is from the kidney compartment.
  4. The APAP dose was changed to 1.4g.
  5. The gut adsorption rate constant (KGutabs), tissue partition coefficients and liver metabolism rate constant (CLmetabolism) were fit using the human in vivo data of Critchley (Critchley, J. A., Critchley, L. A. H., Anderson, P. J., and Tomlinson, B. 2005 Journal of clinical pharmacy and therapeutics, 30(2), 179-184).
  6. To model the extensive re-adsorption of APAP from the kidney tubules back into the blood the QGfr value is modified. This was done by \t\t\t\tchanging the scaling parameter QGFR_ref value from 0.31 to 0.039, resulting in a decrease in the QGfr value of 8 fold (from 7.2 L/hr to 0.91 L/hr).

The parameters in this file are the REFSIM parameters from our publication.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4046, + "tag": "BioModels:BIOMD0000000619" + }, + { + "id": 4047, + "tag": "Drug metabolic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4048, + "tag": "Xenobiotic transporter activity" + } + ], + "timestamp_created": "2025-01-30 14:02:36.978670+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000619", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2910": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2910, + "name": "Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Disease Condition", + "repository_type": "biomodels", + "summary": "
Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Disease Condition
\t

This is the model with disease state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetanoet al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb].\t

\t

This model is described in the article:

Palm\u00e9r R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118.

Abstract:

Recent clinical studies suggest sustained treatment effects of interleukin-1\u03b2 (IL-1\u03b2)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. We propose that improved \u03b2-cell function, rather than mass, is likely to explain the main IL-1\u03b2-blocking effects seen in current clinical data, but that improved \u03b2-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

This model is hosted on BioModels Database and identified by: MODEL1604270002.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4049, + "tag": "BioModels:BIOMD0000000620" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4050, + "tag": "Interleukin-1 beta secretion" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4052, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 14:02:37.513164+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000620", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2911": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2911, + "name": "Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Healthy Condition", + "repository_type": "biomodels", + "summary": "
Palmer2014 - Effect of IL-1\u03b2-Blocking therapies in T2DM - Healthy Condition
\t

This is the model with healthy state initial conditions. A few changes were made to the model equations in order to bypass the circular dependencies apparent in SBML. Coupled algebraic equations for the species Glucose, Insulin and Proinsulin were changed to reactions which represent the ordinary differential equations found in a previously published model by De Gaetanoet al (2008), [MODEL1112110003]. This reference was used by the present authors for the algebraic equations. The original Mathematica code, obtained from the supplementary material of the article can be downloaded from the link below: [Palmer2014_notebook.nb].\t

\t

This model is described in the article:

Palm\u00e9r R, Nyman E, Penney M, Marley A, Cedersund G, Agoram B.
CPT Pharmacometrics Syst Pharmacol. 2014 Jun 11;3:e118.

Abstract:

Recent clinical studies suggest sustained treatment effects of interleukin-1\u03b2 (IL-1\u03b2)-blocking therapies in type 2 diabetes mellitus. The underlying mechanisms of these effects, however, remain underexplored. Using a quantitative systems pharmacology modeling approach, we combined ex vivo data of IL-1\u03b2 effects on \u03b2-cell function and turnover with a disease progression model of the long-term interactions between insulin, glucose, and \u03b2-cell mass in type 2 diabetes mellitus. We then simulated treatment effects of the IL-1 receptor antagonist anakinra. The result was a substantial and partly sustained symptomatic improvement in \u03b2-cell function, and hence also in HbA1C, fasting plasma glucose, and proinsulin-insulin ratio, and a small increase in \u03b2-cell mass. We propose that improved \u03b2-cell function, rather than mass, is likely to explain the main IL-1\u03b2-blocking effects seen in current clinical data, but that improved \u03b2-cell mass might result in disease-modifying effects not clearly distinguishable until >1 year after treatment.

This model is hosted on BioModels Database and identified by: MODEL1604270002.

To cite BioModels Database, please use: BioModels: Content, Features, Functionality and Use.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4051, + "tag": "BioModels:BIOMD0000000621" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4050, + "tag": "Interleukin-1 beta secretion" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4052, + "tag": "Type 2 diabetes mellitus" + } + ], + "timestamp_created": "2025-01-30 14:02:38.016143+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000621", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2912": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2912, + "name": "NguyenLK2011 - Ubiquitination dynamics in Ring1B/Bmi1 system", + "repository_type": "biomodels", + "summary": "
NguyenLK2011 - Ubiquitination dynamics inRing1B-Bmi1 system
This theoretical model investigates thedynamics of Ring1B/Bmi1 ubiquitination to identify bistableswitch-like and oscillatory behaviour in thesystem.\u00a0Michaelis-Menten (MM) equations are used to formulatethe model. However, the authors show that the dynamics persist evenfor Mass-Action kinetics. This SBML file is the MM version of themodel.

This model is described in the article:

Nguyen LK, Mu\u00f1oz-Garc\u00eda J, Maccario H, Ciechanover A, Kolch W, Kholodenko BN.
PLoS Comput. Biol. 2011 Dec; 7(12): e1002317

Abstract:

In an active, self-ubiquitinated state, the Ring1B ligase monoubiquitinates histone H2A playing a critical role in Polycomb-mediated gene silencing. Following ubiquitination by external ligases, Ring1B is targeted for proteosomal degradation. Using biochemical data and computational modeling, we show that the Ring1B ligase can exhibit abrupt switches, overshoot transitions and self-perpetuating oscillations between its distinct ubiquitination and activity states. These different Ring1B states display canonical or multiply branched, atypical polyubiquitin chains and involve association with the Polycomb-group protein Bmi1. Bistable switches and oscillations may lead to all-or-none histone H2A monoubiquitination rates and result in discrete periods of gene (in)activity. Switches, overshoots and oscillations in Ring1B catalytic activity and proteosomal degradation are controlled by the abundances of Bmi1 and Ring1B, and the activities and abundances of external ligases and deubiquitinases, such as E6-AP and USP7.

This model is hosted on BioModels Database and identified by: BIOMD0000000622.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4053, + "tag": "BioModels:BIOMD0000000622" + }, + { + "id": 3942, + "tag": "Protein catabolic process" + }, + { + "id": 4054, + "tag": "Protein ubiquitination" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 14:02:38.578852+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000622", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2913": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2913, + "name": "Orton2009 - Modelling cancerous mutations in the EGFR/ERK pathway - EGF Model", + "repository_type": "biomodels", + "summary": "
Orton2009 - Modelling cancerous mutations inthe EGFR/ERK pathway - EGF Model
This model studies the aberrations inERK signalling for different cancer mutations. The authors alter apreviously existing EGF model (Brown et al 2004) to include newinteractions\u00a0that\u00a0better\u00a0fit empirical data.Predictions show that the ERK signalling is a robust mechanismtaking different courses for different cancer mutations. Mostparameter values are used from the previous model and the newparameters are\u00a0estimated using experimental data performed bythe authors on PC12 cells (adrenal gland, rat).\u00a0The authorsprovide an SBML version of the model in the paper.

This model is described in the article:

Orton RJ, Adriaens ME, Gormand A, Sturm OE, Kolch W, Gilbert DR.
BMC Syst Biol 2009 Oct; 3: 100

Abstract:

The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway.We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors.Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.

This model is hosted on BioModels Database and identified by: BIOMD0000000623.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4055, + "tag": "BioModels:BIOMD0000000623" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:02:39.098146+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000623", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2914": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2914, + "name": "Sluka2016 - Acetaminophen metabolism", + "repository_type": "biomodels", + "summary": "
Sluka2016 - Acetaminophen metabolism
Liver metabolism of Acetaminophen: Acetaminophen (APAP) ismetabolized in the liver in both Phase I and Phase II reactions.Phase II reactions convert APAP to APAP-glucuronide andAPAP-sulfate. Phase I reactions involve Cytochrome P450 mediated(mostly Cyp450-2E1 and -1A2) conversion of APAP toN-acetyle-p-quinoneimine (NAPQI), which goes on to react withcellular nucleophiles such as glutathione (GSH). At high doses ofAPAP significant GSH depletion in hepatocyte occurs resulting incell necrosis and and in extreme cases death.

This model is described in the article:

Sluka JP, Fu X, Swat M, Belmonte JM, Cosmanescu A, Clendenon SG, Wambaugh JF, Glazier JA.
PLoS ONE 2016; 11(9): e0162428

Abstract:

We describe a multi-scale, liver-centric in silico modeling framework for acetaminophen pharmacology and metabolism. We focus on a computational model to characterize whole body uptake and clearance, liver transport and phase I and phase II metabolism. We do this by incorporating sub-models that span three scales; Physiologically Based Pharmacokinetic (PBPK) modeling of acetaminophen uptake and distribution at the whole body level, cell and blood flow modeling at the tissue/organ level and metabolism at the sub-cellular level. We have used standard modeling modalities at each of the three scales. In particular, we have used the Systems Biology Markup Language (SBML) to create both the whole-body and sub-cellular scales. Our modeling approach allows us to run the individual sub-models separately and allows us to easily exchange models at a particular scale without the need to extensively rework the sub-models at other scales. In addition, the use of SBML greatly facilitates the inclusion of biological annotations directly in the model code. The model was calibrated using human in vivo data for acetaminophen and its sulfate and glucuronate metabolites. We then carried out extensive parameter sensitivity studies including the pairwise interaction of parameters. We also simulated population variation of exposure and sensitivity to acetaminophen. Our modeling framework can be extended to the prediction of liver toxicity following acetaminophen overdose, or used as a general purpose pharmacokinetic model for xenobiotics.

This model is hosted on BioModels Database and identified by: BIOMD0000000624.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4056, + "tag": "BioModels:BIOMD0000000624" + }, + { + "id": 4057, + "tag": "Endogenous drug catabolic process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:39.818838+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000624", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2915": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2915, + "name": "Leber2016 - Expanded model of Tfh-Tfr differentiation - Helicobacter pylori infection", + "repository_type": "biomodels", + "summary": "
Leber2016 - Expanded model of Tfh-Tfrdifferentiation - Helicobacter pylori infection

The parameters used in the model wereobtained from experiments conducted by the authors, previouspublications [ 1, 2, 3] andparameter optimisation carried out in the paper using particleswarm and genetic algorithms.\u00a0

This model is described in the article:

Leber A, Abedi V, Hontecillas R, Viladomiu M, Hoops S, Ciupe S, Caughman J, Andrew T, Bassaganya-Riera J.
J. Theor. Biol. 2016 Jun; 398: 74-84

Abstract:

T follicular helper (Tfh) cells are a highly plastic subset of CD4+ T cells specialized in providing B cell help and promoting inflammatory and effector responses during infectious and immune-mediate diseases. Helicobacter pylori is the dominant member of the gastric microbiota and exerts both beneficial and harmful effects on the host. Chronic inflammation in the context of H. pylori has been linked to an upregulation in T helper (Th)1 and Th17 CD4+ T cell phenotypes, controlled in part by the cytokine, interleukin-21. This study investigates the differentiation and regulation of Tfh cells, major producers of IL-21, in the immune response to H. pylori challenge. To better understand the conditions influencing the promotion and inhibition of a chronically elevated Tfh population, we used top-down and bottom-up approaches to develop computational models of Tfh and T follicular regulatory (Tfr) cell differentiation. Stability analysis was used to characterize the presence of two bi-stable steady states in the calibrated Tfh/Tfr models. Stochastic simulation was used to illustrate the ability of the parameter set to dictate two distinct behavioral patterns. Furthermore, sensitivity analysis helped identify the importance of various parameters on the establishment of Tfh and Tfr cell populations. The core network model was expanded into a more comprehensive and predictive model by including cytokine production and signaling pathways. From the expanded network, the interaction between TGFB-Induced Factor Homeobox 1 (Tgif1) and the retinoid X receptor (RXR) was displayed to exert control over the determination of the Tfh response. Model simulations predict that Tgif1 and RXR respectively induce and curtail Tfh responses. This computational hypothesis was validated experimentally by assaying Tgif1, RXR and Tfh in stomachs of mice infected with H. pylori.

The impulse of RXR as shown in the paper(figure 7C) can be implemented by creating an event in the curatedSBML file.

This model is hosted on BioModels Database and identified by: BIOMD0000000625.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4058, + "tag": "BioModels:BIOMD0000000625" + }, + { + "id": 4059, + "tag": "Helicobacter pylori" + }, + { + "id": 4060, + "tag": "Interspecies interaction between organisms" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4645, + "tag": "Duodenal ulcer" + }, + { + "id": 4646, + "tag": "Peptic ulcer disease" + } + ], + "timestamp_created": "2025-01-30 14:02:40.415005+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000625", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2916": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2916, + "name": "Ray2013 - Meiotic initiation in S. cerevisiae", + "repository_type": "biomodels", + "summary": "
Ray2013 - Meiotic initiation in S. cerevisiae

A mathematical representation of early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signalling molecules for regulating protein activities, is described here

This model is described in the article:

Ray D, Su Y, Ye P.
BMC Syst Biol. 2013 May 1;7:37

Abstract:

BACKGROUND: Meiosis is the sexual reproduction process common to eukaryotes. The diploid yeast Saccharomyces cerevisiae undergoes meiosis in sporulation medium to form four haploid spores. Initiation of the process is tightly controlled by intricate networks of positive and negative feedback loops. Intriguingly, expression of early meiotic proteins occurs within a narrow time window. Further, sporulation efficiency is strikingly different for yeast strains with distinct mutations or genetic backgrounds. To investigate signal transduction pathways that regulate transient protein expression and sporulation efficiency, we develop a mathematical model using ordinary differential equations. The model describes early meiotic events, particularly feedback mechanisms at the system level and phosphorylation of signaling molecules for regulating protein activities.

RESULTS: The mathematical model is capable of simulating the orderly and transient dynamics of meiotic proteins including Ime1, the master regulator of meiotic initiation, and Ime2, a kinase encoded by an early gene. The model is validated by quantitative sporulation phenotypes of single-gene knockouts. Thus, we can use the model to make novel predictions on the cooperation between proteins in the signaling pathway. Virtual perturbations on feedback loops suggest that both positive and negative feedback loops are required to terminate expression of early meiotic proteins. Bifurcation analyses on feedback loops indicate that multiple feedback loops are coordinated to modulate sporulation efficiency. In particular, positive auto-regulation of Ime2 produces a bistable system with a normal meiotic state and a more efficient meiotic state.

CONCLUSIONS: By systematically scanning through feedback loops in the mathematical model, we demonstrate that, in yeast, the decisions to terminate protein expression and to sporulate at different efficiencies stem from feedback signals toward the master regulator Ime1 and the early meiotic protein Ime2. We argue that the architecture of meiotic initiation pathway generates a robust mechanism that assures a rapid and complete transition into meiosis. This type of systems-level regulation is a commonly used mechanism controlling developmental programs in yeast and other organisms. Our mathematical model uncovers key regulations that can be manipulated to enhance sporulation efficiency, an important first step in the development of new strategies for producing gametes with high quality and quantity.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000626 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4061, + "tag": "BioModels:BIOMD0000000626" + }, + { + "id": 4062, + "tag": "Meiotic cell cycle" + }, + { + "id": 4063, + "tag": "Regulation of meiotic nuclear division" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:40.997003+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000626", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2917": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2917, + "name": "Winter2017 - Brain Energy Metabolism with PPP", + "repository_type": "biomodels", + "summary": "
Winter2017 - Brain Energy Metabolism with PPP

This model is described in the article:

Felix Winter1,2, Catrin Bludszuweit-Philipp1 and Olaf Wolkenhauer2,3
Journal of Cerebral Blood Flow & Metabolism

Abstract:

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a standard clinical tool for the detection of brain activation. In Alzheimer\u2019s disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia- driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients.

This model is hosted on BioModels Database and identified by: BIOMD0000000627.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4064, + "tag": "BioModels:BIOMD0000000627" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 14:02:41.560652+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000627", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2918": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2918, + "name": "Li2012 Calcium mediated synaptic plasticity", + "repository_type": "biomodels", + "summary": "
Li2012 Calcium mediated synapticplasticity
This model is an extension of\u00a0 BIOMD0000000183.

This model is described in the article:

Li L, Stefan MI, Le Nov\u00e8re N.
PLoS ONE 2012; 7(9): e43810

Abstract:

NMDA receptor dependent long-term potentiation (LTP) and long-term depression (LTD) are two prominent forms of synaptic plasticity, both of which are triggered by post-synaptic calcium elevation. To understand how calcium selectively stimulates two opposing processes, we developed a detailed computational model and performed simulations with different calcium input frequencies, amplitudes, and durations. We show that with a total amount of calcium ions kept constant, high frequencies of calcium pulses stimulate calmodulin more efficiently. Calcium input activates both calcineurin and Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) at all frequencies, but increased frequencies shift the relative activation from calcineurin to CaMKII. Irrespective of amplitude and duration of the inputs, the total amount of calcium ions injected adjusts the sensitivity of the system to calcium input frequencies. At a given frequency, the quantity of CaMKII activated is proportional to the total amount of calcium. Thus, an input of a small amount of calcium at high frequencies can induce the same activation of CaMKII as a larger amount, at lower frequencies. Finally, the extent of activation of CaMKII signals with high calcium frequency is further controlled by other factors, including the availability of calmodulin, and by the potency of phosphatase inhibitors.

This model is hosted on BioModels Database and identified by: BIOMD0000000628.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4065, + "tag": "BioModels:BIOMD0000000628" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:42.200836+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000628", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2919": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2919, + "name": "Haffez2017 - RAR interaction with synthetic analogues", + "repository_type": "biomodels", + "summary": "
Haffez2017 - RAR interaction with syntheticanalogues

This model is described in the article:

Hesham Haffez, David R. Chisholm, Roy Valentine, Ehmke Pohl, Christopher Redfern and Andrew Whiting
MedChemComm

Abstract:

All-trans-retinoic acid (ATRA) and its synthetic analogues EC23 and EC19 direct cellular differentiation by interacting as ligands for the retinoic acid receptor (RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3) family of nuclear receptor proteins. To date, a number of crystal structures of natural and synthetic ligands complexed to their target proteins have been solved, providing molecular level snap-shots of ligand binding. However, a deeper understanding of receptor and ligand flexibility and conformational freedom is required to develop stable and effective ATRA analogues for clinical use. Therefore, we have used molecular modelling techniques to define RAR interactions with ATRA and two synthetic analogues, EC19 and EC23, and compared their predicted biochemical activities to experimental measurements of relative ligand affinity and recruitment of coactivator proteins. A comprehensive molecular docking approach that explored the conformational space of the ligands indicated that ATRA is able to bind the three RAR proteins in a number of conformations with one extended structure being favoured. In contrast the biologically-distinct isomer, 9-cis-retinoic acid (9CRA), showed significantly less conformational flexibility in the RAR binding pockets. These findings were used to inform docking studies of the synthetic retinoids EC23 and EC19, and their respective methyl esters. EC23 was found to be an excellent mimic for ATRA, and occupied similar binding modes to ATRA in all three target RAR proteins. In comparison, EC19 exhibited an alternative binding mode which reduces the strength of key polar interactions in RAR\u00ce\u00b1/\u00ce\u00b3 but is well-suited to the larger RAR\u00ce\u00b2 binding pocket. In contrast, docking of the corresponding esters revealed the loss of key polar interactions which may explain the much reduced biological activity. Our computational results were complemented using an in vitro binding assay based on FRET measurements, which showed that EC23 was a strongly binding, pan-agonist of the RARs, while EC19 exhibited specificity for RAR\u00ce\u00b2, as predicted by the docking studies. These findings can account for the distinct behaviour of EC23 and EC19 in cellular differentiation assays, and additionally, the methods described herein can be further applied to the understanding of the molecular basis for the selectivity of different retinoids to RAR\u00ce\u00b1, \u00ce\u00b2 and \u00ce\u00b3.

This model is hosted on BioModels Database and identified by: BIOMD0000000629.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4066, + "tag": "BioModels:BIOMD0000000629" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 14:02:42.781346+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000629", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2920": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2920, + "name": "Venkatraman2011 - PLS-UPA behaviour in the presence of substrate competition_1_1_1_1", + "repository_type": "biomodels", + "summary": "
Venkatraman2011 - PLS-UPA behaviour in thepresence of substrate competition

The posibility of ultrasensitivity and bistable activation of PLS (Plasmin) and UPA (Urokinase-type plasminogen activator) in the presence of substrate competition is explained here using a mathematical model.\u00a0

This model is described in the article:

Venkatraman L, Li H, Dewey CF Jr, White JK, Bhowmick SS, Yu H, Tucker-Kellogg L.
Biophys. J. 2011 Oct; 101(8): 1825-1834

Abstract:

Plasmin (PLS) and urokinase-type plasminogen activator (UPA) are ubiquitous proteases that regulate the extracellular environment. Although they are secreted in inactive forms, they can activate each other through proteolytic cleavage. This mutual interplay creates the potential for complex dynamics, which we investigated using mathematical modeling and in vitro experiments. We constructed ordinary differential equations to model the conversion of precursor plasminogen into active PLS, and precursor urokinase (scUPA) into active urokinase (tcUPA). Although neither PLS nor UPA exhibits allosteric cooperativity, modeling showed that cooperativity occurred at the system level because of substrate competition. Computational simulations and bifurcation analysis predicted that the system would be bistable over a range of parameters for cooperativity and positive feedback. Cell-free experiments with recombinant proteins tested key predictions of the model. PLS activation in response to scUPA stimulus was found to be cooperative in vitro. Finally, bistability was demonstrated in vitro by the presence of two significantly different steady-state levels of PLS activation for the same levels of stimulus. We conclude that ultrasensitive, bistable activation of UPA-PLS is possible in the presence of substrate competition. An ultrasensitive threshold for activation of PLS and UPA would have ramifications for normal and disease processes, including angiogenesis, metastasis, wound healing, and fibrosis.

The cooperativity parameter \"ci\" was missing in the original model. The parameter \"ci\" has been added to the added.

This model is hosted on BioModels Database and identified by: BIOMD0000000630.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4067, + "tag": "BioModels:BIOMD0000000630" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3042, + "tag": "cellular organisms" + } + ], + "timestamp_created": "2025-01-30 14:02:43.374994+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000630", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2921": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2921, + "name": "DeCaluwe2016 - Circadian Clock", + "repository_type": "biomodels", + "summary": "
DeCaluw\u00e92016 - Circadian Clock

This model is described in the article:

De Caluw\u00e9 J, Xiao Q, Hermans C, Verbruggen N, Leloup JC, Gonze D.
Front Plant Sci 2016; 7: 74

Abstract:

The circadian clock is an endogenous timekeeper that allows organisms to anticipate and adapt to the daily variations of their environment. The plant clock is an intricate network of interlocked feedback loops, in which transcription factors regulate each other to generate oscillations with expression peaks at specific times of the day. Over the last decade, mathematical modeling approaches have been used to understand the inner workings of the clock in the model plant Arabidopsis thaliana. Those efforts have produced a number of models of ever increasing complexity. Here, we present an alternative model that combines a low number of equations and parameters, similar to the very earliest models, with the complex network structure found in more recent ones. This simple model describes the temporal evolution of the abundance of eight clock gene mRNA/protein and captures key features of the clock on a qualitative level, namely the entrained and free-running behaviors of the wild type clock, as well as the defects found in knockout mutants (such as altered free-running periods, lack of entrainment, or changes in the expression of other clock genes). Additionally, our model produces complex responses to various light cues, such as extreme photoperiods and non-24 h environmental cycles, and can describe the control of hypocotyl growth by the clock. Our model constitutes a useful tool to probe dynamical properties of the core clock as well as clock-dependent processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000631.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 3104, + "tag": "Arabidopsis thaliana" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4068, + "tag": "BioModels:BIOMD0000000631" + }, + { + "id": 4069, + "tag": "Entrainment of circadian clock by photoperiod" + }, + { + "id": 3006, + "tag": "Regulation of circadian rhythm" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:43.902154+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000631", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2922": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2922, + "name": "Kollarovic2016 - Cell fate decision at G1-S transition", + "repository_type": "biomodels", + "summary": "
Kollarovic2016 - Cell fate decision at G1-Stransition

This model is described in the article:

Kollarovic G, Studencka M, Ivanova L, Lauenstein C, Heinze K, Lapytsko A, Talemi SR, Figueiredo AS, Schaber J.
Aging (Albany NY) 2016 Jan;

Abstract:

Excessive DNA damage can induce an irreversible cell cycle arrest, called senescence, which is generally perceived as an important tumour-suppressor mechanism. However, it is unclear how cells decide whether to senesce or not after DNA damage. By combining experimental data with a parameterized mathematical model we elucidate this cell fate decision at the G1-S transition. Our model provides a quantitative and conceptually new understanding of how human fibroblasts decide whether DNA damage is beyond repair and senesce. Model and data imply that the G1-S transition is regulated by a bistable hysteresis switch with respect to Cdk2 activity, which in turn is controlled by the Cdk2/p21 ratio rather than cyclin abundance. We experimentally confirm the resulting predictions that to induce senescence i) in healthy cells both high initial and elevated background DNA damage are necessary and sufficient, and ii) in already damaged cells much lower additional DNA damage is sufficient. Our study provides a mechanistic explanation of a) how noise in protein abundances allows cells to overcome the G1-S arrest even with substantial DNA damage, potentially leading to neoplasia, and b) how accumulating DNA damage with age increasingly sensitizes cells for senescence.

This model is hosted on BioModels Database and identified by: BIOMD0000000632.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4070, + "tag": "BioModels:BIOMD0000000632" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:44.412776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000632", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2923": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2923, + "name": "Bulik2016 - Regulation of hepatic glucose metabolism", + "repository_type": "biomodels", + "summary": "
Bulik2016 - Regulation of hepatic glucosemetabolism

This model is described in the article:

Bulik S, Holzh\u00fctter HG, Berndt N.
BMC Biol. 2016 Mar; 14: 15

Abstract:

Adaptation of the cellular metabolism to varying external conditions is brought about by regulated changes in the activity of enzymes and transporters. Hormone-dependent reversible enzyme phosphorylation and concentration changes of reactants and allosteric effectors are the major types of rapid kinetic enzyme regulation, whereas on longer time scales changes in protein abundance may also become operative. Here, we used a comprehensive mathematical model of the hepatic glucose metabolism of rat hepatocytes to decipher the relative importance of different regulatory modes and their mutual interdependencies in the hepatic control of plasma glucose homeostasis.Model simulations reveal significant differences in the capability of liver metabolism to counteract variations of plasma glucose in different physiological settings (starvation, ad libitum nutrient supply, diabetes). Changes in enzyme abundances adjust the metabolic output to the anticipated physiological demand but may turn into a regulatory disadvantage if sudden unexpected changes of the external conditions occur. Allosteric and hormonal control of enzyme activities allow the liver to assume a broad range of metabolic states and may even fully reverse flux changes resulting from changes of enzyme abundances alone. Metabolic control analysis reveals that control of the hepatic glucose metabolism is mainly exerted by enzymes alone, which are differently controlled by alterations in enzyme abundance, reversible phosphorylation, and allosteric effects.In hepatic glucose metabolism, regulation of enzyme activities by changes of reactants, allosteric effects, and reversible phosphorylation is equally important as changes in protein abundance of key regulatory enzymes.

This model is hosted on BioModels Database and identified by: BIOMD0000000633.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4071, + "tag": "BioModels:BIOMD0000000633" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:44.906243+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000633", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2924": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2924, + "name": "Proctor2013 - Effect of A\u03b2 immunisation in Alzheimer's disease (stochastic version)", + "repository_type": "biomodels", + "summary": "
Proctor2013 - Effect of A\u03b2 immunisationin Alzheimer's disease (stochastic version)
Extension of a previously publishedstochastic model (designed to examine some of the key pathwaysinvolved in the aggregation of amyloid-beta (A\u03b2)\u00a0andthe micro-tubular binding protein tau (BIOMD0000000286,BIOMD0000000462))to include the main processes involved in passive and activeimmunisation against A\u03b2and then to demonstrate the effects of this intervention on solubleA\u03b2.\u00a0Thisis the stochastic version of the model, the deterministic versionis BIOMD0000000488.\u00a0

This model is described in the article:

Proctor CJ, Boche D, Gray DA, Nicoll JA.
PLoS ONE 2013; 8(9): e73631

Abstract:

Progress in the development of therapeutic interventions to treat or slow the progression of Alzheimer's disease has been hampered by lack of efficacy and unforeseen side effects in human clinical trials. This setback highlights the need for new approaches for pre-clinical testing of possible interventions. Systems modelling is becoming increasingly recognised as a valuable tool for investigating molecular and cellular mechanisms involved in ageing and age-related diseases. However, there is still a lack of awareness of modelling approaches in many areas of biomedical research. We previously developed a stochastic computer model to examine some of the key pathways involved in the aggregation of amyloid-beta (A\u03b2) and the micro-tubular binding protein tau. Here we show how we extended this model to include the main processes involved in passive and active immunisation against A\u03b2 and then demonstrate the effects of this intervention on soluble A\u03b2, plaques, phosphorylated tau and tangles. The model predicts that immunisation leads to clearance of plaques but only results in small reductions in levels of soluble A\u03b2, phosphorylated tau and tangles. The behaviour of this model is supported by neuropathological observations in Alzheimer patients immunised against A\u03b2. Since, soluble A\u03b2, phosphorylated tau and tangles more closely correlate with cognitive decline than plaques, our model suggests that immunotherapy against A\u03b2 may not be effective unless it is performed very early in the disease process or combined with other therapies.

This model is hosted on BioModels Database and identified by: BIOMD0000000634.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4072, + "tag": "BioModels:BIOMD0000000634" + }, + { + "id": 3320, + "tag": "DNA damage response, signal transduction by p53 class mediator" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3507, + "tag": "Inclusion body assembly" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4588, + "tag": "Alzheimer's disease" + } + ], + "timestamp_created": "2025-01-30 14:02:45.409590+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000634", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2925": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2925, + "name": "Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1 Neuron)", + "repository_type": "biomodels", + "summary": "
Nair2015 - Interaction betweenneuromodulators via GPCRs - Effect on cAMP/PKA signaling (D1Neuron)

This model is described in the article:

Nair AG, Gutierrez-Arenas O, Eriksson O, Vincent P, Hellgren Kotaleski J.
J. Neurosci. 2015 Oct; 35(41): 14017-14030

Abstract:

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000635.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4073, + "tag": "BioModels:BIOMD0000000635" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:45.910764+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000635", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2926": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2926, + "name": "Nair2015 - Interaction between neuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2 Neuron)", + "repository_type": "biomodels", + "summary": "
Nair2015 - Interaction betweenneuromodulators via GPCRs - Effect on cAMP/PKA signaling (D2Neuron)

This model is described in the article:

Nair AG, Gutierrez-Arenas O, Eriksson O, Vincent P, Hellgren Kotaleski J.
J. Neurosci. 2015 Oct; 35(41): 14017-14030

Abstract:

Transient changes in striatal dopamine (DA) concentration are considered to encode a reward prediction error (RPE) in reinforcement learning tasks. Often, a phasic DA change occurs concomitantly with a dip in striatal acetylcholine (ACh), whereas other neuromodulators, such as adenosine (Adn), change slowly. There are abundant adenylyl cyclase (AC) coupled GPCRs for these neuromodulators in striatal medium spiny neurons (MSNs), which play important roles in plasticity. However, little is known about the interaction between these neuromodulators via GPCRs. The interaction between these transient neuromodulator changes and the effect on cAMP/PKA signaling via Golf- and Gi/o-coupled GPCR are studied here using quantitative kinetic modeling. The simulations suggest that, under basal conditions, cAMP/PKA signaling could be significantly inhibited in D1R+ MSNs via ACh/M4R/Gi/o and an ACh dip is required to gate a subset of D1R/Golf-dependent PKA activation. Furthermore, the interaction between ACh dip and DA peak, via D1R and M4R, is synergistic. In a similar fashion, PKA signaling in D2+ MSNs is under basal inhibition via D2R/Gi/o and a DA dip leads to a PKA increase by disinhibiting A2aR/Golf, but D2+ MSNs could also respond to the DA peak via other intracellular pathways. This study highlights the similarity between the two types of MSNs in terms of high basal AC inhibition by Gi/o and the importance of interactions between Gi/o and Golf signaling, but at the same time predicts differences between them with regard to the sign of RPE responsible for PKA activation.Dopamine transients are considered to carry reward-related signal in reinforcement learning. An increase in dopamine concentration is associated with an unexpected reward or salient stimuli, whereas a decrease is produced by omission of an expected reward. Often dopamine transients are accompanied by other neuromodulatory signals, such as acetylcholine and adenosine. We highlight the importance of interaction between acetylcholine, dopamine, and adenosine signals via adenylyl-cyclase coupled GPCRs in shaping the dopamine-dependent cAMP/PKA signaling in striatal neurons. Specifically, a dopamine peak and an acetylcholine dip must interact, via D1 and M4 receptor, and a dopamine dip must interact with adenosine tone, via D2 and A2a receptor, in direct and indirect pathway neurons, respectively, to have any significant downstream PKA activation.

This model is hosted on BioModels Database and identified by: BIOMD0000000636.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4074, + "tag": "BioModels:BIOMD0000000636" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:46.412878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000636", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2927": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2927, + "name": "Bush2016 - Simplified Carrousel model of GPCR", + "repository_type": "biomodels", + "summary": "
Bush2016 - Simplified Carrousel model ofGPCR

This model is described in the article:

Bush A, Vasen G, Constantinou A, Dunayevich P, Patop IL, Blaustein M, Colman-Lerner A.
Mol. Syst. Biol. 2016 Dec; 12(12): 898

Abstract:

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.


This model is hosted on BioModels Database and identified by: BIOMD0000000637.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4075, + "tag": "BioModels:BIOMD0000000637" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:46.899867+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000637", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2928": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2928, + "name": "Bush2016 - Extended Carrousel model of GPCR-RGS", + "repository_type": "biomodels", + "summary": "
Bush2016 - Extended Carrousel model ofGPCR-RGS

This model is described in the article:

Bush A, Vasen G, Constantinou A, Dunayevich P, Patop IL, Blaustein M, Colman-Lerner A.
Mol. Syst. Biol. 2016 Dec; 12(12): 898

Abstract:

According to receptor theory, the effect of a ligand depends on the amount of agonist-receptor complex. Therefore, changes in receptor abundance should have quantitative effects. However, the response to pheromone in Saccharomyces cerevisiae is robust (unaltered) to increases or reductions in the abundance of the G-protein-coupled receptor (GPCR), Ste2, responding instead to the fraction of occupied receptor. We found experimentally that this robustness originates during G-protein activation. We developed a complete mathematical model of this step, which suggested the ability to compute fractional occupancy depends on the physical interaction between the inhibitory regulator of G-protein signaling (RGS), Sst2, and the receptor. Accordingly, replacing Sst2 by the heterologous hsRGS4, incapable of interacting with the receptor, abolished robustness. Conversely, forcing hsRGS4:Ste2 interaction restored robustness. Taken together with other results of our work, we conclude that this GPCR pathway computes fractional occupancy because ligand-bound GPCR-RGS complexes stimulate signaling while unoccupied complexes actively inhibit it. In eukaryotes, many RGSs bind to specific GPCRs, suggesting these complexes with opposing activities also detect fraction occupancy by a ratiometric measurement. Such complexes operate as push-pull devices, which we have recently described.


This model is hosted on BioModels Database and identified by: BIOMD0000000638.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4076, + "tag": "BioModels:BIOMD0000000638" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:02:47.423261+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000638", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2929": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2929, + "name": "Thiaville2016 - Wild type folate pathway model with proposed PanB reaction", + "repository_type": "biomodels", + "summary": "
Thiaville2016 - Wild type folate pathwaymodel with proposed PanB reaction
This is a wild type E. coli model, andis one amongst the three models described in the paper. The othertwo models are\u00a0MODEL1602280002 (wild type with PanB overexpression) and MODEL1602280003 (wild type with PanB overexpression and THF regulation).\u00a0

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: BIOMD0000000639.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4077, + "tag": "BioModels:BIOMD0000000639" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:47.919317+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000639", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2930": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2930, + "name": "DallePezze2016 - Activation of AMPK and mTOR by amino acids", + "repository_type": "biomodels", + "summary": "
DallePezze2016 - Activation of AMPK and mTORby amino acids (Model 3)
This model is as\u00a0described in the Supplementary Software 3 of the reference publication: SBML model similar to Model S2, but including a more complex p70-S6K module.

This model is described in the article:

Dalle Pezze P, Ruf S, Sonntag AG, Langelaar-Makkinje M, Hall P, Heberle AM, Razquin Navas P, van Eunen K, T\u00f6lle RC, Schwarz JJ, Wiese H, Warscheid B, Deitersen J, Stork B, F\u00e4\u00dfler E, Sch\u00e4uble S, Hahn U, Horvatovich P, Shanley DP, Thedieck K.
Nat Commun 2016 Nov; 7: 13254

Abstract:

Amino acids (aa) are not only building blocks for proteins, but also signalling molecules, with the mammalian target of rapamycin complex 1 (mTORC1) acting as a key mediator. However, little is known about whether aa, independently of mTORC1, activate other kinases of the mTOR signalling network. To delineate aa-stimulated mTOR network dynamics, we here combine a computational-experimental approach with text mining-enhanced quantitative proteomics. We report that AMP-activated protein kinase (AMPK), phosphatidylinositide 3-kinase (PI3K) and mTOR complex 2 (mTORC2) are acutely activated by aa-readdition in an mTORC1-independent manner. AMPK activation by aa is mediated by Ca2+/calmodulin-dependent protein kinase kinase ? (CaMKK?). In response, AMPK impinges on the autophagy regulators Unc-51-like kinase-1 (ULK1) and c-Jun. AMPK is widely recognized as an mTORC1 antagonist that is activated by starvation. We find that aa acutely activate AMPK concurrently with mTOR. We show that AMPK under aa sufficiency acts to sustain autophagy. This may be required to maintain protein homoeostasis and deliver metabolite intermediates for biosynthetic processes.

This model is hosted on BioModels Database and identified by: BIOMD0000000640.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4078, + "tag": "BioModels:BIOMD0000000640" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:48.403180+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000640", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2931": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2931, + "name": "Jaiswal2017 - Cell cycle arrest", + "repository_type": "biomodels", + "summary": "
Jaiswal2017 - Cell cycle arrest

This model is described in the article:

Jaiswal H, Benada J, M\u00fcllers E, Akopyan K, Burdova K, Koolmeister T, Helleday T, Medema RH, Macurek L, Lindqvist A.
EMBO J. 2017 Jul; 36(14): 2161-2176

Abstract:

After DNA damage, the cell cycle is arrested to avoid propagation of mutations. Arrest in G2 phase is initiated by ATM-/ATR-dependent signaling that inhibits mitosis-promoting kinases such as Plk1. At the same time, Plk1 can counteract ATR-dependent signaling and is required for eventual resumption of the cell cycle. However, what determines when Plk1 activity can resume remains unclear. Here, we use FRET-based reporters to show that a global spread of ATM activity on chromatin and phosphorylation of ATM targets including KAP1 control Plk1 re-activation. These phosphorylations are rapidly counteracted by the chromatin-bound phosphatase Wip1, allowing cell cycle restart despite persistent ATM activity present at DNA lesions. Combining experimental data and mathematical modeling, we propose a model for how the minimal duration of cell cycle arrest is controlled. Our model shows how cell cycle restart can occur before completion of DNA repair and suggests a mechanism for checkpoint adaptation in human cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000641.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4079, + "tag": "BioModels:BIOMD0000000641" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4080, + "tag": "Mitotic G2 DNA damage checkpoint" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:48.881274+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000641", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2932": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2932, + "name": "Mufudza2012 - Estrogen effect on the dynamics of breast cancer", + "repository_type": "biomodels", + "summary": "
Mufudza2012 - Estrogen effect on the dynamicsof breast cancer
This deterministic model shows thedynamics of breast cancer with immune response. The effects ofestrogen are incorporated to study its effects as a risk factor forthe disease.\u00a0

This model is described in the article:

Mufudza C, Sorofa W, Chiyaka ET.
Comput Math Methods Med 2012; 2012: 473572

Abstract:

Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer.

This model is hosted on BioModels Database and identified by: BIOMD0000000642.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4081, + "tag": "BioModels:BIOMD0000000642" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:49.424822+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000642", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2933": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2933, + "name": "Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions", + "repository_type": "biomodels", + "summary": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000643.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4082, + "tag": "BioModels:BIOMD0000000643" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:49.953424+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000643", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2934": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2934, + "name": "Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3", + "repository_type": "biomodels", + "summary": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions and PKA inhibitsMAST3

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000644.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4084, + "tag": "BioModels:BIOMD0000000644" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:50.454406+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000644", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2935": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2935, + "name": "Musante2017 - Switching behaviour of PP2A inhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3 and dominant negative effect", + "repository_type": "biomodels", + "summary": "
Musante2017 - Switching behaviour of PP2Ainhibition by ARPP-16 - mutual inhibitions and PKA inhibits MAST3and dominant negative effect

This model is described in the article:

Musante V, Li L, Kanyo J, Lam TT, Colangelo CM, Cheng SK, Brody AH, Greengard P, Le Nov\u00e8re N, Nairn AC.
Elife 2017 Jun; 6:

Abstract:

ARPP-16, ARPP-19, and ENSA are inhibitors of protein phosphatase PP2A. ARPP-19 and ENSA phosphorylated by Greatwall kinase inhibit PP2A during mitosis. ARPP-16 is expressed in striatal neurons where basal phosphorylation by MAST3 kinase inhibits PP2A and regulates key components of striatal signaling. The ARPP-16/19 proteins were discovered as substrates for PKA, but the function of PKA phosphorylation is unknown. We find that phosphorylation by PKA or MAST3 mutually suppresses the ability of the other kinase to act on ARPP-16. Phosphorylation by PKA also acts to prevent inhibition of PP2A by ARPP-16 phosphorylated by MAST3. Moreover, PKA phosphorylates MAST3 at multiple sites resulting in its inhibition. Mathematical modeling highlights the role of these three regulatory interactions to create a switch-like response to cAMP. Together, the results suggest a complex antagonistic interplay between the control of ARPP-16 by MAST3 and PKA that creates a mechanism whereby cAMP mediates PP2A disinhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000645.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4085, + "tag": "BioModels:BIOMD0000000645" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:50.964323+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000645", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2936": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2936, + "name": "Barr2016 - All-or-nothing G1/S transition", + "repository_type": "biomodels", + "summary": "
Barr2016 - All-or-nothing G1/Stransition

This model is described in the article:

Barr AR, Heldt FS, Zhang T, Bakal C, Nov\u00e1k B.
Cell Syst 2016 Jan; 2(1): 27-37

Abstract:

The transition from G1 into DNA replication (S phase) is an emergent behavior resulting from dynamic and complex interactions between cyclin-dependent kinases (Cdks), Cdk inhibitors (CKIs), and the anaphase-promoting complex/cyclosome (APC/C). Understanding the cellular decision to commit to S\u00a0phase requires a quantitative description of these interactions. We apply quantitative imaging of single human cells to track the expression of G1/S regulators and use these data to parametrize a stochastic mathematical model of the G1/S transition. We show that a rapid, proteolytic, double-negative feedback loop between Cdk2:Cyclin and the Cdk inhibitor p27(Kip1) drives a switch-like entry into S phase. Furthermore, our model predicts that increasing Emi1 levels throughout S phase are critical in maintaining irreversibility of the G1/S transition, which we validate using Emi1 knockdown and live imaging of G1/S reporters. This work provides insight into the general design principles of the signaling networks governing the temporally abrupt transitions between cell-cycle phases.

This model is hosted on BioModels Database and identified by: BIOMD0000000646.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4086, + "tag": "Altered G1/S transition pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4087, + "tag": "BioModels:BIOMD0000000646" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:51.454589+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000646", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2937": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2937, + "name": "Kwang2003 - The influence of RKIP on the ERK signaling pathway", + "repository_type": "biomodels", + "summary": "
Kwang2003 - The influence of RKIP on the ERKsignaling pathway

This model is described in the article:

Kwang-Hyun Cho, Sung-Young Shin, Hyun-Woo Kim, Olaf Wolkenhauer, Brian McFerran and Walter Kolch
Computational Methods in Systems Biology: First International Workshop, CMSB 2003 Rovereto, Italy, February 24\u00e2??26, 2003 Proceedings

Abstract:

This paper investigates the influence of the Raf Kinase Inhibitor Pro- tein (RKIP) on the Extracellular signal Regulated Kinase (ERK) signaling pathway through mathematical modeling and simulation. Using nonlinear ordi- nary differential equations to represent biochemical reactions in the pathway, we suggest a technique for parameter estimation, utilizing time series data of proteins involved in the signaling pathway. The mathematical model allows the simulation the sensitivity of the ERK pathway to variations of initial RKIP and ERK-PP (phosphorylated ERK) concentrations along with time. Throughout the simulation study, we can qualitatively validate the proposed mathematical model compared with experimental results.

This model is hosted on BioModels Database and identified by: BIOMD0000000647.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4088, + "tag": "BioModels:BIOMD0000000647" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:52.005998+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000647", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2938": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2938, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (normal)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (normal)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways under normal condition.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000648.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4089, + "tag": "BioModels:BIOMD0000000648" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:52.574340+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000648", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2939": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2939, + "name": "Owen1998 - Tumour treatment model", + "repository_type": "biomodels", + "summary": "
Owen1998 - tumour treatment model

This model is described in the article:

Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun; 15(2): 165-185

Abstract:

Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.

This model is hosted on BioModels Database and identified by: MODEL1708250002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4090, + "tag": "BioModels:BIOMD0000000650" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:53.115546+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000650", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2940": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2940, + "name": "Nguyen2016 - Feedback regulation in cell signalling: Lessons for cancer therapeutics", + "repository_type": "biomodels", + "summary": "
Feedback regulation in cell signalling: Lessons for cancer therapeutics

This model is described in the article:

Nguyen LK, Kholodenko BN.
Semin. Cell Dev. Biol. 2016 Feb; 50: 85-94

Abstract:

The notion of feedback is fundamental for understanding signal transduction networks. Feedback loops attenuate or amplify signals, change the network dynamics and modify the input-output relationships between the signal and the target. Negative feedback provides robustness to noise and adaptation to perturbations, but as a double-edged sword can prevent effective pathway inhibition by a drug. Positive feedback brings about switch-like network responses and can convert analog input signals into digital outputs, triggering cell fate decisions and phenotypic changes. We show how a multitude of protein-protein interactions creates hidden feedback loops in signal transduction cascades. Drug treatments that interfere with feedback regulation can cause unexpected adverse effects. Combinatorial molecular interactions generated by pathway crosstalk and feedback loops often bypass the block caused by targeted therapies against oncogenic mutated kinases. We discuss mechanisms of drug resistance caused by network adaptations and suggest that development of effective drug combinations requires understanding of how feedback loops modulate drug responses.

This model is hosted on BioModels Database and identified by: MODEL1708250003.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4091, + "tag": "BioModels:BIOMD0000000651" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:53.633487+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000651", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2941": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2941, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (PI3K mutated)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (PI3K mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with mutated PI3K.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000652.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4092, + "tag": "BioModels:BIOMD0000000652" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:54.338810+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000652", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2942": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2942, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (bRaf mutated)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (bRaf mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with bRaf mutation

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000653.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4093, + "tag": "BioModels:BIOMD0000000653" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:54.876212+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000653", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2943": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2943, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (Ras mutated)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (Ras mutated)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with Ras mutation.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000654.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4094, + "tag": "BioModels:BIOMD0000000654" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:55.378095+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000654", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2944": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2944, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (PTEN mutation)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (PTEN mutation)
Crosstalk model of the ERK, Wnt and AktSignalling pathways with PTEN mutation.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000655.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4095, + "tag": "BioModels:BIOMD0000000655" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:55.887764+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000655", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2945": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2945, + "name": "Padala2017- ERK, PI3K/Akt and Wnt signalling network (EGFR overexpression)", + "repository_type": "biomodels", + "summary": "
Padala2017- ERK, PI3K/Akt and Wnt signallingnetwork (EGFR overexpression)
Crosstalk model of the ERK, Wnt and Aktsignalling pathways with EGFR overexpression.

This model is described in the article:

Padala RR, Karnawat R, Viswanathan SB, Thakkar AV, Das AB.
Mol Biosyst 2017 May; 13(5): 830-840

Abstract:

Perturbations in molecular signaling pathways are a result of genetic or epigenetic alterations, which may lead to malignant transformation of cells. Despite cellular robustness, specific genetic or epigenetic changes of any gene can trigger a cascade of failures, which result in the malfunctioning of cell signaling pathways and lead to cancer phenotypes. The extent of cellular robustness has a link with the architecture of the network such as feedback and feedforward loops. Perturbation in components within feedback loops causes a transition from a regulated to a persistently activated state and results in uncontrolled cell growth. This work represents the mathematical and quantitative modeling of ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk to show the dynamics of signaling responses during genetic and epigenetic changes in cancer. ERK, PI3K/Akt, and Wnt/?-catenin signaling crosstalk networks include both intra and inter-pathway feedback loops which function in a controlled fashion in a healthy cell. Our results show that cancerous perturbations of components such as EGFR, Ras, B-Raf, PTEN, and components of the destruction complex cause extreme fragility in the network and constitutively activate inter-pathway positive feedback loops. We observed that the aberrant signaling response due to the failure of specific network components is transmitted throughout the network via crosstalk, generating an additive effect on cancer growth and proliferation.

This model is hosted on BioModels Database and identified by: BIOMD0000000656.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4096, + "tag": "BioModels:BIOMD0000000656" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:56.400863+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000656", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2946": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 2946, + "name": "Araujo2016 - Positive feedback in Cdk1 signalling keeps mitotic duration short and constant", + "repository_type": "biomodels", + "summary": "
Araujo2016 - Positive feedback in Cdk1signalling keeps mitotic duration short and constant

This model is described in the article:

Araujo AR, Gelens L, Sheriff RS, Santos SD.
Mol. Cell 2016 Oct; 64(2): 362-375

Abstract:

Cell division is characterized by a sequence of events by which a cell gives rise to two daughter cells. Quantitative measurements of cell-cycle dynamics in single cells showed that despite variability in G1-, S-, and G2 phases, duration of mitosis is short and remarkably constant. Surprisingly, there is no correlation between cell-cycle length and mitotic duration, suggesting that mitosis is temporally insulated from variability in earlier cell-cycle phases. By combining live cell imaging and computational modeling, we showed that positive feedback is the molecular mechanism underlying the temporal insulation of mitosis. Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.

This model is hosted on BioModels Database and identified by: BIOMD0000000657.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4097, + "tag": "BioModels:BIOMD0000000657" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:56.901089+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000657", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2947": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2947, + "name": "Lee2003 - Roles of APC and Axin in Wnt Pathway (without regulatory loop)", + "repository_type": "biomodels", + "summary": "
Lee2003 - Roles of APC and Axin in WntPathway (without regulatory loop)

This model is described in the article:

Lee E, Salic A, Kr\u00fcger R, Heinrich R, Kirschner MW.
PLoS Biol. 2003 Oct; 1(1): E10

Abstract:

Wnt signaling plays an important role in both oncogenesis and development. Activation of the Wnt pathway results in stabilization of the transcriptional coactivator beta-catenin. Recent studies have demonstrated that axin, which coordinates beta-catenin degradation, is itself degraded. Although the key molecules required for transducing a Wnt signal have been identified, a quantitative understanding of this pathway has been lacking. We have developed a mathematical model for the canonical Wnt pathway that describes the interactions among the core components: Wnt, Frizzled, Dishevelled, GSK3beta, APC, axin, beta-catenin, and TCF. Using a system of differential equations, the model incorporates the kinetics of protein-protein interactions, protein synthesis/degradation, and phosphorylation/dephosphorylation. We initially defined a reference state of kinetic, thermodynamic, and flux data from experiments using Xenopus extracts. Predictions based on the analysis of the reference state were used iteratively to develop a more refined model from which we analyzed the effects of prolonged and transient Wnt stimulation on beta-catenin and axin turnover. We predict several unusual features of the Wnt pathway, some of which we tested experimentally. An insight from our model, which we confirmed experimentally, is that the two scaffold proteins axin and APC promote the formation of degradation complexes in very different ways. We can also explain the importance of axin degradation in amplifying and sharpening the Wnt signal, and we show that the dependence of axin degradation on APC is an essential part of an unappreciated regulatory loop that prevents the accumulation of beta-catenin at decreased APC concentrations. By applying control analysis to our mathematical model, we demonstrate the modular design, sensitivity, and robustness of the Wnt pathway and derive an explicit expression for tumor suppression and oncogenicity.

This model is hosted on BioModels Database and identified by: BIOMD0000000658.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4098, + "tag": "BioModels:BIOMD0000000658" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4099, + "tag": "Xenopus" + } + ], + "timestamp_created": "2025-01-30 14:02:57.380793+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000658", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2948": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2948, + "name": "Cursons2015 - Regulation of ERK-MAPK signaling in human epidermis", + "repository_type": "biomodels", + "summary": "
Cursons2015 - Regulation of ERK-MAPKsignaling in human epidermis
Model comparing the abundance ofphosphorylated MAPK signalling proteins and calcium signalling inthe epidermis.

This model is described in the article:

Cursons J, Gao J, Hurley DG, Print CG, Dunbar PR, Jacobs MD, Crampin EJ.
BMC Syst Biol 2015; 9: 41

Abstract:

The skin is largely comprised of keratinocytes within the interfollicular epidermis. Over approximately two weeks these cells differentiate and traverse the thickness of the skin. The stage of differentiation is therefore reflected in the positions of cells within the tissue, providing a convenient axis along which to study the signaling events that occur in situ during keratinocyte terminal differentiation, over this extended two-week timescale. The canonical ERK-MAPK signaling cascade (Raf-1, MEK-1/2 and ERK-1/2) has been implicated in controlling diverse cellular behaviors, including proliferation and differentiation. While the molecular interactions involved in signal transduction through this cascade have been well characterized in cell culture experiments, our understanding of how this sequence of events unfolds to determine cell fate within a homeostatic tissue environment has not been fully characterized.We measured the abundance of total and phosphorylated ERK-MAPK signaling proteins within interfollicular keratinocytes in transverse cross-sections of human epidermis using immunofluorescence microscopy. To investigate these data we developed a mathematical model of the signaling cascade using a normalized-Hill differential equation formalism.These data show coordinated variation in the abundance of phosphorylated ERK-MAPK components across the epidermis. Statistical analysis of these data shows that associations between phosphorylated ERK-MAPK components which correspond to canonical molecular interactions are dependent upon spatial position within the epidermis. The model demonstrates that the spatial profile of activation for ERK-MAPK signaling components across the epidermis may be maintained in a cell-autonomous fashion by an underlying spatial gradient in calcium signaling.Our data demonstrate an extended phospho-protein profile of ERK-MAPK signaling cascade components across the epidermis in situ, and statistical associations in these data indicate canonical ERK-MAPK interactions underlie this spatial profile of ERK-MAPK activation. Using mathematical modelling we have demonstrated that spatially varying calcium signaling components across the epidermis may be sufficient to maintain the spatial profile of ERK-MAPK signaling cascade components in a cell-autonomous manner. These findings may have significant implications for the wide range of cancer drugs which therapeutically target ERK-MAPK signaling components.

This model is hosted on BioModels Database and identified by: BIOMD0000000659.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4100, + "tag": "BioModels:BIOMD0000000659" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 2989, + "tag": "MAPK cascade" + }, + { + "id": 4101, + "tag": "MAPK signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:57.876847+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000659", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2949": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2949, + "name": "Barr2017 - Dynamics of p21 in hTert-RPE1 cells", + "repository_type": "biomodels", + "summary": "
Barr2017 - Dynamics of p21 in hTert-RPE1cells
This deteministic model reveals that abistable switch created by Cdt2, promotes irreversible S-phaseentry by keeping p21 levels low, prevents premature S-phase exitupon DNA damage

This model is described in the article:

Barr AR, Cooper S, Heldt FS, Butera F, Stoy H, Mansfeld J, Nov\u00e1k B, Bakal C.
Nat Commun 2017 Mar; 8: 14728

Abstract:

Following DNA damage caused by exogenous sources, such as ionizing radiation, the tumour suppressor p53 mediates cell cycle arrest via expression of the CDK inhibitor, p21. However, the role of p21 in maintaining genomic stability in the absence of exogenous DNA-damaging agents is unclear. Here, using live single-cell measurements of p21 protein in proliferating cultures, we show that naturally occurring DNA damage incurred over S-phase causes p53-dependent accumulation of p21 during mother G2- and daughter G1-phases. High p21 levels mediate G1 arrest via CDK inhibition, yet lower levels have no impact on G1 progression, and the ubiquitin ligases CRL4Cdt2 and SCFSkp2 couple to degrade p21 prior to the G1/S transition. Mathematical modelling reveals that a bistable switch, created by CRL4Cdt2, promotes irreversible S-phase entry by keeping p21 levels low, preventing premature S-phase exit upon DNA damage. Thus, we characterize how p21 regulates the proliferation-quiescence decision to maintain genomic stability.

This model is hosted on BioModels Database and identified by: BIOMD0000000660.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4102, + "tag": "BioModels:BIOMD0000000660" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:58.378829+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000660", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2950": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2950, + "name": "Webb2002 - Fas/FasL mediated tumor T-cell interaction", + "repository_type": "biomodels", + "summary": "
Webb2002 - Fas/FasL mediated tumor T-cell interaction
This deterministic model ofimmunological surveillance involving tumour cell\u2013T-lymphocyteinteraction, cell surface expression of Fas/FasL, and theirsecreted soluble forms.

This model is described in the article:

Webb SD, Sherratt JA, Fish RG.
Math Biosci 2002 Sep-Oct; 179(2): 113-129

Abstract:

One proposed mechanism of tumour escape from immune surveillance is tumour up-regulation of the cell surface ligand FasL, which can lead to apoptosis of Fas receptor (Fas) positive lymphocytes. Based upon this 'counterattack', we have developed a mathematical model involving tumour cell-lymphocyte interaction, cell surface expression of Fas/FasL, and their secreted soluble forms. The model predicts that (a) the production of soluble forms of Fas and FasL will lead to the down-regulation of the immune response; (b) matrix metalloproteinase (MMP) inactivation should lead to increased membrane FasL and result in a higher rate of Fas-mediated apoptosis for lymphocytes than for tumour cells. Recent studies on cancer patients lend support for these predictions. The clinical implications are two-fold. Firstly, the use of broad spectrum MMP inhibitors as anti-angiogenic agents may be compromised by their adverse effect on tumour FasL up-regulation. Also, Fas/FasL interactions may have an impact on the outcome of numerous ongoing immunotherapeutic trials since the final common pathway of all these approaches is the transduction of death signals within the tumour cell.

This model is hosted on BioModels Database and identified by: BIOMD0000000661.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4103, + "tag": "BioModels:BIOMD0000000661" + }, + { + "id": 4104, + "tag": "Extrinsic apoptotic pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 4106, + "tag": "Regulation of Fas signaling pathway" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:58.866928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000661", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2951": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 2951, + "name": "Moore2004 - Chronic Myeloid Leukemic cells and T-lymphocyte interaction", + "repository_type": "biomodels", + "summary": "
Moore2004 - Chronic Myeloid Leukemic cellsand T-lymphocytes interaction
A mathematical model for theinteraction of between cancer cells and immune system, involvingCML cancer cells, naive and effector T-lymphocytes.

This model is described in the article:

Moore H, Li NK.
J. Theor. Biol. 2004 Apr; 227(4): 513-523

Abstract:

In this paper, we propose and analyse a mathematical model for chronic myelogenous leukemia (CML), a cancer of the blood. We model the interaction between naive T cells, effector T cells, and CML cancer cells in the body, using a system of ordinary differential equations which gives rates of change of the three cell populations. One of the difficulties in modeling CML is the scarcity of experimental data which can be used to estimate parameters values. To compensate for the resulting uncertainties, we use Latin hypercube sampling (LHS) on large ranges of possible parameter values in our analysis. A major goal of this work is the determination of parameters which play a critical role in remission or clearance of the cancer in the model. Our analysis examines 12 parameters, and identifies two of these, the growth and death rates of CML, as critical to the outcome of the system. Our results indicate that the most promising research avenues for treatments of CML should be those that affect these two significant parameters (CML growth and death rates), while altering the other parameters should have little effect on the outcome.

This model is hosted on BioModels Database and identified by: BIOMD0000000662.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4107, + "tag": "BioModels:BIOMD0000000662" + }, + { + "id": 4108, + "tag": "Chronic myelogenous leukemia" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:02:59.352341+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000662", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2952": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2952, + "name": "Wodarz2007 - HIV/CD4 T-cell interaction", + "repository_type": "biomodels", + "summary": "
Wodarz2007 - HIV/CD4 T-cell interaction
A deterministic model illustrating howCD4 T-cells can influence HIV infection.

This model is described in the article:

Wodarz D, Hamer DH.
Math Biosci 2007 Sep; 209(1): 14-29

Abstract:

Recent experimental data have shown that HIV-specific CD4 T cells provide a very important target for HIV replication. We use mathematical models to explore the effect of specific CD4 T cell infection on the dynamics of virus spread and immune responses. Infected CD4 T cells can provide antigen for their own stimulation. We show that such autocatalytic cell division can significantly enhance virus spread, and can also provide an additional reservoir for virus persistence during anti-viral drug therapy. In addition, the initial number of HIV-specific CD4 T cells is an important determinant of acute infection dynamics. A high initial number of HIV-specific CD4 T cells can lead to a sudden and fast drop of the population of HIV-specific CD4 T cells which results quickly in their extinction. On the other hand, a low initial number of HIV-specific CD4 T cells can lead to a prolonged persistence of HIV-specific CD4 T cell help at higher levels. The model suggests that boosting the population of HIV-specific CD4 T cells can increase the amount of virus-induced immune impairment, lead to less efficient anti-viral effector responses, and thus speed up disease progression, especially if effector responses such as CTL have not been sufficiently boosted at the same time.

This model is hosted on BioModels Database and identified by: BIOMD0000000663.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4109, + "tag": "BioModels:BIOMD0000000663" + }, + { + "id": 4110, + "tag": "HIV infection" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4111, + "tag": "Human immunodeficiency virus 1" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3783, + "tag": "Viral entry into host cell" + } + ], + "timestamp_created": "2025-01-30 14:03:00.120877+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000663", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2953": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2953, + "name": "Muller2008 - Simplified MAPK activation Dynamics (Model B)", + "repository_type": "biomodels", + "summary": "
Muller2008 - Simplified MAPK activation Dynamics (Model B)
Simplified mathematical model (model B)for predicting MAPK signal expression.

This model is described in the article:

Muller M, Obeyesekere M, Mills GB, Ram PT.
FASEB J. 2008 May; 22(5): 1393-1403

Abstract:

Activation of the fibroblast growth factor (FGFR) and melanocyte stimulating hormone (MC1R) receptors stimulates B-Raf and C-Raf isoforms that regulate the dynamics of MAPK1,2 signaling. Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. Further reducing a 106-node ordinary differential equations network encompassing the complete network to a 6-node network encompassing rate-limiting processes sustains the feedback loops and the bifan, providing sufficient information to predict biological responses.

This model is hosted on BioModels Database and identified by: BIOMD0000000664.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4112, + "tag": "BioModels:BIOMD0000000664" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:00.637581+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000664", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2954": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2954, + "name": "Fallon2000 - Interleukin-2 dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Computational model for effects of ligand/receptor binding properties oninterleukin-2 trafficking dynamics and T cell proliferation response.
Fallon EM, Lauffenburger DA. Biotechnol Prog 2000 Sep-Oct;16(5):905-16 11027188 ,
Abstract:
Multisubunit cytokine receptors such as the heterotrimeric receptor forinterleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance inbiotechnology and are crucial regulators of cell proliferation anddifferentiation behavior. Dynamics of cytokine/receptor endocytic traffickingcan significantly impact cell responses through effects of receptordown-regulation and ligand depletion, and in turn are governed byligand/receptor binding properties. We describe here a computational model fortrafficking dynamics of the IL-2 receptor (IL-2R) system, which is able topredict T cell proliferation responses to IL-2. This model comprises kineticequations describing binding, internalization, and postendocytic sorting of IL-2and IL-2R, including an experimentally derived dependence of cell proliferationrate on these properties. Computational results from this model predict thatIL-2 depletion can be reduced by decreasing its binding affinity for the IL-2Rbetagamma subunit relative to the alpha subunit at endosomal pH, as a result ofenhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2analogue with such altered binding properties should exhibit increased potencyfor stimulating the T cell proliferation response. These results are inagreement with our recent experimental findings for the IL-2 analogue termed 2D1[Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type ofmodel may enable prediction of beneficial cytokine/receptor binding propertiesto aid development of molecular design criteria for improvements in applicationssuch as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Fallon EM, Lauffenburger DA. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4113, + "tag": "BioModels:BIOMD0000000665" + }, + { + "id": 4114, + "tag": "Other interleukin signaling" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:01.176176+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000665", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2955": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2955, + "name": "Pappalardo2016 - PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer", + "repository_type": "biomodels", + "summary": "
Pappalardo2016 - PI3K/AKT and MAPK SignalingPathways in Melanoma Cancer

This model is described in the article:

Pappalardo F, Russo G, Candido S, Pennisi M, Cavalieri S, Motta S, McCubrey JA, Nicoletti F, Libra M.
PLoS ONE 2016; 11(3): e0152104

Abstract:

Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease.Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive.

This model is hosted on BioModels Database and identified by: MODEL1609190000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4115, + "tag": "BioModels:BIOMD0000000666" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:01.679573+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000666", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2956": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2956, + "name": "Hornberg2005 - MAPKsignalling", + "repository_type": "biomodels", + "summary": "
Hornberg2005 - MAPKsignalling
Large model of the ERK signallingnetwork. Results from this model were used to generate a simplifiedversion of the network.

This model is described in the article:

Hornberg JJ, Binder B, Bruggeman FJ, Schoeberl B, Heinrich R, Westerhoff HV.
Oncogene 2005 Aug; 24(36): 5533-5542

Abstract:

Oncogenesis results from changes in kinetics or in abundance of proteins in signal transduction networks. Recently, it was shown that control of signalling cannot reside in a single gene product, and might well be dispersed over many components. Which of the reactions in these complex networks are most important, and how can the existing molecular information be used to understand why particular genes are oncogenes whereas others are not? We implement a new method to help address such questions. We apply control analysis to a detailed kinetic model of the epidermal growth factor-induced mitogen-activated protein kinase network. We determine the control of each reaction with respect to three biologically relevant characteristics of the output of this network: the amplitude, duration and integrated output of the transient phosphorylation of extracellular signal-regulated kinase (ERK). We confirm that control is distributed, but far from randomly: a small proportion of reactions substantially control signalling. In particular, the activity of Raf is in control of all characteristics of the transient profile of ERK phosphorylation, which may clarify why Raf is an oncogene. Most reactions that really matter for one signalling characteristic are also important for the other characteristics. Our analysis also predicts the effects of mutations and changes in gene expression.

This model is hosted on BioModels Database and identified by: BIOMD0000000667.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4116, + "tag": "BioModels:BIOMD0000000667" + }, + { + "id": 4117, + "tag": "Regulation of MAPK cascade" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:02.217468+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000667", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2957": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2957, + "name": "Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - basic PD model", + "repository_type": "biomodels", + "summary": "
Zhu2015 - Combined gemcitabine and birinapantin pancreatic cancer cells - basic PD model
Mathematical model to illustrate theeffectiveness of combination chemotherapy involving gemcitabine andbirinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000668.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4118, + "tag": "BioModels:BIOMD0000000668" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:02.711172+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000668", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2958": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2958, + "name": "Zhu2015 - Combined gemcitabine and birinapant in pancreatic cancer cells - mechanistic PD model", + "repository_type": "biomodels", + "summary": "
Zhu2015 - combined gemcitabine and birinapantin pancreatic cancer cells - mechanistic PD model
Mechanistic mathematical model toillustrate the effectiveness of combination chemotherapy involvinggemcitabine and birinapant against pancreatic cancer.

This model is described in the article:

Zhu X, Straubinger RM, Jusko WJ.
J Pharmacokinet Pharmacodyn 2015 Oct; 42(5): 477-496

Abstract:

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term \u03a8 was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, \u03a8 values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

This model is hosted on BioModels Database and identified by: BIOMD0000000669.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4119, + "tag": "BioModels:BIOMD0000000669" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:03.216555+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000669", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2959": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2959, + "name": "Owen1998 - tumour growth model", + "repository_type": "biomodels", + "summary": "
Owen1998 - tumour growth model
Deterministic model for the early,avascular growth of a tumour, concentrating on the inhibitoryeffect of macrophages.

This model is described in the article:

Owen MR, Sherratt JA.
IMA J Math Appl Med Biol 1998 Jun; 15(2): 165-185

Abstract:

Even in the early stages of their development, tumours are not simply a homogeneous grouping of mutant cells; rather, they develop in tandem with normal tissue cells, and also recruit other cell types including lymphatic cells and the endothelial cells required for the development of a blood supply. It has been repeatedly seen that macrophages form a significant proportion of the tumour mass, and that they can have a variety of effects upon the tumour, leading to a delicate balance between growth promotion and inhibition. This paper develops a model for the early, avascular growth of a tumour, concentrating on the inhibitory effect of macrophages due to their cytolytic activity. It is shown that such an immune response is not sufficient to prevent growth, due to it being a second-order process with respect to the density of the tumour cells present. However, the presence of macrophages does have important effects on the tumour composition, and the authors perform a detailed bifurcation analysis of their model to clarify this. An extended model is also considered which incorporates addition of exogenous chemical regulators. In this case, the model admits the possibility of tumour regression, and the therapeutic implications of this are discussed.

This model is hosted on BioModels Database and identified by: BIOMD0000000670.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4120, + "tag": "BioModels:BIOMD0000000670" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:03.752361+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000670", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2960": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2960, + "name": "Murphy2016 - Differences in predictions of ODE models of tumor growth", + "repository_type": "biomodels", + "summary": "
Murphy2016 - Differences in predictions ofODE models of tumor growth
Comparison of 7 ODE models for tumoursize. This models have been compared to experimental data.

This model is described in the article:

Murphy H, Jaafari H, Dobrovolny HM.
BMC Cancer 2016 Feb; 16: 163

Abstract:

While mathematical models are often used to predict progression of cancer and treatment outcomes, there is still uncertainty over how to best model tumor growth. Seven ordinary differential equation (ODE) models of tumor growth (exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy) have been proposed, but there is no clear guidance on how to choose the most appropriate model for a particular cancer.We examined all seven of the previously proposed ODE models in the presence and absence of chemotherapy. We derived equations for the maximum tumor size, doubling time, and the minimum amount of chemotherapy needed to suppress the tumor and used a sample data set to compare how these quantities differ based on choice of growth model.We find that there is a 12-fold difference in predicting doubling times and a 6-fold difference in the predicted amount of chemotherapy needed for suppression depending on which growth model was used.Our results highlight the need for careful consideration of model assumptions when developing mathematical models for use in cancer treatment planning.

This model is hosted on BioModels Database and identified by: BIOMD0000000671.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4121, + "tag": "BioModels:BIOMD0000000671" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:04.296392+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000671", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2961": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2961, + "name": "Brown1997 - Plasma Melatonin Levels", + "repository_type": "biomodels", + "summary": "
Brown1997 - Plasma Melatonin Levels
A mathematical model that incorporatesa piecewise function for NAT activity to predict melatoninconcentration.

This model is described in the article:

Brown EN, Choe Y, Shanahan TL, Czeisler CA.
Am. J. Physiol. 1997 Mar; 272(3 Pt 1): E506-16

Abstract:

Studies in animals and humans suggest that the diurnal pattern in plasma melatonin levels is due to the hormone's rates of synthesis, circulatory infusion and clearance, circadian control of synthesis onset and offset, environmental lighting conditions, and error in the melatonin immunoassay. A two-dimensional linear differential equation model of the hormone is formulated and is used to analyze plasma melatonin levels in 18 normal healthy male subjects during a constant routine. Recently developed Bayesian statistical procedures are used to incorporate correctly the magnitude of the immunoassay error into the analysis. The estimated parameters [median (range)] were clearance half-life of 23.67 (14.79-59.93) min, synthesis onset time of 2206 (1940-0029), synthesis offset time of 0621 (0246-0817), and maximum N-acetyltransferase activity of 7.17(2.34-17.93) pmol x l(-1) x min(-1). All were in good agreement with values from previous reports. The difference between synthesis offset time and the phase of the core temperature minimum was 1 h 15 min (-4 h 38 min-2 h 43 min). The correlation between synthesis onset and the dim light melatonin onset was 0.93. Our model provides a more physiologically plausible estimate of the melatonin synthesis onset time than that given by the dim light melatonin onset and the first reliable means of estimating the phase of synthesis offset. Our analysis shows that the circadian and pharmacokinetics parameters of melatonin can be reliably estimated from a single model.

This model is hosted on BioModels Database and identified by: BIOMD0000000672.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4122, + "tag": "BioModels:BIOMD0000000672" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3240, + "tag": "Melatonin metabolic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:04.827080+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000672", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2962": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2962, + "name": "Lockwood2006 - Alzheimer's Disease PBPK model", + "repository_type": "biomodels", + "summary": "
Lockwood2006 - AlzheimersDisease PBPKmodel
A mathematical model to predict theeffectiveness of CI-1017 (muscarinic agonist) for Alzheimer'sdisease by evaluating changes in ADAS-cog score.

This model is described in the article:

Lockwood P, Ewy W, Hermann D, Holford N.
Pharm. Res. 2006 Sep; 23(9): 2050-2059

Abstract:

OBJECTIVE: Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials. MATERIALS AND METHODS: A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and literature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p < 0.05) and pair-wise comparisons to placebo (p < 0.025). RESULTS: A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability. CONCLUSION: CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.

This model is hosted on BioModels Database and identified by: BIOMD0000000673.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4123, + "tag": "BioModels:BIOMD0000000673" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4647, + "tag": "Pharmacodynamic model" + } + ], + "timestamp_created": "2025-01-30 14:03:05.433351+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000673", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2963": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2963, + "name": "Reyes-Palomares2012 - a combined model hepatic polyamine and sulfur aminoacid metabolism - version1", + "repository_type": "biomodels", + "summary": "
Reyes-Palomares2012 - a combined modelhepatic polyamine and sulfur aminoacid metabolism - version1

Mammalian polyamine metabolism consists of a bi-cycle with two required entrances, omithine and S-adenosyl methionine (SAM), and several alternative exists. The relevant regulatory roles of the short half-life enzymes ornithine decarboxylase (ODC), S-adenosyl methione decarboxylase (SAMDC) and spermindine/spermine acetyl transferase (SSAT) in polyamine metabolism are well studied, and has been modelled here.

This model is described in the article:

Reyes-Palomares A, Monta\u00f1ez R, S\u00e1nchez-Jim\u00e9nez F, Medina MA.
Amino Acids 2012 Feb; 42(2-3): 597-610

Abstract:

Many molecular details remain to be uncovered concerning the regulation of polyamine metabolism. A previous model of mammalian polyamine metabolism showed that S-adenosyl methionine availability could play a key role in polyamine homeostasis. To get a deeper insight in this prediction, we have built a combined model by integration of the previously published polyamine model and one-carbon and glutathione metabolism model, published by different research groups. The combined model is robust and it is able to achieve physiological steady-state values, as well as to reproduce the predictions of the individual models. Furthermore, a transition between two versions of our model with new regulatory factors added properly simulates the switch in methionine adenosyl transferase isozymes occurring when the liver enters in proliferative conditions. The combined model is useful to support the previous prediction on the role of S-adenosyl methionine availability in polyamine homeostasis. Furthermore, it could be easily adapted to get deeper insights on the connections of polyamines with energy metabolism.

Notes by the author:

This model combines BIOMD0000000190 and BIOMD0000000268 from BioModels Database, both models include corrections respect to their originals publications.

This model is hosted on BioModels Database and identified by: BIOMD0000000674.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4124, + "tag": "BioModels:BIOMD0000000674" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:06.009776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000674", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2964": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2964, + "name": "Chen2000_CellCycle", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Kinetic analysis of a molecular model of the budding yeast cell cycle.
Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. Mol Biol Cell 2000 Jan;11(1):369-91 10637314 ,
Abstract:
The molecular machinery of cell cycle control is known in more detail forbudding yeast, Saccharomyces cerevisiae, than for any other eukaryotic organism.In recent years, many elegant experiments on budding yeast have dissected theroles of cyclin molecules (Cln1-3 and Clb1-6) in coordinating the events of DNAsynthesis, bud emergence, spindle formation, nuclear division, and cellseparation. These experimental clues suggest a mechanism for the principalmolecular interactions controlling cyclin synthesis and degradation. Usingstandard techniques of biochemical kinetics, we convert the mechanism into a setof differential equations, which describe the time courses of three majorclasses of cyclin-dependent kinase activities. Model in hand, we examine themolecular events controlling \"Start\" (the commitment step to a new round ofchromosome replication, bud formation, and mitosis) and \"Finish\" (the transitionfrom metaphase to anaphase, when sister chromatids are pulled apart and the budseparates from the mother cell) in wild-type cells and 50 mutants. The modelaccounts for many details of the physiology, biochemistry, and genetics of cellcycle control in budding yeast.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Chen KC, Csikasz-Nagy A, Gyorffy B, Val J, Novak B, Tyson JJ. (2000) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4125, + "tag": "BioModels:BIOMD0000000675" + }, + { + "id": 3135, + "tag": "Cell cycle checkpoint" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:03:06.671938+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000675", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2965": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2965, + "name": "Chen2006 - Nitric Oxide Release from Endothelial Cells", + "repository_type": "biomodels", + "summary": "
Chen2006 - Nitric Oxide Release fromEndothelial Cells

This model is described in the article:

Chen K, Popel AS.
Free Radic. Biol. Med. 2006 Aug; 41(4): 668-680

Abstract:

Vascular endothelium expressing endothelial nitric oxide synthase (eNOS) produces nitric oxide (NO), which has a number of important physiological functions in the microvasculature. The rate of NO production by the endothelium is a critical determinant of NO distribution in the vascular wall. We have analyzed the biochemical pathways of NO synthesis and formulated a model to estimate NO production by the microvascular endothelium under physiological conditions. The model quantifies the NO produced by eNOS based on the kinetics of NO synthesis and the availability of eNOS and its intracellular substrates. The predicted NO production from microvessels was in the range of 0.005-0.1 microM/s. This range of predicted values is in agreement with some experimental values but is much lower than other rates previously measured or estimated from experimental data with the help of mathematical modeling. Paradoxical discrepancies between the model predictions and previously reported results based on experimental measurements of NO concentration in the vicinity of the arteriolar wall suggest that NO can also be released through eNOS-independent mechanisms, such as catalysis by neuronal NOS (nNOS). We also used our model to test the sensitivity of NO production to substrate availability, eNOS concentration, and potential rate-limiting factors. The results indicated that the predicted low level of NO production can be attributed primarily to a low expression of eNOS in the microvascular endothelial cells.

This model is hosted on BioModels Database and identified by: BIOMD0000000676.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4126, + "tag": "BioModels:BIOMD0000000676" + }, + { + "id": 3786, + "tag": "Nitric oxide biosynthetic process" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:07.252600+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000676", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2966": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2966, + "name": "Holmes2006 - Hill's model of muscle contraction", + "repository_type": "biomodels", + "summary": "
Holmes2006 - Hill's model of muscle contraction

This model is described in the article:

Holmes JW.
Adv Physiol Educ 2006 Jun; 30(2): 67-72

Abstract:

A. V. Hill's 1938 paper \"The heat of shortening and the dynamic constants of muscle\" is an enduring classic, presenting detailed methods, meticulous experiments, and the model of muscle contraction that now bears Hill's name. Pairing a simulation based on Hill's model with a reading of his paper allows students to follow his thought process to discover key principles of muscle physiology and gain insight into how to develop quantitative models of physiological processes. In this article, the experience of the author using this approach in a graduate biomedical engineering course is outlined, along with suggestions for adapting this approach to other audiences.

This model is hosted on BioModels Database and identified by: BIOMD0000000677.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4127, + "tag": "BioModels:BIOMD0000000677" + }, + { + "id": 4128, + "tag": "Muscle contraction" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:08.084363+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000677", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2967": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2967, + "name": "Tomida2003 - Calcium Oscillatory-induced translocation of nuclear factor of activated T cells", + "repository_type": "biomodels", + "summary": "
Tomida2003 - NFAT functions CalciumOscillation

This model is described in the article:

Tomida T, Hirose K, Takizawa A, Shibasaki F, Iino M.
EMBO J. 2003 Aug; 22(15): 3825-3832

Abstract:

Transcription by the nuclear factor of activated T cells (NFAT) is regulated by the frequency of Ca(2+) oscillation. However, why and how Ca(2+) oscillation regulates NFAT activity remain elusive. NFAT is dephosphorylated by Ca(2+)-dependent phosphatase calcineurin and translocates from the cytoplasm to the nucleus to initiate transcription. We analyzed the kinetics of dephosphorylation and translocation of NFAT. We show that Ca(2+)-dependent dephosphorylation proceeds rapidly, while the rephosphorylation and nuclear transport of NFAT proceed slowly. Therefore, after brief Ca(2+) stimulation, dephosphorylated NFAT has a lifetime of several minutes in the cytoplasm. Thus, Ca(2+) oscillation induces a build-up of dephosphorylated NFAT in the cytoplasm, allowing effective nuclear translocation, provided that the oscillation interval is shorter than the lifetime of dephosphorylated NFAT. We also show that Ca(2+) oscillation is more cost-effective in inducing the translocation of NFAT than continuous Ca(2+) signaling. Thus, the lifetime of dephosphorylated NFAT functions as a working memory of Ca(2+) signals and enables the control of NFAT nuclear translocation by the frequency of Ca(2+) oscillation at a reduced cost of Ca(2+) signaling.

This model is hosted on BioModels Database and identified by: BIOMD0000000678.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4129, + "tag": "BioModels:BIOMD0000000678" + }, + { + "id": 4130, + "tag": "Calcineurin-NFAT signaling cascade" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:08.684365+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000678", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2968": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2968, + "name": "Waugh2006 - Diabetic Wound Healing - Macrophage Dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4131, + "tag": "BioModels:BIOMD0000000679" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4132, + "tag": "Regulation of wound healing" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:09.273747+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000679", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2969": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2969, + "name": "Waugh2006 - Diabetic Wound Healing - TGF-B Dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4133, + "tag": "BioModels:BIOMD0000000680" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4132, + "tag": "Regulation of wound healing" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:09.902926+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000680", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2970": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2970, + "name": "Waugh2006 - Diabetic Wound Healing - Treated and Untreated Macrophage Dynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Macrophage dynamics in diabetic wound dealing.
Waugh HV, Sherratt JA. Bull Math Biol 2006 Jan;68(1):197-207 16794927 ,
Abstract:
Wound healing in diabetes is a complex process, characterised by a chronicinflammation phase. The exact mechanism by which this occurs is not fullyunderstood, and whilst several treatments for healing diabetic wounds exist,very little research has been conducted towards the causes of the extendedinflammation phase. We describe a mathematical model which offers a possibleexplanation for diabetic wound healing in terms of the distribution ofmacrophage phenotypes being altered in the diabetic patient compared to normalwound repair. As a consequence of this, we put forward a suggestion fortreatment based on rectifying the macrophage phenotype imbalance.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Waugh HV, Sherratt JA. (2006) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4134, + "tag": "BioModels:BIOMD0000000681" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4132, + "tag": "Regulation of wound healing" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:10.423056+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000681", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2971": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2971, + "name": "Wierschem2004 - Electrical bursting activity in Pancreatic Islets", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Complex bursting in pancreatic islets: a potential glycolytic mechanism.
Wierschem K, Bertram R. J Theor Biol 2004 Jun 21;228(4):513-21 15178199 ,
Abstract:
The electrical activity of insulin-secreting pancreatic islets of Langerhans ischaracterized by bursts of action potentials. Most often this bursting isperiodic, but in some cases it is modulated by an underlying slower rhythm. Wesuggest that the modulatory rhythm for this complex bursting pattern is due tooscillations in glycolysis, while the bursting itself is generated by some otherslow process. To demonstrate this hypothesis, we couple a minimal model ofglycolytic oscillations to a minimal model for activity-dependent bursting inislets. We show that the combined model can reproduce several complex burstingpatterns from mouse islets published in the literature, and we illustrate howthese complex oscillations are produced through the use of a fast/slow analysis.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wierschem K, Bertram R. () - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4135, + "tag": "BioModels:BIOMD0000000682" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4136, + "tag": "Regulation of action potential" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:10.963008+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000682", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2972": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2972, + "name": "Wodarz1999 CTL memory response HIV", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Specific therapy regimes could lead to long-term immunological control of HIV.
Wodarz D, Nowak MA. Proc Natl Acad Sci U S A 1999 Dec 7;96(25):14464-9 10588728 ,
Abstract:
We use mathematical models to study the relationship between HIV and the immunesystem during the natural course of infection and in the context of differentantiviral treatment regimes. The models suggest that an efficient cytotoxic Tlymphocyte (CTL) memory response is required to control the virus. We define CTLmemory as long-term persistence of CTL precursors in the absence of antigen.Infection and depletion of CD4(+) T helper cells interfere with CTL memorygeneration, resulting in persistent viral replication and disease progression.We find that antiviral drug therapy during primary infection can enable thedevelopment of CTL memory. In chronically infected patients, specific treatmentschedules, either including deliberate drug holidays or antigenic boosts of theimmune system, can lead to a re-establishment of CTL memory. Whether suchtreatment regimes would lead to long-term immunologic control deservesinvestigation under carefully controlled conditions.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Nowak MA. (1999) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4137, + "tag": "BioModels:BIOMD0000000683" + }, + { + "id": 4110, + "tag": "HIV infection" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3783, + "tag": "Viral entry into host cell" + } + ], + "timestamp_created": "2025-01-30 14:03:11.505982+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000683", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2973": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2973, + "name": "Wodarz2003 - Immunological Memory", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Evolution of immunological memory and the regulation of competition betweenpathogens.
Wodarz D. Curr Biol 2003 Sep 16;13(18):1648-52 13678598 ,
Abstract:
Memory is a central characteristic of immune responses. It is defined as anelevated number of specific immune cells that remain after resolution ofinfection and can protect the host against reinfection. The evolution ofimmunological memory is subject to debate. The advantages of memory discussed sofar include protection from reinfection, control of chronic infection, and thetransfer of immune function to the next generation. Mathematical models are usedto identify a new force that can drive the evolution of immunological memory:the duration of memory can regulate the degree of competition between differentpathogens. While a long duration of memory provides lasting protection againstreinfection, it may also allow an inferior pathogen species to persist. This canbe detrimental for the host if the inferior pathogen is more virulent. On theother hand, a shorter duration of memory ensures that an inferior pathogenspecies is excluded. This can be beneficial for the host if the inferiorpathogen is more virulent. Thus, while in the absence of pathogen diversitymemory is always expected to evolve to a long duration, under specificcircumstances, memory can evolve toward shorter durations in the presence ofpathogen diversity.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3917, + "tag": "Adaptive immune response" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4138, + "tag": "BioModels:BIOMD0000000684" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:12.090358+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000684", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2974": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2974, + "name": "Wodarz2003 - Cytotoxic T lymphocyte cross-priming", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A dynamical perspective of CTL cross-priming and regulation: implications forcancer immunology.
Wodarz D, Jansen VA. Immunol Lett 2003 May 1;86(3):213-27 12706524 ,
Abstract:
Cytotoxic T lymphocytes (CTL) responses are required to fight many diseases suchas viral infections and tumors. At the same time, they can cause disease wheninduced inappropriately. Which factors regulate CTL and decide whether theyshould remain silent or react is open to debate. The phenomenon calledcross-priming has received attention in this respect. That is, CTL expansionoccurs if antigen is recognized on the surface of professional antigenpresenting cells (APCs). This is in contrast to direct presentation whereantigen is seen on the surface of the target cells (e.g. infected cells or tumorcells). Here we introduce a mathematical model, which takes the phenomenon ofcross-priming into account. We propose a new mechanism of regulation which isimplicit in the dynamics of the CTL: According to the model, the ability of aCTL response to become established depends on the ratio of cross-presentation todirect presentation of the antigen. If this ratio is relatively high, CTLresponses are likely to become established. If this ratio is relatively low,tolerance is the likely outcome. The behavior of the model includes a parameterregion where the outcome depends on the initial conditions. We discuss ourresults with respect to the idea of self/non-self discrimination and the dangersignal hypothesis. We apply the model to study the role of CTL in cancerinitiation, cancer evolution/progression, and therapeutic vaccination againstcancers.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Jansen VA. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 3917, + "tag": "Adaptive immune response" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4139, + "tag": "BioModels:BIOMD0000000685" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:12.584564+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000685", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2975": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2975, + "name": "Wodarz2007 - Basic Model of Cytomegalovirus Infection", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4140, + "tag": "BioModels:BIOMD0000000686" + }, + { + "id": 4141, + "tag": "Mus" + }, + { + "id": 4142, + "tag": "Response to virus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:13.301952+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000686", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2976": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2976, + "name": "Wodarz2007 - Cytomegalovirus infection model with cytotoxic T lymphocyte response", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4143, + "tag": "BioModels:BIOMD0000000687" + }, + { + "id": 4141, + "tag": "Mus" + }, + { + "id": 4142, + "tag": "Response to virus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:13.828021+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000687", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2977": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2977, + "name": "Wodarz2007 - Cytomegalovirus infection model with cytotoxic T lymphocyte and natural killer cell response", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Dynamics of killer T cell inflation in viral infections.
Wodarz D, Sierro S, Klenerman P. J R Soc Interface 2007 Jun 22;4(14):533-43 17251133 ,
Abstract:
Upon acute viral infection, a typical cytotoxic T lymphocyte (CTL) response ischaracterized by a phase of expansion and contraction after which it settles ata relatively stable memory level. Recently, experimental data from mice infectedwith murine cytomegalovirus (MCMV) showed different and unusual dynamics. Afteracute infection had resolved, some antigen specific CTL started to expand overtime despite the fact that no replicative virus was detectable. This phenomenonhas been termed as \"CTL memory inflation\". In order to examine the dynamics ofthis system further, we developed a mathematical model analysing the impact ofinnate and adaptive immune responses. According to this model, a potentiallyimportant contributor to CTL inflation is competition between the specific CTLresponse and an innate natural killer (NK) cell response. Inflation occurs mostreadily if the NK cell response is more efficient than the CTL at reducing virusload during acute infection, but thereafter maintains a chronic virus load whichis sufficient to induce CTL proliferation. The model further suggests thatweaker NK cell mediated protection can correlate with more pronounced CTLinflation dynamics over time. We present experimental data from mice infectedwith MCMV which are consistent with the theoretical predictions. This modelprovides valuable information and may help to explain the inflation of CMVspecific CD8+T cells seen in humans as they age.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Wodarz D, Sierro S, Klenerman P. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4144, + "tag": "BioModels:BIOMD0000000688" + }, + { + "id": 4141, + "tag": "Mus" + }, + { + "id": 4142, + "tag": "Response to virus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:14.346127+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000688", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2978": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2978, + "name": "Thiaville2016 - Folate pathway model (PanB overexpression)", + "repository_type": "biomodels", + "summary": "
Henry2016 Folate pathway model with inducedPanB reaction

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: MODEL1602280002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4145, + "tag": "BioModels:BIOMD0000000689" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:14.875059+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000689", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2979": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2979, + "name": "Thiaville2016 - Folate pathway model (PanB overexpression and THF regulation)", + "repository_type": "biomodels", + "summary": "
Henry2016 Folate pathway model with inducedPanB reaction

This model is described in the article:

Thiaville JJ, Frelin O, Garc\u00eda-Salinas C, Harrison K, Hasnain G, Horenstein NA, D\u00edaz de la Garza RI, Henry CS, Hanson AD, de Cr\u00e9cy-Lagard V.
Front Microbiol 2016; 7: 431

Abstract:

Tetrahydrofolate (THF) and its one-carbon derivatives, collectively termed folates, are essential cofactors, but are inherently unstable. While it is clear that chemical oxidation can cleave folates or damage their pterin precursors, very little is known about enzymatic damage to these molecules or about whether the folate biosynthesis pathway responds adaptively to damage to its end-products. The presence of a duplication of the gene encoding the folate biosynthesis enzyme 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (FolK) in many sequenced bacterial genomes combined with a strong chromosomal clustering of the folK gene with panB, encoding the 5,10-methylene-THF-dependent enzyme ketopantoate hydroxymethyltransferase, led us to infer that PanB has a side activity that cleaves 5,10-methylene-THF, yielding a pterin product that is recycled by FolK. Genetic and metabolic analyses of Escherichia coli strains showed that overexpression of PanB leads to accumulation of the likely folate cleavage product 6-hydroxymethylpterin and other pterins in cells and medium, and-unexpectedly-to a 46% increase in total folate content. In silico modeling of the folate biosynthesis pathway showed that these observations are consistent with the in vivo cleavage of 5,10-methylene-THF by a side-activity of PanB, with FolK-mediated recycling of the pterin cleavage product, and with regulation of folate biosynthesis by folates or their damage products.

This model is hosted on BioModels Database and identified by: MODEL1602280002.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4146, + "tag": "BioModels:BIOMD0000000690" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:15.385010+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000690", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2980": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2980, + "name": "Wolf2000 - Cellular interaction on glycolytic oscillations in yeast", + "repository_type": "biomodels", + "summary": "
Wolf2000 - Cellular interaction on glycolyticoscillations in yeast
A two-cell model of glycolysis.

This model is described in the article:

Wolf J, Heinrich R.
Biochem. J. 2000 Jan; 345 Pt 2: 321-334

Abstract:

On the basis of a detailed model of yeast glycolysis, the effect of intercellular dynamics is analysed theoretically. The model includes the main steps of anaerobic glycolysis, and the production of ethanol and glycerol. Transmembrane diffusion of acetaldehyde is included, since it has been hypothesized that this substance mediates the interaction. Depending on the kinetic parameter, the single-cell model shows both stationary and oscillatory behaviour. This agrees with experimental data with respect to metabolite concentrations and phase shifts. The inclusion of intercellular coupling leads to a variety of dynamical modes, such as synchronous oscillations, and different kinds of asynchronous behavior. These oscillations can co-exist, leading to bi- and tri-rhythmicity. The corresponding parameter regions have been identified by a bifurcation analysis. The oscillatory dynamics of synchronized cell populations are investigated by calculating the phase responses to acetaldehyde pulses. Simulations are performed with respect to the synchronization of two subpopulations that are oscillating out of phase before mixing. The effect of the various process on synchronization is characterized quantitatively. While continuous exchange of acetaldehyde might synchronize the oscillations for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization. for appropriate sets of parameter values, the calculated synchronization time is longer than that observed experimentally. It is concluded either that addition to the transmembrane exchange of acetaldehyde, other processes may contribute to intercellular coupling, or that intracellular regulator feedback plays a role in the acceleration of the synchronization.

This model is hosted on BioModels Database and identified by: BIOMD0000000691.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4147, + "tag": "BioModels:BIOMD0000000691" + }, + { + "id": 4148, + "tag": "Glycolysis" + }, + { + "id": 4149, + "tag": "Glycolysis / Gluconeogenesis" + }, + { + "id": 3061, + "tag": "Glycolytic process" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4150, + "tag": "Saccharomyces" + } + ], + "timestamp_created": "2025-01-30 14:03:15.912509+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000691", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2981": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2981, + "name": "Phillips2003 - The Mechanism of Ras GTPase Activation by Neurofibromin", + "repository_type": "biomodels", + "summary": "
Phillips2003 - The Mechanism of Ras GTPaseActivation by Neurofibromin
A mathematical model for Ras-GTPactivation by neurofibromin and the kinetic rates of the relevantreactions.

This model is described in the article:

Phillips RA, Hunter JL, Eccleston JF, Webb MR.
Biochemistry 2003 Apr; 42(13): 3956-3965

Abstract:

Individual rate constants have been determined for each step of the Ras.GTP hydrolysis mechanism, activated by neurofibromin. Fluorescence intensity and anisotropy stopped-flow measurements used the fluorescent GTP analogue, mantGTP (2'(3')-O-(N-methylanthraniloyl)GTP), to determine rate constants for binding and release of neurofibromin. Quenched flow measurements provided the kinetics of the hydrolytic cleavage step. The fluorescent phosphate sensor, MDCC-PBP was used to measure phosphate release kinetics. Phosphate-water oxygen exchange, using (18)O-substituted GTP and inorganic phosphate (P(i)), was used to determine the extent of reversal of the hydrolysis step and of P(i) binding. The data show that neurofibromin and P(i) dissociate from the NF1.Ras.GDP.P(i) complex with identical kinetics, which are 3-fold slower than the preceding cleavage step. A model is presented in which the P(i) release is associated with the change of Ras from \"GTP\" to \"GDP\" conformation. In this model, the conformation change on P(i) release causes the large change in affinity of neurofibromin, which then dissociates rapidly.

This model is hosted on BioModels Database and identified by: BIOMD0000000692.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4151, + "tag": "BioModels:BIOMD0000000692" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 4152, + "tag": "Regulation of Ras GTPase binding" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:16.442804+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000692", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2982": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2982, + "name": "Wang2008 - Mimicking the inhibitory effect of riluzole on membrane conductance in skeletal fibres", + "repository_type": "biomodels", + "summary": "
Wang2008 - Mimicking the inhibitory effect ofriluzole on membrane conductance in skeletal fibres

This model is described in the article:

Wang YJ, Lin MW, Lin AA, Wu SN.
Life Sci. 2008 Jan; 82(1-2): 11-20

Abstract:

Riluzole is known to be of therapeutic use in the management of amyotrophic lateral sclerosis. In this study, we investigated the effects of riluzole on ion currents in cultured differentiated human skeletal muscle cells (dHSkMCs). Western blotting revealed the protein expression of alpha-subunits for both large-conductance Ca2+-activated K+ (BK(Ca)) channel and Na+ channel (Na(v)1.5) in these cells. Riluzole could reduce the frequency of spontaneous beating in dHSkMCs. In whole-cell configuration, riluzole suppressed voltage-gated Na+ current (I(Na)) in a concentration-dependent manner with an IC50 value of 2.3 microM. Riluzole (10 microM) also effectively increased Ca2+-activated K+ current (I(K(Ca))) which could be reversed by iberiotoxin (200 nM) and paxilline (1 microM), but not by apamin (200 nM). In inside-out patches, when applied to the inside of the cell membrane, riluzole (10 microM) increased BK(Ca)-channel activity with a decrease in mean closed time. Simulation studies also unraveled that both decreased conductance of I(Na) and increased conductance of I(K(Ca)) utilized to mimic riluzole actions in skeletal muscle cells could combine to decrease the amplitude of action potentials and increase the repolarization of action potentials. Taken together, inhibition of I(Na) and stimulation of BK(Ca)-channel activity caused by this drug are partly, if not entirely, responsible for its muscle relaxant actions in clinical setting.

This model is hosted on BioModels Database and identified by: BIOMD0000000693.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 4153, + "tag": "Amyotrophic lateral sclerosis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4154, + "tag": "BioModels:BIOMD0000000693" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3722, + "tag": "Response to drug" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4155, + "tag": "Voltage-gated channel activity" + } + ], + "timestamp_created": "2025-01-30 14:03:16.968577+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000693", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2983": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2983, + "name": "FelixGarza2017 - Blue Light Treatment of Psoriasis (simplified)", + "repository_type": "biomodels", + "summary": "
FelixGarza2017 - Blue Light Treatment ofPsoriasis (simplified)

This model is described in the article:

F\u00e9lix Garza ZC, Liebmann J, Born M, Hilbers PA, van Riel NA.
Front Physiol 2017; 8: 28

Abstract:

Clinical investigations prove that blue light irradiation reduces the severity of psoriasis vulgaris. Nevertheless, the mechanisms involved in the management of this condition remain poorly defined. Despite the encouraging results of the clinical studies, no clear guidelines are specified in the literature for the irradiation scheme regime of blue light-based therapy for psoriasis. We investigated the underlying mechanism of blue light irradiation of psoriatic skin, and tested the hypothesis that regulation of proliferation is a key process. We implemented a mechanistic model of cellular epidermal dynamics to analyze whether a temporary decrease of keratinocytes hyper-proliferation can explain the outcome of phototherapy with blue light. Our results suggest that the main effect of blue light on keratinocytes impacts the proliferative cells. They show that the decrease in the keratinocytes proliferative capacity is sufficient to induce a transient decrease in the severity of psoriasis. To study the impact of the therapeutic regime on the efficacy of psoriasis treatment, we performed simulations for different combinations of the treatment parameters, i.e., length of treatment, fluence (also referred to as dose), and intensity. These simulations indicate that high efficacy is achieved by regimes with long duration and high fluence levels, regardless of the chosen intensity. Our modeling approach constitutes a framework for testing diverse hypotheses on the underlying mechanism of blue light-based phototherapy, and for designing effective strategies for the treatment of psoriasis.

This model is hosted on BioModels Database and identified by: BIOMD0000000695.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4156, + "tag": "BioModels:BIOMD0000000695" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:17.495908+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000695", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2984": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2984, + "name": "Boada2016 - Incoherent type 1 feed-forward loop (I1-FFL)", + "repository_type": "biomodels", + "summary": "
Boada2016 - Incoherent type 1 feed-forwardloop (I1-FFL)
A synthetic-biology mathematicalmodelling framework that was constructed to provide guidelines forexperimental implementation and parameter optimisation resulted ina biological device demonstrating desired behaviour.

This model is described in the article:

Boada Y, Reynoso-Meza G, Pic\u00f3 J, Vignoni A.
BMC Syst Biol 2016 Mar; 10: 27

Abstract:

Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized.We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior.The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.

This model is hosted on BioModels Database and identified by: BIOMD0000000696.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4157, + "tag": "BioModels:BIOMD0000000696" + }, + { + "id": 4158, + "tag": "Modelling framework" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4159, + "tag": "Theoretical Biology" + } + ], + "timestamp_created": "2025-01-30 14:03:18.022117+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000696", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2985": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2985, + "name": "Ciliberto2003 - CyclinE / Cdk2 timer in the cell cycle of Xenopus laevis embryo", + "repository_type": "biomodels", + "summary": "
Ciliberto2003 - CyclinE / Cdk2 timer in thecell cycle of Xenopus laevis embryo

This model is described in the article:

Ciliberto A, Petrus MJ, Tyson JJ, Sible JC.
Biophys. Chem. 2003 Jul; 104(3): 573-589

Abstract:

Early cell cycles of Xenopus laevis embryos are characterized by rapid oscillations in the activity of two cyclin-dependent kinases. Cdk1 activity peaks at mitosis, driven by periodic degradation of cyclins A and B. In contrast, Cdk2 activity oscillates twice per cell cycle, despite a constant level of its partner, cyclin E. Cyclin E degrades at a fixed time after fertilization, normally corresponding to the midblastula transition. Based on published data and new experiments, we constructed a mathematical model in which: (1) oscillations in Cdk2 activity depend upon changes in phosphorylation, (2) Cdk2 participates in a negative feedback loop with the inhibitory kinase Wee1; (3) cyclin E is cooperatively removed from the oscillatory system; and (4) removed cyclin E is degraded by a pathway activated by cyclin E/Cdk2 itself. The model's predictions about embryos injected with Xic1, a stoichiometric inhibitor of cyclin E/Cdk2, were experimentally validated.

This model is hosted on BioModels Database and identified by: BIOMD0000000697.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4160, + "tag": "BioModels:BIOMD0000000697" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-30 14:03:18.539281+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000697", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2986": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2986, + "name": "Reed2004 - Methionine Cycle", + "repository_type": "biomodels", + "summary": "
Reed2004 - Methionine Cycle

This model is described in the article:

Reed MC, Nijhout HF, Sparks R, Ulrich CM.
J. Theor. Biol. 2004 Jan; 226(1): 33-43

Abstract:

Building on the work of Martinov et al. (2000), a mathematical model is developed for the methionine cycle. A large amount of information is available about the enzymes that catalyse individual reaction steps in the cycle, from methionine to S-adenosylmethionine to S-adenosylhomocysteine to homocysteine, and the removal of mass from the cycle by the conversion of homocysteine to cystathionine. Nevertheless, the behavior of the cycle is very complicated since many substrates alter the activities of the enzymes in the reactions that produce them, and some can also alter the activities of other enzymes in the cycle. The model consists of four differential equations, based on known reaction kinetics, that can be solved to give the time course of the concentrations of the four main substrates in the cycle under various circumstances. We show that the behavior of the model in response to genetic abnormalities and dietary deficiencies is similar to the changes seen in a wide variety of experimental studies. We conduct computational \"experiments\" that give understanding of the regulatory behavior of the methionine cycle under normal conditions and the behavior in the presence of genetic variation and dietary deficiencies.

This model is hosted on BioModels Database and identified by: BIOMD0000000698.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4161, + "tag": "BioModels:BIOMD0000000698" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4162, + "tag": "S-methylmethionine cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:19.055928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000698", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2987": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2987, + "name": "Caydasi2012 - Regulation of Tem1 by the GAP complex in spindle position cell cycle checkpoint - Ubiquitous association model", + "repository_type": "biomodels", + "summary": "
Caydasi2012 - Regulation of Tem1 by the GAPcomplex in spindle position cell cycle checkpoint - Ubiquitousassociation model

This model is described in the article:

Caydasi AK, Lohel M, Gr\u00fcnert G, Dittrich P, Pereira G, Ibrahim B.
Mol. Syst. Biol. 2012; 8: 582

Abstract:

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000699.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4163, + "tag": "BioModels:BIOMD0000000699" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:03:19.571247+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000699", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2988": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2988, + "name": "Heldt2018 - Proliferation-quiescence decision in response to DNA damage", + "repository_type": "biomodels", + "summary": "
Heldt2018 - Proliferation-quiescence decisionin response to DNA damage

This model is described in the article:

Heldt FS, Barr AR, Cooper S, Bakal C, Nov\u00e1k B.
Proc. Natl. Acad. Sci. U.S.A. 2018 Feb; :

Abstract:

Human cells that suffer mild DNA damage can enter a reversible state of growth arrest known as quiescence. This decision to temporarily exit the cell cycle is essential to prevent the propagation of mutations, and most cancer cells harbor defects in the underlying control system. Here we present a mechanistic mathematical model to study the proliferation-quiescence decision in nontransformed human cells. We show that two bistable switches, the restriction point (RP) and the G1/S transition, mediate this decision by integrating DNA damage and mitogen signals. In particular, our data suggest that the cyclin-dependent kinase inhibitor p21 (Cip1/Waf1), which is expressed in response to DNA damage, promotes quiescence by blocking positive feedback loops that facilitate G1 progression downstream of serum stimulation. Intriguingly, cells exploit bistability in the RP to convert graded p21 and mitogen signals into an all-or-nothing cell-cycle response. The same mechanism creates a window of opportunity where G1 cells that have passed the RP can revert to quiescence if exposed to DNA damage. We present experimental evidence that cells gradually lose this ability to revert to quiescence as they progress through G1 and that the onset of rapid p21 degradation at the G1/S transition prevents this response altogether, insulating S phase from mild, endogenous DNA damage. Thus, two bistable switches conspire in the early cell cycle to provide both sensitivity and robustness to external stimuli.

This model is hosted on BioModels Database and identified by: MODEL1703030000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4164, + "tag": "BioModels:BIOMD0000000700" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:20.087423+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000700", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2989": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 2989, + "name": "Caydasi2012 - Inhibition of Tem1 by the GAP complex in Spindle Position Checkpoint", + "repository_type": "biomodels", + "summary": "

This model is from the article:
A dynamical model of the spindle position checkpoint
Ayse Koca Caydasi, Maiko Lohel, Gerd Gr\u00fcnert, Peter Dittrich, Gislene Pereira, Bashar Ibrahim Molecular Systems Biology 2012; 582 doi: 10.1038/msb.2012.15
Abstract:
The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1\u2013Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1\u2013Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1\u2013Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4165, + "tag": "BioModels:BIOMD0000000701" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:03:20.600203+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000701", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2990": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2990, + "name": "Caydasi2012 - Regulation of Tem1 by the GAP complex in Spindle Position Checkpoint - Ubiquitous inactive model", + "repository_type": "biomodels", + "summary": "
Regulation of Tem1 by the GAP complex inSpindle Position Checkpoint - Ubiquitous inactive

This model is described in the article:

Caydasi AK, Lohel M, Gr\u00fcnert G, Dittrich P, Pereira G, Ibrahim B.
Mol. Syst. Biol. 2012; 8: 582

Abstract:

The orientation of the mitotic spindle with respect to the polarity axis is crucial for the accuracy of asymmetric cell division. In budding yeast, a surveillance mechanism called the spindle position checkpoint (SPOC) prevents exit from mitosis when the mitotic spindle fails to align along the mother-to-daughter polarity axis. SPOC arrest relies upon inhibition of the GTPase Tem1 by the GTPase-activating protein (GAP) complex Bfa1-Bub2. Importantly, reactions signaling mitotic exit take place at yeast centrosomes (named spindle pole bodies, SPBs) and the GAP complex also promotes SPB localization of Tem1. Yet, whether the regulation of Tem1 by Bfa1-Bub2 takes place only at the SPBs remains elusive. Here, we present a quantitative analysis of Bfa1-Bub2 and Tem1 localization at the SPBs. Based on the measured SPB-bound protein levels, we introduce a dynamical model of the SPOC that describes the regulation of Bfa1 and Tem1. Our model suggests that Bfa1 interacts with Tem1 in the cytoplasm as well as at the SPBs to provide efficient Tem1 inhibition.

This model is hosted on BioModels Database and identified by: BIOMD0000000702.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4166, + "tag": "BioModels:BIOMD0000000702" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:03:21.120513+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000702", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2991": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 2991, + "name": "Diedrichs2018 - A data-entrained computational model for testing the regulatory logic of the vertebrate unfolded protein response", + "repository_type": "biomodels", + "summary": "
A data-entrained computational model fortesting the regulatory logic of the vertebrate unfolded proteinresponse

This model is described in the article:

Diedrichs DR, Gomez JA, Huang CS, Rutkowski DT, Curtu R.
Mol. Biol. Cell 2018 Apr; : mbcE17090565

Abstract:

The vertebrate unfolded protein response (UPR) is characterized by multiple interacting nodes among its three pathways, yet the logic underlying this regulatory complexity is unclear. To begin to address this issue, we created a computational model of the vertebrate UPR that was entrained upon and then validated against experimental data. As part of this validation, the model successfully predicted the phenotypes of cells with lesions in UPR signaling, including a surprising and previously unreported differential role for the eIF2? phosphatase GADD34 in exacerbating severe stress but ameliorating mild stress. We then used the model to test the functional importance of a feed-forward circuit within the PERK/CHOP axis, and of cross-regulatory control of BiP and CHOP expression. We found that the wiring structure of the UPR appears to balance the ability of the response to remain sensitive to ER stress yet also to be rapidly deactivated by improved protein folding conditions. This model should serve as a valuable resource for further exploring the regulatory logic of the UPR.

This model is hosted on BioModels Database and identified by: MODEL1803300000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4167, + "tag": "BioModels:BIOMD0000000703" + }, + { + "id": 4168, + "tag": "Response to unfolded protein" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4169, + "tag": "Unfolded Protein Response (UPR)" + } + ], + "timestamp_created": "2025-01-30 14:03:21.873554+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000703", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2992": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2992, + "name": "Aguda1999 - G2 DNA damage checkpoint", + "repository_type": "biomodels", + "summary": "Baltazar D. Aguda. A quantitative analysis of the kinetics of the G(2) DNA damage checkpoint system. Proceedings of the National Academy of Sciences 96, 20 (1999).

A detailed model of the G(2) DNA damage checkpoint (G2DDC) system is presented that includes complex regulatory networks of the mitotic kinase Cdc2, phosphatase Cdc25, Wee1 kinase, and damage signal transduction pathways involving Chk1 and p53. Assumptions on the kinetic equations of the G2DDC are made, and computer simulations are carried out to demonstrate how the various subsystems operate to delay or arrest cell cycle progression. The detailed model could be used to explain various experiments relevant to G2DDC reported recently, including the nuclear export of 14-3-3-bound Cdc25, the down-regulation of cyclin B1 expression by p53, the effect of Chk1 and p53 on Cdc25 levels, and Wee1 degradation. It also is shown that, under certain conditions, p53 is necessary to sustain a G(2) arrest.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4170, + "tag": "BioModels:BIOMD0000000704" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3487, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-01-30 14:03:22.429188+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000704", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2993": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2993, + "name": "Smith2010 - Response of FOXO Transcription Factors to Post-Translational Modifications Made by Ageing-Related Signalling Pathways", + "repository_type": "biomodels", + "summary": "Graham R. Smith & Daryl P. Shanley. Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways. PLoS ONE 5, 6 (2010).

FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. The use of the ancillary program to generate the SBML is necessary because the possibility that many regulatory PTMs may be added, each independently of the others, means that a large number of chemically distinct forms of the FOXO molecule must be taken into account, and the program is used to generate them. Although the model does not yet include detailed representations of events upstream and downstream of FOXO, we show how it can qualitatively, and in some cases quantitatively, reproduce the known effects of certain treatments that induce various single and multiple PTMs, and allows for a complex spatiotemporal interplay of effects due to the activation of multiple PTM-inducing treatments. Thus, it provides an important framework to integrate current knowledge about the behaviour of FOXO. The approach should be generally applicable to other proteins experiencing multiple regulations.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4171, + "tag": "BioModels:BIOMD0000000705" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:23.122830+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000705", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2994": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 2994, + "name": "Smith2010 - Response of FOXO Transcription Factors to Post-Translational Modifications (with acetylation pathway)", + "repository_type": "biomodels", + "summary": "Graham R. Smith & Daryl P. Shanley. Modelling the response of FOXO transcription factors to multiple post-translational modifications made by ageing-related signalling pathways. PLoS ONE 5, 6 (2010).

FOXO transcription factors are an important, conserved family of regulators of cellular processes including metabolism, cell-cycle progression, apoptosis and stress resistance. They are required for the efficacy of several of the genetic interventions that modulate lifespan. FOXO activity is regulated by multiple post-translational modifications (PTMs) that affect its subcellular localization, half-life, DNA binding and transcriptional activity. Here, we show how a mathematical modelling approach can be used to simulate the effects, singly and in combination, of these PTMs. Our model is implemented using the Systems Biology Markup Language (SBML), generated by an ancillary program and simulated in a stochastic framework. The use of the ancillary program to generate the SBML is necessary because the possibility that many regulatory PTMs may be added, each independently of the others, means that a large number of chemically distinct forms of the FOXO molecule must be taken into account, and the program is used to generate them. Although the model does not yet include detailed representations of events upstream and downstream of FOXO, we show how it can qualitatively, and in some cases quantitatively, reproduce the known effects of certain treatments that induce various single and multiple PTMs, and allows for a complex spatiotemporal interplay of effects due to the activation of multiple PTM-inducing treatments. Thus, it provides an important framework to integrate current knowledge about the behaviour of FOXO. The approach should be generally applicable to other proteins experiencing multiple regulations.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4172, + "tag": "BioModels:BIOMD0000000706" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:23.629009+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000706", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2995": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 2995, + "name": "Revilla2003 - Controlling HIV infection using recombinant viruses", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Fighting a virus with a virus: a dynamic model for HIV-1 therapy.
Revilla T, Garcia-Ramos G. Math Biosci 2003 Oct;185(2):191-203 12941536 ,
Abstract:
A mathematical model examined a potential therapy for controlling viralinfections using genetically modified viruses. The control of the infection isan indirect effect of the selective elimination by an engineered virus ofinfected cells that are the source of the pathogens. Therefore, this engineeredvirus could greatly compensate for a dysfunctional immune system compromised byAIDS. In vitro studies using engineered viruses have been shown to decrease theHIV-1 load about 1000-fold. However, the efficacy of this potential treatmentfor reducing the viral load in AIDS patients is unknown. The present modelstudied the interactions among the HIV-1 virus, its main host cell (activatedCD4+ T cells), and a therapeutic engineered virus in an in vivo context; and itexamined the conditions for controlling the pathogen. This model predicted asignificant drop in the HIV-1 load, but the treatment does not eradicate HIV. Abasic estimation using a currently engineered virus indicated an HIV-1 loadreduction of 92% and a recovery of host cells to 17% of their normal level.Greater success (98% HIV reduction, 44% host cells recovery) is expected as morecompetent engineered viruses are designed. These results suggest that therapyusing viruses could be an alternative to extend the survival of AIDS patients.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Revilla T, Garcia-Ramos G. (2003) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4173, + "tag": "BioModels:BIOMD0000000707" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4111, + "tag": "Human immunodeficiency virus 1" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:24.106406+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000707", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2996": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 2996, + "name": "Liu2017 - Dynamics of Avian Influenza with Logistic Growth", + "repository_type": "biomodels", + "summary": "Sanhong Liu, Shigui Ruan & Xinan Zhang. Nonlinear dynamics of avian influenza epidemic models. Mathematical Biosciences 283 (2017).

Avian influenza is a zoonotic disease caused by the transmission of the avian influenza A virus, such as H5N1 and H7N9, from birds to humans. The avian influenza A H5N1 virus has caused more than 500 human infections worldwide with nearly a 60% death rate since it was first reported in Hong Kong in 1997. The four outbreaks of the avian influenza A H7N9 in China from March 2013 to June 2016 have resulted in 580 human cases including 202 deaths with a death rate of nearly 35%. In this paper, we construct two avian influenza bird-to-human transmission models with different growth laws of the avian population, one with logistic growth and the other with Allee effect, and analyze their dynamical behavior. We obtain a threshold value for the prevalence of avian influenza and investigate the local or global asymptotical stability of each equilibrium of these systems by using linear analysis technique or combining Liapunov function method and LaSalle's invariance principle, respectively. Moreover, we give necessary and sufficient conditions for the occurrence of periodic solutions in the avian influenza system with Allee effect of the avian population. Numerical simulations are also presented to illustrate the theoretical results.", + "tags": [ + { + "id": 4174, + "tag": "Aves" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4175, + "tag": "BioModels:BIOMD0000000708" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:24.632560+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000708", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2997": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 2997, + "name": "Liu2017 - Dynamics of Avian Influenza with Allee Growth Effect", + "repository_type": "biomodels", + "summary": "Sanhong Liu, Shigui Ruan & Xinan Zhang. Nonlinear dynamics of avian influenza epidemic models. Mathematical Biosciences 283 (2017).

Avian influenza is a zoonotic disease caused by the transmission of the avian influenza A virus, such as H5N1 and H7N9, from birds to humans. The avian influenza A H5N1 virus has caused more than 500 human infections worldwide with nearly a 60% death rate since it was first reported in Hong Kong in 1997. The four outbreaks of the avian influenza A H7N9 in China from March 2013 to June 2016 have resulted in 580 human cases including 202 deaths with a death rate of nearly 35%. In this paper, we construct two avian influenza bird-to-human transmission models with different growth laws of the avian population, one with logistic growth and the other with Allee effect, and analyze their dynamical behavior. We obtain a threshold value for the prevalence of avian influenza and investigate the local or global asymptotical stability of each equilibrium of these systems by using linear analysis technique or combining Liapunov function method and LaSalle's invariance principle, respectively. Moreover, we give necessary and sufficient conditions for the occurrence of periodic solutions in the avian influenza system with Allee effect of the avian population. Numerical simulations are also presented to illustrate the theoretical results.", + "tags": [ + { + "id": 4174, + "tag": "Aves" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4176, + "tag": "BioModels:BIOMD0000000709" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:25.155402+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000709", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2998": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 2998, + "name": "Hernandez-Vargas2012 - Innate immune system dynamics to Influenza virus", + "repository_type": "biomodels", + "summary": "A. Esteban Hernandez-Vargas & Michael Meyer-Hermann. Innate Immune System Dynamics to Influenza Virus. IFAC Proceedings Volumes 45, 18 (2012).

The understanding of how influenza virus infection activates the immune system is crucial to designing prophylactic and therapeutic strategies against the infection. Nevertheless, the immune response to influenza virus infection is complex and remains largely unknown. In this paper we focus in the innate immune response to influenza virus using a mathematical model, based on interferon-induced resistance to infection of respiratory epithelial cells and the clearance of infected cells by natural killers. Simulation results show the importance of IFN-I to prevent new infections in epithelial cells and to stop the viral explosion during the first two days after infection. Nevertheless, natural killers response might be the most relevant for the first depletion in viral load due to the elimination of infected cells. Based on the reproductive number, the innate immune response is important to control the infection, although it would not be enough to clear completely the virus. The effective coordination between innate and adaptive immune response is essential for the virus eradication.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4177, + "tag": "BioModels:BIOMD0000000710" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3782, + "tag": "Influenza A virus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:25.648249+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000710", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "2999": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 2999, + "name": "Hancioglu2007 - Human Immune Response to Influenza A virus Infection", + "repository_type": "biomodels", + "summary": "Baris Hancioglu, David Swigon & Gilles Clermont. A dynamical model of human immune response to influenza A virus infection. Journal of Theoretical Biology 246, 1 (2007).

We present a simplified dynamical model of immune response to uncomplicated influenza A virus (IAV) infection, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells (cytotoxic T-cells and natural killer cells). Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov and Romanyukha [1994. Mathematical model of antiviral immune response. III. Influenza A virus infection. J. Theor. Biol. 167, 323-360], the model is constructed as a system of 10 ordinary differential equations with 27 parameters characterizing the rates of various processes contributing to the course of disease. The parameters are derived from published experimental data or estimated so as to reproduce available data about the time course of IAV infection in a na\u00efve host. We explore the effect of initial viral load on the severity and duration of the disease, construct a phase diagram that sheds insight into the dynamics of the disease, and perform sensitivity analysis on the model parameters to explore which ones influence the most the onset, duration and severity of infection. To account for the variability and speed of adaptation of the adaptive response to a particular virus strain, we introduce a variable that quantifies the antigenic compatibility between the virus and the antibodies currently produced by the organism. We find that for small initial viral load the disease progresses through an asymptomatic course, for intermediate value it takes a typical course with constant duration and severity of infection but variable onset, and for large initial viral load the disease becomes severe. This behavior is robust to a wide range of parameter values. The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4178, + "tag": "BioModels:BIOMD0000000711" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3782, + "tag": "Influenza A virus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:26.142849+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000711", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3000": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3000, + "name": "Manchanda2014 - Effect on Immune System by 4 different Influenza A virus strains", + "repository_type": "biomodels", + "summary": "Himanshu Manchanda, Nora Seidel, Andi Krumbholz, Andreas Sauerbrei, Michaela Schmidtke & Reinhard Guthke. Within-host influenza dynamics: a small-scale mathematical modeling approach. Biosystems 118 (2014).

The emergence of new influenza viruses like the pandemic H1N1 influenza A virus in 2009 (A(H1N1)pdm09) with unpredictable difficulties in vaccine coverage and established antiviral treatment protocols emphasizes the need of new murine models to prove the activity of novel antiviral compounds in vivo. The aim of the present study was to develop a small-scale mathematical model based on easily attainable experimental data to explain differences in influenza kinetics induced by different virus strains in mice. To develop a three-dimensional ordinary differential equation model of influenza dynamics, the following variables were included: (i) viral pathogenicity (P), (ii) antiviral immune defense (D), and (iii) inflammation due to pro-inflammatory response (I). Influenza virus-induced symptoms (clinical score S) in mice provided the basis for calculations of P and I. Both, mono- and biphasic course of mild to severe influenza induced by three clinical A(H1N1)pdm09 strains and one European swine H1N2 virus were comparatively and quantitatively studied by fitting the mathematical model to the experimental data. The model hypothesizes reasons for mild and severe influenza with mono- as well as biphasic course of disease. According to modeling results, the second peak of the biphasic course of infection is caused by inflammation. The parameters (i) maximum primary pathogenicity, (ii) viral infection rate, and (iii) rate of activation of the immune system represent most important parameters that quantitatively characterize the different pattern of virus-specific influenza kinetics.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4179, + "tag": "BioModels:BIOMD0000000712" + }, + { + "id": 4180, + "tag": "Influenza A virus (A/Jena/5258/2009(H1N1))" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:26.649577+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000712", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3001": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3001, + "name": "Aston2018 - Dynamics of Hepatitis C Infection", + "repository_type": "biomodels", + "summary": "Philip Aston. A New Model for the Dynamics of Hepatitis C Infection: Derivation, Analysis and Implications. Viruses 10, 4 (2018).

We review various existing models of hepatitis C virus (HCV) infection and show that there are inconsistencies between the models and known behaviour of the infection. A new model for HCV infection is proposed, based on various dynamical processes that occur during the infection that are described in the literature. This new model is analysed, and three steady state branches of solutions are found when there is no stem cell generation of hepatocytes. Unusually, the branch of infected solutions that connects the uninfected branch and the pure infection branch can be found analytically and always includes a limit point, subject to a few conditions on the parameters. When the action of stem cells is included, the bifurcation between the pure infection and infected branches unfolds, leaving a single branch of infected solutions. It is shown that this model can generate various viral load profiles that have been described in the literature, which is confirmed by fitting the model to four viral load datasets. Suggestions for possible changes in treatment are made based on the model.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4181, + "tag": "BioModels:BIOMD0000000713" + }, + { + "id": 4182, + "tag": "Hepacivirus C" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:27.154363+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000713", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3002": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3002, + "name": "Reynolds2006 - Reduced model of the acute inflammatory response", + "repository_type": "biomodels", + "summary": "Angela Reynolds, Jonathan Rubin, Gilles Clermont, Judy Day, Yoram Vodovotz & G. Bard Ermentrout. A reduced mathematical model of the acute inflammatory response: I. Derivation of model and analysis of anti-inflammation. Journal of Theoretical Biology 242, 1 (2006).

The acute inflammatory response, triggered by a variety of biological or physical stresses on an organism, is a delicate system of checks and balances that, although aimed at promoting healing and restoring homeostasis, can result in undesired and occasionally lethal physiological responses. In this work, we derive a reduced conceptual model for the acute inflammatory response to infection, built up from consideration of direct interactions of fundamental effectors. We harness this model to explore the importance of dynamic anti-inflammation in promoting resolution of infection and homeostasis. Further, we offer a clinical correlation between model predictions and potential therapeutic interventions based on modulation of immunity by anti-inflammatory agents.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4183, + "tag": "BioModels:BIOMD0000000714" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:27.638230+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000714", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3003": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3003, + "name": "Huo2017 - SEIS epidemic model with the impact of media", + "repository_type": "biomodels", + "summary": "Hai-Feng Huo, Peng Yang & Hong Xiang. Stability and bifurcation for an SEIS epidemic model with the impact of media. Physica A: Statistical Mechanics and its Applications 490 (2018).

A novel SEIS epidemic model with the impact of media is introduced. By analyzing the characteristic equation of equilibrium, the basic reproduction number is obtained and the stability of the steady states is proved. The occurrence of a forward, backward and Hopf bifurcation is derived. Numerical simulations and sensitivity analysis are performed. Our results manifest that media can regard as a good indicator in controlling the emergence and spread of the epidemic disease.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4184, + "tag": "BioModels:BIOMD0000000715" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:28.144591+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000715", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3004": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3004, + "name": "Lee2018 - Avian human bilinear incidence (BI) model", + "repository_type": "biomodels", + "summary": "Hanl Lee & Angelyn Lao. Transmission dynamics and control strategies assessment of avian influenza A (H5N6) in the Philippines. Infectious Disease Modelling 3 (2018).

Due to the outbreaks of Highly Pathogenic Avian Influenza A (HPAI) H5N6 in the Philippines (particularly in Pampanga and Nueva Ecija) in August 2017, there has been an increase in the need to cull the domestic birds to control the spread of the infection. However, this control method poses a negative impact on the poultry industry. In addition, the pathogenicity and transmissibility of the H5N6 in both the birds and the humans remain largely unknown which call for the necessity to develop more strategic control methods for the virus. In this study, we constructed a mathematical model for the bilinear and half-saturated incidence to compare their corresponding effect on transmission dynamics of H5N6. The simulations of half-saturated incidence model were similar to what occurred during the H5N6 outbreak (2017) in the Philippines. Instead of culling the birds, we implemented other control strategies such as non-medicinal (personal protection and poultry isolation) and medicinal (poultry vaccination) ways to prevent, reduce, and control the rate of the H5N6 virus transmission. Among the proposed control strategies, we have shown that the poultry isolation strategy is still the most effective in reducing the infected birds.", + "tags": [ + { + "id": 4174, + "tag": "Aves" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4185, + "tag": "BioModels:BIOMD0000000716" + }, + { + "id": 4186, + "tag": "H5N6 subtype" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:28.669148+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000716", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3005": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3005, + "name": "Lee2018 - Avian human half-saturated incidence (HSI) model", + "repository_type": "biomodels", + "summary": "Hanl Lee & Angelyn Lao. Transmission dynamics and control strategies assessment of avian influenza A (H5N6) in the Philippines. Infectious Disease Modelling 3 (2018).

Due to the outbreaks of Highly Pathogenic Avian Influenza A (HPAI) H5N6 in the Philippines (particularly in Pampanga and Nueva Ecija) in August 2017, there has been an increase in the need to cull the domestic birds to control the spread of the infection. However, this control method poses a negative impact on the poultry industry. In addition, the pathogenicity and transmissibility of the H5N6 in both the birds and the humans remain largely unknown which call for the necessity to develop more strategic control methods for the virus. In this study, we constructed a mathematical model for the bilinear and half-saturated incidence to compare their corresponding effect on transmission dynamics of H5N6. The simulations of half-saturated incidence model were similar to what occurred during the H5N6 outbreak (2017) in the Philippines. Instead of culling the birds, we implemented other control strategies such as non-medicinal (personal protection and poultry isolation) and medicinal (poultry vaccination) ways to prevent, reduce, and control the rate of the H5N6 virus transmission. Among the proposed control strategies, we have shown that the poultry isolation strategy is still the most effective in reducing the infected birds.", + "tags": [ + { + "id": 4174, + "tag": "Aves" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4187, + "tag": "BioModels:BIOMD0000000717" + }, + { + "id": 4186, + "tag": "H5N6 subtype" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:29.158008+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000717", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3006": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3006, + "name": "Li2008 - Caulobacter Cell Cycle", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells.
Shenghua Li, Paul Brazhnik, Bruno Sobral, John J. Tyson PLoS Comput Biol 2008 Jan 25:4(1): e9 18225942 ,
Abstract:
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genomereplication and partitioning to daughter cells. From published experimental evidence, we propose a molecularmechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on thesynthesis and degradation of three \u2018\u2018master regulator\u2019\u2019 proteins (CtrA, GcrA, and DnaA), is converted into a quantitativemodel, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts forimportant details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. Itreproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, andpredicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating thecell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may beapplicable to other species of importance in agriculture and medicine.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4188, + "tag": "BioModels:BIOMD0000000718" + }, + { + "id": 4189, + "tag": "Caulobacter vibrioides" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:29.634529+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000718", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3007": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3007, + "name": "Tsai2014 - Cell cycle duration control by oscillatory Dynamics in Early Xenopus laevis Embryos", + "repository_type": "biomodels", + "summary": "

During the early development of Xenopus laevis embryos, the first mitotic cell cycle is long (\u223c85 min) and the subsequent 11 cycles are short (\u223c30 min) and clock-like. Here we address the question of how the Cdk1 cell cycle oscillator changes between these two modes of operation. We found that the change can be attributed to an alteration in the balance between Wee1/Myt1 and Cdc25. The change in balance converts a circuit that acts like a positive-plus-negative feedback oscillator, with spikes of Cdk1 activation, to one that acts like a negative-feedback-only oscillator, with a shorter period and smoothly varying Cdk1 activity. Shortening the first cycle, by treating embryos with the Wee1A/Myt1 inhibitor PD0166285, resulted in a dramatic reduction in embryo viability, and restoring the length of the first cycle in inhibitor-treated embryos with low doses of cycloheximide partially rescued viability. Computations with an experimentally parameterized mathematical model show that modest changes in the Wee1/Cdc25 ratio can account for the observed qualitative changes in the cell cycle. The high ratio in the first cycle allows the period to be long and tunable, and decreasing the ratio in the subsequent cycles allows the oscillator to run at a maximal speed. Thus, the embryo rewires its feedback regulation to meet two different developmental requirements during early development.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4190, + "tag": "BioModels:BIOMD0000000719" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-30 14:03:30.109818+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000719", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3008": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3008, + "name": "Yan2012 - Rb-E2F pathway dynamics with miR449", + "repository_type": "biomodels", + "summary": "MiRNAs, which are a family of small non-coding RNAs, regulate a broad array of physiological and developmental processes. However, their regulatory roles have remained largely mysterious. E2F is a positive regulator of cell cycle progression and also a potent inducer of apoptosis. Positive feedback loops in the regulation of Rb-E2F pathway are predicted and shown experimentally. Recently, it has been discovered that E2F induce a cluster of miRNAs called miR449. In turn, E2F is inhibited by miR449 through regulating different transcripts, thus forming negative feedback loops in the interaction network. Here, based on the integration of experimental evidence and quantitative data, we studied Rb-E2F pathway coupling the positive feedback loops and negative feedback loops mediated by miR449. Therefore, a mathematical model is constructed based in part on the model proposed in Yao-Lee et al. (2008) and nonlinear dynamical behaviors including the stability and bifurcations of the model are discussed.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4191, + "tag": "BioModels:BIOMD0000000720" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:30.653435+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000720", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3009": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3009, + "name": "Graham2013 - Role of osteocytes in targeted bone remodeling", + "repository_type": "biomodels", + "summary": "Jason M. Graham, Bruce P. Ayati, Sarah A. Holstein & James A. Martin. The role of osteocytes in targeted bone remodeling: a mathematical model. PLoS ONE 8, 5 (2013).

Until recently many studies of bone remodeling at the cellular level have focused on the behavior of mature osteoblasts and osteoclasts, and their respective precursor cells, with the role of osteocytes and bone lining cells left largely unexplored. This is particularly true with respect to the mathematical modeling of bone remodeling. However, there is increasing evidence that osteocytes play important roles in the cycle of targeted bone remodeling, in serving as a significant source of RANKL to support osteoclastogenesis, and in secreting the bone formation inhibitor sclerostin. Moreover, there is also increasing interest in sclerostin, an osteocyte-secreted bone formation inhibitor, and its role in regulating local response to changes in the bone microenvironment. Here we develop a cell population model of bone remodeling that includes the role of osteocytes, sclerostin, and allows for the possibility of RANKL expression by osteocyte cell populations. We have aimed to give a simple, yet still tractable, model that remains faithful to the underlying system based on the known literature. This model extends and complements many of the existing mathematical models for bone remodeling, but can be used to explore aspects of the process of bone remodeling that were previously beyond the scope of prior modeling work. Through numerical simulations we demonstrate that our model can be used to explore theoretically many of the qualitative features of the role of osteocytes in bone biology as presented in recent literature.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4192, + "tag": "BioModels:BIOMD0000000721" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:31.171495+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000721", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3010": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3010, + "name": "Bianchi2015 -Model for lymphangiogenesis in normal and diabetic wounds", + "repository_type": "biomodels", + "summary": "Arianna Bianchi, Kevin J. Painter & Jonathan A. Sherratt. A mathematical model for lymphangiogenesis in normal and diabetic wounds. Journal of Theoretical Biology 383 (2015).

Several studies suggest that one possible cause of impaired wound healing is failed or insufficient lymphangiogenesis, that is the formation of new lymphatic capillaries. Although many mathematical models have been developed to describe the formation of blood capillaries (angiogenesis) very few have been proposed for the regeneration of the lymphatic network. Moreover, lymphangiogenesis is markedly distinct from angiogenesis, occurring at different times and in a different manner. Here a model of five ordinary differential equations is presented to describe the formation of lymphatic capillaries following a skin wound. The variables represent different cell densities and growth factor concentrations, and where possible the parameters are estimated from experimental and clinical data. The system is then solved numerically and the results are compared with the available biological literature. Finally, a parameter sensitivity analysis of the model is taken as a starting point for suggesting new therapeutic approaches targeting the enhancement of lymphangiogenesis in diabetic wounds. The work provides a deeper understanding of the phenomenon in question, clarifying the main factors involved. In particular, the balance between TGF-\u03b2 and VEGF levels, rather than their absolute values, is identified as crucial to effective lymphangiogenesis. In addition, the results indicate lowering the macrophage-mediated activation of TGF-\u03b2 and increasing the basal lymphatic endothelial cell growth rate, inter alia, as potential treatments. It is hoped the findings of this paper may be considered in the development of future experiments investigating novel lymphangiogenic therapies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4193, + "tag": "BioModels:BIOMD0000000722" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:31.684492+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000722", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3011": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3011, + "name": "Weis2014 - Data driven Mammalian Cell Cycle Model", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A Data-Driven, Mathematical Model of Mammalian Cell Cycle Regulation.
Michael C. Weis, Jayant Avva, James W. Jacobberger, Sree N. Sreenath PLoS ONE 2014 May 13: 9(5): e97130 24824602 ,
Abstract:
Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genomereplication and partitioning to daughter cells. From published experimental evidence, we propose a molecularmechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on thesynthesis and degradation of three \u2018\u2018master regulator\u2019\u2019 proteins (CtrA, GcrA, and DnaA), is converted into a quantitativemodel, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts forimportant details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. Itreproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, andpredicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating thecell cycle of C. crescentus are conserved among many genera of a-proteobacteria, the proposed mechanism may beapplicable to other species of importance in agriculture and medicine.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4194, + "tag": "BioModels:BIOMD0000000723" + }, + { + "id": 4195, + "tag": "Cell Cycle Pathway" + }, + { + "id": 2985, + "tag": "Cell cycle" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:32.193877+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000723", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3012": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3012, + "name": "Theinmozhi2018 - Mechanism of PD1 inhibiting TCR signaling in Tumor immune regulation", + "repository_type": "biomodels", + "summary": "
Its a Deterministic ODE model showcasing mechanism of PDL1 induced TCR and CD38 signalling inhibition. The model also contains the LCK activation and inactivation phenomenon dependent on the particular phosphorylation site. This model is relevant in immunotherapy. 
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4196, + "tag": "BioModels:BIOMD0000000724" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:32.699549+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000724", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3013": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3013, + "name": "Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation", + "repository_type": "biomodels", + "summary": "
Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation

This model is described in the article:

Enrique Salcedo-Sora J, Mc Auley MT.
Mol Biosyst. 2016 Jan 21.

Abstract:

The metabolic biochemistry of folate biosynthesis and utilisation has evolved into a complex network of reactions. Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole.

This model is hosted on BioModels Database and identified by: MODEL1511020000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4197, + "tag": "BioModels:BIOMD0000000725" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:33.201617+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000725", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3014": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3014, + "name": "Ruan2017 - Transmission dynamics and control of rabies in China", + "repository_type": "biomodels", + "summary": "Shigui Ruan. Modeling the transmission dynamics and control of rabies in China. Mathematical Biosciences 286 (2017).

Human rabies was first recorded in ancient China in about 556 BC and is still one of the major public-health problems in China. From 1950 to 2015, 130,494 human rabies cases were reported in Mainland China with an average of 1977 cases per year. It is estimated that 95% of these human rabies cases are due to dog bites. The purpose of this article is to provide a review about the models, results, and simulations that we have obtained recently on studying the transmission of rabies in China. We first construct a basic susceptible, exposed, infectious, and recovered (SEIR) type model for the spread of rabies virus among dogs and from dogs to humans and use the model to simulate the human rabies data in China from 1996 to 2010. Then we modify the basic model by including both domestic and stray dogs and apply the model to simulate the human rabies data from Guangdong Province, China. To study the seasonality of rabies, in Section\u00a04 we further propose a SEIR model with periodic transmission rates and employ the model to simulate the monthly data of human rabies cases reported by the Chinese Ministry of Health from January 2004 to December 2010. To understand the spatial spread of rabies, in Section\u00a05 we add diffusion to the dog population in the basic SEIR model to obtain a reaction-diffusion equation model and determine the minimum wave speed connecting the disease-free equilibrium to the endemic equilibrium. Finally, in order to investigate how the movement of dogs affects the geographically inter-provincial spread of rabies in Mainland China, in Section\u00a06 we propose a multi-patch model to describe the transmission dynamics of rabies between dogs and humans and use the two-patch submodel to investigate the rabies virus clades lineages and to simulate the human rabies data from Guizhou and Guangxi, Hebei and Fujian, and Sichuan and Shaanxi, respectively. Some discussions are provided in Section\u00a07.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4198, + "tag": "BioModels:BIOMD0000000726" + }, + { + "id": 3903, + "tag": "Canis lupus familiaris" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4199, + "tag": "Rabies lyssavirus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:33.725711+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000726", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3015": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3015, + "name": "Li2009- Assymetric Caulobacter cell cycle", + "repository_type": "biomodels", + "summary": "
The asymmetric cell division cycle of Caulobacter crescentus is orchestrated by an elaborate gene-protein regulatory network, centered on three major control proteins, DnaA, GcrA and CtrA. The regulatory network is cast into a quantitative computational model to investigate in a systematic fashion how these three proteins control the relevant genetic, biochemical and physiological properties of proliferating bacteria. Different controls for both swarmer and stalked cell cycles are represented in the mathematical scheme. The model is validated against observed phenotypes of wild-type cells and relevant mutants, and it predicts the phenotypes of novel mutants and of known mutants under novel experimental conditions. Because the cell cycle control proteins of Caulobacter are conserved across many species of alpha-proteobacteria, the model we are proposing here may be applicable to other genera of importance to agriculture and medicine
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4200, + "tag": "BioModels:BIOMD0000000727" + }, + { + "id": 4189, + "tag": "Caulobacter vibrioides" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:34.197490+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000727", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3016": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3016, + "name": "Norel1990 - MPF and Cyclin Oscillations", + "repository_type": "biomodels", + "summary": "
A mathematical model of cell cycle progression is presented, which integrates recent biochemical information on the interaction of the maturation promotion factor (MPF) and cyclin. The model retrieves the dynamics observed in early embryos and explains how multiple cycles of MPF activity can be produced and how the internal clock that determines durations and number of cycles can be adjusted by modulating the rate of change in MPF or cyclin concentrations. Experiments are suggested for verifying the role of MPF activity in determining the length of the somatic cell cycle.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4201, + "tag": "BioModels:BIOMD0000000728" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:34.691340+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000728", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3017": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3017, + "name": "Goldbeter1996 - Cyclin Cdc2 kinase Oscillations", + "repository_type": "biomodels", + "summary": "

We consider a minimal cascade model previously proposed for the mitotic oscillator driving the embryonic cell division cycle. The model is based on a bicyclic phosphorylation-dephosphorylation cascade involving cyclin and cdc2 kinase. By constructing stability diagrams showing domains of periodic behavior as a function of the maximum rates of the kinases and phosphatases involved in the two cycles of the cascade, we investigate the role of these converter enzymes in the oscillatory mechanism. Oscillations occur when the balance of kinase and phosphatase rates in each cycle is in a range bounded by two critical values. The results suggest ways to arrest the mitotic oscillator by altering the maximum rates of the converter enzymes. These results bear on the control of cell proliferation.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4202, + "tag": "BioModels:BIOMD0000000729" + }, + { + "id": 3165, + "tag": "Eukaryota" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:35.174418+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000729", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3018": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3018, + "name": "Gerard2009 - An Integrated Mammalian Cell Cycle Model", + "repository_type": "biomodels", + "summary": "

We propose an integrated computational model for the network of cyclin-dependent kinases (Cdks) that controls the dynamics of the mammalian cell cycle. The model contains four Cdk modules regulated by reversible phosphorylation, Cdk inhibitors, and protein synthesis or degradation. Growth factors (GFs) trigger the transition from a quiescent, stable steady state to self-sustained oscillations in the Cdk network. These oscillations correspond to the repetitive, transient activation of cyclin D/Cdk4-6 in G(1), cyclin E/Cdk2 at the G(1)/S transition, cyclin A/Cdk2 in S and at the S/G(2) transition, and cyclin B/Cdk1 at the G(2)/M transition. The model accounts for the following major properties of the mammalian cell cycle: (i) repetitive cell cycling in the presence of suprathreshold amounts of GF; (ii) control of cell-cycle progression by the balance between antagonistic effects of the tumor suppressor retinoblastoma protein (pRB) and the transcription factor E2F; and (iii) existence of a restriction point in G(1), beyond which completion of the cell cycle becomes independent of GF. The model also accounts for endoreplication. Incorporating the DNA replication checkpoint mediated by kinases ATR and Chk1 slows down the dynamics of the cell cycle without altering its oscillatory nature and leads to better separation of the S and M phases. The model for the mammalian cell cycle shows how the regulatory structure of the Cdk network results in its temporal self-organization, leading to the repetitive, sequential activation of the four Cdk modules that brings about the orderly progression along cell-cycle phases.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4203, + "tag": "BioModels:BIOMD0000000730" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:35.646984+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000730", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3019": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3019, + "name": "Robertson-Tessi M 2012 A model of tumor Immune interaction", + "repository_type": "biomodels", + "summary": "

Its a mathematical model presenting the interaction between a growing tumor and immune system. Model involves tumor cells, dendritic cell, helper Tcells, regulatory Tcells, effector cells and certain cytokines (e.g. TGFbeta, IL10, IL2 ) produced by these cells. It represent a dynamic regulation of tumor production/killing by different immune cells and cytokines.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4204, + "tag": "BioModels:BIOMD0000000731" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:36.137905+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000731", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3020": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3020, + "name": "Kirschner1998_Immunotherapy_Tumour", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling immunotherapy of the tumor-immune interaction.
Kirschner D, Panetta JC. J Math Biol 1998 Sep;37(3):235-52 9785481 ,
Abstract:
A number of lines of evidence suggest that immunotherapy with the cytokineinterleukin-2 (IL-2) may boost the immune system to fight tumors. CD4+ T cells,the cells that orchestrate the immune response, use these cytokines as signalingmechanisms for immune-response stimulation as well as lymphocyte stimulation,growth, and differentiation. Because tumor cells begin as 'self', the immunesystem may not respond in an effective way to eradicate them. Adoptive cellularimmunotherapy can potentially restore or enhance these effects. We illustratethrough mathematical modeling the dynamics between tumor cells, immune-effectorcells, and IL-2. These efforts are able to explain both short tumor oscillationsin tumor sizes as well as long-term tumor relapse. We then explore the effectsof adoptive cellular immunotherapy on the model and describe under whatcircumstances the tumor can be eliminated.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Kirschner D, Panetta JC. (1998) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model attempts to fit the radioiron tracer data from Lopes et al. 2010 for mice fed iron deficient and rich diets by adjusting the rate of iron intake (vDiet) and the hepcidin synthesis rate (vhepcidin) independently for each experiment. All other parameters are those that provide the best fit for the adequate diet.

This model includes the radioiron tracer species.


Differences in parameter values between deficient, rich, and adequate diets:

Diet vDiet vhepcidin
Adequate 0.00377422 1.7393e-08
Deficient 0 8.54927e-09
Rich 0.00415624 2.30942e-08
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Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes normal iron and radioactive labelled tracer iron species and was used for parameter estimation given the data from Lopes et al. 2010 for mice fed an adequate iron diet.


", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4209, + "tag": "BioModels:BIOMD0000000735" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:38.175353+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000735", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3024": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3024, + "name": "Mouse Iron Distribution - Adequate iron diet (No Tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed an adequate iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


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Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed a deficient iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4211, + "tag": "BioModels:BIOMD0000000737" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:39.618234+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000737", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3026": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3026, + "name": "Mouse Iron Distribution - Rich iron diet (No Tracer)", + "repository_type": "biomodels", + "summary": "

Mouse Iron Distribution Dynamics

Dynamic model of iron distribution in mice. This model includes only normal iron with the parameters that fit the data from Lopes et al. 2010 for mice fed a rich iron diet.

This model does not include the radioiron tracer species. It is appropriate to study the properties in conditions where no tracers are used (for example for steady state analysis).


", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4212, + "tag": "BioModels:BIOMD0000000738" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:40.096881+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000738", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3027": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3027, + "name": "Bravo2012 - Modelling blood coagulation factor Va inactivation by APC", + "repository_type": "biomodels", + "summary": "Mathematical model of blood coagulation factor Va and Va fragment inactivation by APC, reactions with Xa and prothrombinase-prothrombin complex formation.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4213, + "tag": "BioModels:BIOMD0000000739" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:40.569235+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000739", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3028": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3028, + "name": "Panteleev2010 - Blood Coagulation: Full Model", + "repository_type": "biomodels", + "summary": "Full and reduced mathematical model of blood coagulation focusing on fibrin formation and the response to varied TF and V.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4214, + "tag": "BioModels:BIOMD0000000740" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:41.053488+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000740", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3029": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3029, + "name": "Eftimie2018 - Cancer and Immune biomarkers", + "repository_type": "biomodels", + "summary": "
The paper describes a model on the detection of cancer based on cancer and immune biomarkers. Created by COPASI 4.25 (Build 207) This model is described in the article: Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach Raluca Eftimie and and Esraa Hassanein J Transl Med (2018) 16:73 Abstract: Background: Early cancer diagnosis is one of the most important challenges of cancer research, since in many can- cers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers. Methods: Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection. Results: We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solu- tion diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolu- tion of cancer biomarkers, thus allowing us to make predictions on cancer detection times. Conclusions: Combining cancer and immune biomarkers could improve cancer detection times, and any predic- tions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific. Keywords: Ovarian cancer, Mathematical model, CA-125 biomarker, IL-7 biomarker, Cancer detection times This model is hosted on BioModels Database and identified by: MODEL1907050002. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4215, + "tag": "BioModels:BIOMD0000000741" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:41.563549+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000741", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3030": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "13", + "id": 3030, + "name": "Garcia2018basic - cancer and immune cell count basic model", + "repository_type": "biomodels", + "summary": "
The paper describes a basic model of immune-tumor cell interactions. Created by COPASI 4.25 (Build 207) This model is described in the article: Cancer-Induced Immunosuppression can enable Effectiveness of Immunotherapy through Bistability Generation: a mathematical and computational Examination Victor Garcia, Sebastian Bonhoeffer and Feng Fu bioRxiv, 2018 Abstract: Cancer immunotherapies rely on how interactions between cancer and immune system cells are constituted. The more essential to the emergence of the dynamical behavior of cancer growth these are, the more effectively they may be used as mechanisms for interventions. Mathematical modeling can help unearth such connections, and help explain how they shape the dynamics of cancer growth. Here, we explored whether there exist simple, consistent properties of cancer-immune system interaction (CISI) models that might be harnessed to devise effective immunotherapy approaches. We did this for a family of three related models of increasing complexity. To this end, we developed a base model of CISI, which captures some essential features of the more complex models built on it. We find that the base model and its derivates can reproduce biologically plausible behavior. This behavior is consistent with situations in which the suppressive effects exerted by cancer cells on immune cells dominate their proliferative effects. Under these circumstances, the model family may display a pattern of bistability, where two distinct, stable states (a cancer-free, and a full-grown cancer state) are possible, consistent with the notion of an immunological barrier. Increasing the effectiveness of immune-caused cancer cell killing may remove the basis for bistability, and abruptly tip the dynamics of the system into cancer-free state. In combination with the administration of immune effector cells, modifications in cancer cell killing may also be harnessed for immunotherapy without resolving the bistability. We use these ideas to test immunotherapeutic interventions in silico in a stochastic version of the base model. This bistability-reliant approach to cancer interventions might offer advantages over those that comprise gradual declines in cancer cell numbers. This model is hosted on BioModels Database and identified by: MODEL1907050005. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4216, + "tag": "BioModels:BIOMD0000000742" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:42.099153+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000742", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3031": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3031, + "name": "Gallaher2018 - Tumor\u2013Immune dynamics in multiple myeloma", + "repository_type": "biomodels", + "summary": "

The paper describes a model on the key components for tumor\u2013immune dynamics in multiple myeloma.Created by COPASI 4.25 (Build 207)This model is described in the article:Methods for determining key components in a mathematical model for tumor\u2013immune dynamics in multiple myelomaJill Gallaher, Kamila Larripa, Marissa Renardy, Blerta Shtylla, Nessy Tania, Diana White, Karen Wood, Li Zhu, Chaitali Passey, Michael Robbins, Natalie Bezman, Suresh Shelat, Hearn Jay Choo, Helen MooreJournal of Theoretical Biology 458 (2018) 31\u201346Abstract:In this work, we analyze a mathematical model we introduced previously for the dynamics of multiple myeloma and the immune system. We focus on four main aspects: (1) obtaining and justifying ranges and values for all parameters in the model; (2) determining a subset of parameters to which the model is most sensitive; (3) determining which parameters in this subset can be uniquely estimated given cer- tain types of data; and (4) exploring the model numerically. Using global sensitivity analysis techniques, we found that the model is most sensitive to certain growth, loss, and efficacy parameters. This anal- ysis provides the foundation for a future application of the model: prediction of optimal combination regimens in patients with multiple myeloma.This model is hosted on BioModels Database and identified by: MODEL1907050001To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4217, + "tag": "BioModels:BIOMD0000000743" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:42.586078+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000743", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3032": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3032, + "name": "Hu2019 - Pancreatic cancer dynamics", + "repository_type": "biomodels", + "summary": "
The paper describes a model on the size of pancreatic tumour. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling Pancreatic Cancer Dynamics with Immunotherapy Xiaochuan Hu, Guoyi Ke and Sophia R.-J. Jang Bulletin of Mathematical Biology (2019) 81:1885\u20131915 Abstract: We develop a mathematical model of pancreatic cancer that includes pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor- suppressing cytokines to investigate the effects of immunotherapies on patient survival. The model is first validated using the survival data of two clinical trials. Local sen- sitivity analysis of the parameters indicates there exists a critical activation rate of pro-tumor cytokines beyond which the cancer can be eradicated if four adoptive trans- fers of immune cells are applied. Optimal control theory is explored as a potential tool for searching the best adoptive cellular immunotherapies. Combined immunother- apies between adoptive ex vivo expanded immune cells and TGF-\u03b2 inhibition by siRNA treatments are investigated. This study concludes that mono-immunotherapy is unlikely to control the pancreatic cancer and combined immunotherapies between anti-TGF-\u03b2 and adoptive transfers of immune cells can prolong patient survival. We show through numerical explorations that how these two types of immunotherapies are scheduled is important to survival. Applying TGF-\u03b2 inhibition first followed by adoptive immune cell transfers can yield better survival outcomes. This model is hosted on BioModels Database and identified by: MODEL1907050003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4218, + "tag": "BioModels:BIOMD0000000744" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:43.079784+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000744", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3033": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3033, + "name": "Jarrett2018 - trastuzumab-induced immune response in murine HER2+ breast cancer model", + "repository_type": "biomodels", + "summary": "
The paper describes a model on the trastuzumab-induced immune response in murine(mouse) HER2+ breast cancer.Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer Angela M. Jarrett, Meghan J. Bloom, Wesley Godfrey, Anum K. Syed, David A. Ekrut, Lauren I. Ehrlich, Thomas E. Yankeelov, Anna G. Sorace Mathematical Medicine and Biology: A Journal of the IMA (2018) 00, 1\u201330 Abstract: The goal of this study is to develop an integrated, mathematical\u2013experimental approach for understanding the interactions between the immune system and the effects of trastuzumab on breast cancer that overexpresses the human epidermal growth factor receptor 2 (HER2+). A system of coupled, ordinary differential equations was constructed to describe the temporal changes in tumour growth, along with intratumoural changes in the immune response, vascularity, necrosis and hypoxia. The mathematical model is calibrated with serially acquired experimental data of tumour volume, vascularity, necrosis and hypoxia obtained from either imaging or histology from a murine model of HER2+ breast cancer. Sensitivity analysis shows that model components are sensitive for 12 of 13 parameters, but accounting for uncertainty in the parameter values, model simulations still agree with the experimental data. Given theinitial conditions, the mathematical model predicts an increase in the immune infiltrates over time in the treated animals. Immunofluorescent staining results are presented that validate this prediction by showing an increased co-staining of CD11c and F4/80 (proteins expressed by dendritic cells and/or macrophages) in the total tissue for the treated tumours compared to the controls. We posit that the proposed mathematical\u2013experimental approach can be used to elucidate driving interactions between the trastuzumab-induced responses in the tumour and the immune system that drive the stabilization of vasculature while simultaneously decreasing tumour growth\u2014conclusions revealed by the mathematical model that were not deducible from the experimental data alone. This model is hosted on BioModels Database and identified by: MODEL1907050004. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4219, + "tag": "BioModels:BIOMD0000000745" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:43.619242+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000745", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3034": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3034, + "name": "Saad2017 - immune checkpoint and BCG in superficial bladder cancer", + "repository_type": "biomodels", + "summary": "The paper describes a model on the Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of Superficial Bladder Cancer. Created by COPASI 4.25 (Build 207) This model is described in the article: Dynamics of Immune Checkpoints, Immune System, and BCG in the Treatment of Superficial Bladder CancerFarouk Tijjani Saad, Evren Hincal, and Bilgen KaymakamzadeComputational and Mathematical Methods in Medicine, vol. 2017, no. 3573082Abstract: This paper aims to study the dynamics of immune suppressors/checkpoints, immune system, and BCG in the treatment of superficial bladder cancer. Programmed cell death protein-1 (PD-1), cytotoxic T-lymphocyte-associated antigen 4 (CTLA4), and transforming growth factor-beta (TGF-b) are some of the examples of immune suppressors/checkpoints. They are responsible for deactivating the immune system and enhancing immunological tolerance. Moreover, they categorically downregulate and suppress the immune system by preventing and blocking the activation of T-cells, which in turn decreases autoimmunity and enhances self- tolerance. In cancer immunotherapy, the immune checkpoints/suppressors prevent and block the immune cells from attacking, spreading, and killing the cancer cells, which leads to cancer growth and development. We formulate a mathematical model that studies three possible dynamics of the treatment and establish the effects of the immune checkpoints on the immune system and the treatment at large. Although the effect cannot be seen explicitly in the analysis of the model, we show it by numerical simulations.This model is hosted on BioModels Database and identified by: MODEL1907100001.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4220, + "tag": "BioModels:BIOMD0000000746" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:44.098908+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000746", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3035": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3035, + "name": "Nagashima2002 - Simulating blood coagulation inhibitory effects", + "repository_type": "biomodels", + "summary": "Mathematical model of blood coagulation and the effects of inhibitors of Xa, Va:Xa and IIa.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4221, + "tag": "BioModels:BIOMD0000000747" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:44.582627+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000747", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3036": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3036, + "name": "Phan2017 - innate immune in oncolytic virotherapy", + "repository_type": "biomodels", + "summary": "The paper describes a model on the key components for tumor\u2013immune dynamics in multiple myeloma. Created by COPASI 4.25 (Build 207) This model is described in the article: The Role of the Innate Immune System in Oncolytic Virotherapy Tuan Anh Phan and Jianjun Paul TianComputational and Mathematical Methods in Medicine (2017) 6587258Abstract: The complexity of the immune responses is a major challenge in current virotherapy. This study incorporates the innate immune response into our basic model for virotherapy and investigates how the innate immunity affects the outcome of virotherapy. The viral therapeutic dynamics is largely determined by the viral burst size, relative innate immune killing rate, and relative innate immunity decay rate. The innate immunity may complicate virotherapy in the way of creating more equilibria when the viral burst size is not too big, while the dynamics is similar to the system without innate immunity when the viral burst size is big.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4222, + "tag": "BioModels:BIOMD0000000748" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4223, + "tag": "Oncolytic Virus Therapy" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:45.067596+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000748", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3037": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3037, + "name": "Reppas2015 - tumor control via alternating immunostimulating and immunosuppressive phases", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor control via alternating immunostimulating and immunosuppressive phases. Created by COPASI 4.25 (Build 207) This model is described in the article: In silico tumor control induced via alternating immunostimulating and immunosuppressive phasesAI Reppas, JCL Alfonso, and H HatzikirouVirulence 7:2, 174--186Abstract: Despite recent advances in the field of Oncoimmunology, the success potential of immunomodulatory therapies against cancer remains to be elucidated. One of the reasons is the lack of understanding on the complex interplay between tumor growth dynamics and the associated immune system responses. Toward this goal, we consider a mathematical model of vascularized tumor growth and the corresponding effector cell recruitment dynamics. Bifurcation analysis allows for the exploration of model\u2019s dynamic behavior and the determination of these parameter regimes that result in immune-mediated tumor control. In this work, we focus on a particular tumor evasion regime that involves tumor and effector cell concentration oscillations of slowly increasing and decreasing amplitude, respectively. Considering a temporal multiscale analysis, we derive an analytically tractable mapping of model solutions onto a weakly negatively damped harmonic oscillator. Based on our analysis, we propose a theory-driven intervention strategy involving immunostimulating and immunosuppressive phases to induce long-term tumor control.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4224, + "tag": "BioModels:BIOMD0000000749" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:45.553584+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000749", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3038": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3038, + "name": "Lolas2016 - tumour-induced neoneurogenesis and perineural tumour growth", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumour-induced neoneurogenesis and perineural tumour growth. Created by COPASI 4.25 (Build 207) This model is described in the article: Tumour-induced neoneurogenesis and perineural tumour growth: a mathematical approachGeorgios Lolas, Arianna Bianchi and Konstantinos N. SyrigosScientific Reports 6:20684Abstract:It is well-known that tumours induce the formation of a lymphatic and a blood vasculature around themselves. A similar but far less studied process occurs in relation to the nervous system and is referred to as neoneurogenesis. The relationship between tumour progression and the nervous system is still poorly understood and is likely to involve a multitude of factors. It is therefore relevant to study tumour-nerve interactions through mathematical modelling: this may reveal the most significant factors of the plethora of interacting elements regulating neoneurogenesis. The present work is a first attempt to model the neurobiological aspect of cancer development through a system of differential equations. The model confirms the experimental observations that a tumour is able to promote nerve formation/elongation around itself, and that high levels of nerve growth factor and axon guidance molecules are recorded in the presence of a tumour. Our results also reflect the observation that high stress levels (represented by higher norepinephrine release by sympathetic nerves) contribute to tumour development and spread, indicating a mutually beneficial relationship between tumour cells and neurons. The model predictions suggest novel therapeutic strategies, aimed at blocking the stress effects on tumour growth and dissemination.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4225, + "tag": "BioModels:BIOMD0000000750" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4226, + "tag": "Host-Tumor Interaction" + }, + { + "id": 1693, + "tag": "Neurogenesis" + }, + { + "id": 4227, + "tag": "Perineural Invasion" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:46.056368+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000750", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3039": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3039, + "name": "Wilkie2013b - immune-induced cancer dormancy and immune evasion-basic", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of immune-induced cancer dormancy and immune evasion. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical models of immune-induced cancer dormancy and the emergence of immune evasionKathleen P. Wilkie and Philip HahnfeldtInterface Focus 3: 20130010Abstract: Cancer dormancy, a state in which cancer cells persist in a host without sig- nificant growth, is a natural forestallment of progression to manifest disease and is thus of great clinical interest. Experimental work in mice suggests that in immune-induced dormancy, the longer a cancer remains dormant in a host, the more resistant the cancer cells become to cytotoxic T-cell-mediated killing. In this work, mathematical models are used to analyse the possible causative mechanisms of cancer escape from immune-induced dormancy. Using a data-driven approach, both decaying efficacy in immune predation and immune recruitment are analysed with results suggesting that decline in recruitment is a stronger determinant of escape than increased resistance to predation. Using a mechanistic approach, the existence of an immune- resistant cancer cell subpopulation is considered, and the effects on cancer dormancy and potential immunoediting mechanisms of cancer escape are analysed and discussed. The immunoediting mechanism assumes that the immune system selectively prunes the cancer of immune-sensitive cells, which is shown to cause an initially heterogeneous population to become a more homogeneous, and more resistant, population. The fact that this selec- tion may result in the appearance of decreasing efficacy in T-cell cytotoxic effect with time in dormancy is also demonstrated. This work suggests that through actions that temporarily delay cancer growth through the targeted removal of immune-sensitive subpopulations, the immune response may actually progress the cancer to a more aggressive state.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4228, + "tag": "BioModels:BIOMD0000000751" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:46.590193+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000751", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3040": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3040, + "name": "Wilkie2013r - immune-induced cancer dormancy and immune evasion-resistance", + "repository_type": "biomodels", + "summary": "The paper describes a model of immune-induced cancer dormancy and immune evasion with resistance. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical models of immune-induced cancer dormancy and the emergence of immune evasionKathleen P. Wilkie and Philip HahnfeldtInterface Focus 3: 20130010Abstract: Cancer dormancy, a state in which cancer cells persist in a host without sig- nificant growth, is a natural forestallment of progression to manifest disease and is thus of great clinical interest. Experimental work in mice suggests that in immune-induced dormancy, the longer a cancer remains dormant in a host, the more resistant the cancer cells become to cytotoxic T-cell-mediated killing. In this work, mathematical models are used to analyse the possible causative mechanisms of cancer escape from immune-induced dormancy. Using a data-driven approach, both decaying efficacy in immune predation and immune recruitment are analysed with results suggesting that decline in recruitment is a stronger determinant of escape than increased resistance to predation. Using a mechanistic approach, the existence of an immune- resistant cancer cell subpopulation is considered, and the effects on cancer dormancy and potential immunoediting mechanisms of cancer escape are analysed and discussed. The immunoediting mechanism assumes that the immune system selectively prunes the cancer of immune-sensitive cells, which is shown to cause an initially heterogeneous population to become a more homogeneous, and more resistant, population. The fact that this selec- tion may result in the appearance of decreasing efficacy in T-cell cytotoxic effect with time in dormancy is also demonstrated. This work suggests that through actions that temporarily delay cancer growth through the targeted removal of immune-sensitive subpopulations, the immune response may actually progress the cancer to a more aggressive state.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4229, + "tag": "BioModels:BIOMD0000000752" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:47.093118+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000752", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3041": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3041, + "name": "Figueredo2013/1 - immunointeraction base model", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of immune-itumor interaction.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4230, + "tag": "BioModels:BIOMD0000000753" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:47.808466+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000753", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3042": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3042, + "name": "Figueredo2013/2 - immunointeraction model with IL2", + "repository_type": "biomodels", + "summary": "The paper describes a model of immune-itumor interaction with IL2.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4231, + "tag": "BioModels:BIOMD0000000754" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:48.311266+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000754", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3043": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3043, + "name": "Hansen2019 - Nine species reduced model of blood coagulation", + "repository_type": "biomodels", + "summary": "
its a nine species reduced model of Hockin 2002. Model uses different level of reduction (5,7,9,11) and testing the best alignment with Hockin model results
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4232, + "tag": "BioModels:BIOMD0000000755" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:48.801444+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000755", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3044": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3044, + "name": "Figueredo2013/3 - immunointeraction full model", + "repository_type": "biomodels", + "summary": "The paper describes a full model of immune-itumor interaction.Created by COPASI 4.25 (Build 207) This model is described in the article: Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspectiveGrazziela P Figueredo, Peer-Olaf Siebers, Uwe Aickelin Kathleen BMC Bioinformatics 2013, 14(Suppl 6):S6Abstract: Many advances in research regarding immuno-interactions with cancer were developed with the help of ordinary differential equation (ODE) models. These models, however, are not effectively capable of representing problems involving individual localisation, memory and emerging properties, which are common characteristics of cells and molecules of the immune system. Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations. In this paper we investigate the potential contribution of agent-based modelling and simulation when compared to ODE modelling and simulation. We seek answers to the following questions: Is it possible to obtain an equivalent agent-based model from the ODE formulation? Do the outcomes differ? Are there any benefits of using one method compared to the other? To answer these questions, we have considered three case studies using established mathematical models of immune interactions with early-stage cancer. These case studies were re-conceptualised under an agent-based perspective and the simulation results were then compared with those from the ODE models. Our results show that it is possible to obtain equivalent agent-based models (i.e. implementing the same mechanisms); the simulation output of both types of models however might differ depending on the attributes of the system to be modelled. In some cases, additional insight from using agent-based modelling was obtained. Overall, we can confirm that agent-based modelling is a useful addition to the tool set of immunologists, as it has extra features that allow for simulations with characteristics that are closer to the biological phenomena.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4233, + "tag": "BioModels:BIOMD0000000756" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4105, + "tag": "Immune response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:49.311392+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000756", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3045": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3045, + "name": "Abernathy2016 - glioblastoma treatment", + "repository_type": "biomodels", + "summary": "The paper describes a model of glioblastoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling the Treatment of Glioblastoma Multiforme and Cancer Stem Cells with Ordinary Differential EquationsKristen Abernathy and Jeremy Burke BMC Computational and Mathematical Methods in Medicine Volume 2016, Article ID 1239861, 11 pagesAbstract: Despite improvements in cancer therapy and treatments, tumor recurrence is a common event in cancer patients. One explanation of recurrence is that cancer therapy focuses on treatment of tumor cells and does not eradicate cancer stem cells (CSCs). CSCs are postulated to behave similar to normal stem cells in that their role is to maintain homeostasis. That is, when the population of tumor cells is reduced or depleted by treatment, CSCs will repopulate the tumor, causing recurrence. In this paper, we study the application of the CSC Hypothesis to the treatment of glioblastoma multiforme by immunotherapy. We extend the work of Kogan et al. (2008) to incorporate the dynamics of CSCs, prove the existence of a recurrence state, and provide an analysis of possible cancerous states and their dependence on treatment levels.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4234, + "tag": "BioModels:BIOMD0000000757" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:49.870945+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000757", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3046": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3046, + "name": "Babbs2012 - immunotherapy", + "repository_type": "biomodels", + "summary": "The paper describes a simple model of tumor immunotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Predicting success or failure of immunotherapy for cancer: insights from a clinically applicable mathematical model Charles F BabbsAm J Cancer Res 2012;2(2):204-213Abstract: The objective of this study was to create a clinically applicable mathematical model of immunotherapy for cancer and use it to explore differences between successful and unsuccessful treatment scenarios. The simplified predator-prey model includes four lumped parameters: tumor growth rate, g; immune cell killing efficiency, k; immune cell signaling factor, \u03bb; and immune cell half-life decay, \u03bc. The predator-prey equations as functions of time, t, for nor- malized tumor cell numbers, y, (the prey) and immunocyte numbers, x, (the predators) are: dy/dt = gy \u2013 kx and dx/dt = \u03bbxy \u2013 \u03bcx. A parameter estimation procedure that capitalizes on available clinical data and the timing of clinically observable phenomena gives mid-range benchmarks for parameters representing the unstable equilibrium case in which the tumor neither grows nor shrinks. Departure from this equilibrium results in oscillations in tumor cell num- bers and in many cases complete elimination of the tumor. Several paradoxical phenomena are predicted, including increasing tumor cell numbers prior to a population crash, apparent cure with late recurrence, one or more cycles of tumor growth prior to eventual tumor elimination, and improved tumor killing with initially weaker immune parame- ters or smaller initial populations of immune cells. The model and the parameter estimation techniques are easily adapted to various human cancers that evoke an immune response. They may help clinicians understand and predict certain strange and unexpected effects in the world of tumor immunity and lead to the design of clinical trials to test improved treatment protocols for patients.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4235, + "tag": "BioModels:BIOMD0000000758" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:50.636992+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000758", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3047": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3047, + "name": "Cappuccio2006 - Cancer immunotherapy by interleukin-21", + "repository_type": "biomodels", + "summary": "This model describes the effects of Il-21 on tumor eradication via natural killer cell-mediated and CD8+ T-cell-mediated lysis of tumor cells. The model demonstrates changes in growth dynamics in nonimmunogenic B16 melanoma and the immunogenic MethA and MCA205 fibrosarcomas, showing a strong dependence of the NK-cell/CD8+ T-cell balance on tumor immunogenicity.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4236, + "tag": "BioModels:BIOMD0000000761" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:51.151776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000761", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3048": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3048, + "name": "Kuznetsov1994 - Nonlinear dynamics of immunogenic tumors", + "repository_type": "biomodels", + "summary": "
This mathematical model describes the response of cytotoxic T lymphocytes to the growth of an immunogenic tumor, with the inclusion on a number of in vivo phenomena, such as the immunostimulation of tumor growth, evasion of immune function by the tumor, and the formation of a tumor \"dormant state\". Specifically, this model is used to describe the kinetics of growth and regression of the B-cell lymphoma BCL1 in the spleen of mice.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4237, + "tag": "BioModels:BIOMD0000000762" + }, + { + "id": 4238, + "tag": "Ordinary differential equation model" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4239, + "tag": "T cell mediated immune response to tumor cell" + } + ], + "timestamp_created": "2025-01-30 14:03:51.679524+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000762", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3049": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3049, + "name": "Dritschel2018 - A mathematical model of cytotoxic and helper T cell interactions in a tumour microenvironment", + "repository_type": "biomodels", + "summary": "
This model examines the role of helper and cytotoxic T cells in an anti-tumour response, with implicit inclusions of immunosuppressive effects. The model demonstrates the dependence of immunoediting on infilftration by helper and cytotoxic T cells, as well as the importance of these cells in mediating tumour elimination.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4240, + "tag": "BioModels:BIOMD0000000763" + }, + { + "id": 4238, + "tag": "Ordinary differential equation model" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4239, + "tag": "T cell mediated immune response to tumor cell" + } + ], + "timestamp_created": "2025-01-30 14:03:52.318810+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000763", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3050": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3050, + "name": "Malinzi2019 - chemovirotherapy", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virothherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical Analysis of a Mathematical Model of Chemovirotherapy: Effect of Drug Infusion Method Joseph Malinzi Computational and Mathematical Methods in Medicine Volume 2019, Article ID 7576591, 16 pages Abstract: IA mathematical model for the treatment of cancer using chemovirotherapy is developed with the aim of determining the efficacy of three drug infusion methods: constant, single bolus, and periodic treatments. The model is in the form of ODEs and is further extended into DDEs to account for delays as a result of the infection of tumor cells by the virus and chemotherapeutic drug responses. Analysis of the model is carried out for each of the three drug infusion methods. Analytic solutions are determined where possible and stability analysis of both steady state solutions for the ODEs and DDEs is presented. The results indicate that constant and periodic drug infusion methods are more efficient compared to a single bolus injection. Numerical simulations show that with a large virus burst size, irrespective of the drug infusion method, chemovirotherapy is highly effective compared to either treatments. The simulations further show that both delays increase the period within which a tumor can be cleared from body tissue.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4241, + "tag": "BioModels:BIOMD0000000764" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:52.882205+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000764", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3051": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3051, + "name": "Macnamara2015/1 - virotherapy full model", + "repository_type": "biomodels", + "summary": "The paper describes a full model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Memory versus effector immune responses in oncolytic virotherapiesCicely Macnamara, Raluca Eftimie Abstract: The main priority when designing cancer immuno-therapies has been to seek viable biological mechanisms that lead to permanent cancer eradica- tion or cancer control. Understanding the delicate balance between the role of effector and memory cells on eliminating cancer cells remains an elusive problem in immunology. Here we make an initial investigation into this problem with the help of a mathematical model for oncolytic virotherapy; although the model can in fact be made general enough to be applied also to other immunological problems. Our results show that long-term cancer con- trol is associated with a large number of persistent effector cells (irrespective of the initial peak in effector cell numbers). However, this large number of persistent effector cells is sustained by a relatively large number of memory cells. Moreover, we show that cancer control from a dormant state cannot be predicted by the size of the memory population.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4242, + "tag": "BioModels:BIOMD0000000766" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:53.384758+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000766", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3052": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3052, + "name": "Macnamara2015/2 - virotherapy virus-free submodel", + "repository_type": "biomodels", + "summary": "The paper describes a submodel of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Memory versus effector immune responses in oncolytic virotherapiesCicely Macnamara, Raluca Eftimie Abstract: The main priority when designing cancer immuno-therapies has been to seek viable biological mechanisms that lead to permanent cancer eradica- tion or cancer control. Understanding the delicate balance between the role of effector and memory cells on eliminating cancer cells remains an elusive problem in immunology. Here we make an initial investigation into this problem with the help of a mathematical model for oncolytic virotherapy; although the model can in fact be made general enough to be applied also to other immunological problems. Our results show that long-term cancer con- trol is associated with a large number of persistent effector cells (irrespective of the initial peak in effector cell numbers). However, this large number of persistent effector cells is sustained by a relatively large number of memory cells. Moreover, we show that cancer control from a dormant state cannot be predicted by the size of the memory population.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4243, + "tag": "BioModels:BIOMD0000000767" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:53.948122+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000767", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3053": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3053, + "name": "Eftimie2010 - immunity to melanoma", + "repository_type": "biomodels", + "summary": "The paper describes a model of immunity to melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modeling anti-tumor Th1 and Th2 immunity in the rejection of melanoma Raluca Eftimie, Jonathan L. Bramson, David J.D. Earn Journal of Theoretical Biology 265 (2010) 467\u2013480Abstract: Recent experiments indicate that CD4+ Th2 cells can reject skin tumors in mice, while CD4+ Th1 cells cannot (Mattes et al., 2003; Zhang et al., 2009). These results are surprising because CD4+ Th1 cells are typically considered to be capable of tumor rejection. We used mathematical models to investigate this unexpected outcome. We found that neither CD4+ Th1 nor CD4+ Th2 cells could eliminate the cancer cells when acting alone, but that tumor elimination could be induced by recruitment of eosinophils by the Th2 cells. These recruited eosinophils had unexpected indirect effects on the decay rate of type 2 cytokines and the rate at which Th2 cells are inactivated through interactions with cancer cells. Strikingly, the presence of eosinophils impacted tumor growth more significantly than the release of tumor-suppressing cytokines such as IFN-g and TNF-a. Our simulations suggest that novel strategies to enhance eosinophil recruitment into skin tumors may improve cancer immunotherapies. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4244, + "tag": "BioModels:BIOMD0000000768" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:54.454454+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000768", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3054": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3054, + "name": "Eftimie2017/2 - interaction of Th and macrophage in melanoma", + "repository_type": "biomodels", + "summary": "The paper describes a model of interaction of Th cells and macrophage in melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling and investigation of the CD4 T cells \u2013 Macrophages paradox in melanoma immunotherapiesRaluca Eftimie, Haneen HamamJournal of Theoretical Biology 420 (2017) 82\u2013104 Abstract: It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10\u201315 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. In some studies, elimination of B16 melanoma was associated with a Th1 immune response (i.e., elimination occurred in the presence of cytokines produced by Th1 cells), while in other studies melanoma elimination was associated with a Th2 immune response (i.e., elimination occurred in the presence of cytokines produced by Th2 cells). Moreover, macrophages have been shown to be present inside the tumours, during both Th1 and Th2 immune responses. To investigate the possible biological mechanisms behind these apparently contradictory results, we develop a class of mathematical models for the dynamics of Th1 and Th2 cells, and M1 and M2 macrophages in the presence/absence of tumour cells. Using this mathematical model, we show that depending on the re- polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type- II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4245, + "tag": "BioModels:BIOMD0000000769" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:55.006004+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000769", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3055": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3055, + "name": "Eftimie2017/1 - interaction of Th and macrophage", + "repository_type": "biomodels", + "summary": "The paper describes a model of interaction of Th cells and macrophage in melanoma. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling and investigation of the CD4 T cells \u2013 Macrophages paradox in melanoma immunotherapiesRaluca Eftimie, Haneen HamamJournal of Theoretical Biology 420 (2017) 82\u2013104 Abstract: It is generally accepted that tumour cells can be eliminated by M1 anti-tumour macrophages and CD8+ T cells. However, experimental results over the past 10\u201315 years have shown that B16 mouse melanoma cells can be eliminated by the CD4+ T cells alone (either Th1 or Th2 sub-types), in the absence of CD8+ T cells. In some studies, elimination of B16 melanoma was associated with a Th1 immune response (i.e., elimination occurred in the presence of cytokines produced by Th1 cells), while in other studies melanoma elimination was associated with a Th2 immune response (i.e., elimination occurred in the presence of cytokines produced by Th2 cells). Moreover, macrophages have been shown to be present inside the tumours, during both Th1 and Th2 immune responses. To investigate the possible biological mechanisms behind these apparently contradictory results, we develop a class of mathematical models for the dynamics of Th1 and Th2 cells, and M1 and M2 macrophages in the presence/absence of tumour cells. Using this mathematical model, we show that depending on the re- polarisation rates between M1 and M2 macrophages, we obtain tumour elimination in the presence of a type-I immune response (i.e., more Th1 and M1 cells, compared to the Th2 and M2 cells), or in the presence of a type- II immune response (i.e., more Th2 and M2 cells). Moreover, tumour elimination is also possible in the presence of a mixed type-I/type-II immune response. Tumour growth always occurs in the presence of a type-II immune response, as observed experimentally. Finally, tumour dormancy is the result of a delicate balance between the pro-tumour effects of M2 cells and the anti-tumour effects of M1 and Th1 cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4246, + "tag": "BioModels:BIOMD0000000770" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:55.488232+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000770", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3056": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3056, + "name": "Bajzer2008 - Modeling of cancer virotherapy with recombinant measles viruses", + "repository_type": "biomodels", + "summary": "
This model describes the interactions between tumor cells and virus particles, with particular reference to virus-induced syncytium formation and ultimately death of tumor cells. Dynamics of infected cells, and production of new virus particules by infected cells, is also included.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4247, + "tag": "BioModels:BIOMD0000000771" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:55.958245+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000771", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3057": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3057, + "name": "Wang2019 - A mathematical model of oncolytic virotherapy with time delay", + "repository_type": "biomodels", + "summary": "
A mathematical model describing oncolytic virotherapy with incorporation the viral lytic cycle and the virus-specific CTL response. The thresholds for viral treatment and virus-specific CTl response are also obtained.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4248, + "tag": "BioModels:BIOMD0000000772" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:56.451696+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000772", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3058": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3058, + "name": "Wodarz2018/2 - model with transit amplifying cells", + "repository_type": "biomodels", + "summary": "The paper describes a model of effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells. Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulationDominik Wodarz J Theor Biol. 2018 July 07; 448: 86\u201393Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4249, + "tag": "BioModels:BIOMD0000000773" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:56.941382+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000773", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3059": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3059, + "name": "Wodarz2018/1 - simple model", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells. Created by COPASI 4.25 (Build 207) This model is described in the article: Effect of cellular de-differentiation on the dynamics and evolution of tissue and tumor cells in mathematical models with feedback regulationDominik Wodarz J Theor Biol. 2018 July 07; 448: 86\u201393Abstract: Tissues are maintained by adult stem cells that self-renew and also differentiate into functioning tissue cells. Homeostasis is achieved by a set of complex mechanisms that involve regulatory feedback loops. Similarly, tumors are believed to be maintained by a minority population of cancer stem cells, while the bulk of the tumor is made up of more differentiated cells, and there is indication that some of the feedback loops that operate in tissues continue to be functional in tumors. Mathematical models of such tissue hierarchies, including feedback loops, have been analyzed in a variety of different contexts. Apart from stem cells giving rise to differentiated cells, it has also been observed that more differentiated cells can de-differentiate into stem cells, both in healthy tissue and tumors, aspects of which have also been investigated mathematically. This paper analyses the effect of de-differentiation on the basic and evolutionary dynamics of cells in the context of tissue hierarchy models that include negative feedback regulation of the cell populations. The models predict that in the presence of de-differentiation, the fixation probability of a neutral mutant is lower than in its absence. Therefore, if de-differentiation occurs, a mutant with identical parameters compared to the wild-type cell population behaves like a disadvantageous mutant. Similarly, the process of de-differentiation is found to lower the fixation probability of an advantageous mutant. These results indicate that the presence of de- differentiation can lower the rates of tumor initiation and progression in the context of the models considered here.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4250, + "tag": "BioModels:BIOMD0000000774" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:57.451257+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000774", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3060": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3060, + "name": "Iarosz2015 - brain tumor", + "repository_type": "biomodels", + "summary": "The paper describes a model of brain tumor. Created by COPASI 4.25 (Build 207) This model is described in the article: Mathematical model of brain tumour with glia-neuron interactions and chemotherapy treatmentKelly C. Iarosz, Fernando S. Borges, Antonio M. Batista, Murilo S. Baptista, Regiane A. N. Siqueira, Ricardo L. Viana, Sergio R. LopesAbstract: In recent years, it became clear that a better understanding of the interactions among the main elements involved in the cancer network is necessary for the treatment of cancer and the suppression of cancer growth. In this work we propose a system of coupled differential equations that model brain tumour under treatment by chemotherapy, which considers interactions among the glial cells, the glioma, the neurons, and the chemotherapeutic agents. We study the conditions for the glioma growth to be eliminated, and identify values of the parameters for which the inhibition of the glioma growth is obtained with a minimal loss of healthy cells.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4251, + "tag": "BioModels:BIOMD0000000775" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:58.028941+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000775", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3061": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3061, + "name": "Monro2008 - chemotherapy resistance", + "repository_type": "biomodels", + "summary": "The paper describes a model of resistance of cancer to chemotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: Modelling chemotherapy resistance in palliation and failed cure Helen C. Monro, Eamonn A. Gaffney J Theor Biol. 2009, 257 (2), pp.292 Abstract: The goal of palliative cancer chemotherapy treatment is to prolong survival and improve quality of life when tumour eradication is not feasible. Chemotherapy protocol design is considered in this context using a simple, robust, model of advanced tumour growth with Gompertzian dynamics, taking into account the effects of drug resistance. It is predicted that reduced chemotherapy protocols can readily lead to improved survival times due to the effects of competition between resistant and sensitive tumour cells. Very early palliation is also predicted to quickly yield near total tumour resistance and thus decrease survival duration. Finally, our simulations indicate that failed curative attempts using dose densification, a common protocol escalation strategy, can reduce survival times.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4252, + "tag": "BioModels:BIOMD0000000776" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:58.653982+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000776", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3062": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3062, + "name": "Chakrabarty2010 - A control theory approach to cancer remission aided by an optimal therapy", + "repository_type": "biomodels", + "summary": "
This is a reinvestigation of a previous model depicting cancer remission. It involves application of mathematical tools from control theory to assess the optimal approach during the use of Adaptive Cellular Immunotherapy and interleukin-2 treatment.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4253, + "tag": "BioModels:BIOMD0000000777" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:59.264818+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000777", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3063": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3063, + "name": "Wei2017 - tumor, T cell and cytokine interaction", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor-immune interaction. Created by COPASI 4.25 (Build 207) This model is described in the article: Periodically Pulsed Immunotherapy in a Mathematical Model of Tumor, CD4+ T Cells, and Antitumor Cytokine InteractionsHsiu-Chuan Wei, Jui-Ling Yu, Chia-Yu Hsu Computational and Mathematical Methods in Medicine Volume 2017, Article ID 2906282, 12 pages Abstract: Immunotherapy is one of the most recent approaches for controlling and curing malignant tumors. In this paper, we consider a mathematical model of periodically pulsed immunotherapy using CD4+ T cells and an antitumor cytokine. Mathematical analyses are performed to determine the threshold of a successful treatment. The interindividual variability is explored by one-, two-, and three-parameter bifurcation diagrams for a nontreatment case. Numerical simulation conducted in this paper shows that (i) the tumor can be regulated by administering CD4+ T cells alone in a patient with a strong immune system or who has been diagnosed at an early stage, (ii) immunotherapy with a large amount of an antitumor cytokine can boost the immune system to remit or even to suppress tumor cells completely, and (iii) through polytherapy the tumor can be kept at a smaller size with reduced dosages.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4254, + "tag": "BioModels:BIOMD0000000778" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:03:59.826174+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000778", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3064": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3064, + "name": "dePillis2009 - Mathematical model creation for cancer chemo-immunotherapy", + "repository_type": "biomodels", + "summary": "
This is an updated version of a previous model that described the dynamics of cancer treatment, with descriptions of tumour cell numbers, specific and non-specific immune cells (NK, CD8+ T cells and other lymphocytes) with inclusion of chemo- and immunotherapy. This model incorporates new data to provide an improved and more comprehensive model, with specific emphasis on better descriptions of IL-2 dynamics.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4255, + "tag": "BioModels:BIOMD0000000779" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:00.386021+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000779", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3065": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3065, + "name": "Wang2016/1 - oncolytic efficacy of M1 virus-SNTM model", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4256, + "tag": "BioModels:BIOMD0000000780" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:01.243379+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000780", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3066": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3066, + "name": "Wang2016/2 - oncolytic efficacy of M1 virus-SNT model", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4257, + "tag": "BioModels:BIOMD0000000781" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:01.834604+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000781", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3067": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3067, + "name": "Wang2016/3 - oncolytic efficacy of M1 virus-SN model", + "repository_type": "biomodels", + "summary": "The paper describes a basic model of oncolytic virotherapy. Created by COPASI 4.25 (Build 207) This model is described in the article: A mathematical model verifying potent oncolytic efficacy of M1 virusZizi Wang, Zhiming Guo, Huaqin Peng Mathematical Biosciences 276 (2016) 19\u201327Abstract: Motivated by the latest findings in a recent medical experiment [19] which identify a naturally occurring alphavirus (M1) as a novel selective killer targeting zinc-finger antiviral protein (ZAP)-deficient cancer cells, we propose a mathematical model to illustrate the growth of normal cells, tumor cells and the M1 virus with limited nutrient. In order to better understand biological mechanisms, we discuss two cases of the model: without competition and with competition. In the first part, the explicit threshold condi- tions for the persistence of normal cells (or tumor cells) is obtained accompanying with the biological explanations. The second part indicates that when competing with tumor cells, the normal cells will ex- tinct if M1 virus is ignored; Whereas, when M1 virus is considered, the growth trend of normal cells is similar to the one without competition. And by using uniformly strong repeller theorem, the minimum effective dosage of medication is explicitly found which is not reported in [19]. Furthermore, numerical simulations and corresponding biological interpretations are given to support our results.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4258, + "tag": "BioModels:BIOMD0000000782" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:02.356273+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000782", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3068": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3068, + "name": "Dong2014 - Mathematical modeling on helper t cells in a tumor immune system", + "repository_type": "biomodels", + "summary": "
This model gives a mathematical description of the interactions between tumor cells, cytotoxic T lymphocytes and helper T cells (HTCs) within the tumor microenvironment, with emphasis on the role played by HTCs. The effects and dynamics of adoptive cell immunotherapy and HTC recruitment are also specifically discussed.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4259, + "tag": "BioModels:BIOMD0000000783" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:02.880950+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000783", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3069": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3069, + "name": "Lopez2014 - A Validated Mathematical Model of Tumor Growth Including Tumor-Host Interaction and Cell-Mediated Immune Response", + "repository_type": "biomodels", + "summary": "
This is a dynamical model of cancer growth that includes three interacting cell populations of tumor cells, healthy host cells and immune effector cells. The tumor-immune and the tumor-host interactions are characterized to reproduce experimental results.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4260, + "tag": "BioModels:BIOMD0000000784" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:03.414096+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000784", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3070": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3070, + "name": "Sotolongo-Costa2003 - Behavior of tumors under nonstationary therapy", + "repository_type": "biomodels", + "summary": "
This model describes the interaction dynamics of a lymphocyte-tumor cell population.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4261, + "tag": "BioModels:BIOMD0000000785" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:03.978762+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000785", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3071": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3071, + "name": "Lipniacki2004 - Mathematical model of NFKB regulatory module", + "repository_type": "biomodels", + "summary": "
its a mathematical model studying impact of TNF on NFKB nuclear dynamics. This model is derived from Hoffmann2002 (PMID:12424381).
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4262, + "tag": "BioModels:BIOMD0000000786" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:04.680548+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000786", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3072": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3072, + "name": "Frascoli2014 - A dynamical model of tumour immunotherapy", + "repository_type": "biomodels", + "summary": "
This is a coupled ordinary differential equation model of tumour-immune dynamics, accounting for biological and clinical factors which regulate the interaction rates of cytotoxic T lymphocytes on the surface of the tumour mass.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4263, + "tag": "BioModels:BIOMD0000000787" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:05.190577+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000787", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3073": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3073, + "name": "Schropp2019 - Target-Mediated Drug Disposition Model for Bispecific Antibodies", + "repository_type": "biomodels", + "summary": "
This model presents a general target-mediated drug disposition (TMDD) model for bispecific antibodies (BsAbs), which bind to two different targets on different cell membranes. The model includes four different binding events for BsAbs, turnover of the targets, and internalization of the complexes. In addition, a quasi-equilibrium (QE) approximation with decreased number of binding parameters is also present.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4264, + "tag": "BioModels:BIOMD0000000788" + }, + { + "id": 4265, + "tag": "Bispecific Antibody" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:05.698652+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000788", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3074": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3074, + "name": "Jenner2018 - treatment of oncolytic virus", + "repository_type": "biomodels", + "summary": "The paper describes a model of oncolytic virotherapy. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical Modelling of the Interaction Between Cancer Cells and an Oncolytic Virus: Insights into the Effects of Treatment ProtocolsAdrianne L. Jenner, Chae-Ok Yun, Peter S. Kim, Adelle C. F. CosterBull Math Biol (2018) 80:1615\u20131629Abstract: Oncolyticvirotherapyisanexperimentalcancertreatmentthatusesgenet- ically engineered viruses to target and kill cancer cells. One major limitation of this treatment is that virus particles are rapidly cleared by the immune system, preventing them from arriving at the tumour site. To improve virus survival and infectivity Kim et al. (Biomaterials 32(9):2314\u20132326, 2011) modified virus particles with the polymer polyethylene glycol (PEG) and the monoclonal antibody herceptin. Whilst PEG mod- ification appeared to improve plasma retention and initial infectivity, it also increased the virus particle arrival time. We derive a mathematical model that describes the inter- action between tumour cells and an oncolytic virus. We tune our model to represent the experimental data by Kim et al. (2011) and obtain optimised parameters. Our model provides a platform from which predictions may be made about the response of cancer growth to other treatment protocols beyond those in the experiments. Through model simulations, we find that the treatment protocol affects the outcome dramatically. We quantify the effects of dosage strategy as a function of tumour cell replication and tumour carrying capacity on the outcome of oncolytic virotherapy as a treatment. The relative significance of the modification of the virus and the crucial role it plays in optimising treatment efficacy are explored.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4266, + "tag": "BioModels:BIOMD0000000789" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:06.247201+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000789", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3075": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3075, + "name": "Alvarez2019 - A nonlinear mathematical model of cell-mediated immune response for tumor phenotypic heterogeneity", + "repository_type": "biomodels", + "summary": "
This is a non-linear mathematical model of cancer immunosurveillance that takes into account intratumoral phenotypic heterogeneity, such as differential expression of cell surface receptors and growth factors, according to cell-mediated immune responses. The model describes phenomena that have also been observed in vivo, such as tumor dormancy, cancer immunoediting, and a strong sensitivity to initial conditions.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4267, + "tag": "BioModels:BIOMD0000000790" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:06.827198+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000790", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3076": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3076, + "name": "Wilson2012 - tumor vaccine efficacy", + "repository_type": "biomodels", + "summary": "The paper describes a model of antitumor vaccine therapy.Created by COPASI 4.25 (Build 207) This model is described in the article: A Mathematical Model of the Enhancement of Tumor Vaccine Efficacy by ImmunotherapyShelby Wilson and Doron LevyBull Math Biol. 2012 July ; 74(7)Abstract: TGF-\u03b2 is an immunoregulatory protein that contributes to inadequate antitumor immune responses in cancer patients. Recent experimental data suggests that TGF-\u03b2 inhibition alone, provides few clinical benefits, yet it can significantly amplify the anti-tumor immune response when combined with a tumor vaccine. We develop a mathematical model in order to gain insight into the cooperative interaction between anti-TGF-\u03b2 and vaccine treatments. The mathematical model follows the dynamics of the tumor size, TGF-\u03b2 concentration, activated cytotoxic effector cells, and regulatory T cells. Using numerical simulations and stability analysis, we study the following scenarios: a control case of no treatment, anti-TGF-\u03b2 treatment, vaccine treatment, and combined anti-TGF-\u03b2 vaccine treatments. We show that our model is capable of capturing the observed experimental results, and hence can be potentially used in designing future experiments involving this approach to immunotherapy.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4268, + "tag": "BioModels:BIOMD0000000791" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:07.417596+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000791", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3077": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3077, + "name": "Hu2019 - Modeling Pancreatic Cancer Dynamics with Immunotherapy", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of pancreatic cancer that includes descriptions of pancreatic cancer cells, pancreatic stellate cells, effector cells and tumor-promoting and tumor-suppressing cytokines to investigate the effects of immunotherapies on patient survival.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4269, + "tag": "BioModels:BIOMD0000000792" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:07.929674+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000792", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3078": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3078, + "name": "Chen2011/1 - bone marrow invasion absolute model", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is described in the article: Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principlesKun-Wan Chen, Kenneth J Pienta Theoretical Biology and Medical Modelling 2011, 8:36 Abstract: Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4270, + "tag": "BioModels:BIOMD0000000793" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:08.451187+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000793", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3079": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3079, + "name": "Benary2019 - Controlling NFKB dynamics by B-TrCP", + "repository_type": "biomodels", + "summary": "
its a mathematical model studying impact of b_TrCP on NFKB nuclear dynamics. This model is derived from Lipniacki2004 (PMID:15094015).
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4271, + "tag": "BioModels:BIOMD0000000794" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:09.039401+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000794", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3080": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3080, + "name": "Chen2011/2 - bone marrow invasion relative model", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor invasion to bone marrow. Created by COPASI 4.26 (Build 213) This model is described in the article: Modeling invasion of metastasizing cancer cells to bone marrow utilizing ecological principlesKun-Wan Chen, Kenneth J Pienta Theoretical Biology and Medical Modelling 2011, 8:36 Abstract: Background: The invasion of a new species into an established ecosystem can be directly compared to the steps involved in cancer metastasis. Cancer must grow in a primary site, extravasate and survive in the circulation to then intravasate into target organ (invasive species survival in transport). Cancer cells often lay dormant at their metastatic site for a long period of time (lag period for invasive species) before proliferating (invasive spread). Proliferation in the new site has an impact on the target organ microenvironment (ecological impact) and eventually the human host (biosphere impact).Results: Tilman has described mathematical equations for the competition between invasive species in a structured habitat. These equations were adapted to study the invasion of cancer cells into the bone marrow microenvironment as a structured habitat. A large proportion of solid tumor metastases are bone metastases, known to usurp hematopoietic stem cells (HSC) homing pathways to establish footholds in the bone marrow. This required accounting for the fact that this is the natural home of hematopoietic stem cells and that they already occupy this structured space. The adapted Tilman model of invasion dynamics is especially valuable for modeling the lag period or dormancy of cancer cells.Conclusions: The Tilman equations for modeling the invasion of two species into a defined space have been modified to study the invasion of cancer cells into the bone marrow microenvironment. These modified equations allow a more flexible way to model the space competition between the two cell species. The ability to model initial density, metastatic seeding into the bone marrow and growth once the cells are present, and movement of cells out of the bone marrow niche and apoptosis of cells are all aspects of the adapted equations. These equations are currently being applied to clinical data sets for verification and further refinement of the models.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4272, + "tag": "BioModels:BIOMD0000000795" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:09.695359+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000795", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3081": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3081, + "name": "Yang2012 - cancer growth with angiogenesis", + "repository_type": "biomodels", + "summary": "The paper describes a model of tumor growth with angiogenesis. Created by COPASI 4.26 (Build 213) This model is described in the article: Mathematical modeling of solid cancer growth with angiogenesisHyun M Yang Theoretical Biology and Medical Modelling 2012, 9:2 Abstract: Background: Cancer arises when within a single cell multiple malfunctions of control systems occur, which are, broadly, the system that promote cell growth and the system that protect against erratic growth. Additional systems within the cell must be corrupted so that a cancer cell, to form a mass of any real size, produces substances that promote the growth of new blood vessels. Multiple mutations are required before a normal cell can become a cancer cell by corruption of multiple growth-promoting systems.Methods: We develop a simple mathematical model to describe the solid cancer growth dynamics inducing angiogenesis in the absence of cancer controlling mechanisms.Results: The initial conditions supplied to the dynamical system consist of a perturbation in form of pulse: The origin of cancer cells from normal cells of an organ of human body. Thresholds of interacting parameters were obtained from the steady states analysis. The existence of two equilibrium points determine the strong dependency of dynamical trajectories on the initial conditions. The thresholds can be used to control cancer.Conclusions: Cancer can be settled in an organ if the following combination matches: better fitness of cancer cells, decrease in the efficiency of the repairing systems, increase in the capacity of sprouting from existing vascularization, and higher capacity of mounting up new vascularization. However, we show that cancer is rarely induced in organs (or tissues) displaying an efficient (numerically and functionally) reparative or regenerative mechanism.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4273, + "tag": "BioModels:BIOMD0000000796" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:10.241412+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000796", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3082": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3082, + "name": "Hu2018 - Dynamics of tumor-CD4+-cytokine-host cells interactions with treatments", + "repository_type": "biomodels", + "summary": "
This is a proposed mathematical model describing interactions between tumor cells, CD4+ T cells, cytokines, and host cells within the context of CD4+ T cells inducing tumor regression. A platform is provided to assess the effectiveness of single or combination therapy with CD4+ T cells and/or cytokines.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4274, + "tag": "BioModels:BIOMD0000000797" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:10.788194+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000797", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3083": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3083, + "name": "Sharp2019 - AML", + "repository_type": "biomodels", + "summary": "The paper describes a model of acute myeloid leukaemia. Created by COPASI 4.26 (Build 213) This model is described in the article: Optimal control of acute myeloid leukaemia Jesse A. Sharp, Alexander P Browning, Tarunendu Mapder, Kevin Burrage, Matthew J SimpsonJournal of Theoretical Biology 470 (2019) 30\u201342 Abstract: Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corre- sponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin\u2019s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4275, + "tag": "BioModels:BIOMD0000000798" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:11.295188+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000798", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3084": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3084, + "name": "Cucuianu2010 - A hypothetical-mathematical model of acute myeloid leukaemia pathogenesis", + "repository_type": "biomodels", + "summary": "
This is a simple mathematical model describing the growth and removal of normal and leukemic haematopoietic stem cell populations and the role of these cellular processes in generating monoclonal leukemic patterns.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4276, + "tag": "BioModels:BIOMD0000000799" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:11.868156+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000799", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3085": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3085, + "name": "Precup2012 - Mathematical modeling of cell dynamics after allogeneic bone marrow transplantation", + "repository_type": "biomodels", + "summary": "
This is a basic mathematical model describing the dynamics of three cell lines (normal host cells, leukemic host cells and donor cells) after allogeneic stem cell transplantation.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4277, + "tag": "BioModels:BIOMD0000000800" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:12.403732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000800", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3086": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3086, + "name": "Sturrock2015 - glioma growth", + "repository_type": "biomodels", + "summary": "The paper describes a model of glioma. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of pre-diagnostic glioma growth Marc Sturrock, Wenrui Hao, Judith Schwartzbaum, Grzegorz A. Rempala J Theor Biol. 2015 September 7; 380: 299\u2013308 Abstract: Due to their location, the malignant gliomas of the brain in humans are very difficult to treat in advanced stages. Blood-based biomarkers for glioma are needed for more accurate evaluation of treatment response as well as early diagnosis. However, biomarker research in primary brain tumors is challenging given their relative rarity and genetic diversity. It is further complicated by variations in the permeability of the blood brain barrier that affects the amount of marker released into the bloodstream. Inspired by recent temporal data indicating a possible decrease in serum glucose levels in patients with gliomas yet to be diagnosed, we present an ordinary differential equation model to capture early stage glioma growth. The model contains glioma-glucose-immune interactions and poses a potential mechanism by which this glucose drop can be explained. We present numerical simulations, parameter sensitivity analysis, linear stability analysis and a numerical experiment whereby we show how a dormant glioma can become malignant.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4278, + "tag": "BioModels:BIOMD0000000801" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:12.914128+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000801", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3087": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3087, + "name": "Hoffman2018- ADCC against cancer", + "repository_type": "biomodels", + "summary": "The paper describes a model of ADCC. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of antibody-dependent cellular cytotoxicity (ADCC) F. Hoffman, D. Gavaghan, J. Osborne, I.P. Barrett, T. You, H. Ghadially, R. Sainson, R.W. Wilkinson, H.M. ByrneJournal of Theoretical Biology 436 (2018) 39\u201350 Abstract: Immunotherapies exploit the immune system to target and kill cancer cells, while sparing healthy tis- sue. Antibody therapies, an important class of immunotherapies, involve the binding to specific antigens on the surface of the tumour cells of antibodies that activate natural killer (NK) cells to kill the tu- mour cells. Preclinical assessment of molecules that may cause antibody-dependent cellular cytotoxicity (ADCC) involves co-culturing cancer cells, NK cells and antibody in vitro for several hours and measuring subsequent levels of tumour cell lysis. Here we develop a mathematical model of such an in vitro ADCC assay, formulated as a system of time-dependent ordinary differential equations and in which NK cells kill cancer cells at a rate which depends on the amount of antibody bound to each cancer cell. Numerical simulations generated using experimentally-based parameter estimates reveal that the system evolves on two timescales: a fast timescale on which antibodies bind to receptors on the surface of the tumour cells, and NK cells form complexes with the cancer cells, and a longer time-scale on which the NK cells kill the cancer cells. We construct approximate model solutions on each timescale, and show that they are in good agreement with numerical simulations of the full system. Our results show how the processes involved in ADCC change as the initial concentration of antibody and NK-cancer cell ratio are varied. We use these results to explain what information about the tumour cell kill rate can be extracted from the cytotoxicity assays.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4279, + "tag": "BioModels:BIOMD0000000802" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:13.443927+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000802", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3088": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3088, + "name": "Park2019 - IL7 receptor signaling in T cells", + "repository_type": "biomodels", + "summary": "
This model is an attempt to provide a mathematical description of IL-7 dependent T cell homeostasis at the molecular and cellular level, with inclusion of gamma-chain and ligand binding in the context of receptors for IL-7 and IL-15 receptors.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4280, + "tag": "BioModels:BIOMD0000000803" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:13.948493+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000803", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3089": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3089, + "name": "Koenders2015 - multiple myeloma", + "repository_type": "biomodels", + "summary": "The paper describes a model of multiple myeloma. Created by COPASI 4.26 (Build 213) This model is described in the article: A mathematical model of cell equilibrium and joint cell formation in multiple myelomaM.A. Koenders, R. Saso Journal of Theoretical Biology 390 (2016) 73\u201379Abstract: In Multiple Myeloma Bone Disease healthy bone remodelling is affected by tumour cells by means of paracrine cytokinetic signalling in such a way that osteoclast formation is enhanced and the growth of osteoblast cells inhibited. The participating cytokines are described in the literature. Osteoclast-induced myeloma cell growth is also reported. Based on existing mathematical models for healthy bone remo- delling a three-way equilibrium model is presented for osteoclasts, osteoblasts and myeloma cell populations to describe the progress of the illness in a scenario in which there is a secular increase in the cytokinetic interactive effectiveness of paracrine processes. The equilibrium state for the system is obtained. The paracrine interactive effectiveness is explored by parameter variation and the stable region in the parameter space is identified. Then recently-discovered joint myeloma\u2013osteoclast cells are added to the model to describe the populations inside lytic lesions. It transpires that their presence expands the available parameter space for stable equilibrium, thus permitting a detrimental, larger population of osteoclasts and myeloma cells. A possible relapse mechanism for the illness is explored by letting joint cells dissociate. The mathematics then permits the evaluation of the evolution of the cell populations as a function of time during relapse.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4281, + "tag": "BioModels:BIOMD0000000804" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:14.449691+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000804", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3090": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3090, + "name": "Al-Husari2013 - pH and lactate in tumor", + "repository_type": "biomodels", + "summary": "The paper describes a model of pH control in tumor. Created by COPASI 4.26 (Build 213) This model is described in the article: Regulation of tumour intracellular pH: A mathematical model examining the interplay between H and lactateMaymona Al-Husari, Steven D. WebbJournal of Theoretical Biology 322 (2013) 58\u201371 Abstract:Non-invasive measurements of pH have shown that both tumour and normal cells have intracellular pH (pHi) that lies on the alkaline side of neutrality (7.1\u20137.2). However, extracellular pH (pHe) is reported to be more acidic in some tumours compared to normal tissues. Many cellular processes and therapeutic agents are known to be tightly pH dependent which makes the study of intracellular pH regulation of paramount importance. We develop a mathematical model that examines the role of various membrane-based ion transporters in tumour pH regulation, in particular, with a focus on the interplay between lactate and H ions and whether the lactate/H symporter activity is sufficient to give rise to the observed reversed pH gradient that is seen is some tumours. Using linear stability analysis and H ions. We extend this analysis using perturbation techniques to specifically examine a rapid change in H-ion concentrations relative to variations in lactate. We then perform a parameter sensitivity analysis to explore solution robustness to parameter variations. An important result from our study is that a reversed pH gradient is possible in our system but for unrealistic parameter estimates\u2014pointing to the possible involvement of other mechanisms in cellular pH gradient reversal, for example acidic vesicles, lysosomes, golgi and endosomes.To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4282, + "tag": "BioModels:BIOMD0000000805" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:15.010502+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000805", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3091": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3091, + "name": "Eftimie2019-Macrophages Plasticity", + "repository_type": "biomodels", + "summary": "This paper describes the complex interactions between two extreme types of macrophages (M1 and M2 cells), effector T cells and an oncolytic Vesicular Stomatitis Virus (VSV), on the growth/elimination of B16F10 melanoma. The mathematics model descirbes, in terms of VSV and macrophages levels, two different types of immune responses which could ensure tumour control and eventual elimination. It shows that both innate and adaptive anti-tumour immune responses, as well as the oncolytic virus, could be very important in delaying tumour relapse and eventually eliminating the tumour. Overall this study supports the use mathematical modelling to increase our understanding of the complex immune interaction following oncolytic virotherapies. However, the complexity of the model combined with a lack of sufficient data for model parametrisation has an impact on the possibility of making quantitative predictions. The Model was created using COPASI version 4.24 (Build 197)", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4283, + "tag": "BioModels:BIOMD0000000806" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4284, + "tag": "Vesicular stomatitis virus" + }, + { + "id": 4603, + "tag": "Cancer" + }, + { + "id": 4650, + "tag": "Skin cancer" + } + ], + "timestamp_created": "2025-01-30 14:04:15.831405+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000806", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3092": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3092, + "name": "Fassoni2019 - Oncogenesis encompassing mutations and genetic instability", + "repository_type": "biomodels", + "summary": "This model describes the multistep process that transform a normal cell and its descendants into a malignant tumour by considering three populations: normal, premalignant and cancer cells. Created by COPASI 4.24(Build 197)Abstract:Tumorigenesis has been described as a multistep process, where each step is associated with a genetic alteration, in the direction to progressively transform a normal cell and its descendants into a malignant tumour. Into this work, we propose a mathematical model for cancer onset and development, considering three populations: normal, premalignant and cancer cells. The model takes into account three hallmarks of cancer: self-sufficiency on growth signals, insensibility to anti-growth signals and evading apoptosis. By using a nonlinear expression to describe the mutation from premalignant to cancer cells, the model includes genetic instability as an enabling characteristic of tumour progression. Mathematical analysis was performed in detail. Results indicate that apoptosis and tissue repair system are the first barriers against tumour progression. One of these mechanisms must be corrupted for cancer to develop from a single mutant cell. The results also show that the presence of aggressive cancer cells opens way to survival of less adapted premalignant cells. Numerical simulations were performed with parameter values based on experimental data of breast cancer, and the necessary time taken for cancer to reach a detectable size from a single mutant cell was estimated with respect to some parameters. We find that the rates of apoptosis and mutations have a large influence on the pace of tumour progression and on the time it takes to become clinically detectable.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4285, + "tag": "BioModels:BIOMD0000000807" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:16.372770+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000807", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3093": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3093, + "name": "Kronik2008 - Improving alloreactive CTL immunotherapy for malignant gliomas using a simulation model of their interactive dynamics", + "repository_type": "biomodels", + "summary": "
This mathematical model describes interactions between glioma tumors and the immune system that may occur following direct intra-tumoral administration of ex-vivo activated alloreactive cytotoxic-T-lymphocytes (aCTLs) as part of adoptive immunotherapy. The model includes descriptions of aCTL, neoplastic cells, MHC class I and II molecules, TGF-beta and IFN-gamma.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4286, + "tag": "BioModels:BIOMD0000000808" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:16.923760+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000808", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3094": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3094, + "name": "Malinzi2018 - tumour-immune interaction model", + "repository_type": "biomodels", + "summary": "The paper describes a spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Created by COPASI 4.24 (Build 197)Abstract:A spatio-temporal mathematical model, in the form of a moving boundary problem, to explain cancer dormancy is developed. Analysis of the model is carried out for both temporal and spatio-temporal cases. Stability analysis and numerical simulations of the temporal model replicate experimental observations of immune-induced tumour dormancy. Travelling wave solutions of the spatio-temporal model are determined using the hyperbolic tangent method and minimum wave speeds of invasion are calculated. Travelling wave analysis depicts that cell invasion dynamics are mainly driven by their motion and growth rates. A stability analysis of the spatio-temporal model shows a possibility of dynamical stabilization of the tumour-free steady state. Simulation results reveal that the tumour swells to a dormant level.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4287, + "tag": "BioModels:BIOMD0000000809" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:04:17.424899+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000809", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3095": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3095, + "name": "Ganguli2018-immuno regulatory mechanisms in tumor microenvironment", + "repository_type": "biomodels", + "summary": "This model describes the concept of Cancer Stem Cells(CSC) differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Created by COPASI 4.24(Build197)Abstract:The tumor microenvironment comprising of the immune cells and cytokines acts as the 'soil' that nourishes a developing tumor. Lack of a comprehensive study of the interactions of this tumor microenvironment with the heterogeneous sub-population of tumor cells that arise from the differentiation of Cancer Stem Cells (CSC), i.e. the 'seed', has limited our understanding of the development of drug resistance and treatment failures in Cancer. Based on this seed and soil hypothesis, for the very first time, we have captured the concept of CSC differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Using this model we report that as the CSC differentiation shifts from symmetric to asymmetric pattern, resistant cancer cells start accumulating in the tumor that makes it refractory to therapeutic interventions. Model analyses unveiled the presence of feedback loops that establish the dual role of M2 macrophages in regulating tumor proliferation. The study further revealed oscillations in the tumor sub-populations in the presence of TH1 derived IFN-\u03b3 that eliminates CSC; and the role of IL10 feedback in the regulation of TH1/TH2 ratio. These analyses expose important observations that are indicative of Cancer prognosis. Further, the model has been used for testing known treatment protocols to explore the reasons of failure of conventional treatment strategies and propose an improvised protocol that shows promising results in suppressing the proliferation of all the cellular sub-populations of the tumor and restoring a healthy TH1/TH2 ratio that assures better Cancer remission.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4288, + "tag": "BioModels:BIOMD0000000810" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4289, + "tag": "breast cancer" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:04:17.916060+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000810", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3096": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3096, + "name": "He2017 - A mathematical model of pancreatic cancer with two kinds of treatments", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of pancreatic cancer which includes descriptions of regulatory T cell activity and inhibition therapy. Descriptions of cytokine induced killer immunotherapy are also included.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4290, + "tag": "BioModels:BIOMD0000000811" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:18.426585+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000811", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3097": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3097, + "name": "Galante2012 - B7-H1 and a Mathematical Model for Cytotoxic T Cell and Tumor Cell Interaction", + "repository_type": "biomodels", + "summary": "
This is a mathematical model that describes the interactions between cytotoxic T cells and tumor cells as influenced by B7-H1 (PD-L1) activity, with a focus on how B7-H1 affects cancer cells apoptosis.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4291, + "tag": "BioModels:BIOMD0000000812" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:18.937576+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000812", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3098": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3098, + "name": "Anderson2015 - Qualitative behavior of systems of tumor-CD4+-cytokine interactions with treatments", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing tumor-CD4+-cytokine interactions, with specific emphasis on the role that CD4+ T lymphocytes play in tumor regression.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4292, + "tag": "BioModels:BIOMD0000000813" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:19.453265+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000813", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3099": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3099, + "name": "Perez-Garcia19 - Computational design of improved standardized chemotherapy protocols for grade 2 oligodendrogliomas", + "repository_type": "biomodels", + "summary": "This is a model built by COPASI4.24(Build 197)This a model from the article: Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomasV\u00edctor M. P\u00e9rez-Garc\u00eda, Luis E. Ayala-Hern\u00e1ndez, Juan Belmonte-Beitia, Philippe Schucht, Michael Murek, Andreas Raabe, Juan Sep\u00falveda. PLoS Comput Biol. 2019 Jul; 15(7): e1006778.Abstract: Here we put forward a mathematical model describing the response of low-grade (WHO grade II) oligodendrogliomas (LGO) to temozolomide (TMZ). The model describes the longitudinal volumetric dynamics of tumor response to TMZ of a cohort of 11 LGO patients treated with TMZ. After finding patient-specific parameters, different therapeutic strategies were tried computationally on the \u2018in-silico twins\u2019 of those patients. Chemotherapy schedules with larger-than-standard rest periods between consecutive cycles had either the same or better long-term efficacy than the standard 28-day cycles. The results were confirmed in a large trial of 2000 virtual patients. These long-cycle schemes would also have reduced toxicity and defer the appearance of resistances. On the basis of those results, a combination scheme consisting of five induction TMZ cycles given monthly plus 12 maintenance cycles given every three months was found to provide substantial survival benefits for the in-silico twins of the 11 LGO patients (median 5.69 years, range: 0.67 to 68.45 years) and in a large virtual trial including 2000 patients. We used 220 sets of experiments in-silico to show that a clinical trial incorporating 100 patients per arm (standard intensive treatment versus 5 + 12 scheme) could demonstrate the superiority of the novel scheme after a follow-up period of 10 years. Thus, the proposed treatment plan could be the basis for a standardized TMZ treatment for LGO patients with survival benefits.This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4293, + "tag": "BioModels:BIOMD0000000814" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:19.963755+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000814", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3100": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3100, + "name": "Chrobak2011 - A mathematical model of induced cancer-adaptive immune system competition", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing competition between an artificially induced tumor and the adaptive immune system based on the use of an autonomous system of ordinary differential equations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4294, + "tag": "BioModels:BIOMD0000000815" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:20.445714+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000815", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3101": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3101, + "name": "Gevertz2018 - Cancer Treatment with Oncolytic Viruses and Dendritic Cell injections original model", + "repository_type": "biomodels", + "summary": "The model is based on 'Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections', PMID:30510594. Author:Jana L.Gevertz and Joanna R.Wares. This model describes the original mathematical model described in section 2.1. Built by COPASI 4.24( Build 197)Abstract:Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4295, + "tag": "BioModels:BIOMD0000000816" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4651, + "tag": "Melanoma" + } + ], + "timestamp_created": "2025-01-30 14:04:20.935689+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000816", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3102": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3102, + "name": "Gevertz2018 - cancer treatment with oncolytic viruses and dendritic cell injections minimal model", + "repository_type": "biomodels", + "summary": "The model is based on 'Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections', PMID:30510594. Author:Jana L.Gevertz and Joanna R.Wares. This model describes the minimal model described in section 2.1. Built by COPASI 4.24( Build 197)Abstract:Mathematical models of biological systems must strike a balance between being sufficiently complex to capture important biological features, while being simple enough that they remain tractable through analysis or simulation. In this work, we rigorously explore how to balance these competing interests when modeling murine melanoma treatment with oncolytic viruses and dendritic cell injections. Previously, we developed a system of six ordinary differential equations containing fourteen parameters that well describes experimental data on the efficacy of these treatments. Here, we explore whether this previously developed model is the minimal model needed to accurately describe the data. Using a variety of techniques, including sensitivity analyses and a parameter sloppiness analysis, we find that our model can be reduced by one variable and three parameters and still give excellent fits to the data. We also argue that our model is not too simple to capture the dynamics of the data, and that the original and minimal models make similar predictions about the efficacy and robustness of protocols not considered in experiments. Reducing the model to its minimal form allows us to increase the tractability of the system in the face of parametric uncertainty.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4296, + "tag": "BioModels:BIOMD0000000817" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4651, + "tag": "Melanoma" + } + ], + "timestamp_created": "2025-01-30 14:04:21.428732+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000817", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3103": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3103, + "name": "Lee2008 - ERK and PI3K signal integration by Myc", + "repository_type": "biomodels", + "summary": "
Mechanisitc model of PI3K and ERK signal integration by Myc. ERK and PI3K regulated Myc satbility by phosphorylating the same. (PMID:18463697)
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4297, + "tag": "BioModels:BIOMD0000000818" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:21.962713+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000818", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3104": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3104, + "name": "Nazari2018 - IL6 mediated stem cell driven tumor growth and targeted treatment", + "repository_type": "biomodels", + "summary": "This a model from the article: A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatmentFereshteh Nazari, Alexander T. Pearson, Jacques Eduardo Nor, Trachette L. Jackson. PloS Computational Biology, 2018 Jan.Abstract:Targeting key regulators of the cancer stem cell phenotype to overcome their critical influence on tumor growth is a promising new strategy for cancer treatment. Here we present a modeling framework that operates at both the cellular and molecular levels, for investigating IL-6 mediated, cancer stem cell driven tumor growth and targeted treatment with anti-IL6 antibodies. Our immediate goal is to quantify the influence of IL-6 on cancer stem cell self-renewal and survival, and to characterize the subsequent impact on tumor growth dynamics. By including the molecular details of IL-6 binding, we are able to quantify the temporal changes in fractional occupancies of bound receptors and their influence on tumor volume. There is a strong correlation between the model output and experimental data for primary tumor xenografts. We also used the model to predict tumor response to administration of the humanized IL-6R monoclonal antibody, tocilizumab (TCZ), and we found that as little as 1mg/kg of TCZ administered weekly for 7 weeks is sufficient to result in tumor reduction and a sustained deceleration of tumor growth.Author Summary:A small population of cancer stem cells that share many of the biological characteristics of normal adult stem cells are believed to initiate and sustain tumor growth for a wide variety of malignancies. Growth and survival of these cancer stem cells is highly influenced by tumor micro-environmental factors and molecular signaling initiated by cytokines and growth factors. This work focuses on quantifying the influence of IL-6, a pleiotropic cytokine secreted by a variety of cell types, on cancer stem cell self-renewal and survival. We present a mathematical model for IL-6 mediated, cancer stem cell driven tumor growth that operates at the following levels: (1) the molecular level\u2014capturing cell surface dynamics of receptor-ligand binding and receptor activation that lead to intra-cellular signal transduction cascades; and (2) the cellular level\u2014describing tumor growth, cellular composition, and response to treatments targeted against IL-6.This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.For more information see the terms of use.To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4298, + "tag": "BioModels:BIOMD0000000819" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:22.442640+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000819", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3105": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3105, + "name": "West2019 - Cellular interactions constrain tumor growth", + "repository_type": "biomodels", + "summary": "These selections of models are described in the paper:Cellular interactions constrain tumor growthby Jeffrey West and Paul K. NewtonSignificance:A mathematical model relating tumor heterogeneity at the cellular level to tumor growth at the macroscopic level is described based on a statistical mechanics framework. The model takes into account the number of accessible states available to each cell as well as their long-range coupling (population cooperation) to other cells. We show that the degree to which cell populations cooperate determines the number of independent cell states, which in turn dictates the macroscopic (volumetric) growth law. It follows that targeting cell-to-cell interactions or functional coupling among cell populations could be a way of mitigating and controlling tumor growth.Abstract:A tumor is made up of a heterogeneous collection of cell types, all competing on a fitness landscape mediated by microenvironmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling, cellular cooperation, and the functional constraints that affect cellular behavior, the specifics of how these constraints (and the range over which they act) affect the macroscopic tumor growth laws that govern total volume, mass, and carrying capacity remain poorly understood. We develop a statistical mechanics approach that focuses on the total number of possible states each cell can occupy and show how different assumptions on correlations of these states give rise to the many different macroscopic tumor growth laws used in the literature. Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of states at the cellular level is what determines macroscopic growth characteristics.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4299, + "tag": "BioModels:BIOMD0000000820" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:22.917807+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000820", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3106": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3106, + "name": "Yazdjer2019 - reinforcement learning-based control of tumor growth under anti-angiogenic therapy", + "repository_type": "biomodels", + "summary": "This model is based on:Reinforcement learning-based control of tumor growth under anti-angiogenic therapyAuthors: Parisa Yazdjerdi, Nader Meskin, Mohammad Al-Naemi, Ala-Eddin Al Moustafa, Levente KovacsAbstract:Background and objectives: In recent decades, cancer has become one of the most fatal and destructive diseases which is threatening humans life. Accordingly, different types of cancer treatment are studied with the main aim to have the best treatment with minimum side effects. Anti-angiogenic is a molecular targeted therapy which can be coupled with chemotherapy and radiotherapy. Although this method does not eliminate the whole tumor, but it can keep the tumor size in a given state by preventing the formation of new blood vessels. In this paper, a novel model-free method based on reinforcement learning (RL) framework is used to design a closed-loop control of anti-angiogenic drug dosing administration.Methods: A Q-learning algorithm is developed for the drug dosing closed-loop control. This controller is designed using two different values of the maximum drug dosage to reduce the tumor volume up to a desired value. The mathematical model of tumor growth under anti-angiogenic inhibitor is used to simulate a real patient.Results: The effectiveness of the proposed method is shown through in silico simulation and its robustness to patient parameters variation is demonstrated. It is demonstrated that the tumor reaches its minimal volume in 84 days with maximum drug inlet of 30 mg/kg/day. Also, it is shown that the designed controller is robust with respect to \u202f\u00b1\u202f20% of tumor growth parameters changes.Conclusion: The proposed closed-loop reinforcement learning-based controller for cancer treatment using anti-angiogenic inhibitor provides an effective and novel result such that with a clinically valid and safe dosage of drug, the volume reduces up to 1mm3 in a reasonable short period compared to the literature.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4300, + "tag": "BioModels:BIOMD0000000821" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:23.426316+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000821", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3107": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3107, + "name": "Dorvash2019 - Dynamic modeling of signal transduction by mTOR complexes in cancer", + "repository_type": "biomodels", + "summary": "This model is based on:Dynamic modeling of signal transduction by mTOR complexes in cancerAuthor:Mohammadreza Dorvash, Mohammad Farahmandnia, Pouria Mosaddeghi, Mitra Farahmandnejad, Hosein Saber, Mohammadhossein Khorraminejad-Shirazi, Amir Azadi, Iman TavassolyAbstract:Signal integration has a crucial role in the cell fate decision and dysregulation of the cellular signaling pathways is a primary characteristic of cancer. As a signal integrator, mTOR shows a complex dynamical behavior which determines the cell fate at different cellular processes levels, including cell cycle progression, cell survival, cell death, metabolic reprogramming, and aging. The dynamics of the complex responses to rapamycin in cancer cells have been attributed to its differential time-dependent inhibitory effects on mTORC1 and mTORC2, the two main complexes of mTOR. Two explanations were previously provided for this phenomenon: 1-Rapamycin does not inhibit mTORC2 directly, whereas it prevents mTORC2 formation by sequestering free mTOR protein (Le Chatelier\u2019s principle). 2-Components like Phosphatidic Acid (PA) further stabilize mTORC2 compared with mTORC1. To understand the mechanism by which rapamycin differentially inhibits the mTOR complexes in the cancer cells, we present a mathematical model of rapamycin mode of action based on the first explanation, i.e., Le Chatelier\u2019s principle. Translating the interactions among components of mTORC1 and mTORC2 into a mathematical model revealed the dynamics of rapamycin action in different doses and time-intervals of rapamycin treatment. This model shows that rapamycin has stronger effects on mTORC1 compared with mTORC2, simply due to its direct interaction with free mTOR and mTORC1, but not mTORC2, without the need to consider other components that might further stabilize mTORC2. Based on our results, even when mTORC2 is less stable compared with mTORC1, it can be less inhibited by rapamycin.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4301, + "tag": "BioModels:BIOMD0000000822" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:23.921672+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000822", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3108": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3108, + "name": "Varusai2018 - Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR", + "repository_type": "biomodels", + "summary": "
This is a mathematical describing the effect that DEP domain-containing mTOR-interacting protein (DEPTOR) has on the mammalian target of rapamycin (mTOR) signalling network.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4302, + "tag": "BioModels:BIOMD0000000823" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:24.408267+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000823", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3109": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3109, + "name": "Lewkiewics2019 - effects of aging on naive T cell populations and diversity", + "repository_type": "biomodels", + "summary": "This model is built by COPASI 4.24(Build197), based on paper:A mathematical model of the effects of aging on naive T-cell population and diversityAuthors:Stephanie Lewkiewicz, Yao-li Chuang, Tom ChouAbstract:The human adaptive immune response is known to weaken in advanced age, resulting in increased severity of pathogen-born illness, poor vaccine efficacy, and a higher prevalence of cancer in the elderly. Age-related erosion of the T cell compartment has been implicated as a likely cause, but the underlying mechanisms driving this immunosenescence have not been quantitatively modeled and systematically analyzed. T cell receptor diversity, or the extent of pathogen-derived antigen responsiveness of the T cell pool, is known to diminish with age, but inherent experimental difficulties preclude accurate analysis on the full organismal level. In this paper, we formulate a mechanistic mathematical model of T cell population dynamics on the immunoclonal subpopulation level, which provides quantitative estimates of diversity. We define different estimates for diversity that depend on the individual number of cells in a specific immunoclone. We show that diversity decreases with age primarily due to diminished thymic output of new T cells and the resulting overall loss of small immunoclones.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4303, + "tag": "BioModels:BIOMD0000000824" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4304, + "tag": "Thymus" + } + ], + "timestamp_created": "2025-01-30 14:04:24.948217+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000824", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3110": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3110, + "name": "Greene2019 - Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment", + "repository_type": "biomodels", + "summary": "This model is built by COPASI 4.24(Build 197), based on paper:Mathematical Approach to Differentiate Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment.Author:James M. Greene, Jana L. Gevertz, Eduardo D. sontagAbstract:PURPOSE:Drug resistance is a major impediment to the success of cancer treatment. Resistance is typically thought to arise from random genetic mutations, after which mutated cells expand via Darwinian selection. However, recent experimental evidence suggests that progression to drug resistance need not occur randomly, but instead may be induced by the treatment itself via either genetic changes or epigenetic alterations. This relatively novel notion of resistance complicates the already challenging task of designing effective treatment protocols. MATERIALS AND METHODS:To better understand resistance, we have developed a mathematical modeling framework that incorporates both spontaneous and drug-induced resistance. RESULTS:Our model demonstrates that the ability of a drug to induce resistance can result in qualitatively different responses to the same drug dose and delivery schedule. We have also proven that the induction parameter in our model is theoretically identifiable and propose an in vitro protocol that could be used to determine a treatment's propensity to induce resistance.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4305, + "tag": "BioModels:BIOMD0000000825" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:25.443239+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000825", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3111": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3111, + "name": "Shin_2018_EGFR-PYK2-c-Met interaction network_model", + "repository_type": "biomodels", + "summary": "
Systems modelling of the EGFR-PYK2-c-Met interaction network predicted and prioritized synergistic drug combinations for Triple-negative breast cancer
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4306, + "tag": "BioModels:BIOMD0000000826" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:25.948104+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000826", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3112": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3112, + "name": "Ito2019 - gefitnib resistance of lung adenocarcinoma caused by MET amplification", + "repository_type": "biomodels", + "summary": "The model is based on publication:Mathematical analysis of gefitinib resistance of lung adenocarcinoma caused by MET amplificationAbstract:Gefitinib, one of the tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR), is effective for treating lung adenocarcinoma harboring EGFR mutation; but later, most cases acquire a resistance to gefitinib. One of the mechanisms conferring gefitinib resistance to lung adenocarcinoma is the amplification of the MET gene, which is observed in 5\u201322% of gefitinib-resistant tumors. A previous study suggested that MET amplification could cause gefitinib resistance by driving ErbB3-dependent activation of the PI3K pathway. In this study, we built a mathematical model of gefitinib resistance caused by MET amplification using lung adenocarcinoma HCC827-GR (gefitinib resistant) cells. The molecular reactions involved in gefitinib resistance consisted of dimerization and phosphorylation of three molecules, EGFR, ErbB3, and MET were described by a series of ordinary differential equations. To perform a computer simulation, we quantified each molecule on the cell surface using flow cytometry and estimated unknown parameters by dimensional analysis. Our simulation showed that the number of active ErbB3 molecules is around a hundred-fold smaller than that of active MET molecules. Limited contribution of ErbB3 in gefitinib resistance by MET amplification is also demonstrated using HCC827-GR cells in culture experiments. Our mathematical model provides a quantitative understanding of the molecular reactions underlying drug resistance.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4307, + "tag": "BioModels:BIOMD0000000827" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:26.470628+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000827", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3113": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3113, + "name": "Jung2019 - Regulating glioblastoma signaling pathways and anti-invasion therapy - core control model", + "repository_type": "biomodels", + "summary": "This model is based on paper:Strategies in regulating glioblastoma signaling pathways and anti-invasion therapyAbstract:Glioblastoma multiforme is one of the most invasive type of glial tumors, which rapidly grows and commonly spreads into nearby brain tissue. It is a devastating brain cancer that often results in death within approximately 12 to 15 months after diagnosis. In this work, optimal control theory was applied to regulate intracellular signaling pathways of miR-451\u2013AMPK\u2013mTOR\u2013cell cycle dynamics via glucose and drug intravenous administration infusions. Glucose level is controlled to activate miR-451 in the up-stream pathway of the model. A potential drug blocking the inhibitory pathway of mTOR by AMPK complex is incorporated to explore regulation of the down-stream pathway to the cell cycle. Both miR-451 and mTOR levels are up-regulated inducing cell proliferation and reducing invasion in the neighboring tissues. Concomitant and alternating glucose and drug infusions are explored under various circumstances to predict best clinical outcomes with least administration costs.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4308, + "tag": "BioModels:BIOMD0000000828" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:26.948136+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000828", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3114": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3114, + "name": "Jung2019 - egulating glioblastoma signaling pathways and anti-invasion therapy cell cycle dynamics model", + "repository_type": "biomodels", + "summary": "This model is based on paper, based on its cell cycle dynamics model:Strategies in regulating glioblastoma signaling pathways and anti-invasion therapyAbstract:Glioblastoma multiforme is one of the most invasive type of glial tumors, which rapidly grows and commonly spreads into nearby brain tissue. It is a devastating brain cancer that often results in death within approximately 12 to 15 months after diagnosis. In this work, optimal control theory was applied to regulate intracellular signaling pathways of miR-451\u2013AMPK\u2013mTOR\u2013cell cycle dynamics via glucose and drug intravenous administration infusions. Glucose level is controlled to activate miR-451 in the up-stream pathway of the model. A potential drug blocking the inhibitory pathway of mTOR by AMPK complex is incorporated to explore regulation of the down-stream pathway to the cell cycle. Both miR-451 and mTOR levels are up-regulated inducing cell proliferation and reducing invasion in the neighboring tissues. Concomitant and alternating glucose and drug infusions are explored under various circumstances to predict best clinical outcomes with least administration costs.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4309, + "tag": "BioModels:BIOMD0000000829" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:27.429340+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000829", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3115": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3115, + "name": "GiantsosAdams2013 - Growth of glycocalyx under static conditions", + "repository_type": "biomodels", + "summary": "
Giantsos-Adams2013 - Growth of glycocalyxunder static conditions

This model is described in the article:

Giantsos-Adams KM, Koo AJ, Song S, Sakai J, Sankaran J, Shin JH, Garcia-Cardena G, Dewey CF.
Cell Mol Bioeng 2013 Jun; 6(2): 160-174

Abstract:

The local hemodynamic shear stress waveforms present in an artery dictate the endothelial cell phenotype. The observed decrease of the apical glycocalyx layer on the endothelium in atheroprone regions of the circulation suggests that the glycocalyx may have a central role in determining atherosclerotic plaque formation. However, the kinetics for the cells' ability to adapt its glycocalyx to the environment have not been quantitatively resolved. Here we report that the heparan sulfate component of the glycocalyx of HUVECs increases by 1.4-fold following the onset of high shear stress, compared to static cultured cells, with a time constant of 19\u00a0h. Cell morphology experiments show that 12\u00a0h are required for the cells to elongate, but only after 36\u00a0h have the cells reached maximal alignment to the flow vector. Our findings demonstrate that following enzymatic degradation, heparan sulfate is restored to the cell surface within 12\u00a0h under flow whereas the time required is 20\u00a0h under static conditions. We also propose a model describing the contribution of endocytosis and exocytosis to apical heparan sulfate expression. The change in HS regrowth kinetics from static to high-shear EC phenotype implies a differential in the rate of endocytic and exocytic membrane turnover.

This model is hosted on BioModels Database and identified by: MODEL1609100001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4310, + "tag": "BioModels:BIOMD0000000830" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:27.920151+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000830", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3116": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3116, + "name": "Smith1980 - Hypothalamic Regulation", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Hypothalamic regulation of pituitary secretion of luteinizing hormone.II. Feedback control of gonadotropin secretion.
Smith WR Bull Math Biol. (1980) 42(1): 57-78 6986927 ,
Abstract:
No Abstract Available

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: smith,1980,version02
The original CellML model was created by:
Lloyd, Catherine, May
c.lloyd@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4311, + "tag": "BioModels:BIOMD0000000831" + }, + { + "id": 3337, + "tag": "Bos taurus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:28.407587+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000831", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3117": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3117, + "name": "Shin2016 - Unveiling Hidden Dynamics of Hippo Signalling", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing Hippo signalling pathway activity. It includes descriptions of crosstalk with the Akt and ERK MAPK pathways; crosstalk activity is described using complex regulatory mechanisms comprised of competitive protein-protein interactions and phosphorylation mediated feedback loops.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4312, + "tag": "BioModels:BIOMD0000000832" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:28.899377+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000832", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3118": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3118, + "name": "DiCamillo2016 - Insulin signalling pathway - Rule-based model", + "repository_type": "biomodels", + "summary": "Barbara Di Camillo, Azzurra Carlon, Federica Eduati & Gianna Maria Toffolo. A rule-based model of insulin signalling pathway. BMC Systems Biology 10, 1 (2016).

The insulin signalling pathway (ISP) is an important biochemical pathway, which regulates some fundamental biological functions such as glucose and lipid metabolism, protein synthesis, cell proliferation, cell differentiation and apoptosis. In the last years, different mathematical models based on ordinary differential equations have been proposed in the literature to describe specific features of the ISP, thus providing a description of the behaviour of the system and its emerging properties. However, protein-protein interactions potentially generate a multiplicity of distinct chemical species, an issue referred to as \"combinatorial complexity\", which results in defining a high number of state variables equal to the number of possible protein modifications. This often leads to complex, error prone and difficult to handle model definitions.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4313, + "tag": "BioModels:BIOMD0000000833" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:29.394318+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000833", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3119": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3119, + "name": "Verma2016 - Ca(2+) Signal Propagation Along Hepatocyte Cords", + "repository_type": "biomodels", + "summary": "
Verma2016 - Ca(2+) Signal Propagation Along Hepatocyte Cords

This model is described in the article:

Verma A, Makadia H, Hoek JB, Ogunnaike BA, Vadigepalli R.
IEEE Trans Biomed Eng 2016 Oct; 63(10): 2047-2055

Abstract:

The purpose of this study is to model the dynamics of lobular Ca(2+) wave propagation induced by an extracellular stimulus, and to analyze the effect of spatially systematic variations in cell-intrinsic signaling parameters on sinusoidal Ca(2+) response.We developed a computational model of lobular scale Ca(2+) signaling that accounts for receptor- mediated initiation of cell-intrinsic Ca(2+) signal in hepatocytes and its propagation to neighboring hepatocytes through gap junction-mediated molecular exchange.Analysis of the simulations showed that a pericentral-to-periportal spatial gradient in hormone sensitivity and/or rates of IP3 synthesis underlies the Ca(2+) wave propagation. We simulated specific cases corresponding to localized disruptions in the graded pattern of these parameters along a hepatic sinusoid. Simulations incorporating locally altered parameters exhibited Ca(2+) waves that do not propagate throughout the hepatic plate. Increased gap junction coupling restored normal Ca(2+) wave propagation when hepatocytes with low Ca(2+) signaling ability were localized in the midlobular or the pericentral region.Multiple spatial patterns in intracellular signaling parameters can lead to Ca(2+) wave propagation that is consistent with the experimentally observed spatial patterns of Ca(2+) dynamics. Based on simulations and analysis, we predict that increased gap junction-mediated intercellular coupling can induce robust Ca(2+) signals in otherwise poorly responsive hepatocytes, at least partly restoring the sinusoidally oriented Ca (2+) waves.Our bottom-up model of agonist-evoked spatial Ca(2+) patterns can be integrated with detailed descriptions of liver histology to study Ca(2+) regulation at the tissue level.

This model is hosted on BioModels Database and identified by: MODEL1603110003.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4314, + "tag": "BioModels:BIOMD0000000834" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:29.886364+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000834", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3120": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3120, + "name": "Rao2014 - Fatty acid beta-oxidation (reduced model)", + "repository_type": "biomodels", + "summary": "
This represents the reduced version of the \"time course model\" of Van Eunen et al (2013): Biochemical competition makes fatty-acid beta-oxidation vulnerable to substrate overload. The SBML was created from that of the original model and produces identical results when a time-course of 25 mins is run in COPASI
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This model is from the article:
Epidemics of panic during a bioterrorist attack--a mathematical model.
Radosavljevic V, Radunovic D, Belojevic G. Med Hypotheses 2009 Sep;73(3):342-6 19423234 ,
Abstract:
A bioterrorist attacks usually cause epidemics of panic in a targeted population. We have presented epidemiologic aspect of this phenomenon as a three-component model--host, information on an attack and social network. We have proposed a mathematical model of panic and counter-measures as the function of time in a population exposed to a bioterrorist attack. The model comprises ordinary differential equations and graphically presented combinations of the equations parameters. Clinically, we have presented a model through a sequence of psychic conditions and disorders initiated by an act of bioterrorism. This model might be helpful for an attacked community to timely and properly apply counter-measures and to minimize human mental suffering during a bioterrorist attack.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This model provides an in silico mathematical platform to explore the interactions between chimeric antigen receptor-modified T cells, inflammatory toxicitiy, and the tumour burdens of individual patients.
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This is a simple mathematical population model for pembrolizumab-treated advanced melanoma patients, used to predict the response of melanoma patients to immune checkpoint inhibitors.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4321, + "tag": "BioModels:BIOMD0000000838" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:31.992359+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000838", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3124": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3124, + "name": "Almeida2019 - Transcription-based circadian mechanism controls the duration of molecular clock states in response to signaling inputs", + "repository_type": "biomodels", + "summary": "
This is a transcriptional-based mathematical model centered on linear combinations of the clock controlled elements (CCEs): E-box, R-box and D-box, used to identify the essential interactions needed to generate phase opposition between the activating CLOCK:BMAL1 and the repressing PER:CRY complexes.
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This is a mathematical model of Vicodin use and abuse used to investigate methods of combating Vicodin abuse in a population of patients who have obtained the drug through prescription. Mathematical descriptions of transitions through acute, chronic, abusive, and in-treatment populations are included.
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This is a delay differential equation model showing how non-coding RNA, acting as microRNA (miRNA) sponges in a conserved RNA-transcription factor feedback motif, can five rise to oscillatory behaviour.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4324, + "tag": "BioModels:BIOMD0000000841" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:33.433329+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000841", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3127": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3127, + "name": "Heitzler2012 - GPCR signalling", + "repository_type": "biomodels", + "summary": "

This model is from the article:
Competing G protein-coupled receptor kinases balance G protein and \u03b2-arrestin signaling
Heitzler D, Durand G, Gallay N, Rizk A, Ahn S, Kim J, Violin JD, Dupuy L, Gauthier C, Piketty V, Cr\u00e9pieux P, Poupon A, Cl\u00e9ment F, Fages F, Lefkowitz RJ, Reiter E. Mol Syst Biol. 2012; 8: 590. 22735336 ,
Abstract:
Seven-transmembrane receptors (7TMRs) are involved in nearly all aspects of chemical communications and represent major drug targets. 7TMRs transmit their signals not only via heterotrimeric G proteins but also through \u03b2-arrestins, whose recruitment to the activated receptor is regulated by G protein-coupled receptor kinases (GRKs). In this paper, we combined experimental approaches with computational modeling to decipher the molecular mechanisms as well as the hidden dynamics governing extracellular signal-regulated kinase (ERK) activation by the angiotensin II type 1A receptor (AT(1A)R) in human embryonic kidney (HEK)293 cells. We built an abstracted ordinary differential equations (ODE)-based model that captured the available knowledge and experimental data. We inferred the unknown parameters by simultaneously fitting experimental data generated in both control and perturbed conditions. We demonstrate that, in addition to its well-established function in the desensitization of G-protein activation, GRK2 exerts a strong negative effect on \u03b2-arrestin-dependent signaling through its competition with GRK5 and 6 for receptor phosphorylation. Importantly, we experimentally confirmed the validity of this novel GRK2-dependent mechanism in both primary vascular smooth muscle cells naturally expressing the AT(1A)R, and HEK293 cells expressing other 7TMRs.

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This is a mathematical model of Hsp70 induction. To model heat shock effects, the model incorporates temperature dependencies in transcirption to Hsp70 mRNA and in dissociation of transcriptional complexes, in addition to a formal expression relating temperature to protein denaturation.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4326, + "tag": "BioModels:BIOMD0000000843" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:34.431080+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000843", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3129": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3129, + "name": "Viertel2019 - A Computational model of the mammalian external tufted cell", + "repository_type": "biomodels", + "summary": "
This is a mathematical conductance-based model of the bursting activity in external tufted (ET) cells of the olfactory bulb. The model includes ion-current based descriptions of the mechanisms underlying bursting in ET cells, with facilitation of blocking various currents to characterise bursting behaviour.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4327, + "tag": "BioModels:BIOMD0000000844" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:34.939923+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000844", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3130": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3130, + "name": "Gulbudak2019.1 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Lytic)", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing describing the population dynamics of microbes infected by lytic viruses.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4328, + "tag": "BioModels:BIOMD0000000845" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:35.461262+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000845", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3131": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3131, + "name": "Gulbudak2019.2 - Heterogeneous viral strategies promote coexistence in virus-microbe systems (Chronic)", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing describing the population dynamics of microbes infected by chronically infecting viruses.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4329, + "tag": "BioModels:BIOMD0000000846" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:36.052812+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000846", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3132": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3132, + "name": "Adams2019 - The regulatory role of shikimate in plant phenylalanine metabolism", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of phenylalanine metabolism in plants as influenced by shikimate, with specific evidence of how shikimate dynamics influence phenylalanine metabolism as a function of phenylalanine availability.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4330, + "tag": "BioModels:BIOMD0000000847" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:36.757913+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000847", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3133": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3133, + "name": "FatehiChenar2018 - Mathematical model of immune response to hepatitis B", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the dynamics of the immune response to hepatitis B, which takes into account contributions form innate and adaptive immune responses, as well as cytokines.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4331, + "tag": "BioModels:BIOMD0000000848" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:37.380311+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000848", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3134": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3134, + "name": "Potassium balance in lactating and non-lactating dairy cows", + "repository_type": "biomodels", + "summary": "M. Berg, J. Pl\u00f6ntzke, S. Leonhard-Marek, K.E. M\u00fcller & S. R\u00f6blitz. A dynamic model to simulate potassium balance in dairy cows. Journal of Dairy Science 100, 12 (2017).

High-performing dairy cows require a particular composition of nutritional ingredients, adapted to their individual requirements and depending on their production status. The optimal dimensioning of minerals in the diet, one being potassium, is indispensable for the prevention of imbalances. Potassium balance in cows is the result of potassium intake, distribution in the organism, and excretion, and it is closely related to glucose and electrolyte metabolism. In this paper, we present a dynamical model for potassium balance in lactating and nonlactating dairy cows based on ordinary differential equations. Parameter values were obtained from clinical trial data and from the literature. To verify the consistency of the model, we present simulation outcomes for 3 different scenarios: potassium balance in (1) nonlactating cows with varying feed intake, (2) nonlactating cows with varying potassium fraction in the diet, and (3) lactating cows with varying milk production levels. The results give insights into the short- and long-term potassium metabolism, providing an important step toward the understanding of the potassium network, the design of prophylactic feed additives, and possible treatment strategies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4332, + "tag": "BioModels:BIOMD0000000849" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:37.895146+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000849", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3135": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3135, + "name": "Jenner2019 - Oncolytic virotherapy for tumours following a Gompertz growth law", + "repository_type": "biomodels", + "summary": "
This is a mathematical model using a Gompertz growth law to describe the in vivo dynamics of a cancer under treatment with an oncolytic virus.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4333, + "tag": "BioModels:BIOMD0000000850" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:38.444030+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000850", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3136": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3136, + "name": "Ho2019 - Mathematical models of transmission dynamics and vaccine strategies in Hong Kong during the 2017-2018 winter influenza season (Simple)", + "repository_type": "biomodels", + "summary": "
This is the simple version of the two mathematical models presented by Ho et al. It is a model comprised of simple ordinary differential equations describing the overall epidemic dynamics of influenza infection in the 2017-2018 winter influenza season in Hong Kong.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4334, + "tag": "BioModels:BIOMD0000000851" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:38.990828+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000851", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3137": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3137, + "name": "Andersen2017 - Mathematical modelling as a proof of concept for MPNs as a human inflammation model for cancer development", + "repository_type": "biomodels", + "summary": "
This is a mathematical model investigating the role of chronic inflammation in the development and progression of myeloproliferative neoplasms (MPNs). The model describes the proliferation from stem cells to mature cells, including mutations of healthy stem cells to become malignant stem cells. The model also features a simple inflammatory coupling coping with cell death and affecting the basic model beneath.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4335, + "tag": "BioModels:BIOMD0000000852" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:39.863798+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000852", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3138": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3138, + "name": "Smolen2018 - Paradoxical LTP maintenance with inhibition of protein synthesis and the proteasome", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the formation of long-term potentiation (LTP) at the Schaffer collateral of CA1 pyramidal cell synapse. The model consists of nine ordinary differential equations that denote synaptic states associated with different degrees of LTP, as well as the levels of regulatory proteins involved in the formation and maintenance of LTP.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4336, + "tag": "BioModels:BIOMD0000000853" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:40.758728+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000853", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3139": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3139, + "name": "Gray2016 - The Akt switch model", + "repository_type": "biomodels", + "summary": "
This is a simple, linear, four-compartment ordinary differential equation (ODE) model Akt activation that tracks both the phosphorylation state and the physical location (cytosol, plasma membrane) of Akt.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4337, + "tag": "BioModels:BIOMD0000000854" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:41.321029+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000854", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3140": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3140, + "name": "Cooper2015 - Modeling the effects of systemic mediators on the inflammatory phase of wound healing", + "repository_type": "biomodels", + "summary": "
This is an ordinary differential equation-based mathematical model describing the inflammatory phase of the wound healing response. The model describes the interactions in the wound between wound debris, pathogens, neutrophils and macrophages, as well as the modulation of these interactions by estrogen and cortisol.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4338, + "tag": "BioModels:BIOMD0000000855" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:41.839418+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000855", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3141": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3141, + "name": "Budding yeast size control by titration of nuclear sites", + "repository_type": "biomodels", + "summary": "
This model is decribed in the article:Dilution and titration of cell-cycle regulators may control cell size in budding yeastFrank S. Heldt, Reece Lunstone, John J. Tyson, Bela NovakPLoS Comput Biol, October 2018, 14(10), e1006548, doi: 10.1371/journal.pcbi.1006548Abstract:The size of a cell sets the scale for all biochemical processes within it, thereby affecting cellular fitness and survival. Hence, cell size needs to be kept within certain limits and relatively constant over multiple generations. However, how cells measure their size and use this information to regulate growth and division remains controversial. Here, we present two mechanistic mathematical models of the budding yeast (S. cerevisiae) cell cycle to investigate competing hypotheses on size control: inhibitor dilution and titration of nuclear sites. Our results suggest that an inhibitor-dilution mechanism, in which cell growth dilutes the transcriptional inhibitor Whi5 against the constant activator Cln3, can facilitate size homeostasis. This is achieved by utilising a positive feedback loop to establish a fixed size threshold for the START transition, which efficiently couples cell growth to cell cycle progression. Yet, we show that inhibitor dilution cannot reproduce the size of mutants that alter the cell\u2019s overall ploidy and WHI5 gene copy number. By contrast, size control through titration of Cln3 against a constant number of genomic binding sites for the transcription factor SBF recapitulates both size homeostasis and the size of these mutant strains. Moreover, this model produces an imperfect \u2018sizer\u2019 behaviour in G1 and a \u2018timer\u2019 in S/G2/M, which combine to yield an \u2018adder\u2019 over the whole cell cycle; an observation recently made in experiments. Hence, our model connects these phenomenological data with the molecular details of the cell cycle, providing a systems-level perspective of budding yeast size control.
", + "tags": [ + { + "id": 4339, + "tag": "\nSaccharomyces cerevisiae" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4340, + "tag": "BioModels:BIOMD0000000856" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:42.579515+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000856", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3142": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3142, + "name": "Larbat2016.1 - Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light or dark conditions (Base Model)", + "repository_type": "biomodels", + "summary": "
This is a global mathematical model describing metabolic partitioning of carbon resources in plants between growth and defense, as a function of nitrate fertilization. The model hinges on the dynamics of sucrose inflow/outflow properties.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4341, + "tag": "BioModels:BIOMD0000000857" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:43.087555+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000857", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3143": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3143, + "name": "Larbat2016.2 - Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light or dark conditions (Light Dark Cycles)", + "repository_type": "biomodels", + "summary": "
This is a global mathematical model describing metabolic partitioning of carbon resources in plants between growth and defense, as a function of nitrate fertilization. The model hinges on the dynamics of sucrose inflow/outflow properties. The model also accounts for variations in photosynthetic activity as dictated by a daily light/dark cycle.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4342, + "tag": "BioModels:BIOMD0000000858" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:43.593694+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000858", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3144": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3144, + "name": "Larbat2016.3 - Modeling the diversion of primary carbon flux into secondary metabolism under variable nitrate and light or dark conditions (Light Dark Cycles with Minimum Starch Adaption)", + "repository_type": "biomodels", + "summary": "
This is a global mathematical model describing metabolic partitioning of carbon resources in plants between growth and defense, as a function of nitrate fertilization. The model hinges on the dynamics of sucrose inflow/outflow properties. The model also accounts for variations in photosynthetic activity as dictated by a daily light/dark cycle. Furthermore, the adaptive changes in starch metabolism that occur with changing light durations (short days vs long days) are also investigated.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4343, + "tag": "BioModels:BIOMD0000000859" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:44.065067+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000859", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3145": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3145, + "name": "Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedforward Incoherent By MicroRNA)_1", + "repository_type": "biomodels", + "summary": "
Proctor2017- Role of microRNAs inosteoarthritis (Positive Feedforward Incoherent By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100004.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4344, + "tag": "BioModels:BIOMD0000000860" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:44.537337+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000860", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3146": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3146, + "name": "Bachmann2011 - Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range", + "repository_type": "biomodels", + "summary": "
This is a dynamic pathway model examining the roles of of the two transcriptional negative feedback regulators of the suppressor of cytokine signaling (SOCS) family, CIS and SOCS3, in JAK/STAT5 signaling, within the context of primary erythroid progenitor cells.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4345, + "tag": "BioModels:BIOMD0000000861" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:45.026990+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000861", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3147": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3147, + "name": "Proctor2017- Role of microRNAs in osteoarthritis (Positive Feedback By Micro RNA)", + "repository_type": "biomodels", + "summary": "
Proctor2017- Role of microRNAs inosteoarthritis (Positive Feedback By Micro RNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100000.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4346, + "tag": "BioModels:BIOMD0000000862" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:45.513851+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000862", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3148": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3148, + "name": "Kosinsky2018 - Radiation and PD-(L)1 treatment combinations", + "repository_type": "biomodels", + "summary": "This is a quantitative systems pharmacology (QSP) model that describes key elements of the cancer immunity cycle and the tumor microenvironment, tumor growth, as well as dose-exposure-target modulation features. The model describes the synergistic kinetic effects underlying immune cell interactions as linked to tumor size modulation under immunotherapeutic and radiological treatments.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4347, + "tag": "BioModels:BIOMD0000000863" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4652, + "tag": "Radiation Therapy" + } + ], + "timestamp_created": "2025-01-30 14:04:46.031050+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000863", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3149": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3149, + "name": "Proctor2017- Role of microRNAs in osteoarthritis (Negative Feedback By MicroRNA)", + "repository_type": "biomodels", + "summary": "
Proctor2017- Role of microRNAs inosteoarthritis (Negative Feedback By MicroRNA)

This model is described in the article:

Proctor CJ, Smith GR.
PLoS ONE 2017; 12(11): e0187568

Abstract:

The aim of this study was to show how computational models can be used to increase our understanding of the role of microRNAs in osteoarthritis (OA) using miR-140 as an example. Bioinformatics analysis and experimental results from the literature were used to create and calibrate models of gene regulatory networks in OA involving miR-140 along with key regulators such as NF-?B, SMAD3, and RUNX2. The individual models were created with the modelling standard, Systems Biology Markup Language, and integrated to examine the overall effect of miR-140 on cartilage homeostasis. Down-regulation of miR-140 may have either detrimental or protective effects for cartilage, indicating that the role of miR-140 is complex. Studies of individual networks in isolation may therefore lead to different conclusions. This indicated the need to combine the five chosen individual networks involving miR-140 into an integrated model. This model suggests that the overall effect of miR-140 is to change the response to an IL-1 stimulus from a prolonged increase in matrix degrading enzymes to a pulse-like response so that cartilage degradation is temporary. Our current model can easily be modified and extended as more experimental data become available about the role of miR-140 in OA. In addition, networks of other microRNAs that are important in OA could be incorporated. A fully integrated model could not only aid our understanding of the mechanisms of microRNAs in ageing cartilage but could also provide a useful tool to investigate the effect of potential interventions to prevent cartilage loss.

This model is hosted on BioModels Database and identified by: MODEL1610100001.

To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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This is a mathematical mechanistic immunobiochemical model that incorporates T cell pathways that control programmed cell death protein 1 (PD-1) expression. A core component of the model is a kinetic motif, termed a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), which reflects known interactions between IRF4, Blimp-1, and Bcl-6.
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This model represents NIK-dependent p100 processing into p52 with Michaelis-Menten kinetics. While this model shows identical dose-response to the mass action representation, when IkBd degradation is included the dose-response is no-longer monotonic in mass action models due to substrate complex competition.

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This is a mathematical model comprised of non-linear ordinary differential equations describing the dynamic relationship between hypoxia-inducible factor-1 alpha (HIF-1a) mRNA, HIF-1a protein, and interleukin-15-mediated upstream signalling events in natural killer cells from human blood. Regulatory expressions are also included for mammalian target of rapamycin (mTOR), nuclear factor-kappa beta, and signal transducer and activator of transcription 3 (STAT3).
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This model represents NIK-dependent p100 processing into p52 with mass action kinetics. While this model shows identical dose-response to the Michaelis-Menten representation, when IkBd degradation is included the dose-response is no longer monotonic in mass action models due to substrate complex competition.

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This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with Michaelis-Menten kinetics. Compare this Michaelis-Menten representation to the mass action model in which the dose-response to increasing p100 mRNA is no longer monotonic due to substrate complex competition.

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This model represents NIK-dependent p100 processing into p52 and NIK-dependent IkBd degradation with mass action kinetics. Compare this mass action representation to the Michaelis-Menten model. In this model the dose-response to increasing p100 mRNA is not monotonic due to substrate complex competition.

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This model represents NIK-dependent p100 processing into p52 followed by binding to RelB and NIK-dependent IkBd degradation. This model assesses the impact of substrate complex competition on RelB-p52.

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This is a COPASI version of the HIV/HPV coinfection model submitted to PLoS One.Title: Modeling the mechanisms by which HIV-associated immunosuppression influences HPV persistence at the oral mucosaAuthors: Meghna Verma*, Samantha Erwin*, Vida Abedi, Raque Hontecillas, Stefan Hoops, Andrew Leber, Josep Bassaganya-Riera, Stanca Ciupe* Contributed equally to the workCorresponding Authors: Josep Bassaganya-Riera and Stanca Ciupe
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IL-6 has been proposed to favor the development of Th2 responses and play an important role in the communication between cells of multicellular organisms. They are involved in the regulation of complex cellular processes such as proliferation, differentiation and act as key player during inflammation and immune response. Th2 cytokines play an immunoregulatory role in early infection. Literature says in mice infected with L. major, IL-6 may promote the development of both Th1 and Th2 responses. IL-4 is also considered to be the signature cytokine of Th-2 response. IL6 was initially characterized as a Th1 cytokine but later on it was proved to be a pleiotropic cytokine, secreted from different cell types including the macrophages. A major challenge is to understand how these complex non-linear processes are connected and regulated. Systems biology approaches may be used to tackle this challenge in an iterative process of quantitative mathematical analysis. In this study, we created an in silico model of IL6 mediated macrophage activation which suffers from an excessive impact of the negative feedback loop involving SOCS1. The strategy adopted in this framework may help to reduce the complexity of the leishmanial IL6 model analysis and also laydown various physiological or pathological conditions of IL6 signaling in future.

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This a model from the article:
Dynamics of HIV infection of CD4+ T cells.
Perelson AS, Kirschner DE, De Boer R. Math Biosci 1993 Mar;114(1):81-125 8096155 ,
Abstract:
We examine a model for the interaction of HIV with CD4+ T cells that considersfour populations: uninfected T cells, latently infected T cells, activelyinfected T cells, and free virus. Using this model we show that many of thepuzzling quantitative features of HIV infection can be explained simply. We alsoconsider effects of AZT on viral growth and T-cell population dynamics. Themodel exhibits two steady states, an uninfected state in which no virus ispresent and an endemically infected state, in which virus and infected T cellsare present. We show that if N, the number of infectious virions produced peractively infected T cell, is less a critical value, Ncrit, then the uninfectedstate is the only steady state in the nonnegative orthant, and this state isstable. For N > Ncrit, the uninfected state is unstable, and the endemicallyinfected state can be either stable, or unstable and surrounded by a stablelimit cycle. Using numerical bifurcation techniques we map out the parameterregimes of these various behaviors. oscillatory behavior seems to lie outsidethe region of biologically realistic parameter values. When the endemicallyinfected state is stable, it is characterized by a reduced number of T cellscompared with the uninfected state. Thus T-cell depletion occurs through theestablishment of a new steady state. The dynamics of the establishment of thisnew steady state are examined both numerically and via the quasi-steady-stateapproximation. We develop approximations for the dynamics at early times inwhich the free virus rapidly binds to T cells, during an intermediate time scalein which the virus grows exponentially, and a third time scale on which viralgrowth slows and the endemically infected steady state is approached. Using thequasi-steady-state approximation the model can be simplified to two ordinarydifferential equations the summarize much of the dynamical behavior. We computethe level of T cells in the endemically infected state and show how that levelvaries with the parameters in the model. The model predicts that different viralstrains, characterized by generating differing numbers of infective virionswithin infected T cells, can cause different amounts of T-cell depletion andgenerate depletion at different rates. Two versions of the model are studied. Inone the source of T cells from precursors is constant, whereas in the other thesource of T cells decreases with viral load, mimicking the infection and killingof T-cell precursors.(ABSTRACT TRUNCATED AT 400 WORDS)

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Perelson AS, Kirschner DE, De Boer R. (1993) - version=1.0
The original CellML model was created by:
Ethan Choi
mcho099@aucklanduni.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is the general model without delay described by the equation system (1) in: A model of HIV-1 pathogenesis that includes an intracellular delay.
Nelson PW, Murray JD, Perelson AS; Math Biosci. 2000 Feb;163(2):201-15. PMID: 10701304 ; doi: 10.1016/S0025-5564(99)00055-3
Abstract:
Mathematical modeling combined with experimental measurements have yielded important insights into HIV-1 pathogenesis. For example, data from experiments in which HIV-infected patients are given potent antiretroviral drugs that perturb the infection process have been used to estimate kinetic parameters underlying HIV infection. Many of the models used to analyze data have assumed drug treatments to be completely efficacious and that upon infection a cell instantly begins producing virus. We consider a model that allows for less then perfect drug effects and which includes a delay in the initiation of virus production. We present detailed analysis of this delay differential equation model and compare the results to a model without delay. Our analysis shows that when drug efficacy is less than 100%, as may be the case in vivo, the predicted rate of decline in plasma virus concentration depends on three factors: the death rate of virus producing cells, the efficacy of therapy, and the length of the delay. Thus, previous estimates of infected cell loss rates can be improved upon by considering more realistic models of viral infection.
Author Keywords: HIV; Delay; Viral life cycle; T-cells

As there are no results given for this model in the article it cannot be checked for MIRIAM compliance. The SBML file should be equivalent to the described ODE file though.

This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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This is a mathematical model comprised of a simple system of four ordinary differential equations that account for the two opposing roles of infected and healthy cytotoxic T lymphocytes within the context of HIV infection, with each type responsible for the production of virus and the activation and stimulation of immune responses, respectively.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4361, + "tag": "BioModels:BIOMD0000000876" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:52.642087+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000876", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3162": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3162, + "name": "Ontah2019 - Dynamic analysis of a tumor treatment model using oncolytic virus and chemotherapy with saturated infection rate", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the treatment of tumors using oncolytic virus and chemotherapy. The model is comprised of nonlinear ordinary differential equations describing the interactions between uninfected tumor cells, infected tumor cells, an oncolytic virus, and chemotherapy.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4362, + "tag": "BioModels:BIOMD0000000877" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:53.578995+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000877", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3163": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3163, + "name": "Lenbury2001_InsulinKineticsModel_A", + "repository_type": "biomodels", + "summary": "

This a model from the article:
Modeling insulin kinetics: responses to a single oral glucose administration or ambulatory-fed conditions.
Lenbury Y, Ruktamatakul S, Amornsamarnkul S. Biosystems. 2001 Jan;59(1):15-25. 11226623 ,
Abstract:
This paper presents a nonlinear mathematical model of the glucose-insulin feedback system, which has been extended to incorporate the beta-cells' function on maintaining and regulating plasma insulin level in man. Initially, a gastrointestinal absorption term for glucose is utilized to effect the glucose absorption by the intestine and the subsequent release of glucose into the bloodstream, taking place at a given initial rate and falling off exponentially with time. An analysis of the model is carried out by the singular perturbation technique in order to derive boundary conditions on the system parameters which identify, in particular, the existence of limit cycles in our model system consistent with the oscillatory patterns often observed in clinical data. We then utilize a sinusoidal term to incorporate the temporal absorption of glucose in order to study the responses in the patients under ambulatory-fed conditions. A numerical investigation is carried out in this case to construct a bifurcation diagram to identify the ranges of parametric values for which chaotic behavior can be expected, leading to interesting biological interpretations.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: lenbury_ruktamatakul_amornsamarnkul_2001_A
The original CellML model was created by:
Catherine Lloyd
c.lloyd@aukland.ac.nz
The University of Auckland
The Bioengineering Institute

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

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THis is a simple ordinary differential equation model describing chemoimmunotherapy of chronic lymphocytic leukemia, including descriptions of the combinatorial effects of chemotherapy and adoptive cellular immunotherapy.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4366, + "tag": "BioModels:BIOMD0000000879" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:54.614533+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000879", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3165": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3165, + "name": "Trisilowati2018 - Optimal control of tumor-immune system interaction with treatment", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of a growing tumor and its interaction with the immune system. The model consists of four populations: tumor cells, dendritic cells (representing the innate immune system), cytotoxic T cells, and helper T cells (as the specific immune system). The model is comprised of a system of ordinary differential equations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4367, + "tag": "BioModels:BIOMD0000000880" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:55.126783+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000880", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3166": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3166, + "name": "Kogan2013 - A mathematical model for the immunotherapeutic control of the TH1 TH2 imbalance in melanoma", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the imbalance between T helper (Th1/Th2) cell types in melanome patients, together with its regulation via IL-12 treatment. The model focuses on the interactions between the two T helper cell types as mediated by their respective key cytokines, interferon gamma and IL-10.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4368, + "tag": "BioModels:BIOMD0000000881" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:55.599076+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000881", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3167": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3167, + "name": "Munz2009 - Zombie SIZRC", + "repository_type": "biomodels", + "summary": "
Munz2009 - Zombie SIZRC This is the model with an latent infection and cure for zombies described in the article. This model was originally created by libAntimony v1.4 (using libSBML 3.4.1). This model is described in the article: When zombies attack!: Mathematical modelling of an outbreak of zombie infection P. Munz, I. Hudea, J. Imad and R.J. Smith? Infectious Disease Modelling Research Progress 2009, chapter 4, pp 133-150. Editors: Jean Michel Tchuenche and C. Chiyaka; Nova Science Publishers, Inc., NY, USA. Abstract: Zombies are a popular figure in pop culture/entertainment and they are usually portrayed as being brought about through an outbreak or epidemic. Consequently, we model a zombie attack, using biological assumptions based on popular zombie movies. We introduce a basic model for zombie infection, determine equilibria and their stability, and illustrate the outcome with numerical solutions. We then refine the model to introduce a latent period of zombification, whereby humans are infected, but not infectious, before becoming undead. We then modify the model to include the effects of possible quarantine or a cure. Finally, we examine the impact of regular, impulsive reductions in the number of zombies and derive conditions under which eradication can occur. We show that only quick, aggressive attacks can stave off the doomsday scenario: the collapse of society as zombies overtake us all. This model is hosted on BioModels Database and identified by: MODEL1008060001 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4369, + "tag": "BioModels:BIOMD0000000882" + }, + { + "id": 4370, + "tag": "Disease by infectious agent" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:56.147416+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000882", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3168": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3168, + "name": "Giani2019 - Computational modeling to predict MAP3K8 effects as mediator of resistance to vemurafenib in thyroid cancer stem cells", + "repository_type": "biomodels", + "summary": "
Computational modeling to predict MAP3K8 effects as mediator of resistance to vemurafenib in thyroid cancer stem cells
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4371, + "tag": "BioModels:BIOMD0000000883" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:56.655587+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000883", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3169": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3169, + "name": "Cortes2019 - Optimality of the spontaneous prophage induction rate.", + "repository_type": "biomodels", + "summary": "
Optimality of the spontaneous prophage induction rate.Cortes MG1, Krog J2, Bal\u00e1zsi G3.1 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.2 The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA.3 Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA. Electronic address: gabor.balazsi@stonybrook.edu.AbstractLysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage \u03bb and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.Copyright \u00a9 2019 The Author(s). Published by Elsevier Ltd.. All rights reserved.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4372, + "tag": "BioModels:BIOMD0000000884" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:57.144249+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000884", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3170": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3170, + "name": "Sumana2018 - Mathematical modeling of cancer-immune system, considering the role of antibodies.", + "repository_type": "biomodels", + "summary": "
Mathematical modeling of cancer-immune system, considering the role of antibodies.Ghosh S1, Banerjee S2.Author information1    Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal, 247667, India.2    Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttaranchal, 247667, India. sandofma@iitr.ac.in.AbstractA mathematical model for the quantitative analysis of cancer-immune interaction, considering the role of antibodies has been proposed in this paper. The model is based on the clinical evidence, which states that antibodies can directly kill cancerous cells (Ivano et al. in J Clin Investig 119(8):2143-2159, 2009). The existence of transcritical bifurcation, which has been proved using Sotomayor theorem, provides strong biological implications. Through numerical simulations, it has been illustrated that under certain therapy (like monoclonal antibody therapy), which is capable of altering the parameters of the system, cancer-free state can be obtained.KEYWORDS:Antibodies; B cells; Cancer cells; Global stability; Plasma cells; Transcritical bifurcation
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4373, + "tag": "BioModels:BIOMD0000000885" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:57.674799+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000885", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3171": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3171, + "name": "Dubey2008 - Modeling the interaction between avascular cancerous cells and acquired immune response", + "repository_type": "biomodels", + "summary": "
MODELING THE INTERACTION BETWEEN AVASCULAR CANCEROUS CELLS AND ACQUIRED IMMUNE RESPONSEB. DUBEY, UMA S. DUBEY and SANDIP BANERJEEAbstractThis paper deals with the interaction between dispersed cancer cells and the major populations of the immune system, namely, the T helper cells, T Cytotoxic cells, B cells, and antibodies produced. The system is described by a set of five ordinary differential equations. Both local and global stability of the system has been investigated. It has been observed that under appropriate conditions this interaction is capable of controlling the growth of these cancer cells. The analytical findings are supported by numerical and computational analytical methods.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4374, + "tag": "BioModels:BIOMD0000000886" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:58.199916+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000886", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3172": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3172, + "name": "Lim2014 - HTLV-I infection A dynamic struggle between viral persistence and host immunity", + "repository_type": "biomodels", + "summary": "
This is a four-dimensional, non-linear system of ordinary differential equations that describes the dynamic interactions among viral expression, infected target cell activation, and the HTLV-I-specific CTL response.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4375, + "tag": "BioModels:BIOMD0000000887" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:58.686501+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000887", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3173": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3173, + "name": "Unni2019 - Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug Interventions", + "repository_type": "biomodels", + "summary": "
Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug InterventionsPranav Unni 1 and Padmanabhan SeshaiyerAbstractOver the last few decades, there have been significant developments in theoretical, experimental, and clinical approaches to understand the dynamics of cancer cells and their interactions with the immune system. These have led to the development of important methods for cancer therapy including virotherapy, immunotherapy, chemotherapy, targeted drug therapy, and many others. Along with this, there have also been some developments on analytical and computational models to help provide insights into clinical observations. This work develops a new mathematical model that combines important interactions between tumor cells and cells in the immune systems including natural killer cells, dendritic cells, and cytotoxic CD8+ T cells combined with drug delivery to these cell sites. These interactions are described via a system of ordinary differential equations that are solved numerically. A stability analysis of this model is also performed to determine conditions for tumor-free equilibrium to be stable. We also study the influence of proliferation rates and drug interventions in the dynamics of all the cells involved. Another contribution is the development of a novel parameter estimation methodology to determine optimal parameters in the model that can reproduce a given dataset. Our results seem to suggest that the model employed is a robust candidate for studying the dynamics of tumor cells and it helps to provide the dynamic interactions between the tumor cells, immune system, and drug-response systems.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4376, + "tag": "BioModels:BIOMD0000000888" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:59.176501+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000888", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3174": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3174, + "name": "Fribourg2014 - Model of influenza A virus infection dynamics of viral antagonism and innate immune response.", + "repository_type": "biomodels", + "summary": "
This is an ordinary differential equation mathematical model investigating the early responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4377, + "tag": "BioModels:BIOMD0000000889" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:04:59.689405+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000889", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3175": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3175, + "name": "Bhattacharya2014 - A mathematical model of the sterol regulatory element binding protein 2 cholesterol biosynthesis pathway", + "repository_type": "biomodels", + "summary": "
This is a deterministic nonlinear ordinary differential equation mathematical model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in a hepatocyte.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4378, + "tag": "BioModels:BIOMD0000000890" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:00.174074+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000890", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3176": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3176, + "name": "Khajanchi2019 - Stability Analysis of a Mathematical Model forGlioma-Immune Interaction under OptimalTherapy", + "repository_type": "biomodels", + "summary": "
Stability Analysis of a Mathematical Model for Glioma-Immune Interaction under Optimal TherapySubhas KhajanchiAbstractWe investigate a mathematical model using a system of coupled ordinary differential equations, which describes the interplay of malignant glioma cells, macrophages, glioma specific CD8+T cells and the immunotherapeutic drug Adoptive Cellular Immunotherapy (ACI). To better understand under what circumstances the glioma cells can be eliminated, we employ the theory of optimal control. We investigate the dynamics of the system by observing biologically feasible equilibrium points and their stability analysis before administration of the external therapy ACI. We solve an optimal control problem with an objective functional which minimizes the glioma cell burden as well as the side effects of the treatment. We characterize our optimal control in terms of the solutions to the optimality system, in which the state system coupled with the adjoint system. Our model simulation demonstrates that the strength of treatment u1(t) plays an important role to eliminate the glioma cells. Finally, we derive an optimal treatment strategy and then solve it numerically.Keywords: malignant gliomas; stability analysis; optimal control; adoptive cellular immunotherapy
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4379, + "tag": "BioModels:BIOMD0000000891" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:00.690482+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000891", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3177": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3177, + "name": "Sandip2013 - Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.", + "repository_type": "biomodels", + "summary": "
Modeling the dynamics of hepatitis C virus with combined antiviral drug therapy: interferon and ribavirin.Banerjee S1, Keval R, Gakkhar S.Author information1    Department of Mathematics, Indian Institute of Technology Roorkee (IITR), Roorkee 247667, Uttaranchal, India. Electronic address: sandofma@iitr.ernet.in.AbstractA mathematical modeling of hepatitis C virus (HCV) dynamics and antiviral therapy has been presented in this paper. The proposed model, which involves four coupled ordinary differential equations, describes the interaction of target cells (hepatocytes), infected cells, infectious virions and non-infectious virions. The model takes into consideration the addition of ribavirin to interferon therapy and explains the dynamics regarding a biphasic and triphasic decline of viral load in the model. A critical drug efficacy parameter has been defined and it is shown that for an efficacy above this critical value, HCV is eradicated whereas for efficacy lower this critical value, a new steady state for infectious virions is reached, which is lower than the previous steady state value.Copyright \u00a9 2013 Elsevier Inc. All rights reserved.KEYWORDS:Hepatitis C virus (HCV); Infected cells; Infectious virions; Interferon; Noninfectious virions; Ribavirin; Target cells
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4380, + "tag": "BioModels:BIOMD0000000892" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:01.246239+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000892", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3178": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3178, + "name": "GonzalezMiranda2013 - The effect of circadian oscillations on biochemical cell signaling by NF-\u03baB", + "repository_type": "biomodels", + "summary": "
This is a mathematical model for NF-\u03baB oscillations, described by a set of ordinary nonlinear differential equations, when perturbed by a circadian oscillation.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4381, + "tag": "BioModels:BIOMD0000000893" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:01.971967+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000893", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3179": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3179, + "name": "Bose2011 - Noise-assisted interactions of tumor and immune cells", + "repository_type": "biomodels", + "summary": "
Noise-assisted interactions of tumor and immune cells.Bose T1, Trimper S.Author information1    Institute of Physics, Martin-Luther-University, D-06099 Halle, Germany. thomas.bose@physik.uni-halle.deAbstractWe consider a three-state model comprising tumor cells, effector cells, and tumor-detecting cells under the influence of noises. It is demonstrated that inevitable stochastic forces existing in all three cell species are able to suppress tumor cell growth completely. Whereas the deterministic model does not reveal a stable tumor-free state, the auto-correlated noise combined with cross-correlation functions can either lead to tumor-dormant states, tumor progression, as well as to an elimination of tumor cells. The auto-correlation function exhibits a finite correlation time \u03c4, while the cross-correlation functions shows a white-noise behavior. The evolution of each of the three kinds of cells leads to a multiplicative noise coupling. The model is investigated by means of a multivariate Fokker-Planck equation for small \u03c4. The different behavior of the system is, above all, determined by the variation of the correlation time and the strength of the cross-correlation between tumor and tumor-detecting cells. The theoretical model is based on a biological background discussed in detail, and the results are tested using realistic parameters from experimental observations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4382, + "tag": "BioModels:BIOMD0000000894" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:02.466808+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000894", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3180": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3180, + "name": "Schokker2013 - A mathematical model representing cellular immune development and response to Salmonella of chicken intestinal tissue", + "repository_type": "biomodels", + "summary": "
This is a dynamic mathematical model describing the development of the cellular branch of the intestinal immune system of poultry during the first 42 days of life, and of its response towards an oral infection with Salmonella enterica serovar Enteritidis.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4383, + "tag": "BioModels:BIOMD0000000895" + }, + { + "id": 4384, + "tag": "Gallus gallus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:02.996942+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000895", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3181": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3181, + "name": "Szymanska2009 - Mathematical modeling of heat shock protein synthesis in response to temperature change", + "repository_type": "biomodels", + "summary": "
This is a mathematical model of heat shock protein synthesis induced by an external temperature stimulus. The model consists of a system of nine nonlinear ordinary differential equations describing the temporal evolution of key variables involved in the regulation of HSP synthesis.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4385, + "tag": "BioModels:BIOMD0000000896" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:03.480773+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000896", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3182": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3182, + "name": "Khajanchi2015 - The combined effects of optimal control in cancer remission", + "repository_type": "biomodels", + "summary": "
The combined effects of optimal control in cancer remissionSubhasKhajanchiDibakarGhoshAbstractWe investigate a mathematical model depicting the nonlinear dynamics of immunogenic tumors as envisioned by Kuznetsov et al. [1]. To understand the dynamics under what circumstances the cancer cells can be eliminated, we implement the theory of optimal control. We design two types of external treatment strategies, one is Adoptive Cellular Immunotherapy and another is interleukin-2. Our aim is to establish the treatment regimens that maximize the effector cell count and minimize the tumor cell burden and the deleterious effects of the total amount of drugs. We derive the existence of an optimal control by using the boundedness of solutions. We characterize the optimality system, in which the state system is coupled with co-states. The uniqueness of an optimal control of our problem is also analyzed. Finally, we demonstrate the numerical illustrations that the optimal regimens reduce the tumor burden under different scenarios.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4386, + "tag": "BioModels:BIOMD0000000897" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:03.985324+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000897", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3183": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3183, + "name": "Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia", + "repository_type": "biomodels", + "summary": "
This is a mathematical model describing the hematopoietic lineages with leukemia lineages, as controlled by end-product negative feedback inhibition. Variables include hematopoietic stem cells, progenitor cells, terminally differentiated HSCs, leukemia stem cells, and terminally differentiated leukemia stem cells.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4387, + "tag": "BioModels:BIOMD0000000898" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:04.471545+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000898", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3184": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3184, + "name": "Ota2015 - Positive regulation of Rho GTPase activity by RhoGDIs as a result of their direct interaction with GAPs (GDI integrated)", + "repository_type": "biomodels", + "summary": "
This is a ordinary differential equation mathematical model describing the Rho GTPase cycle in which Rho GDP-dissociation inhibitors (RhoGDIs) inhibit the regulatory activities of guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) by interacting with them directly as well as by sequestering the Rho GTPases. The model was constructed with the intent of analyzing the role of RhoGDIs in Rho GTPase signaling.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4388, + "tag": "BioModels:BIOMD0000000899" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:04.951839+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000899", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3185": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3185, + "name": "Bianca2013 - Persistence analysis in a Kolmogorov-type model for cancer-immune system competition", + "repository_type": "biomodels", + "summary": "
Persistence analysis in a Kolmogorov-type model for cancer-immune system competitionAIP Conference Proceedings 1558, 1797 (2013); https://doi.org/10.1063/1.4825874C. BiancaDipartimento di Scienze Matematiche, Politecnico di Torino, Torino, ItalyF. PappalardoDipartimento di Scienza del Farmaco, Universit\u00e0 degli Studi di Catania, Catania, ItalyM. Pennisi and M. A. RagusaDipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Catania, Catania, Italy
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4389, + "tag": "BioModels:BIOMD0000000900" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:05.444486+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000900", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3186": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3186, + "name": "ChowHall2008 Dynamics of Human Weight Change_ODE_1", + "repository_type": "biomodels", + "summary": "This ODE model is a representation of the two compartment macronutrient partition model that Chow and Hall outlined in their 2008 publication. It is one of three models that they outlined in that publication. The model was constructed by a team of five as part of a Workshop on Modelling conducted at Indian Institute of Technology Madras by the EBI.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4390, + "tag": "BioModels:BIOMD0000000901" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:05.989763+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000901", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3187": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3187, + "name": "Wang2019 - A mathematical model of oncolytic virotherapy with time delay", + "repository_type": "biomodels", + "summary": "
A mathematical model describing oncolytic virotherapy with incorporation the viral lytic cycle and the virus-specific CTL response. The thresholds for viral treatment and virus-specific CTl response are also obtained.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4391, + "tag": "BioModels:BIOMD0000000902" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:06.557730+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000902", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3188": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3188, + "name": "Solis-perez2019 - A fractional mathematical model of breast cancer competition model", + "repository_type": "biomodels", + "summary": "
A fractional mathematical model of breast cancer competition modelAuthor links open overlay panelJ.E.Sol\u00eds-P\u00e9rezaJ.F.G\u00f3mez-AguilarbA.Atanganaca    Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xicob    CONACyT-Tecnol\u00f3gico Nacional de M\u00e9xico/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, M\u00e9xicoc    Institute for Groundwater Studies, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9300, South AfricaAbstractIn this paper, a mathematical model which considers population dynamics among cancer stem cells, tumor cells, healthy cells, the effects of excess estrogen and the body\u2019s natural immune response on the cell populations was considered. Fractional derivatives with power law and exponential decay law in Liouville\u2013Caputo sense were considered. Special solutions using an iterative scheme via Laplace transform were obtained. Furthermore, numerical simulations of the model considering both derivatives were obtained using the Atangana\u2013Toufik numerical method. Also, random model described by a system of random differential equations was presented. The use of fractional derivatives provides more useful information about the complexity of the dynamics of the breast cancer competition model.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4392, + "tag": "BioModels:BIOMD0000000903" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:07.048201+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000903", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3189": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3189, + "name": "Admon2017 - Modelling tumor growth with immune response and drug using ordinary differential equations", + "repository_type": "biomodels", + "summary": "
Modelling tumor growth with immune response and drug using ordinary differential equationsMohd Rashid Admon, Normah MaanThis is a mathematical study about tumor growth from a different perspective, with the aim of predicting and/or controlling the disease. The focus is on the effect and interaction of tumor cell with immune and drug. This paper presents a mathematical model of immune response and a cycle phase specific drug using a system of ordinary differential equations. Stability analysis is used to produce stability regions for various values of certain parameters during mitosis. The stability region of the graph shows that the curve splits the tumor decay and growth regions in the absence of immune response. However, when immune response is present, the tumor growth region is decreased. When drugs are considered in the system, the stability region remains unchanged as the system with the presence of immune response but the population of tumor cells at interphase and metaphase is reduced with percentage differences of 1.27 and 1.53 respectively. The combination of immunity and drug to fight cancer provides a better method to reduce tumor population compared to immunity alone.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4393, + "tag": "BioModels:BIOMD0000000904" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:07.602211+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000904", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3190": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3190, + "name": "Dubey2007 - A mathematical model for the effect of toxicant on the immune system (with toxicant effect) Model2", + "repository_type": "biomodels", + "summary": "
In this paper, a nonlinear mathematical model is proposed and analyzed to study theeffect of environmental toxicant on the immune response of the body. Criteria for localstability, instability and global stability are obtained. It is shown that the immuneresponse of the body decreases as the concentration of environmental toxicant increases,and certain criteria are obtained under which it settles down at its equilibrium level.In the absence of toxicant, an oscillatory behavior of immune system and pathogenicgrowth is observed. However, in the presence of toxicant, oscillatory behavior is notobserved. These studies show that the toxicant may have a grave effect on our body\u2019sdefense mechanism.Keywords: Pathogen; Immune Response; Toxicant; Stability.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4394, + "tag": "BioModels:BIOMD0000000905" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:08.163216+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000905", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3191": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3191, + "name": "Dubey2007 - A mathematical model for the effect of toxicant on the immune system (without toxicant effect) Model1", + "repository_type": "biomodels", + "summary": "
In this paper, a nonlinear mathematical model is proposed and analyzed to study theeffect of environmental toxicant on the immune response of the body. Criteria for localstability, instability and global stability are obtained. It is shown that the immuneresponse of the body decreases as the concentration of environmental toxicant increases,and certain criteria are obtained under which it settles down at its equilibrium level.In the absence of toxicant, an oscillatory behavior of immune system and pathogenicgrowth is observed. However, in the presence of toxicant, oscillatory behavior is notobserved. These studies show that the toxicant may have a grave effect on our body\u2019sdefense mechanism.Keywords: Pathogen; Immune Response; Toxicant; Stability.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4395, + "tag": "BioModels:BIOMD0000000906" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:08.963531+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000906", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3192": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3192, + "name": "HeberleRazquinNavas2019 - The PI3K and MAPK/p38 pathways control stress granuleassembly in a hierarchical manner model 3", + "repository_type": "biomodels", + "summary": "
All cells and organisms exhibit stress-coping mechanisms toensure survival. Cytoplasmic protein-RNA assemblies termedstress granules are increasingly recognized to promote cellularsurvival under stress. Thus, they might represent tumor vul-nerabilities that are currently poorly explored. The translation-inhibitory eIF2\u03b1kinases are established as main drivers ofstress granule assembly. Using a systems approach, we identifythe translation enhancers PI3K and MAPK/p38 as pro-stress-granule-kinases. They act through the metabolic master regu-lator mammalian target of rapamycin complex 1 (mTORC1) topromote stress granule assembly. When highly active, PI3K is themain driver of stress granules; however, the impact of p38becomes apparent as PI3K activity declines. PI3K and p38 thusact in a hierarchical manner to drive mTORC1 activity and stressgranule assembly. Of note, this signaling hierarchy is also presentin human breast cancer tissue. Importantly, only the recognition ofthe PI3K-p38 hierarchy under stress enabled the discovery of p38\u2019srole in stress granule formation. In summary, we assign a new pro-survival function to the key oncogenic kinases PI3K and p38, as theyhierarchically promote stress granule formation
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4396, + "tag": "BioModels:BIOMD0000000907" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:09.437277+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000907", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3193": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3193, + "name": "dePillis2013 - Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment", + "repository_type": "biomodels", + "summary": "
Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatmentLisette dePillis 1, , Trevor Caldwell 2, , Elizabeth Sarapata 2, and  Heather Williams 2,1. \tDepartment of Mathematics, Harvey Mudd College, Claremont, CA 917112. \tHarvey Mudd College, Claremont, CA 91711, United States, United States, United States    AbstractWe present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not.    Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response.Keywords: Renal cell carcinoma, mathematical modeling., sunitinib, immune system, regulatory T cells.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4397, + "tag": "BioModels:BIOMD0000000908" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:09.927976+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000908", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3194": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3194, + "name": "dePillis2003 - The dynamics of an optimally controlled tumor model: A case study", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <pre>The dynamics of an optimally controlled tumor model: A case studyL.GDe PillisabARadunskayaahttps://doi.org/10.1016/S0895-7177(03)00133-XAbstractWe present a phase-space analysis of a mathematical model of tumor growth with an immune response and chemotherapy. We prove that all orbits are bounded and must converge to one of several possible equilibrium points. Therefore, the long-term behavior of an orbit is classified according to the basin of attraction in which it starts. The addition of a drug term to the system can move the solution trajectory into a desirable basin of attraction. We show that the solutions of the model with a time-varying drug term approach the solutions of the system without the drug once traatment has stopped. We present numerical experiments in which optimal control therapy is able to drive the system into a desirable basin of attraction, whereas traditional pulsed chemotherapy is not.</pre> </body> </notes>", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4398, + "tag": "BioModels:BIOMD0000000909" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:10.431150+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000909", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3195": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3195, + "name": "Isaeva2008 - Modelling of Anti-Tumour Immune Response Immunocorrective Effect of Weak Centimetre Electromagnetic Waves", + "repository_type": "biomodels", + "summary": "
Modelling of anti-tumour immune response: Immunocorrective effectof weak centimetre electromagnetic wavesO.G. Isaeva* and V.A. OsipovBogoliubov Laboratory of Theoretical Physics, Joint Institute for Nuclear Research, Dubna,Moscow Region, RussiaWe formulate the dynamical model for the anti-tumour immune response based onintercellular cytokine-mediated interactions with the interleukin-2 (IL-2) taken intoaccount. The analysis shows that the expression level of tumour antigens on antigenpresenting cells has a distinct influence on the tumour dynamics. At low antigenpresentation, a progressive tumour growth takes place to the highest possible value.At high antigen presentation, there is a decrease in tumour size to some value when thedynamical equilibrium between the tumour and the immune system is reached. In thecase of the medium antigen presentation, both these regimes can be realized dependingon the initial tumour size and the condition of the immune system. A pronouncedimmunomodulating effect (the suppression of tumour growth and the normalization ofIL-2 concentration) is established by considering the influence of low-intensityelectromagnetic microwaves as a parametric perturbation of the dynamical system. Thisfinding is in qualitative agreement with the recent experimental results onimmunocorrective effects of centimetre electromagnetic waves in tumour-bearing mice.Keywords: carcinogenesis; interleukin-2; modelling; anti-tumour immunity;electromagnetic waves
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4399, + "tag": "BioModels:BIOMD0000000910" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4653, + "tag": "Electromagnetic Radiation" + } + ], + "timestamp_created": "2025-01-30 14:05:10.961868+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000910", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3196": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3196, + "name": "Merola2008 - An insight into tumor dormancy equilibrium via the analysis of its domain of attraction", + "repository_type": "biomodels", + "summary": "
An insight into tumor dormancy equilibrium via the analysis of its domain ofattractionA. Merola, C. Cosentino *, F. AmatoSchool of Computer and Biomedical Engineering, Universita` degli Studi Magna Gr\u00e6cia di Catanzaro, Campus \u2018\u2018Salvatore Venuta\u2019\u2019, 88100 Catanzaro, ItalyA B S T R A C TThe trajectories of the dynamic system which regulates the competition between the populations ofmalignant cells and immune cells may tend to an asymptotically stable equilibrium in which the sizes ofthese populations do not vary, which is called tumor dormancy. Especially for lower steady-state sizes ofthe population of malignant cells, this equilibrium represents a desirable clinical condition since thetumor growth is blocked. In this context, it is of mandatory importance to analyze the robustness of thisclinical favorable state of health in the face of perturbations. To this end, the paper presents anoptimization technique to determine whether an assigned rectangular region, which surrounds anasymptotically stable equilibrium point of a quadratic systems, is included into the domain of attractionof the equilibrium itself. The biological relevance of the application of this technique to the analysis oftumor growth dynamics is shown on the basis of a recent quadraticmodel of the tumor\u2013immune systemcompetition dynamics. Indeed the application of the proposedmethodology allows to ensure that a givensafety region, determined on the basis of clinical considerations, belongs to the domain of attraction ofthe tumor blocked equilibrium; therefore for the set of perturbed initial conditions which belong to suchregion, the convergence to the healthy steady state is guaranteed. The proposed methodology can alsoprovide an optimal strategy for cancer treatment.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4400, + "tag": "BioModels:BIOMD0000000911" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4654, + "tag": "Tumor Model" + } + ], + "timestamp_created": "2025-01-30 14:05:11.499628+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000911", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3197": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3197, + "name": "Caravagna2010 - Tumour suppression by immune system", + "repository_type": "biomodels", + "summary": "
Tumour suppression by immune system through stochastic oscillationsGiulioCaravagnaa Albertod\u2019Onofriob PaoloMilazzoaRobertoBarbutiahttps://doi.org/10.1016/j.jtbi.2010.05.013AbstractThe well-known Kirschner\u2013Panetta model for tumour\u2013immune System interplay [Kirschner, D., Panetta, J.C., 1998. Modelling immunotherapy of the tumour\u2013immune interaction. J. Math. Biol. 37 (3), 235\u2013252] reproduces a number of features of this essential interaction, but it excludes the possibility of tumour suppression by the immune system in the absence of therapy. Here we present a hybrid\u2013stochastic version of that model. In this new framework, we show that in reality the model is also able to reproduce the suppression, through stochastic extinction after the first spike of an oscillation.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4401, + "tag": "BioModels:BIOMD0000000912" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:11.988163+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000912", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3198": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3198, + "name": "dePillis2008 - Optimal control of mixed immunotherapy and chemotherapy of tumors", + "repository_type": "biomodels", + "summary": "&lt;notes xmlns=&quot;http://www.sbml.org/sbml/level2/version4&quot;&gt; &lt;body xmlns=&quot;http://www.w3.org/1999/xhtml&quot;&gt; &lt;pre&gt;Optimal control of mixed immunotherapy and chemotherapy of tumorsLisette Depillis, K. R. Fister , W. Gu, Tiffany Head, Kenny Maples, Todd Neal, Anand Murugan and Kenji KozaiAbstractWe investigate a mathematical population model of tumor-immune interactions. Thepopulations involved are tumor cells, specific and non-specific immune cells, and con-centrations of therapeutic treatments. We establish the existence of an optimal con-trol for this model and provide necessary conditions for the optimal control triple forsimultaneous application of chemotherapy, tumor infiltrating lymphocyte (TIL) ther-apy, and interleukin-2 (IL-2) treatment. We discuss numerical results for the combina-tion of the chemo-immunotherapy regimens. We find that the qualitative nature of ourresults indicates that chemotherapy is the dominant intervention with TIL interactingin a complementary fashion with the chemotherapy. However, within the optimal con-trol context, the interleukin-2 treatment does not become activated for the estimatedparameter ranges.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4402, + "tag": "BioModels:BIOMD0000000913" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:12.511009+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000913", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3199": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3199, + "name": "Parra_Guillen2013 - Mathematical model approach to describe tumour response in mice after vaccine administration_model1", + "repository_type": "biomodels", + "summary": "
Mathematical model approach to describe tumour response in mice after vaccine administration and its applicability to immune-stimulatory cytokine-based strategies.Parra-Guillen ZP1, Berraondo P, Grenier E, Ribba B, Troconiz IF.Author informationAbstractImmunotherapy is a growing therapeutic strategy in oncology based on the stimulation of innate and adaptive immune systems to induce the death of tumour cells. In this paper, we have developed a population semi-mechanistic model able to characterize the mechanisms implied in tumour growth dynamic after the administration of CyaA-E7, a vaccine able to target antigen to dendritic cells, thus triggering a potent immune response. The mathematical model developed presented the following main components: (1) tumour progression in the animals without treatment was described with a linear model, (2) vaccine effects were modelled assuming that vaccine triggers a non-instantaneous immune response inducing cell death. Delayed response was described with a series of two transit compartments, (3) a resistance effect decreasing vaccine efficiency was also incorporated through a regulator compartment dependent upon tumour size, and (4) a mixture model at the level of the elimination of the induced signal vaccine (k 2) to model tumour relapse after treatment, observed in a small percentage of animals (15.6%). The proposed model structure was successfully applied to describe antitumor effect of IL-12, suggesting its applicability to different immune-stimulatory therapies. In addition, a simulation exercise to evaluate in silico the impact on tumour size of possible combination therapies has been shown. This type of mathematical approaches may be helpful to maximize the information obtained from experiments in mice, reducing the number of animals and the cost of developing new antitumor immunotherapies.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4403, + "tag": "BioModels:BIOMD0000000914" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:13.050588+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000914", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3200": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3200, + "name": "Sun2018 - Instantaneous mutation rate in cancer initiation and progression", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <p>BackgroundCancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes.ResultsTo accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time.ConclusionsThe proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.</p> </body> </notes>", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4404, + "tag": "BioModels:BIOMD0000000915" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:13.579919+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000915", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3201": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3201, + "name": "Kraan199_Kinetics of Cortisol Metabolism and Excretion.", + "repository_type": "biomodels", + "summary": "
A new model is proposed to study the kinetics of [3H]cortisol metabolism by using urinary data only. The model consists of 5 pools, in which changes of the fractions of dose are given by a system of 5 ordinary differential equations. After i.v. administration of [3H]cortisol to 8 multiple pituitary deficient (MPD) patients (group I) the urines from each patient were collected in 9-15 portions during the following 3 days. From the urinary data the rate constants of cortisol metabolism were calculated. A published set of urinary data from patients with a normal cortisol metabolism (group II) was used for comparison. The overall half-life of the label in the circulation was 30 min for both groups; the half-life of the label excretion by both groups was 6 h and the time of maximal activity in the main metabolizing pool was 1.8 h in group I and 1.5 h in group II. The 20% of normal cortisol production rate (CPR) in the 8 MPD patients amounted to 7.2 + 1.9/zmol/(m2*d). Therefore, the low CPR but normal rate constants, i.e. a normal metabolic clearance rate of cortisol, in the MPD patients suggest a sensitive adjustment of the cortisol response in the target organs.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4405, + "tag": "BioModels:BIOMD0000000916" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:14.091235+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000916", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3202": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3202, + "name": "Phillips2007_AscendingArousalSystem_SleepWakeDynamics", + "repository_type": "biomodels", + "summary": "

This a model from the article:
A quantitative model of sleep-wake dynamics based on the physiology of thebrainstem ascending arousal system.
Phillips AJ, Robinson PA. J Biol Rhythms 2007 Apr;22(2):167-79 17440218 ,
Abstract:
A quantitative, physiology-based model of the ascending arousal system isdeveloped, using continuum neuronal population modeling, which involvesaveraging properties such as firing rates across neurons in each population. Themodel includes the ventrolateral preoptic area (VLPO), where circadian andhomeostatic drives enter the system, the monoaminergic and cholinergic nuclei ofthe ascending arousal system, and their interconnections. The human sleep-wakecycle is governed by the activities of these nuclei, which modulate thebehavioral state of the brain via diffuse neuromodulatory projections. The modelparameters are not free since they correspond to physiological observables.Approximate parameter bounds are obtained by requiring consistency withphysiological and behavioral measures, and the model replicates the humansleep-wake cycle, with physiologically reasonable voltages and firing rates.Mutual inhibition between the wake-promoting monoaminergic group andsleep-promoting VLPO causes ;;flip-flop'' behavior, with most time spent in 2stable steady states corresponding to wake and sleep, with transitions betweenthem on a timescale of a few minutes. The model predicts hysteresis in thesleep-wake cycle, with a region of bistability of the wake and sleep states.Reducing the monoaminergic-VLPO mutual inhibition results in a smallerhysteresis loop. This makes the model more prone to wake-sleep transitions inboth directions and makes the states less distinguishable, as in narcolepsy. Themodel behavior is robust across the constrained parameter ranges, but withsufficient flexibility to describe a wide range of observed phenomena.

This model was taken from the CellML repository and automatically converted to SBML.
The original model was: Phillips AJ, Robinson PA. (2007) - version=1.0
The original CellML model was created by:
Catherine Lloyd
c.lloyd@auckland.ac.nz
The University of Auckland

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
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In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4406, + "tag": "BioModels:BIOMD0000000917" + }, + { + "id": 4407, + "tag": "Circadian sleep/wake cycle process" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:14.594233+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000917", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3203": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3203, + "name": "Schwarz2018-Cdk Activity Threshold Determines Passage through the Restriction Point", + "repository_type": "biomodels", + "summary": "
At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is passed when growth factor signaling initiates a positive feedback loop of Cdk activity. However, recent studies have cast doubt on this model by claiming R occurs prior to positive feedback activation in G1 or even before completion of the previous cell cycle. Here we reconcile these results and show that whereas many commonly used cell lines do not exhibit a G1 R, primary fibroblasts have a G1 R that is defined by a precise Cdk activity threshold and the activation of cell-cycle-dependent transcription. A simple threshold model, based solely on Cdk activity, predicted with more than 95% accuracy whether individual cells had passed R. That a single measurement accurately predicted cell fate shows that the state of complex regulatory networks can be assessed using a few critical protein activities.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4408, + "tag": "BioModels:BIOMD0000000918" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4655, + "tag": "mammalian" + } + ], + "timestamp_created": "2025-01-30 14:05:15.328204+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000918", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3204": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3204, + "name": "Ledzewicz2013 - On optimal chemotherapy with a strongly targeted agent for a model of tumor immune system interactions with generalized logistic growth", + "repository_type": "biomodels", + "summary": "
On optimal chemotherapy with a strongly targeted agent for a model of tumor-immune system interactions with generalized logistic growth.Ledzewicz U1, Olumoye O, Sch\u00e4ttler H.1Dept. of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois 62026-1653, USA. uledzew@siue.eduAbstractIn this paper, a mathematical model for chemotherapy that takes tumor immune-system interactions into account is considered for a strongly targeted agent. We use a classical model originally formulated by Stepanova, but replace exponential tumor growth with a generalised logistic growth model function depending on a parameter v. This growth function interpolates between a Gompertzian model (in the limit v \u2192 0) and an exponential model (in the limit v \u2192 \u221e). The dynamics is multi-stable and equilibria and their stability will be investigated depending on the parameter v. Except for small values of v, the system has both an asymptotically stable microscopic (benign) equilibrium point and an asymptotically stable macroscopic (malignant) equilibrium point. The corresponding regions of attraction are separated by the stable manifold of a saddle. The optimal control problem of moving an initial condition that lies in the malignant region into the benign region is formulated and the structure of optimal singular controls is determined
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4409, + "tag": "BioModels:BIOMD0000000919" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:15.813200+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000919", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3205": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3205, + "name": "Jarrett2015 - Modelling the interaction between immune response, bacterial dynamics and inflammatory damage", + "repository_type": "biomodels", + "summary": "Mathematical model of pro- and anti-inflammatory response, inflammation/damage and infection dynamics in BALB/c mouse with Staphylococcal aureus infection.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4410, + "tag": "BioModels:BIOMD0000000920" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3925, + "tag": "Regulation of inflammatory response" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:16.341351+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000920", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3206": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3206, + "name": "Khajanchi2017 - Uniform Persistence and Global Stability for a Brain Tumor and Immune System Interaction", + "repository_type": "biomodels", + "summary": "This paper describes the synergistic interaction between the growth of malignant gliomas and the immune system interactions using a system of coupled ordinary di\u00aeerential equations (ODEs). The proposed mathematical model comprises the interaction of glioma cells, macrophages, activated Cytotoxic T-Lymphocytes (CTLs), the immunosuppressive factor TGF- and the immuno-stimulatory factor IFN-. The dynamical behavior of the proposed system both analytically and numerically is investigated from the point of view of stability. By constructing Lyapunov functions, the global behavior of the glioma-free and the interior equilibrium point have been analyzed under some assumptions. Finally, we perform numerical simulations in order to illustrate our analytical \u00afndings by varying the system parameters.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4411, + "tag": "BioModels:BIOMD0000000921" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:16.866742+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000921", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3207": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3207, + "name": "Turner2015-Human/Mosquito ELP Model", + "repository_type": "biomodels", + "summary": "
the growth of the mosquito population is directly related to the spread of malaria, the Four Stage Life Cycle is incorporated to model the effects of climate change and interspecies competition within the mosquito life cycle stages of Egg, Larvae, and Pupae.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4412, + "tag": "BioModels:BIOMD0000000922" + }, + { + "id": 4413, + "tag": "Lutzia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:17.348962+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000922", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3208": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3208, + "name": "Li\u00f22012_Modelling osteomyelitis_Control Model", + "repository_type": "biomodels", + "summary": "
BackgroundThis work focuses on the computational modelling of osteomyelitis, a bone pathology caused by bacteria infection (mostly Staphylococcus aureus). The infection alters the RANK/RANKL/OPG signalling dynamics that regulates osteoblasts and osteoclasts behaviour in bone remodelling, i.e. the resorption and mineralization activity. The infection rapidly leads to severe bone loss, necrosis of the affected portion, and it may even spread to other parts of the body. On the other hand, osteoporosis is not a bacterial infection but similarly is a defective bone pathology arising due to imbalances in the RANK/RANKL/OPG molecular pathway, and due to the progressive weakening of bone structure.ResultsSince both osteoporosis and osteomyelitis cause loss of bone mass, we focused on comparing the dynamics of these diseases by means of computational models. Firstly, we performed meta-analysis on a gene expression data of normal, osteoporotic and osteomyelitis bone conditions. We mainly focused on RANKL/OPG signalling, the TNF and TNF receptor superfamilies and the NF-kB pathway. Using information from the gene expression data we estimated parameters for a novel model of osteoporosis and of osteomyelitis. Our models could be seen as a hybrid ODE and probabilistic verification modelling framework which aims at investigating the dynamics of the effects of the infection in bone remodelling. Finally we discuss different diagnostic estimators defined by formal verification techniques, in order to assess different bone pathologies (osteopenia, osteoporosis and osteomyelitis) in an effective way.ConclusionsWe present a modeling framework able to reproduce aspects of the different bone remodeling defective dynamics of osteomyelitis and osteoporosis. We report that the verification-based estimators are meaningful in the light of a feed forward between computational medicine and clinical bioinformaticsModel is encoded by Ruby and submitted and curated to BioModels by Ahmad Zyoud
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4414, + "tag": "BioModels:BIOMD0000000923" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:17.836444+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000923", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3209": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3209, + "name": "Smith2011 - Three Stage Innate Immune Response to a Pneumococcal Lung Infection", + "repository_type": "biomodels", + "summary": "
Pneumococcal pneumonia is a leading cause of death and a major source of human morbidity. The initial immune response plays a central role in determining the course and outcome of pneumococcal disease. We combine bacterial titer measurements from mice infected with Streptococcus pneumoniae with mathematical modeling to investigate the coordination of immune responses and the effects of initial inoculum on outcome. To evaluate the contributions of individual components, we systematically build a mathematical model from three subsystems that describe the succession of defensive cells in the lung: resident alveolar macrophages, neutrophils and monocyte-derived macrophages. The alveolar macrophage response, which can be modeled by a single differential equation, can by itself rapidly clear small initial numbers of pneumococci. Extending the model to include the neutrophil response required additional equations for recruitment cytokines and host cell status and damage. With these dynamics, two outcomes can be predicted: bacterial clearance or sustained bacterial growth. Finally, a model including monocyte-derived macrophage recruitment by neutrophils suggests that sustained bacterial growth is possible even in their presence. Our model quantifies the contributions of cytotoxicity and immune-mediated damage in pneumococcal pathogenesis.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4415, + "tag": "BioModels:BIOMD0000000924" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:18.345034+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000924", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3210": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3210, + "name": "Dunster2016 - Nondimensional Coagulation Model", + "repository_type": "biomodels", + "summary": "
We undertake a mathematical investigation of a model for the generation of thrombin, an enzyme central to haemostatic blood coagulation, as well as to thrombotic disorders, that is the end product of a complicated protein cascade with multiple feedbacks that ensures its production in the right place at the right time. In a laboratory setting, its central role is reflected in thrombin evolution over time being used as a measure of the ability of a patient's blood to clot. Here, we present a model for the generation of thrombin (based on earlier work) and analyse it using the method of matched asymptotic expansions to derive a sequence of simplified models that characterize the roles of distinct interactions over various timescales. In particular, we are able through the asymptotic analysis to provide simplified models that are an excellent substitute for the full model (capturing the explosive growth and decay of thrombin) and approximations for the key experimental measurements used to describe thrombin's characteristic evolution over time. The asymptotic results are validated against numerical simulations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4416, + "tag": "BioModels:BIOMD0000000925" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:18.945856+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000925", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3211": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3211, + "name": "Rhodes2019 - Immune-Mediated theory of Metastasis", + "repository_type": "biomodels", + "summary": "<notes xmlns="http://www.sbml.org/sbml/level2/version4"> <body xmlns="http://www.w3.org/1999/xhtml"> <p>Abstract:Accumulating experimental and clinical evidence suggest that the immune response to cancer is not exclusively anti-tumor. Indeed, the pro-tumor roles of the immune system - as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example - have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed "immune-mediated" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor "education" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. Our results suggest that further work is warranted to fully elucidate the pro-tumor effects of the immune system in metastatic cancer.</p> </body> </notes>", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4417, + "tag": "BioModels:BIOMD0000000926" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:19.456666+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000926", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3212": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3212, + "name": "Grigolon2018-Responses to auxin signals", + "repository_type": "biomodels", + "summary": "
Plants depend on the signalling of the phytohormone auxin for their development and for responding to environmental perturbations. The associated biomolecular signalling network involves a negative feedback on Aux/IAA proteins which mediate the influence of auxin (the signal) on the auxin response factor (ARF) transcription factors (the drivers of the response). To probe the role of this feedback, we consider alternative in silico signalling networks implementing different operating principles. By a comparative analysis, we find that the presence of a negative feedback allows the system to have a far larger sensitivity in its dynamical response to auxin and that this sensitivity does not prevent the system from being highly resilient. Given this insight, we build a new biomolecular signalling model for quantitatively describing such Aux/IAA and ARF responses.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4418, + "tag": "BioModels:BIOMD0000000927" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4419, + "tag": "unidentified plant" + } + ], + "timestamp_created": "2025-01-30 14:05:19.966767+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000927", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3213": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3213, + "name": "Baker2017 - The role of cytokines, MMPs and fibronectin fragments osteoarthritis", + "repository_type": "biomodels", + "summary": "
Baker2017 - The role of cytokines, MMPs andfibronectin fragments osteoarthritis

This model is described in the article:

Baker M, Brook BS, Owen MR.
J Math Biol 2017 Feb; :

Abstract:

Osteoarthritis (OA) is a degenerative disease which causes pain and stiffness in joints. OA progresses through excessive degradation of joint cartilage, eventually leading to significant joint degeneration and loss of function. Cytokines, a group of cell signalling proteins, present in raised concentrations in OA joints, can be classified into pro-inflammatory and anti-inflammatory groups. They mediate cartilage degradation through several mechanisms, primarily the up-regulation of matrix metalloproteinases (MMPs), a group of collagen-degrading enzymes. In this paper we show that the interactions of cytokines within cartilage have a crucial role to play in OA progression and treatment. We develop a four-variable ordinary differential equation model for the interactions between pro- and anti-inflammatory cytokines, MMPs and fibronectin fragments (Fn-fs), a by-product of cartilage degradation and up-regulator of cytokines. We show that the model has four classes of dynamic behaviour: homoeostasis, bistable inflammation, tristable inflammation and persistent inflammation. We show that positive and negative feedbacks controlling cytokine production rates can determine either a pre-disposition to OA or initiation of OA. Further, we show that manipulation of cytokine, MMP and Fn-fs levels can be used to treat OA, but we suggest that multiple treatment targets may be essential to halt or slow disease progression.

This model is hosted on BioModels Database and identified by: MODEL1704120000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4420, + "tag": "BioModels:BIOMD0000000928" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4656, + "tag": "Inflammatory Response Pathway" + } + ], + "timestamp_created": "2025-01-30 14:05:20.493762+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000928", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3214": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3214, + "name": "Li2016 - Model for pancreatic cancer patients receiving immunotherapy", + "repository_type": "biomodels", + "summary": "
immunotherapy offers a better prognosis for pancreatic cancer patients. As a direct extension of this work, various new therapy methods that are under exploration and clinical trials could be assessed or evaluated using the newly developed mathematical prognosis model.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4421, + "tag": "BioModels:BIOMD0000000929" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + }, + { + "id": 4639, + "tag": "Pancreatic cancer" + } + ], + "timestamp_created": "2025-01-30 14:05:21.029684+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000929", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3215": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3215, + "name": "Liu2017 - chemotherapy targeted model of tumor immune system", + "repository_type": "biomodels", + "summary": "
Its a mathematical model reflecting chemotherapy response in tumor immune interaction system. Model encoded by Sarubini Kananathan and annotated+submitted to Biomodels by Krishna Tiwari
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4422, + "tag": "BioModels:BIOMD0000000930" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:21.522928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000930", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3216": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3216, + "name": "Voliotis2019-GnRH Pulse Generation", + "repository_type": "biomodels", + "summary": "
Fertility critically depends on the gonadotropin-releasing hormone (GnRH) pulse generator, a neural construct comprised of hypothalamic neurons coexpressing kisspeptin, neurokoinin-B and dynorphin. Here, using mathematical modeling and in vivo optogenetics we reveal for the first time how this neural construct initiates and sustains the appropriate ultradian frequency essential for reproduction. Prompted by mathematical modeling, we show experimentally using female estrous mice that robust pulsatile release of luteinizing hormone, a proxy for GnRH, emerges abruptly as we increase the basal activity of the neuronal network using continuous low-frequency optogenetic stimulation. Further increase in basal activity markedly increases pulse frequency and eventually leads to pulse termination. Additional model predictions that pulsatile dynamics emerge from nonlinear positive and negative feedback interactions mediated through neurokinin-B and dynorphin signaling respectively are confirmed neuropharmacologically. Our results shed light on the long-elusive GnRH pulse generator offering new horizons for reproductive health and wellbeing.SIGNIFICANCE STATEMENT The gonadotropin-releasing hormone (GnRH) pulse generator controls the pulsatile secretion of the gonadotropic hormones LH and FSH and is critical for fertility. The hypothalamic arcuate kisspeptin neurons are thought to represent the GnRH pulse generator, since their oscillatory activity is coincident with LH pulses in the blood; a proxy for GnRH pulses. However, the mechanisms underlying GnRH pulse generation remain elusive. We developed a mathematical model of the kisspeptin neuronal network and confirmed its predictions experimentally, showing how LH secretion is frequency-modulated as we increase the basal activity of the arcuate kisspeptin neurons in vivo using continuous optogenetic stimulation. Our model provides a quantitative framework for understanding the reproductive neuroendocrine system and opens new horizons for fertility regulationModel is encoded by Johannes and submitted to BioModels by Ahmad Zyoud.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4423, + "tag": "BioModels:BIOMD0000000931" + }, + { + "id": 4141, + "tag": "Mus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:22.213361+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000931", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3217": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3217, + "name": "Garde2020-Minimal model describing metabolic oscillations in Bacillus subtilis biofilms", + "repository_type": "biomodels", + "summary": "
Biofilms offer an excellent example of ecological interaction among bacteria. Temporal and spatial oscillations in biofilms are an emerging topic. In this paper, we describe the metabolic oscillations in Bacillus subtilis biofilms by applying the smallest theoretical chemical reaction system showing Hopf bifurcation proposed by Wilhelm and Heinrich in 1995. The system involves three differential equations and a single bilinear term. We specifically select parameters that are suitable for the biological scenario of biofilm oscillations. We perform computer simulations and a detailed analysis of the system including bifurcation analysis and quasi-steady-state approximation. We also discuss the feedback structure of the system and the correspondence of the simulations to biological observations. Our theoretical work suggests potential scenarios about the oscillatory behaviour of biofilms and also serves as an application of a previously described chemical oscillator to a biological system.
", + "tags": [ + { + "id": 3412, + "tag": "Bacillus subtilis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4424, + "tag": "BioModels:BIOMD0000000932" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:22.799411+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000932", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3218": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3218, + "name": "Kosiuk2015-Geometric analysis of the Goldbeter minimal model for the embryonic cell cycle", + "repository_type": "biomodels", + "summary": "
A minimal model describing the embryonic cell division cycle at the molecular level in eukaryotes is analyzed mathematically. It is known from numerical simulations that the corresponding three-dimensional system of ODEs has periodic solutions in certain parameter regimes. We prove the existence of a stable limit cycle and provide a detailed description on how the limit cycle is generated. The limit cycle corresponds to a relaxation oscillation of an auxiliary system, which is singularly perturbed and has the same orbits as the original model. The singular perturbation character of the auxiliary problem is caused by the occurrence of small Michaelis constants in the model. Essential pieces of the limit cycle of the auxiliary problem consist of segments of slow motion close to several branches of a two dimensional critical manifold which are connected by fast jumps. In addition, a new phenomenon of exchange of stability occurs at lines, where the branches of the two-dimensional critical manifold intersect. This novel type of relaxation oscillations is studied by combining standard results from geometric singular perturbation with several suitable blow-up transformations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4425, + "tag": "BioModels:BIOMD0000000933" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4426, + "tag": "unclassified eukaryotes" + } + ], + "timestamp_created": "2025-01-30 14:05:23.305001+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000933", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3219": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3219, + "name": "Linke2017 - Synchronization of Cyclins' expression by the Fkh2 transcription factor in the budding yeast cell cycle", + "repository_type": "biomodels", + "summary": "

Modeling of the expression of cyclin B2 and B3 in the budding yeast cell cycle

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4427, + "tag": "BioModels:BIOMD0000000934" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3039, + "tag": "Saccharomyces cerevisiae" + } + ], + "timestamp_created": "2025-01-30 14:05:23.789355+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000934", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3220": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3220, + "name": "Ferrel2011 - Cdk1 and APC regulation in cell cycle in Xenopus laevis", + "repository_type": "biomodels", + "summary": "Mathematical model of the regulation of Cdk1 and APC in cell cycle in Xenopus Laevis", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4428, + "tag": "BioModels:BIOMD0000000935" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-30 14:05:24.487547+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000935", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3221": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3221, + "name": "ferrel2011 - autonomous biochemical oscillator in cell cycle in Xenopus laevis v2", + "repository_type": "biomodels", + "summary": "
Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. The simplest examples of the eukaryotic cell cycle operate like autonomous oscillators. Here, we present the basic theory of oscillatory biochemical circuits in the context of the Xenopus embryonic cell cycle. We examine Boolean models, delay differential equation models, and especially ordinary differential equation (ODE) models. For ODE models, we explore what it takes to get oscillations out of two simple types of circuits (negative feedback loops and coupled positive and negative feedback loops). Finally, we review the procedures of linear stability analysis, which allow one to determine whether a given ODE model and a particular set of kinetic parameters will produce oscillations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4429, + "tag": "BioModels:BIOMD0000000936" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-30 14:05:25.169431+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000936", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3222": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3222, + "name": "Ferrel2011 - Autonomous biochemical oscillator in regulation of CDK1, Plk1, and APC in Xenopus Laevis cell cycle", + "repository_type": "biomodels", + "summary": "
Computational modeling and the theory of nonlinear dynamical systems allow one to not simply describe the events of the cell cycle, but also to understand why these events occur, just as the theory of gravitation allows one to understand why cannonballs fly in parabolic arcs. The simplest examples of the eukaryotic cell cycle operate like autonomous oscillators. Here, we present the basic theory of oscillatory biochemical circuits in the context of the Xenopus embryonic cell cycle. We examine Boolean models, delay differential equation models, and especially ordinary differential equation (ODE) models. For ODE models, we explore what it takes to get oscillations out of two simple types of circuits (negative feedback loops and coupled positive and negative feedback loops). Finally, we review the procedures of linear stability analysis, which allow one to determine whether a given ODE model and a particular set of kinetic parameters will produce oscillations.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4430, + "tag": "BioModels:BIOMD0000000937" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-30 14:05:25.668034+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000937", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3223": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3223, + "name": "Gerard2013 - Model 3 - Embryonic-type eukaryotic Cell Cycle regulation based on negative feedback between Cdk/cyclin and APC and competitive inhibition between Cdk/cyclin and securin for polyubiquitylation_1", + "repository_type": "biomodels", + "summary": "
The eukaryotic cell cycle is characterized by alternating oscillations in the activities of cyclin-dependent kinase (Cdk) and the anaphase-promoting complex (APC). Successful completion of the cell cycle is dependent on the precise, temporally ordered appearance of these activities. A modest level of Cdk activity is sufficient to initiate DNA replication, but mitosis and APC activation require an elevated Cdk activity. In present-day eukaryotes, this temporal order is provided by a complex network of regulatory proteins that control both Cdk and APC activities via sharp thresholds, bistability, and time delays. Using simple computational models, we show here that these dynamical features of cell-cycle organization could emerge in a control system driven by a single Cdk/cyclin complex and APC wired in a negative-feedback loop. We show that ordered phosphorylation of cellular proteins could be explained by multisite phosphorylation/dephosphorylation and competition of substrates for interconverting kinase (Cdk) and phosphatase. In addition, the competition of APC substrates for ubiquitylation can create and maintain sustained oscillations in cyclin levels. We propose a sequence of models that gets closer and closer to a realistic model of cell-cycle control in yeast. Since these models lack the elaborate control mechanisms characteristic of modern eukaryotes, they suggest that bistability and time delay
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4431, + "tag": "BioModels:BIOMD0000000938" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:26.196386+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000938", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3224": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3224, + "name": "Iwamoto2010 - Cell cycle reponse to DNA damage", + "repository_type": "biomodels", + "summary": "
After DNA damage, cells activate p53, a tumor suppressor gene, and select a cell fate (e.g., DNA repair, cell cycle arrest, or apoptosis). Recently, a p53 oscillatory behavior was observed following DNA damage. However, the relationship between this p53 oscillation and cell-fate selection is unclear. Here, we present a novel model of the DNA damage signaling pathway that includes p53 and whole cell cycle regulation and explore the relationship between p53 oscillation and cell fate selection. The simulation run without DNA damage qualitatively realized experimentally observed data from several cell cycle regulators, indicating that our model was biologically appropriate. Moreover, the comprehensive sensitivity analysis for the proposed model was implemented by changing the values of all kinetic parameters, which revealed that the cell cycle regulation system based on the proposed model has robustness on a fluctuation of reaction rate in each process. Simulations run with four different intensities of DNA damage, i.e. Low-damage, Medium-damage, High-damage, and Excess-damage, realized cell cycle arrest in all cases. Low-damage, Medium-damage, High-damage, and Excess-damage corresponded to the DNA damage caused by 100, 200, 400, and 800 J/m(2) doses of UV-irradiation, respectively, based on expression of p21, which plays a crucial role in cell cycle arrest. In simulations run with High-damage and Excess-damage, the length of the cell cycle arrest was shortened despite the severe DNA damage, and p53 began to oscillate. Cells initiated apoptosis and were killed at 400 and 800 J/m(2) doses of UV-irradiation, corresponding to High-damage and Excess-damage, respectively. Therefore, our model indicated that the oscillatory mode of p53 profoundly affects cell fate selection.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4432, + "tag": "BioModels:BIOMD0000000939" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:26.710800+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000939", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3225": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3225, + "name": "Tang2019 - Pharmacology modelling of AURKB and ZAK interaction in TNBC", + "repository_type": "biomodels", + "summary": "

Aurora Kinase B and ZAK interaction model

Equivalent of the stochastic model used in \"Network pharmacology model predicts combined Aurora B and ZAK inhibition in MDA-MB-231 breast cancer cells\" by Tang et. al. 2018.The only difference is cell division and partitioning of the components, which are available in the original model for SGNS2.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4433, + "tag": "BioModels:BIOMD0000000940" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:27.224856+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000940", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3226": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3226, + "name": "Gerard2010 - Progression of mammalian cell cycle by successive activation of various cyclin cdk complexes", + "repository_type": "biomodels", + "summary": "
We previously proposed a detailed, 39-variable model for the network of cyclin-dependent kinases (Cdks) that controls progression along the successive phases of the mammalian cell cycle. Here, we propose a skeleton, 5-variable model for the Cdk network that can be seen as the backbone of the more detailed model for the mammalian cell cycle. In the presence of sufficient amounts of growth factor, the skeleton model also passes from a stable steady state to sustained oscillations of the various cyclin/Cdk complexes. This transition corresponds to the switch from quiescence to cell proliferation. Sequential activation of the cyclin/Cdk complexes allows the ordered progression along the G1, S, G2 and M phases of the cell cycle. The 5-variable model can also account for the existence of a restriction point in G1, and for endoreplication. Like the detailed model, it contains multiple oscillatory circuits and can display complex oscillatory behaviour such as quasi-periodic oscillations and chaos. We compare the dynamical properties of the skeleton model with those of the more detailed model for the mammalian cell cycle.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4434, + "tag": "BioModels:BIOMD0000000941" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:27.708627+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000941", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3227": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3227, + "name": "Sible2007 - Mitotic cell cycle mecanism in Xenopus Laevis", + "repository_type": "biomodels", + "summary": "
Although not a traditional experimental \"method,\" mathematical modeling can provide a powerful approach for investigating complex cell signaling networks, such as those that regulate the eukaryotic cell division cycle. We describe here one modeling approach based on expressing the rates of biochemical reactions in terms of nonlinear ordinary differential equations. We discuss the steps and challenges in assigning numerical values to model parameters and the importance of experimental testing of a mathematical model. We illustrate this approach throughout with the simple and well-characterized example of mitotic cell cycles in frog egg extracts. To facilitate new modeling efforts, we describe several publicly available modeling environments, each with a collection of integrated programs for mathematical modeling. This review is intended to justify the place of mathematical modeling as a standard method for studying molecular regulatory networks and to guide the non-expert to initiate modeling projects in order to gain a systems-level perspective for complex control systems.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4435, + "tag": "BioModels:BIOMD0000000942" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 2990, + "tag": "Xenopus laevis" + } + ], + "timestamp_created": "2025-01-30 14:05:28.207739+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000942", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3228": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3228, + "name": "Hat2016 - Reponse of p53 System to irradiation in cell fate decision making", + "repository_type": "biomodels", + "summary": "
The p53 transcription factor is a regulator of key cellular processes including DNA repair, cell cycle arrest, and apoptosis. In this theoretical study, we investigate how the complex circuitry of the p53 network allows for stochastic yet unambiguous cell fate decision-making. The proposed Markov chain model consists of the regulatory core and two subordinated bistable modules responsible for cell cycle arrest and apoptosis. The regulatory core is controlled by two negative feedback loops (regulated by Mdm2 and Wip1) responsible for oscillations, and two antagonistic positive feedback loops (regulated by phosphatases Wip1 and PTEN) responsible for bistability. By means of bifurcation analysis of the deterministic approximation we capture the recurrent solutions (i.e., steady states and limit cycles) that delineate temporal responses of the stochastic system. Direct switching from the limit-cycle oscillations to the \"apoptotic\" steady state is enabled by the existence of a subcritical Neimark-Sacker bifurcation in which the limit cycle loses its stability by merging with an unstable invariant torus. Our analysis provides an explanation why cancer cell lines known to have vastly diverse expression levels of Wip1 and PTEN exhibit a broad spectrum of responses to DNA damage: from a fast transition to a high level of p53 killer (a p53 phosphoform which promotes commitment to apoptosis) in cells characterized by high PTEN and low Wip1 levels to long-lasting p53 level oscillations in cells having PTEN promoter methylated (as in, e.g., MCF-7 cell line).
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4436, + "tag": "BioModels:BIOMD0000000943" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:28.751827+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000943", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3229": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3229, + "name": "Goldbeter2013-Oscillatory activity of cyclin-dependent kinases in the cell cycle", + "repository_type": "biomodels", + "summary": "
A model for oscillations of Cdc2 kinase in embryonic cell cycles based on Michaelis\u2013Menten phosphorylation\u2013dephosphorylation kinetics shows that the occurrence and amplitude of the oscillations strongly depend on the ultrasensitivity of the enzymatic cascade that controls the activity of the cyclin-dependent kinase.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4437, + "tag": "BioModels:BIOMD0000000944" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4657, + "tag": "Amphibians" + } + ], + "timestamp_created": "2025-01-30 14:05:29.308403+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000944", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3230": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3230, + "name": "Evans2004 - Cell based mathematical model of topotecan", + "repository_type": "biomodels", + "summary": "

A two compartment mathematical model of the antineoplastic compound topotecan

", + "tags": [ + { + "id": 4438, + "tag": "Antineoplastic drug pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4439, + "tag": "BioModels:BIOMD0000000945" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:29.877812+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000945", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3231": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3231, + "name": "Evans2005 - Compartmental model for antineoplastic drug topotecan in breast cancer cells", + "repository_type": "biomodels", + "summary": "

Compartment model for the antineoplastic drug topotecan. Modelling drug in its active lactone and inactive hydroxy acid forms in the medium, extracellular, cytoplasm and nucleus.

", + "tags": [ + { + "id": 4438, + "tag": "Antineoplastic drug pathway" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4440, + "tag": "BioModels:BIOMD0000000946" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:30.438686+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000946", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3232": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3232, + "name": "Lee2017 - Paracetamol first-pass metabolism PK model", + "repository_type": "biomodels", + "summary": "Authors developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. The study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4441, + "tag": "BioModels:BIOMD0000000947" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4442, + "tag": "Pharmacokinetic Study" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:30.983091+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000947", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3233": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3233, + "name": "Landberg2009 - Alkylresorcinol Dose Response", + "repository_type": "biomodels", + "summary": "Pharmacokinetic model of alkylresorcinols. Both plasma AR concentrations and urinary metabolites in 24-h samples showed a dose-response relation to increased AR intake, which strongly supports the hypothesis that ARs and their metabolites may be useful as biomarkers of whole-grain wheat and rye intakes.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4443, + "tag": "BioModels:BIOMD0000000948" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:31.556934+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000948", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3234": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3234, + "name": "Chitnis2008 - Mathematical model of malaria transmission", + "repository_type": "biomodels", + "summary": "Mathematical model of malaria transmission for low and high transmission rates.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4444, + "tag": "BioModels:BIOMD0000000949" + }, + { + "id": 4445, + "tag": "Disease Transmission" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:32.199030+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000949", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3235": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3235, + "name": "Chitnis2012 - Model Rift Valley Fever transmission between cattle and mosquitoes (Model 1)", + "repository_type": "biomodels", + "summary": "Mathematical model for Rift Valley Fever transmission between cattle and mosquitoes without infectious eggs.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4446, + "tag": "BioModels:BIOMD0000000950" + }, + { + "id": 4445, + "tag": "Disease Transmission" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:32.793125+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000950", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3236": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3236, + "name": "Mitrophanov2015 - Simulating extended Hockin Blood Coagulation Model under varied pH", + "repository_type": "biomodels", + "summary": "Mathematical model of the blood coagulation cascade with new kinetic rates to simulate acidosis. Extended Hockin2002 model. Reused Mitrophanov2011 model with new parameters k: 5, 6, 7, 10, 15, 16, 17, 22, 26, 31, 32, 43 and 44.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4447, + "tag": "BioModels:BIOMD0000000951" + }, + { + "id": 3592, + "tag": "Blood coagulation" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:33.346424+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000951", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3237": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3237, + "name": "Rodenfels2019 - Heat Oscillations Driven by the Embryonic Cell Cycle Reveal the Energetic Costs of Signaling", + "repository_type": "biomodels", + "summary": "
All living systems function out of equilibrium and exchange energy in the form of heat with their environment. Thus, heat flow can inform on the energetic costs of cellular processes, which are largely unknown. Here, we have repurposed an isothermal calorimeter to measure heat flow between developing zebrafish embryos and the surrounding medium. Heat flow increased over time with cell number. Unexpectedly, a prominent oscillatory component of the heat flow, with periods matching the synchronous early reductive cleavage divisions, persisted even when DNA synthesis and mitosis were blocked by inhibitors. Instead, the heat flow oscillations were driven by the phosphorylation and dephosphorylation reactions catalyzed by the cell-cycle oscillator, the biochemical network controlling mitotic entry and exit. We propose that the high energetic cost of cell-cycle signaling reflects the significant thermodynamic burden of imposing accurate and robust timing on cell proliferation during development.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4448, + "tag": "BioModels:BIOMD0000000952" + }, + { + "id": 4449, + "tag": "Danio rerio" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:33.915139+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000952", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3238": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3238, + "name": "Queralt2006 - Initiation of mitotic exit by downregulation of PP2A in budding yeast", + "repository_type": "biomodels", + "summary": "

Mathematical model of mitotic exit in budding yeast.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4450, + "tag": "BioModels:BIOMD0000000953" + }, + { + "id": 4451, + "tag": "Negative regulation of mitotic nuclear division" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3799, + "tag": "Saccharomyces cerevisiae (strain ATCC 204508 / S288c)" + } + ], + "timestamp_created": "2025-01-30 14:05:34.491184+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000953", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3239": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3239, + "name": "Pandey2018-reversible transition between quiescence and proliferation", + "repository_type": "biomodels", + "summary": "Cells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the restriction point (R-point), cells become irreversibly committed to the completion of the cell cycle independent of mitogen. The mechanism involving hyper-phosphorylation of retinoblastoma (Rb) and activation of transcription factor E2F is linked to the R-point passage. However, stress stimuli trigger exit from the cell cycle back to the mitogen-sensitive quiescent state after Rb hyper-phosphorylation but only until APC/CCdh1 inactivation. In this study, we developed a mathematical model to investigate the reversible transition between quiescence and proliferation in mammalian cells with respect to mitogen and stress signals. The model integrates the current mechanistic knowledge and accounts for the recent experimental observations with cells exiting quiescence and proliferating cells. We show that Cyclin E:Cdk2 couples Rb-E2F and APC/CCdh1 bistable switches and temporally segregates the R-point and the G1/S transition. A redox-dependent mutual antagonism between APC/CCdh1 and its inhibitor Emi1 makes the inactivation of APC/CCdh1 bistable. We show that the levels of Cdk inhibitor (CKI) and mitogen control the reversible transition between quiescence and proliferation. Further, we propose that shifting of the mitogen-induced transcriptional program to G2-phase in proliferating cells might result in an intermediate Cdk2 activity at the mitotic exit and in the immediate inactivation of APC/CCdh1. Our study builds a coherent framework and generates hypotheses that can be further explored by experiments.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4452, + "tag": "BioModels:BIOMD0000000954" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:35.029076+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000954", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3240": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3240, + "name": "Giordano2020 - SIDARTHE model of COVID-19 spread in Italy", + "repository_type": "biomodels", + "summary": "In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4453, + "tag": "BioModels:BIOMD0000000955" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:35.580775+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000955", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3241": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3241, + "name": "Bertozzi2020 - SIR model of scenarios of COVID-19 spread in CA and NY", + "repository_type": "biomodels", + "summary": "The coronavirus disease 2019 (COVID-19) pandemic has placed epidemic modeling at the forefront of worldwide public policy making. Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge. Here, we detail three regional scale models for forecasting and assessing the course of the pandemic. This work demonstrates the utility of parsimonious models for early-time data and provides an accessible framework for generating policy-relevant insights into its course. We show how these models can be connected to each other and to time series data for a particular region. Capable of measuring and forecasting the impacts of social distancing, these models highlight the dangers of relaxing nonpharmaceutical public health interventions in the absence of a vaccine or antiviral therapies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4455, + "tag": "BioModels:BIOMD0000000956" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:36.430455+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000956", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3242": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3242, + "name": "Roda2020 - SIR model of COVID-19 spread in Wuhan", + "repository_type": "biomodels", + "summary": "=Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4456, + "tag": "BioModels:BIOMD0000000957" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:37.014152+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000957", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3243": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "11", + "id": 3243, + "name": "Ndairou2020 - early-stage transmission dynamics of COVID-19 in Wuhan", + "repository_type": "biomodels", + "summary": "We propose a compartmental mathematical model for the spread of the COVID-19 disease with special focus on the transmissibility of super-spreaders individuals. We compute the basic reproduction number threshold, we study the local stability of the disease free equilibrium in terms of the basic reproduction number, and we investigate the sensitivity of the model with respect to the variation of each one of its parameters. Numerical simulations show the suitability of the proposed COVID-19 model for the outbreak that occurred in Wuhan, China", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4457, + "tag": "BioModels:BIOMD0000000958" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:37.527891+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000958", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3244": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3244, + "name": "Kok2020 - IFNalpha-induced signaling in Huh7.5 cells", + "repository_type": "biomodels", + "summary": "
The proposed ODE model describes dynamics of IFNalpha-induced signaling in Huh7.5 cells for a time scale up to 32 hours after stimulation with IFNalpha. The model consists of an IFN receptor model, formation/degradation and cytoplasmic/nuclear shuttling of STAT1-homodimers, STAT1-STAT2-heterodimers and STAT1-STAT2-IRF9 (ISGF3) complexes. On top, formation of feedback proteins STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3 and IRF2 and corresponding influences on IFNalpha signaling dynamics was incorporated. The model was calibrated by dose response and time course measurements over 32 hours as well as time courses for USP18 inhibition and overexpression experiments. As a special focus, the model is able to describe dose-dependent sensitization and desensitization of IFNalpha signaling in form of double treatment experiments at 0h and 24h.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4458, + "tag": "BioModels:BIOMD0000000959" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:38.057107+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000959", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3245": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3245, + "name": "Paiva2020 - SEIAHRD model of transmission dynamics of COVID-19", + "repository_type": "biomodels", + "summary": "This paper proposes a dynamic model to describe and forecast the dynamics of the coronavirus disease COVID-19 transmission. The model is based on an approach previously used to describe the Middle East Respiratory Syndrome (MERS) epidemic. This methodology is used to describe the COVID-19 dynamics in six countries where the pandemic is widely spread, namely China, Italy, Spain, France, Germany, and the USA. For this purpose, data from the European Centre for Disease Prevention and Control (ECDC) are adopted. It is shown how the model can be used to forecast new infection cases and new deceased and how the uncertainties associated to this prediction can be quantified. This approach has the advantage of being relatively simple, grouping in few mathematical parameters the many conditions which affect the spreading of the disease. On the other hand, it requires previous data from the disease transmission in the country, being better suited for regions where the epidemic is not at a very early stage. With the estimated parameters at hand, one can use the model to predict the evolution of the disease, which in turn enables authorities to plan their actions. Moreover, one key advantage is the straightforward interpretation of these parameters and their influence over the evolution of the disease, which enables altering some of them, so that one can evaluate the effect of public policy, such as social distancing. The results presented for the selected countries confirm the accuracy to perform predictions.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4459, + "tag": "BioModels:BIOMD0000000960" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:38.664144+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000960", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3246": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 3246, + "name": "McDougal2017 - Metabolism in ischemic cardiomyocytes", + "repository_type": "biomodels", + "summary": "Heart disease remains the leading cause of death globally. Although reperfusion following myocardial ischemia can prevent death by restoring nutrient flow, ischemia/reperfusion injury can cause significant heart damage. The mechanisms that drive ischemia/reperfusion injury are not well understood; currently, few methods can predict the state of the cardiac muscle cell and its metabolic conditions during ischemia. Here, we explored the energetic sustainability of cardiomyocytes, using a model for cellular metabolism to predict the levels of ATP following hypoxia. We modeled glycolytic metabolism with a system of coupled ordinary differential equations describing the individual metabolic reactions within the cardiomyocyte over time. Reduced oxygen levels and ATP consumption rates were simulated to characterize metabolite responses to ischemia. By tracking biochemical species within the cell, our model enables prediction of the cell\u2019s condition up to the moment of reperfusion. The simulations revealed a distinct transition between energetically sustainable and unsustainable ATP concentrations for various energetic demands. Our model illustrates how even low oxygen concentrations allow the cell to perform essential functions. We found that the oxygen level required for a sustainable level of ATP increases roughly linearly with the ATP consumption rate. An extracellular O2 concentration of ~0.007 mM could supply basic energy needs in non-beating cardiomyocytes, suggesting that increased collateral circulation may provide an important source of oxygen to sustain the cardiomyocyte during extended ischemia. Our model provides a time-dependent framework for studying various intervention strategies to change the outcome of reperfusion.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4460, + "tag": "BioModels:BIOMD0000000961" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:39.222455+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000961", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3247": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3247, + "name": "Zhao2020 - SUQC model of COVID-19 transmission dynamics in Wuhan, Hubei, and China", + "repository_type": "biomodels", + "summary": "Background - The coronavirus disease 2019 (COVID-19) is rapidly spreading in China and more than 30 countries over last two months. COVID-19 has multiple characteristics distinct from other infectious diseases, including high infectivity during incubation, time delay between real dynamics and daily observed number of confirmed cases, and the intervention effects of implemented quarantine and control measures. Methods - We develop a Susceptible, Un-quanrantined infected, Quarantined infected, Confirmed infected (SUQC) model to characterize the dynamics of COVID-19 and explicitly parameterize the intervention effects of control measures, which is more suitable for analysis than other existing epidemic models. Results - The SUQC model is applied to the daily released data of the confirmed infections to analyze the outbreak of COVID-19 in Wuhan, Hubei (excluding Wuhan), China (excluding Hubei) and four first-tier cities of China. We found that, before January 30, 2020, all these regions except Beijing had a reproductive number R &amp;gt; 1, and after January 30, all regions had a reproductive number R lesser than 1, indicating that the quarantine and control measures are effective in preventing the spread of COVID-19. The confirmation rate of Wuhan estimated by our model is 0.0643, substantially lower than that of Hubei excluding Wuhan (0.1914), and that of China excluding Hubei (0.2189), but it jumps to 0.3229 after February 12 when clinical evidence was adopted in new diagnosis guidelines. The number of unquarantined infected cases in Wuhan on February 12, 2020 is estimated to be 3,509 and declines to 334 on February 21, 2020. After fitting the model with data as of February 21, 2020, we predict that the end time of COVID-19 in Wuhan and Hubei is around late March, around mid March for China excluding Hubei, and before early March 2020 for the four tier-one cities. A total of 80,511 individuals are estimated to be infected in China, among which 49,510 are from Wuhan, 17,679 from Hubei (excluding Wuhan), and the rest 13,322 from other regions of China (excluding Hubei). Note that the estimates are from a deterministic ODE model and should be interpreted with some uncertainty. Conclusions - We suggest that rigorous quarantine and control measures should be kept before early March in Beijing, Shanghai, Guangzhou and Shenzhen, and before late March in Hubei. The model can also be useful to predict the trend of epidemic and provide quantitative guide for other countries at high risk of outbreak, such as South Korea, Japan, Italy and Iran.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4461, + "tag": "BioModels:BIOMD0000000962" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:39.731729+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000962", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3248": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3248, + "name": "Weitz2020 - SIR model of COVID-19 transmission with shielding", + "repository_type": "biomodels", + "summary": "The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 2020. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, developing what we term \u2018shield immunity\u2019 at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence and in the potential development of plasma-based therapies.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4462, + "tag": "BioModels:BIOMD0000000963" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:40.289042+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000963", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3249": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3249, + "name": "Mwalili2020 - SEIR model of COVID-19 transmission and environmental pathogen prevalence", + "repository_type": "biomodels", + "summary": "Objective: Coronavirus disease 2019 (COVID-19) is a pandemic respiratory illness spreading from person-to-person caused by a novel coronavirus and poses a serious public health risk. The goal of this study was to apply a modified susceptible-exposed-infectious-recovered (SEIR) compartmental mathematical model for prediction of COVID-19 epidemic dynamics incorporating pathogen in the environment and interventions. The next generation matrix approach was used to determine the basic reproduction number R0. The model equations are solved numerically using fourth and ffth order Runge\u2013Kutta methods. Results: We found an R0 of 2.03, implying that the pandemic will persist in the human population in the absence of strong control measures. Results after simulating various scenarios indicate that disregarding social distancing and hygiene measures can have devastating effects on the human population. The model shows that quarantine of contacts and isolation of cases can help halt the spread on novel coronavirus.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4463, + "tag": "BioModels:BIOMD0000000964" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:40.845256+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000964", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3250": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3250, + "name": "LeBeau1999 - IP3-dependent intracellular calcium oscillations due to agonist stimulation from Cholecytokinin", + "repository_type": "biomodels", + "summary": "The properties of inositol 1,4,5-trisphosphate (IP3)-dependent intracellular calcium oscillations in pancreatic acinar cells depend crucially on the agonist used to stimulate them. Acetylcholine or carbachol (CCh) cause high-frequency (10\u201312-s period) calcium oscillations that are superimposed on a raised baseline, while cholecystokinin (CCK) causes long-period (.100-s period) baseline spiking. We show that physiological concentrations of CCK induce rapid phosphorylation of the IP3 receptor, which is not true of physiological concentrations of CCh. Based on this and other experimental data, we construct a mathematical model of agonist-specific intracellular calcium oscillations in pancreatic acinar cells. Model simulations agree with previous experimental work on the rates of activation and inactivation of the IP3 receptor by calcium (DuFour, J.-F., I.M. Arias, and T.J. Turner. 1997. J. Biol. Chem. 272:2675\u20132681), and reproduce both short-period, raised baseline oscillations, and long-period baseline spiking. The steady state open probability curve of the model IP3 receptor is an increasing function of calcium concentration, as found for type-III IP3 receptors by Hagar et al. (Hagar, R.E., A.D. Burgstahler, M.H. Nathanson, and B.E. Ehrlich. 1998. Nature. 396:81\u201384). We use the model to predict the effect of the removal of external calcium, and this prediction is confirmed experimentally. We also predict that, for type-III IP3 receptors, the steady state open probability curve will shift to lower calcium concentrations as the background IP3 concentration increases. We conclude that the differences between CCh- and CCK-induced calcium oscillations in pancreatic acinar cells can be explained by two principal mechanisms: (a) CCK causes more phosphorylation of the IP3 receptor than does CCh, and the phosphorylated receptor cannot pass calcium current; and (b) the rate of calcium ATPase pumping and the rate of calcium influx from the outside the cell are greater in the presence of CCh than in the presence of CCK.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4464, + "tag": "BioModels:BIOMD0000000965" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:41.456180+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000965", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3251": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3251, + "name": "Cui2008 - in vitro transcriptional response of zinc homeostasis system in Escherichia coli", + "repository_type": "biomodels", + "summary": "BACKGROUND: The zinc homeostasis system in Escherichia coli is one of the most intensively studied prokaryotic zinc homeostasis systems. Its underlying regulatory machine consists of repression on zinc influx through ZnuABC by Zur (Zn2+ uptake regulator) and activation on zinc efflux via ZntA by ZntR (a zinc-responsive regulator). Although these transcriptional regulations seem to be well characterized, and there is an abundance of detailed in vitro experimental data available, as yet there is no mathematical model to help interpret these data. To our knowledge, the work described here is the first attempt to use a mathematical model to simulate these regulatory relations and to help explain the in vitro experimental data. RESULTS: We develop a unified mathematical model consisting of 14 reactions to simulate the in vitro transcriptional response of the zinc homeostasis system in E. coli. Firstly, we simulate the in vitro Zur-DNA interaction by using two of these reactions, which are expressed as 4 ordinary differential equations (ODEs). By imposing the conservation restraints and solving the relevant steady state equations, we find that the simulated sigmoidal curve matches the corresponding experimental data. Secondly, by numerically solving the ODEs for simulating the Zur and ZntR run-off transcription experiments, and depicting the simulated concentrations of zntA and znuC transcripts as a function of free zinc concentration, we find that the simulated curves fit the corresponding in vitro experimental data. Moreover, we also perform simulations, after taking into consideration the competitive effects of ZntR with the zinc buffer, and depict the simulated concentration of zntA transcripts as a function of the total ZntR concentration, both in the presence and absence of Zn(II). The obtained simulation results are in general agreement with the corresponding experimental data. CONCLUSION: Simulation results show that our model can quantitatively reproduce the results of several of the in vitro experiments conducted by Outten CE and her colleagues. Our model provides a detailed insight into the dynamics of the regulatory system and also provides a general framework for simulating in vitro metal-binding and transcription experiments and interpreting the relevant experimental data.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4465, + "tag": "BioModels:BIOMD0000000966" + }, + { + "id": 2994, + "tag": "Escherichia coli" + }, + { + "id": 4238, + "tag": "Ordinary differential equation model" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4466, + "tag": "Zinc ion homeostasis" + } + ], + "timestamp_created": "2025-01-30 14:05:42.084484+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000966", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3252": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3252, + "name": "McLean1991 - Behaviour of HIV in the presence of zidovudine", + "repository_type": "biomodels", + "summary": "A new mechanism is proposed for the apparent breakthrough of HIV that occurs approximately 6 months after the commencement of therapy with zidovudine (AZT). Using a simple mathematical model of the interacting population dynamics of HIV and its major host cell in the circulation (the CD4+ lymphocyte), predicted patterns of HIV plasma viraemia in the weeks following treatment with zidovudine are generated. These are in close agreement with observed patterns despite the fact that the model contains no mechanisms for the development of drug-resistant strains of virus. It is suggested that the patterns of viral abundance observed during the first 6 months after treatment may be the result of non-linearities in the interactions between HIV and CD4+ cells, and that it is only after the first post-treatment burst of viral production that drug resistance plays an important role.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4467, + "tag": "BioModels:BIOMD0000000967" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4468, + "tag": "Human immunodeficiency virus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:42.572564+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000967", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3253": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3253, + "name": "Palmer2008 - Negative Feedback in IL-7 mediated Jak-Stat signaling", + "repository_type": "biomodels", + "summary": "Interleukin-7 (IL-7) is an essential cytokine for the development and homeostatic maintenance of T and B lymphocytes. Binding of IL-7 to its cognate receptor, the IL-7 receptor (IL-7R), activates multiple pathways that regulate lymphocyte survival, glucose uptake, proliferation and differentiation. There has been much interest in understanding how IL-7 receptor signaling is modulated at multiple interconnected network levels. This review examines how the strength of the signal through the IL-7 receptor is modulated in T and B cells, including the use of shared receptor components, signaling crosstalk, shared interaction domains, feedback loops, integrated generegulation, multimerization and ligand competition. We discuss how these network control mechanisms could integrate to govern the properties of IL-7R signaling in lymphocytes in health and disease. Analysis of IL-7 receptor signaling at a network level in a systematic manner will allow for a comprehensive approach to understanding the impact of multiple signaling pathways on lymphocyte biology.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4469, + "tag": "BioModels:BIOMD0000000968" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:43.069570+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000968", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3254": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3254, + "name": "Cuadros2020 - SIHRD spatiotemporal model of COVID-19 transmission in Ohio", + "repository_type": "biomodels", + "summary": "The role of geospatial disparities in the dynamics of the COVID-19 pandemic is poorly understood. We developed a spatially-explicit mathematical model to simulate transmission dynamics of COVID-19 disease infection in relation with the uneven distribution of the healthcare capacity in Ohio, U.S. The results showed substantial spatial variation in the spread of the disease, with localized areas showing marked differences in disease attack rates. Higher COVID-19 attack rates experienced in some highly connected and urbanized areas (274 cases per 100,000 people) could substantially impact the critical health care response of these areas regardless of their potentially high healthcare capacity compared to more rural and less connected counterparts (85 cases per 100,000). Accounting for the spatially uneven disease diffusion linked to the geographical distribution of the critical care resources is essential in designing effective prevention and control programmes aimed at reducing the impact of COVID-19 pandemic.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4470, + "tag": "BioModels:BIOMD0000000969" + }, + { + "id": 2456, + "tag": "COVID-19" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:43.592028+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000969", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3255": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3255, + "name": "Hou2020 - SEIR model of COVID-19 transmission in Wuhan", + "repository_type": "biomodels", + "summary": "A novel coronavirus pneumonia, first identified in Wuhan City and referred to as COVID-19 by the World Health Organization, has been quickly spreading to other cities and countries. To control the epidemic, the Chinese government mandated a quarantine of the Wuhan city on January 23, 2020. To explore the effectiveness of the quarantine of the Wuhan city against this epidemic, transmission dynamics of COVID-19 have been estimated. A well-mixed "susceptible exposed infectious recovered" (SEIR) compartmental model was employed to describe the dynamics of the COVID-19 epidemic based on epidemiological characteristics of individuals, clinical progression of COVID-19, and quarantine intervention measures of the authority. Considering infected individuals as contagious during the latency period, the well-mixed SEIR model fitting results based on the assumed contact rate of latent individuals are within 6-18, which represented the possible impact of quarantine and isolation interventions on disease infections, whereas other parameter were suppose as unchanged under the current intervention. The present study shows that, by reducing the contact rate of latent individuals, interventions such as quarantine and isolation can effectively reduce the potential peak number of COVID-19 infections and delay the time of peak infection.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4471, + "tag": "BioModels:BIOMD0000000970" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:44.092021+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000970", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3256": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3256, + "name": "Tang2020 - Estimation of transmission risk of COVID-19 and impact of public health interventions", + "repository_type": "biomodels", + "summary": "Since the emergence of the first cases in Wuhan, China, the novel coronavirus (2019-nCoV) infection has been quickly spreading out to other provinces and neighboring countries. Estimation of the basic reproduction number by means of mathematical modeling can be helpful for determining the potential and severity of an outbreak and providing critical information for identifying the type of disease interventions and intensity. A deterministic compartmental model was devised based on the clinical progression of the disease, epidemiological status of the individuals, and intervention measures. The estimations based on likelihood and model analysis show that the control reproduction number may be as high as 6.47 (95% CI 5.71\u20137.23). Sensitivity analyses show that interventions, such as intensive contact tracing followed by quarantine and isolation, can effectively reduce the control reproduction number and transmission risk, with the effect of travel restriction adopted by Wuhan on 2019-nCoV infection in Beijing being almost equivalent to increasing quarantine by a 100 thousand baseline value. It is essential to assess how the expensive, resource-intensive measures implemented by the Chinese authorities can contribute to the prevention and control of the 2019-nCoV infection, and how long they should be maintained. Under the most restrictive measures, the outbreak is expected to peak within two weeks (since 23 January 2020) with a significant low peak value. With travel restriction (no imported exposed individuals to Beijing), the number of infected individuals in seven days will decrease by 91.14% in Beijing, compared with the scenario of no travel restriction.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4472, + "tag": "BioModels:BIOMD0000000971" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:44.585919+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000971", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3257": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3257, + "name": "Tang2020 - Estimation of transmission risk of COVID-19 and impact of public health interventions - update", + "repository_type": "biomodels", + "summary": "The basic reproduction number of an infectious agent is the average number of infections one case can generate over the course of the infectious period, in a na\u00efve, uninfected population. It is well-known that the estimation of this number may vary due to several methodological issues, including different assumptions and choice of parameters, utilized models, used datasets and estimation period. With the spreading of the novel coronavirus (2019-nCoV) infection, the reproduction number has been found to vary, reflecting the dynamics of transmission of the coronavirus outbreak as well as the case reporting rate. Due to significant variations in the control strategies, which have been changing over time, and thanks to the introduction of detection technologies that have been rapidly improved, enabling to shorten the time from infection/symptoms onset to diagnosis, leading to faster confirmation of the new coronavirus cases, our previous estimations on the transmission risk of the 2019-nCoV need to be revised. By using time-dependent contact and diagnose rates, we refit our previously proposed dynamics transmission model to the data available until January 29th, 2020 and re-estimated the effective daily reproduction ratio that better quantifies the evolution of the interventions. We estimated when the effective daily reproduction ratio has fallen below 1 and when the epidemics will peak. Our updated findings suggest that the best measure is persistent and strict self-isolation. The epidemics will continue to grow, and can peak soon with the peak time depending highly on the public health interventions practically implemented.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4473, + "tag": "BioModels:BIOMD0000000972" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:45.078006+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000972", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3258": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3258, + "name": "Dasgupta2020 - Reduced model of receptor clusturing and aggregation", + "repository_type": "biomodels", + "summary": "a simple kinetic mass-action-law-based model could be utilized to adequately describe clustering inresponse to activation both in 2D and in 3D", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4474, + "tag": "BioModels:BIOMD0000000973" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:45.585436+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000973", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3259": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3259, + "name": "Carcione2020 - Deterministic SEIR simulation of a COVID-19 outbreak", + "repository_type": "biomodels", + "summary": "An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4475, + "tag": "BioModels:BIOMD0000000974" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:46.130327+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000974", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3260": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3260, + "name": "Leloup2004 - Mammalian Circadian Rhythm models for 23.8 and 24.2 hours timeperiod", + "repository_type": "biomodels", + "summary": "We extend the study of a computational model recently proposed for the mammalian circadian clock (Proc. Natl Acad. Sci. USA 100 (2003) 7051). The model, based on the intertwined positive and negative regulatory loops involving the Per, Cry, Bmal1, and Clock genes, can give rise to sustained circadian oscillations in conditions of continuous darkness. These limit cycle oscillations correspond to circadian rhythms autonomously generated by suprachiasmatic nuclei and by some peripheral tissues. By using different sets of parameter values producing circadian oscillations, we compare the effect of the various parameters and show that both the occurrence and the period of the oscillations are generally most sensitive to parameters related to synthesis or degradation of Bmal1 mRNA and BMAL1 protein. The mechanism of circadian oscillations relies on the formation of an inactive complex between PER and CRY and the activators CLOCK and BMAL1 that enhance Per and Cry expression. Bifurcation diagrams and computer simulations nevertheless indicate the possible existence of a second source of oscillatory behavior. Thus, sustained oscillations might arise from the sole negative autoregulation of Bmal1 expression. This second oscillatory mechanism may not be functional in physiological conditions, and its period need not necessarily be circadian. When incorporating the light-induced expression of the Per gene, the model accounts for entrainment of the oscillations by light-dark (LD) cycles. Long-term suppression of circadian oscillations by a single light pulse can occur in the model when a stable steady state coexists with a stable limit cycle. The phase of the oscillations upon entrainment in LD critically depends on the parameters that govern the level of CRY protein. Small changes in the parameters governing CRY levels can shift the peak in Per mRNA from the L to the D phase, or can prevent entrainment. The results are discussed in relation to physiological disorders of the sleep-wake cycle linked to perturbations of the human circadian clock, such as the familial advanced sleep phase syndrome or the non-24h sleep-wake syndrome.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4476, + "tag": "BioModels:BIOMD0000000975" + }, + { + "id": 4069, + "tag": "Entrainment of circadian clock by photoperiod" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:46.642495+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000975", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3261": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3261, + "name": "Ghanbari2020 - forecasting the second wave of COVID-19 in Iran", + "repository_type": "biomodels", + "summary": "One of the common misconceptions about COVID-19 disease is to assume that we will not see a recurrence after the first wave of the disease has subsided. This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. From the results, improving the recovery rate of people with weak immune systems via appropriate medical incentives is resulted as one of the most effective prescriptions to prevent the widespread unbridled outbreak of the second wave of COVID-19.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4477, + "tag": "BioModels:BIOMD0000000976" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:47.132200+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000976", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3262": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3262, + "name": "Sarkar2020 - SAIR model of COVID-19 transmission with quarantine measures in India", + "repository_type": "biomodels", + "summary": "In India, 100,340 confirmed cases and 3155 confirmed deaths due to COVID-19 were reported as of May 18, 2020. Due to absence of specific vaccine or therapy, non-pharmacological interventions including so- cial distancing, contact tracing are essential to end the worldwide COVID-19. We propose a mathematical model that predicts the dynamics of COVID-19 in 17 provinces of India and the overall India. A complete scenario is given to demonstrate the estimated pandemic life cycle along with the real data or history to date, which in turn divulges the predicted inflection point and ending phase of SARS-CoV-2. The proposed model monitors the dynamics of six compartments, namely susceptible (S), asymptomatic (A), recovered (R), infected (I), isolated infected ( I q ) and quarantined susceptible ( S q ), collectively expressed SARII q S q . A sensitivity analysis is conducted to determine the robustness of model predictions to parameter values and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in India. Our re- sults reveal that achieving a reduction in the contact rate between uninfected and infected individuals by quarantined the susceptible individuals, can effectively reduce the basic reproduction number. Our model simulations demonstrate that the elimination of ongoing SARS-CoV-2 pandemic is possible by combining the restrictive social distancing and contact tracing. Our predictions are based on real data with reason- able assumptions, whereas the accurate course of epidemic heavily depends on how and when quaran- tine, isolation and precautionary measures are enforced.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4478, + "tag": "BioModels:BIOMD0000000977" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:47.622482+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000977", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3263": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3263, + "name": "Mukandavire2020 - SEIR model of early COVID-19 transmission in South Africa", + "repository_type": "biomodels", + "summary": "The emergence and fast global spread of COVID-19 has presented one of the greatest public health challenges in modern times with no proven cure or vaccine. Africa is still early in this epidemic, therefore the extent of disease severity is not yet clear. We used a mathematical model to fit to the observed cases of COVID-19 in South Africa to estimate the basic reproductive number and critical vaccination coverage to control the disease for different hypothetical vaccine efficacy scenarios. We also estimated the percentage reduction in effective contacts due to the social distancing measures implemented. Early model estimates show that COVID-19 outbreak in South Africa had a basic reproductive number of 2.95 (95% credible interval [CrI] 2.83\u20133.33). A vaccine with 70% efficacy had the capacity to contain COVID-19 outbreak but at very higher vaccination coverage 94.44% (95% Crl 92.44\u201399.92%) with a vaccine of 100% efficacy requiring 66.10% (95% Crl 64.72\u201369.95%) coverage. Social distancing measures put in place have so far reduced the number of social contacts by 80.31% (95% Crl 79.76\u201380.85%). These findings suggest that a highly efficacious vaccine would have been required to contain COVID-19 in South Africa. Therefore, the current social distancing measures to reduce contacts will remain key in controlling the infection in the absence of vaccines and other therapeutics.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4479, + "tag": "BioModels:BIOMD0000000978" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:48.119088+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000978", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3264": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3264, + "name": "Malkov2020 - SEIRS model of COVID-19 transmission with reinfection", + "repository_type": "biomodels", + "summary": "Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully protected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious-Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are indistinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4480, + "tag": "BioModels:BIOMD0000000979" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:48.606092+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000979", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3265": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3265, + "name": "Malkov2020 - SEIRS model of COVID-19 transmission with time-varying R values and reinfection", + "repository_type": "biomodels", + "summary": "Epidemiological models of COVID-19 transmission assume that recovered individuals have a fully pro- tected immunity. To date, there is no definite answer about whether people who recover from COVID-19 can be reinfected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the absence of a clear answer about the risk of reinfection, it is instructive to consider the possible scenarios. To study the epidemiological dynamics with the possibility of reinfection, I use a Susceptible-Exposed-Infectious- Resistant-Susceptible model with the time-varying transmission rate. I consider three different ways of modeling reinfection. The crucial feature of this study is that I explore both the difference between the reinfection and no-reinfection scenarios and how the mitigation measures affect this difference. The principal results are the following. First, the dynamics of the reinfection and no-reinfection scenarios are in- distinguishable before the infection peak. Second, the mitigation measures delay not only the infection peak, but also the moment when the difference between the reinfection and no-reinfection scenarios becomes prominent. These results are robust to various modeling assumptions.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4481, + "tag": "BioModels:BIOMD0000000980" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:49.113648+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000980", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3266": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3266, + "name": "Wan2020 - risk estimation and prediction of the transmission of COVID-19 in maninland China excluding Hubei province", + "repository_type": "biomodels", + "summary": "Background: In December 2019, an outbreak of coronavirus disease (later named as COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in the mainland of China excluding Hubei province based on the published data and a novel mathematical model.Methods: A novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of the mainland of China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases between 20 January and 3 March 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number (Rc), as well as the effective daily reproduction ratio- Re(t), of the disease transmission in the mainland of China excluding Hubei province.Results: The estimation outcomes indicate that Rc is 3.36 (95% CI: 3.20-3.64) and Re(t) has dropped below 1 since 31 January 2020, which implies that the containment strategies implemented by the Chinese government in the mainland of China are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to a prolonged disease transmission period and more people would be infected, and may even cause a second wave of epidemic or outbreaks. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020.Conclusions: To ensure the pandemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, keeping social distance, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in the mainland of China apart from Hubei province.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4482, + "tag": "BioModels:BIOMD0000000981" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:49.680192+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000981", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3267": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3267, + "name": "Law2020 - SIR model of COVID-19 transmission in Malyasia with time-varying parameters", + "repository_type": "biomodels", + "summary": "The susceptible-infectious-removed (SIR) model offers the simplest framework to study transmission dynamics of COVID-19, however, it does not factor in its early depleting trend observed during a lockdown. We modified the SIR model to specifically simulate the early depleting transmission dynamics of COVID-19 to better predict its temporal trend in Malaysia. The classical SIR model was fitted to observed total (I total), active (I) and removed (R) cases of COVID-19 before lockdown to estimate the basic reproduction number. Next, the model was modified with a partial time-varying force of infection, given by a proportionally depleting transmission coefficient, [Formula: see text] and a fractional term, z. The modified SIR model was then fitted to observed data over 6 weeks during the lockdown. Model fitting and projection were validated using the mean absolute percent error (MAPE). The transmission dynamics of COVID-19 was interrupted immediately by the lockdown. The modified SIR model projected the depleting temporal trends with lowest MAPE for I total, followed by I, I daily and R. During lockdown, the dynamics of COVID-19 depleted at a rate of 4.7% each day with a decreased capacity of 40%. For 7-day and 14-day projections, the modified SIR model accurately predicted I total, I and R. The depleting transmission dynamics for COVID-19 during lockdown can be accurately captured by time-varying SIR model. Projection generated based on observed data is useful for future planning and control of COVID-19.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4483, + "tag": "BioModels:BIOMD0000000982" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:50.220609+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000982", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3268": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3268, + "name": "Zongo2020 - model of COVID-19 transmission dynamics under containment measures in France", + "repository_type": "biomodels", + "summary": "The main objective of this paper is to address the following question: are the containment measures imposed by most of the world governments effective and sufficient to stop the epidemic of COVID-19 beyond the lock-down period? In this paper, we propose a mathematical model which allows us to investigate and analyse this problem. We show by means of the reproductive number, R0 that the containment measures appear to have slowed the growth of the outbreak. Nevertheless, these measures remain only effective as long as a very large fraction of population, p, greater than the critical value 1 \u2212 1/R0 remains confined. Using French current data, we give some simulation experiments with five scenarios including: (i) the validation of model with p estimated to 93%, (ii) the study of the effectiveness of containment measures, (iii) the study of the effectiveness of the large-scale testing, (iv) the study of the social distancing and wearing masks measures and (v) the study taking into account the combination of the large-scale test of detection of infected individuals and the social distancing with linear progressive easing of restrictions. The latter scenario was shown to be effective at overcoming the outbreak if the transmission rate decreases to 75% and the number of tests of detection is multiplied by three. We also noticed that if the measures studied in our five scenarios are taken separately then the second wave might occur at least as far as the parameter values remain unchanged.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4484, + "tag": "BioModels:BIOMD0000000983" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:50.755412+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000983", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3269": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3269, + "name": "Fang2020 - SEIR model of COVID-19 transmission considering government interventions in Wuhan", + "repository_type": "biomodels", + "summary": "Using the parameterized susceptible\u2010exposed\u2010infectious\u2010recovered model, we simulated the spread dynamics of coronavirus disease 2019 (COVID\u201019) outbreak and impact of different control measures, conducted the sensitivity analysis to identify the key factor, plotted the trend curve of effective reproductive number (R), and performed data fitting after the simulation. By simulation and data fitting, the model showed the peak existing confirmed cases of 59 769 arriving on 15 February 2020, with the coefficient of determination close to 1 and the fitting bias 3.02%, suggesting high precision of the datafitting results. More rigorous government control policies were associated with a slower increase in the infected population. Isolation and protective procedures would be less effective as more cases accrue, so the optimization of the treatment plan and the development of specific drugs would be of more importance. There was an upward trend of R in the beginning, followed by a downward trend, a temporary rebound, and another continuous decline. The feature of high infectiousness for severe acute respiratory syndrome coronavirus 2(SARS\u2010CoV\u20102) led to an upward trend, and government measures contributed to the temporary rebound and declines. The declines of R could be exploited as strong evidence for the effectiveness of the interventions. Evidence from the fourphase stringent measures showed that it was significant to ensure early detection, early isolation, early treatment, adequate medical supplies, patients\u2019 being admitted to designated hospitals, and comprehensive therapeutic strategy. Collaborative efforts are required to combat the novel coronavirus, focusing on both persistent strict domestic interventions and vigilance against exogenous imported cases.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4485, + "tag": "BioModels:BIOMD0000000984" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:51.251248+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000984", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3270": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3270, + "name": "Gex-Fabry1984 - model of receptor-mediated endocytosis of EGF in BALB/c 3T3 cells", + "repository_type": "biomodels", + "summary": "We present a mathematical model for analyzing, simulating, and quantitating the dynamic and steady-state characteristics of receptor-mediated endocytosis. The basic processes considered by the model are ligand-receptor binding, diffusion of receptors and ligand-receptor complexes in the plane of the membrane toward and away from coated pits, binding of ligand-receptor complexes to coated pit proteins, endocytosis of coated pit contents, degradation of ligand, and recycling of undegraded receptors. The model accounts quantitatively for a wide variety of kinetic data and makes new predictions about steady-state characteristics. We show that for homogeneous receptors the slope of the Scatchard plot is not necessarily constant but can have a positive or negative derivative, depending on the concentration of coated pit proteins and their reactivity. This finding suggests that binding data, which show linear and concave curves, might be explainable be a simple coated pit-related mechanism. Similarly the relationship between the x-intercept and the number of receptors is also affected by kinetic parameters controlling endocytosis. We briefly discuss these results in terms of possible mechanisms for the action of tumor promoters, the large variations in receptor number and affinity in the literature, and methods for quantitative characterization of parameters.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4486, + "tag": "BioModels:BIOMD0000000985" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:51.808776+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000985", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3271": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3271, + "name": "Aubry1995 - Multi-compartment model of fluid-phase endocytosis kinetics in Dictyostelium discoideum", + "repository_type": "biomodels", + "summary": "Fluid-phase endoeytosis (pinocytosis) kinetics were studied in Dictyostelium discoideum amoebae from the axenic strain Ax-2 that exhibits high rates of fluid-phase endoeytosis when cultured in liquid nutrient media. Fluorescein-labelled dextran (FITC-dextran) was used as a marker in continuous uptake- and in pulse-chase exocytosis experiments. In the latter case, efflux of the marker was monitored on cells loaded for short periods of time and resuspended in marker-free medium. A multicompartmental model was developed which describes satisfactorily fluid-phase endocytosis kinetics. In particular, it accounts correctly for the extended latency period before exocytosis in pulse-chase experiments and it suggests the existence of some sorts of maturation stages in the pathway.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4487, + "tag": "BioModels:BIOMD0000000986" + }, + { + "id": 4488, + "tag": "Dictyostelium discoideum AX2" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:52.387993+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000986", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3272": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3272, + "name": "Aubry1995 - Nine-compartment model of fluid-phase endocytosis kinetics in Dictyostelium discoideum", + "repository_type": "biomodels", + "summary": "Fluid-phase endoeytosis (pinocytosis) kinetics were studied in Dictyostelium discoideum amoebae from the axenic strain Ax-2 that exhibits high rates of fluid-phase endoeytosis when cultured in liquid nutrient media. Fluorescein-labelled dextran (FITC-dextran) was used as a marker in continuous uptake- and in pulse-chase exocytosis experiments. In the latter case, efflux of the marker was monitored on cells loaded for short periods of time and resuspended in marker-free medium. A multicompartmental model was developed which describes satisfactorily fluid-phase endocytosis kinetics. In particular, it accounts correctly for the extended latency period before exocytosis in pulse-chase experiments and it suggests the existence of some sorts of maturation stages in the pathway.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4489, + "tag": "BioModels:BIOMD0000000987" + }, + { + "id": 4488, + "tag": "Dictyostelium discoideum AX2" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:52.953007+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000987", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3273": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3273, + "name": "Westerhoff2020 - systems biology model of the coronavirus pandemic 2020", + "repository_type": "biomodels", + "summary": "

Using standard systems biology methodologies a 14-compartment dynamic model was developed for the Corona virus epidemic. The model predicts that: (i) it will be impossible to limit lockdown intensity such that sufficient herd immunity develops for this epidemic to die down, (ii) the death toll from the SARS-CoV-2 virus decreases very strongly with increasing intensity of the lockdown, but (iii) the duration of the epidemic increases at first with that intensity and then decreases again, such that (iv) it may be best to begin with selecting a lockdown intensity beyond the intensity that leads to the maximum duration, (v) an intermittent lockdown strategy should also work and might be more acceptable socially and economically, (vi) an initially intensive but adaptive lockdown strategy should be most efficient, both in terms of its low number of casualties and shorter duration, (vii) such an adaptive lockdown strategy offers the advantage of being robust to unexpected imports of the virus, e.g. due to international travel, (viii) the eradication strategy may still be superior as it leads to even fewer deaths and a shorter period of economic downturn, but should have the adaptive strategy as backup in case of unexpected infection imports, (ix) earlier detection of infections is the most effective way in which the epidemic can be controlled, whilst waiting for vaccines.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4490, + "tag": "BioModels:BIOMD0000000988" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:53.493132+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000988", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3274": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "12", + "id": 3274, + "name": "Strasen2018 - TGFb SMAD Signalling - Dose dependent dynamics upon TGFb stimulation", + "repository_type": "biomodels", + "summary": "
This model simulates TGFb dose dependent kinetics of The SMADs.  TGFb ligand dose applied are  1pM, 2.5pM, 5pM, 25pM, and 100pM as explained in the manuscript.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4491, + "tag": "BioModels:BIOMD0000000989" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:54.011378+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000989", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3275": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "13", + "id": 3275, + "name": "Strasen2018 - TGFb SMAD Signalling - Degradation of 25pM ligand (TGFb)", + "repository_type": "biomodels", + "summary": "
MOdel simulates 25pM ligand degradation kinetics as shown in Figure 4D
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4492, + "tag": "BioModels:BIOMD0000000990" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:54.528327+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000990", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3276": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3276, + "name": "Okuonghae2020 - SEAIR model of COVID-19 transmission in Lagos, Nigeria", + "repository_type": "biomodels", + "summary": "This work examines the impact of various non-pharmaceutical control measures (government and personal) on the population dynamics of the novel coronavirus disease 2019 (COVID-19) in Lagos, Nigeria, using an appropriately formulated mathematical model. Using the available data, since its first reported case on 16 March 2020, we seek to develop a predicative tool for the cumulative number of reported cases and the number of active cases in Lagos; we also estimate the basic reproduction number of the disease outbreak in the aforementioned State in Nigeria. Using numerical simulations, we show the effect of control measures, specifically the common social distancing, use of face mask and case detection (via contact tracing and subsequent testings) on the dynamics of COVID-19. We also provide forecasts for the cumulative number of reported cases and active cases for different levels of the control measures being implemented. Numerical simulations of the model show that if at least 55% of the population comply with the social distancing regulation with about 55% of the population effectively making use of face masks while in public, the disease will eventually die out in the population and that, if we can step up the case detection rate for symptomatic individuals to about 0.8 per day, with about 55% of the population complying with the social distancing regulations, it will lead to a great decrease in the incidence (and prevalence) of COVID-19.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4493, + "tag": "BioModels:BIOMD0000000991" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4454, + "tag": "Severe acute respiratory syndrome coronavirus 2" + } + ], + "timestamp_created": "2025-01-30 14:05:55.105473+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000991", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3277": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "15", + "id": 3277, + "name": "Strasen2018 - TGFb SMAD Signalling - Restimulation with 5pM TGFb at 3hr", + "repository_type": "biomodels", + "summary": "
 Restimulation with 5pM TGF\u03b2 at 3hr - Figure 4E
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4494, + "tag": "BioModels:BIOMD0000000994" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:55.603455+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000994", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3278": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3278, + "name": "Strasen2018 - TGFb SMAD Signalling - Restimulation with 5pM TGFb at 8hr", + "repository_type": "biomodels", + "summary": "
Restimulation with 5pM TGF\u03b2 at 8 hrs, shown in Figure 4F  
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4495, + "tag": "BioModels:BIOMD0000000995" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:56.106688+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000995", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3279": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3279, + "name": "Strasen2018 - TGFb SMAD Signalling - Restimulation with 100pM TGFb at 6hr", + "repository_type": "biomodels", + "summary": "
Restimulation with 100pM TGF\u03b2 at 6hr- figure 4G
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4496, + "tag": "BioModels:BIOMD0000000996" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:56.641878+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000996", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3280": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3280, + "name": "Strasen2018 - TGFb SMAD Signalling - DRB treatment", + "repository_type": "biomodels", + "summary": "
 Simulates figure 4H. DRB = 1 simulates the wild type condition and DRB = 0.2 simulates the DRB treated condition.  
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4497, + "tag": "BioModels:BIOMD0000000997" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:57.164692+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000997", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3281": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3281, + "name": "Strasen2018 - TGFb SMAD Signalling Class 1", + "repository_type": "biomodels", + "summary": "Model depicting response Class 1 defined by a minimal response to stimulation which is considered as non\u2010responders.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4498, + "tag": "BioModels:BIOMD0000000998" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:57.685421+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000998", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3282": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3282, + "name": "Strasen2018 - TGFb SMAD Signalling Class 2", + "repository_type": "biomodels", + "summary": "Model depicting class 2 signaling class observed upon stimulation with 100 pM TGF\u03b21.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4499, + "tag": "BioModels:BIOMD0000000999" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:05:58.186560+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000999", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3283": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3283, + "name": "Strasen2018 - TGFb SMAD Signalling Class 3", + "repository_type": "biomodels", + "summary": "
Simulates signaling class 3
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a dynamic description for the core molecular mechanisms steering Th17 cell differentiation and use mathematical modeling to quantitatively predict the resulting molecular dynamics
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4504, + "tag": "BioModels:BIOMD0000001004" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:00.713742+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001004", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3288": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3288, + "name": "Bae2017 - Mathematical analysis of circadian disruption and metabolic re-entrainment of hepatic gluconeogenesis", + "repository_type": "biomodels", + "summary": "

The circadian rhythms influence the metabolic activity from molecular level to tissue, organ, and host level. Disruption of the circadian rhythms manifests to the host's health as metabolic syndromes, including obesity, diabetes, and elevated plasma glucose, eventually leading to cardiovascular diseases. Therefore, it is imperative to understand the mechanism behind the relationship between circadian rhythms and metabolism. To start answering this question, we propose a semimechanistic mathematical model to study the effect of circadian disruption on hepatic gluconeogenesis in humans. Our model takes the light-dark cycle and feeding-fasting cycle as two environmental inputs that entrain the metabolic activity in the liver. The model was validated by comparison with data from mice and rat experimental studies. Formal sensitivity and uncertainty analyses were conducted to elaborate on the driving forces for hepatic gluconeogenesis. Furthermore, simulating the impact of Clock gene knockout suggests that modification to the local pathways tied most closely to the feeding-fasting rhythms may be the most efficient way to restore the disrupted glucose metabolism in liver.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4505, + "tag": "BioModels:BIOMD0000001005" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 3073, + "tag": "Rattus norvegicus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:01.283128+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001005", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3289": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3289, + "name": "Ciliberto2005 - Steady states and oscillations in the p53/Mdm2 network", + "repository_type": "biomodels", + "summary": "
Its a mathematial model studying steady state and oscialltions in p53-MDM2 network triggered by IR induced DNA Damage.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4506, + "tag": "BioModels:BIOMD0000001006" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:01.841047+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001006", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3290": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3290, + "name": "Zhang2007 - Mechanism of DNA damage response (Model1)", + "repository_type": "biomodels", + "summary": "
Its a mechanistic model explaining the impact of p53 om apoptosis decision. This model represents schema 1 of manuscript.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4507, + "tag": "BioModels:BIOMD0000001007" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:02.340910+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001007", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3291": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3291, + "name": "Scaramellini1997 - Two-receptor:One-transducer (2R1T) model for analysis of interactions between agonists", + "repository_type": "biomodels", + "summary": "The two-receptor:one-transducerm odel (Leff, 1987) is here extended to analyze interactions between agonistsd isplaying E/[A] curves of different shapes, by incorporating slope factors into the separate and common parts of the transduction pathway. Interactions were modelled as the effect of one agonist, at fixed concentration, on the curve to the other. A variety of patterns of position and slope changes are predicted. These do not depend on the shape of the control curve, rather, they depend on the slope factors in the separate and common pathways. The following specific predictions are made: (1) when the common pathway is steep, curves undergo potentiation and flattening; (2) when the common pathway is flat, curves undergo right-shift and steepening; (3) when the common pathway is hyperbolic, curves undergo right-shift, with no slope change; (4) when the slope depends on the separate pathways, curves only undergo right-shift with no change in slope. The model provides a sound basis for classifying agonist interactions and for detecting additional, synergistic or antagonistic properties. This analysisin dicatest hat methodsb asedo n dose-additivity or independencea re less reliable for these purposes. The model provides a practical test, based on slope changes, to detect and quantify additional properties", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4508, + "tag": "BioModels:BIOMD0000001008" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:03.050209+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001008", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3292": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3292, + "name": "Zhang2007 - Mechanism of DNA damage response (Model2)", + "repository_type": "biomodels", + "summary": "
Its a mechanistic model explaining the impact of p53 on apoptosis decision. This model represents schema 2 of manuscript.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4509, + "tag": "BioModels:BIOMD0000001009" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:03.565500+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001009", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3293": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3293, + "name": "Zhang2007 - Mechanism of DNA damage response (Model3)", + "repository_type": "biomodels", + "summary": "It's a mechanistic model explaining the impact of p53 on apoptosis decision. This model represents schema 3 of manuscript.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4510, + "tag": "BioModels:BIOMD0000001010" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:04.071734+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001010", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3294": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3294, + "name": "Leon-Triana2020 - CAR T-cell therapy in B-cell acute lymphoblastic leukaemia", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models\".Odelaisy Le\u00f3n-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Mar\u00eda Rosa, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ramirez-Orellana, V\u00edctor M. P\u00e9rez-Garc\u00edadoi: 10.1016/j.cnsns.2020.105570 Comment:This model is based on equations (4a)-(4c).Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4511, + "tag": "BioModels:BIOMD0000001011" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4658, + "tag": "Lymphoid leukemia" + } + ], + "timestamp_created": "2025-01-30 14:06:04.567055+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001011", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3295": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3295, + "name": "Leon-Triana2020 - CAR T-cell therapy in B-cell acute lymphoblastic leukaemia with contribution from immature B cells", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"CAR T cell therapy in B-cell acute lymphoblastic leukaemia: Insights from mathematical models\".Odelaisy Le\u00f3n-Triana, Soukaina Sabir, Gabriel F. Calvo, Juan Belmonte-Beitia, Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Mar\u00eda Rosa, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ramirez-Orellana, V\u00edctor M. P\u00e9rez-Garc\u00edadoi: 10.1016/j.cnsns.2020.105570 Comment:This model is based on equations (3a)-(3c) from the paper.Abstract: Immunotherapies use components of the patient immune system to selectively target can- cer cells. The use of chimeric antigenic receptor (CAR) T cells to treat B-cell malignancies \u2013leukaemias and lymphomas\u2013is one of the most successful examples, with many patients experiencing long-lasting full responses to this therapy. This treatment works by extract- ing the patient\u2019s T cells and transducing them with the CAR, enabling them to recognize and target cells carrying the antigen CD19 + , which is expressed in these haematological cancers. Here we put forward a mathematical model describing the time response of leukaemias to the injection of CAR T cells. The model accounts for mature and progenitor B-cells, leukaemic cells, CAR T cells and side effects by including the main biological processes involved. The model explains the early post-injection dynamics of the different compart- ments and the fact that the number of CAR T cells injected does not critically affect the treatment outcome. An explicit formula is found that gives the maximum CAR T cell ex- pansion in vivo and the severity of side effects. Our mathematical model captures other known features of the response to this immunotherapy. It also predicts that CD19 + cancer relapses could be the result of competition between leukaemic and CAR T cells, analogous to predator-prey dynamics. We discuss this in the light of the available evidence and the possibility of controlling relapses by early re-challenging of the leukaemia cells with stored CAR T cells.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4512, + "tag": "BioModels:BIOMD0000001012" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4658, + "tag": "Lymphoid leukemia" + } + ], + "timestamp_created": "2025-01-30 14:06:05.044203+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001012", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3296": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3296, + "name": "Leon-Triana2021 - Competition between tumour cells and single-target CAR T-cells", + "repository_type": "biomodels", + "summary": "
This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article:\"Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept\"Odelaisy Le\u00f3n-Triana, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ram\u00edrez-Orellana and V\u00edctor M. P\u00e9rez-Garc\u00edaCancers 2021, 13, 703.; doi: 10.3390/cancers13040703Comment:This is the first mathematical model, derived from equations 1 and 2, used in the paper.Reproduction of Fig. 5a was achieved by setting alpha_1 = 0.04, different to the value quoted in the article caption for Fig. 5.Abstract:Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4513, + "tag": "BioModels:BIOMD0000001013" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:06:05.532006+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001013", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3297": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3297, + "name": "Leon-Triana2021 - Competition between tumour cells and dual-target CAR T-cells", + "repository_type": "biomodels", + "summary": "
This model of the use of chimeric antigen receptor (CAR)-T cell therapy in the treatment of solid tumours is described in the article:\"Dual-Target CAR-Ts with On- and Off-Tumour Activity May Override Immune Suppression in Solid Cancers: A Mathematical Proof of Concept\"Odelaisy Le\u00f3n-Triana, Antonio P\u00e9rez-Mart\u00ednez, Manuel Ram\u00edrez-Orellana and V\u00edctor M. P\u00e9rez-Garc\u00edaCancers 2021, 13, 703.; doi: 10.3390/cancers13040703Comment:This is the second mathematical model, derived from equations 3 to 6, used in the paper.Reproduction of Figure 5b was achieved by setting alpha_1 = 0.183, in substitution for alpha_1 = 0.2 as quoted in the article.Abstract:Chimeric antigen receptor (CAR)-T cell-based therapies have achieved substantial success against B-cell malignancies, which has led to a growing scientific and clinical interest in extending their use to solid cancers. However, results for solid tumours have been limited up to now, in part due to the immunosuppressive tumour microenvironment, which is able to inactivate CAR-T cell clones. In this paper we put forward a mathematical model describing the competition of CAR-T and tumour cells, taking into account their immunosuppressive capacity. Using the mathematical model, we show that the use of large numbers of CAR-T cells targetting the solid tumour antigens could overcome the immunosuppressive potential of cancer. To achieve such high levels of CAR-T cells we propose, and study computationally, the manufacture and injection of CAR-T cells targetting two antigens: CD19 and a tumour-associated antigen. We study in silico the resulting dynamics of the disease after the injection of this product and find that the expansion of the CAR-T cell population in the blood and lymphopoietic organs could lead to the massive production of an army of CAR-T cells targetting the solid tumour, and potentially overcoming its immune suppression capabilities. This strategy could benefit from the combination with PD-1 inhibitors and low tumour loads. Our computational results provide theoretical support for the treatment of different types of solid tumours using T cells engineered with combination treatments of dual CARs with on- and off-tumour activity and anti-PD-1 drugs after completion of classical cytoreductive treatments.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4514, + "tag": "BioModels:BIOMD0000001014" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4659, + "tag": "Glioblastoma multiforme" + } + ], + "timestamp_created": "2025-01-30 14:06:06.069158+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001014", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3298": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3298, + "name": "Jarrah2014 - mathematical model of the immune response in muscle degeneration and subsequent regeneration in Duchenne muscular dystrophy in mdx mice", + "repository_type": "biomodels", + "summary": "Duchenne muscular dystrophy (DMD) is a genetic disease that results in the death of affected boys by early adulthood.The genetic defect responsible for DMD has been known for over 25 years, yet at present there is neither cure nor effective treatment for DMD. During early disease onset, the mdx mouse has been validated as an animal model for DMD and use of this model has led to valuable but incomplete insights into the disease process. For example, immune cells are thought to be responsible for a significant portion of muscle cell death in the mdx mouse; however, the role and time course of the immune response in the dystrophic process have not been well described. In this paper we constructed a simple mathematical model to investigate the role of the immune response in muscle degeneration and subsequent regeneration in the mdx mouse model of Duchenne muscular dystrophy. Our model suggests that the immune response contributes substantially to the muscle degeneration and regeneration processes. Furthermore, the analysis of the model predicts that the immune system response oscillates throughout the life of the mice, and the damaged fibers are never completely cleared.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4515, + "tag": "BioModels:BIOMD0000001015" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:06.719105+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001015", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3299": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3299, + "name": "Bakshi2020 - Truncated minimal model of alternative pathway of complement system", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"Mathematical Modelling of Alternative Pathway of Complement System\".Suruchi Bakshi, Fraser Cunningham, Eva-Maria Nichols, Marta Biedzka-Sarek, Jessica Neisen, Sebastien Petit-Frere, Christina Bessant, Loveleena Bansal, Lambertus A Peletier, Stefano Zamuner, Piet H van der GraafDOI: 10.1007/s11538-020-00708-zComment:This model is based on the truncated minimal model equations (Eq. B.1) from the manuscript, which simulate depletion of factor H.Abstract:The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4516, + "tag": "BioModels:BIOMD0000001016" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4660, + "tag": "Complement deficiency" + } + ], + "timestamp_created": "2025-01-30 14:06:07.207527+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001016", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3300": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3300, + "name": "Bakshi2020 - Minimal model of alternative pathway of complement system", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"Mathematical Modelling of Alternative Pathway of Complement System\".Suruchi Bakshi, Fraser Cunningham, Eva-Maria Nichols, Marta Biedzka-Sarek, Jessica Neisen, Sebastien Petit-Frere, Christina Bessant, Loveleena Bansal, Lambertus A Peletier, Stefano Zamuner, Piet H van der GraafDOI: 10.1007/s11538-020-00708-zComment:This model is based on the truncated minimal model equations (Eq. B.1) from the manuscript, which simulate depletion of factor H.Abstract:The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4517, + "tag": "BioModels:BIOMD0000001017" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:07.713765+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001017", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3301": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3301, + "name": "Bakshi2020 - Properdin model of alternative pathway of complement system", + "repository_type": "biomodels", + "summary": "
This model is based on the publication:\"Mathematical Modelling of Alternative Pathway of Complement System\".Suruchi Bakshi, Fraser Cunningham, Eva-Maria Nichols, Marta Biedzka-Sarek, Jessica Neisen, Sebastien Petit-Frere, Christina Bessant, Loveleena Bansal, Lambertus A Peletier, Stefano Zamuner, Piet H van der GraafDOI: 10.1007/s11538-020-00708-zComment:Correction to the original manuscript is accessible hereModel schematics in Figure 3 and parameter d4 were corrected.Abstract:The complement system (CS) is an integral part of innate immunity and can be activated via three different pathways. The alternative pathway (AP) has a central role in the function of the CS. The AP of complement system is implicated in several human disease pathologies. In the absence of triggers, the AP exists in a time-invariant resting state (physiological steady state). It is capable of rapid, potent and transient activation response upon challenge with a trigger. Previous models of AP have focused on the activation response. In order to understand the molecular machinery necessary for AP activation and regulation -of a physiological steady state, we built parsimonious AP models using experimentally supported kinetic parameters. The models further allowed us to test quantitative roles played by negative and positive regulators of the pathway in order to test hypotheses regarding their mechanisms of action, thus providing more insight into the complex regulation of AP.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4518, + "tag": "BioModels:BIOMD0000001018" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:08.210961+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001018", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3302": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3302, + "name": "Barros2021 - CARTmath, Mathematical Model of CAR-T Immunotherapy in HDLM-2 cell line", + "repository_type": "biomodels", + "summary": "A mathematical model (CART-math) studying the impact of CAR-T cells therapy on haematological cancer cell line which in this case is HDLM-2.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4519, + "tag": "BioModels:BIOMD0000001019" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:08.720118+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001019", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3303": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3303, + "name": "Barros2021 - CARTmath, Mathematical Model of CAR-T Immunotherapy in Raji Cell Line", + "repository_type": "biomodels", + "summary": "A mathematical model (CART-math) studying the impact of CAR-T cells therapy on haematological cancer cell lines which in this case is RAJI.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4520, + "tag": "BioModels:BIOMD0000001020" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:09.214146+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001020", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3304": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3304, + "name": "Lavigne2021 - Non-spatial model of viral infection dynamics and interferon response of well-mixed viral infection", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model is described in the following article:\"Autocrine and paracrine interferon signalling as \u2018ring vaccination\u2019 and \u2018contact tracing\u2019 strategies to suppress virus infection in a host\"G. Michael Lavigne, Hayley Russell, Barbara Sherry and Ruian KeDOI: 10.1098/rspb.2020.3002Comment:This model is based on the ordinary differential equations of the non-spatial model of well-mixed viral infection stated in the manuscript (Eq. 2.1 in the article).Abstract:The innate immune response, particularly the interferon response, represents a first line of defence against viral infections. The interferon molecules produced from infected cells act through autocrine and paracrine signalling to turn host cells into an antiviral state. Although the molecular mechanisms of IFN signalling have been well characterized, how the interferon response collectively contribute to the regulation of host cells to stop or suppress viral infection during early infection remain unclear. Here, we use mathematical models to delineate the roles of the autocrine and the paracrine signalling, and show that their impacts on viral spread are dependent on how infection proceeds. In particular, we found that when infection is well-mixed, the paracrine signalling is not as effective; by contrast, when infection spreads in a spatial manner, a likely scenario during initial infection in tissue, the paracrine signalling can impede the spread of infection by decreasing the number of susceptible cells close to the site of infection. Furthermore, we argue that the interferon response can be seen as a parallel to population-level epidemic prevention strategies such as \u2018contact tracing\u2019 or \u2018ring vaccination\u2019. Thus, our results here may have implications for the outbreak control at the population scale more broadly.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4521, + "tag": "BioModels:BIOMD0000001021" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:09.706928+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001021", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3305": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "10", + "id": 3305, + "name": "Creemers2021 - Tumor-immune dynamics and implications on immunotherapy responses", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication:Creemers JHA, Lesterhuis WJ, Mehra N, et al.\"A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery.\"Journal for ImmunoTherapy of Cancer 2021;9:e002032.doi:10.1136/jitc-2020-002032Comment:Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used:Reproduction of Fig. 1(C), xi = 0.0005Reproduction of Fig. 1(D), xi = 0.00025Abstract:Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice.Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs.Results: Our model shows that a tipping point\u2014a sharp state transition between immune control and immune evasion\u2014induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments.Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient\u2019s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4522, + "tag": "BioModels:BIOMD0000001022" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:10.223444+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001022", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3306": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3306, + "name": "Alharbi2020 - An ODE-based model of the dynamics of tumor cell progression and its effects on normal cell growth and immune system functionality", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of tumor cell growth, called the normal-tumor-immune-unhealthy diet model (NTIUNHDM), is described by the publication:Alharbi, S.A.; Rambely, A.S.\"A New ODE-Based Model for Tumor Cells and Immune System Competition\"Mathematics 2020, 8, 1285.doi:10.3390/math8081285Abstract:Changes in diet are heavily associated with high mortality rates in several types of cancer. In this paper, a new mathematical model of tumor cells growth is established to dynamically demonstrate the effects of abnormal cell progression on the cells affected by the tumor in terms of the immune system\u2019s functionality and normal cells\u2019 dynamic growth. This model is called the normal-tumor-immune-unhealthy diet model (NTIUNHDM) and governed by a system of ordinary differential equations. In the NTIUNHDM, there are three main populations normal cells, tumor cell and immune cells. The model is discussed analytically and numerically by utilizing a fourth-order Runge\u2013Kutta method. The dynamic behavior of the NTIUNHDM is discussed by analyzing the stability of the system at various equilibrium points and the Mathematica software is used to simulate the model. From analysis and simulation of the NTIUNHDM, it can be deduced that instability of the response stage, due to a weak immune system, is classified as one of the main reasons for the coexistence of abnormal cells and normal cells. Additionally, it is obvious that the NTIUNHDM has only one stable case when abnormal cells begin progressing into early stages of tumor cells such that the immune cells are generated once. Thus, early boosting of the immune system might contribute to reducing the risk of cancer.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4523, + "tag": "BioModels:BIOMD0000001023" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:06:10.710787+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001023", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3307": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3307, + "name": "Chaudhury2020 - Lotka-Volterra mathematical model of CAR-T cell and tumour kinetics", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the cellular kinetics and pharmacodynamics of CAR-T cell therapy is described in the publication:Chaudhury, A., Zhu, X., Chu, L., Goliaei, A., June, C., Kearns, J. and Stein, A., 2020. Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic\u2010Pharmacodynamic Modeling Approaches. The Journal of Clinical Pharmacology, 60(S1).DOI: 10.1002/jcph.1691Comment:This model is based on equations 4-5 from the manuscript.Abstract:Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4524, + "tag": "BioModels:BIOMD0000001024" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4661, + "tag": "Leukemia" + }, + { + "id": 4662, + "tag": "Lymphoma" + } + ], + "timestamp_created": "2025-01-30 14:06:11.199058+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001024", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3308": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3308, + "name": "Chaudhury2020 - EC50 expansion and killing mathematical model of CAR-T cell and tumour kinetics", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the cellular kinetics and pharmacodynamics of CAR-T cell therapy is described in the publication:Chaudhury, A., Zhu, X., Chu, L., Goliaei, A., June, C., Kearns, J. and Stein, A., 2020. Chimeric Antigen Receptor T Cell Therapies: A Review of Cellular Kinetic\u2010Pharmacodynamic Modeling Approaches. The Journal of Clinical Pharmacology, 60(S1).DOI: 10.1002/jcph.1691Comment:This model is based on equations 7-9 from the manuscript.Abstract:Chimeric antigen receptor T cell (CAR-T cell) therapies have shown significant efficacy in CD19+ leukemias and lymphomas. There remain many challenges and questions for improving next-generation CAR-T cell therapies, and mathematical modeling of CAR-T cells may play a role in supporting further development. In this review, we introduce a mathematical modeling taxonomy for a set of relatively simple cellular kinetic-pharmacodynamic models that describe the in vivo dynamics of CAR-T cell and their interactions with cancer cells. We then discuss potential extensions of this model to include target binding, tumor distribution, cytokine-release syndrome, immunophenotype differentiation, and genotypic heterogeneity.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4525, + "tag": "BioModels:BIOMD0000001025" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4661, + "tag": "Leukemia" + }, + { + "id": 4662, + "tag": "Lymphoma" + } + ], + "timestamp_created": "2025-01-30 14:06:11.749060+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001025", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3309": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3309, + "name": "Kurlovics2021 - Metformin partitioning between plasma and RBC with independent Kin and Kout coefficients", + "repository_type": "biomodels", + "summary": "
This model is a supplementary material of a manuscript\"Diffusion driven metformin exchange transport rates between plasma and red blood cells\"by Janis Kurlovics, Darta Maija Zake, Linda Zaharenko, Kristaps Berzins, Janis Klovins, Egils StalidzansThe setting of the model correspond to Fig.2 for the case with a single coefficient for experimental values of average concentration curve. Parameter estimation can be executed using experimental data file \"average_exp_data.txt\".A=B if metformin concentration is 0 at t=0.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4526, + "tag": "BioModels:BIOMD0000001026" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:12.425275+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001026", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3310": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3310, + "name": "Zake2021 - PBPK model of metformin in mice: single dose peroral", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, Janis Kurlovics, Vitalijs Komasilovs, Janis Klovins and Egils Stalidzans.The model is pre-set for simulation of a single peroral dose.This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments. The model consists of 20 compartments (\u201cCompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 20 species (\u201cSpecies\u201d in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 33 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 52 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing metformin dose \u2013 either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg). 2.Parameter describing mice physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots).", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4527, + "tag": "BioModels:BIOMD0000001027" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:06:12.907216+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001027", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3311": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3311, + "name": "Zake2021 - PBPK model of metformin in humans, single PO dose", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments. The model consists of 21 compartments (\u201ccompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 21 species (\u201cspecies\u201d in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 35 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 62 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).2.Parameter describing human physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Parameters involved in the scale-up of the model\u2022Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.\u2022Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions \u201c13.4. KidneyPlasma -> KidneyTissue\u201d and \u201c13.5. KidneyTissue -> KidneyTubular\u201d. This parameter was determined using parameter estimation. \u2022Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine. 4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). The time-course also includes multiple \"Sliders\" that allow to easily change 3 parameters - \"Body Weight\", \"Cardiac Output\", \"Metformin Dose in Lumen in mg\".", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4528, + "tag": "BioModels:BIOMD0000001028" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:44:59.636511+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001028", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3312": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3312, + "name": "Zake2021 - PBPK model of metformin in humans, eight PO administrations with 12h interval", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the human body. The model is in the form of Ordinary differential equations and describes metformin concentration in 21 compartments. The model consists of 21 compartments (\u201ccompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, red blood cells, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder, urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1L, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 21 species (\u201cspecies\u201d in COPASI model) that correspond to the metformin concentrations in the 21 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 35 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 62 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing peroral metformin dose (Metformin Dose in Lumen in mg).2.Parameter describing human physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Parameters involved in the scale-up of the model\u2022Tissue:plasma partition coefficients (Ktp) that were estimated in the mice model.\u2022Kidney coefficient that is used for the scale-up of metformin elimination and is involved in the calculation of the rate parameters in the reactions \u201c13.4. KidneyPlasma -> KidneyTissue\u201d and \u201c13.5. KidneyTissue -> KidneyTubular\u201d. This parameter was determined using parameter estimation. \u2022Intestine coefficient that is involved in the calculation of the intestinal reaction rates of the reactions (03.2. IntestineLumen -> Enterocytes (PMAT OCT3), 03.3. Enterocytes -> IntestineVascular (OCT1), 03.4. IntestineLumen -> IntestineVascular (Saturable), 03.6. IntestineLumen -> Enterocytes (Diffusion) , 03.7. IntestineLumen -> IntestineVascular (Diffusion)). The parmaeters for these reactions are taken from Proctor publication and the intectine coefficient is used for the scale-up from the cell-culture to the human intestine. 4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. The current model has 14 events and is set for a multiple-dose regimen for 7-day long twice-daily metformin administration. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 21 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). The time-course also includes multiple \"Sliders\" that allow to easily change 3 parameters - \"Body Weight\", \"Cardiac Output\", \"Metformin Dose in Lumen in mg\".", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4529, + "tag": "BioModels:BIOMD0000001029" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:00.261466+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001029", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3313": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3313, + "name": "Sontag2017 - Dynamic model of immune responses to antigen presentation by tumor or pathogen", + "repository_type": "biomodels", + "summary": "
This model of the immune system response to antigen presentation is based on the publication:Eduardo D.Sontag (2017) 'A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination', Cell Systems, 4(2)DOI: 10.1016/j.cels.2016.12.003Comment:This model is based on the \"toy model\" as described by Equations 1A-1C from the manuscript and the system represented by Equation 2, which exhibits a type of incoherent feedforward loop (IFFL).Abstract:The immune system must discriminate between agents of disease and an organism\u2019s healthy cells. While the identification of an antigen as self/non-self is critically important, the dynamic features of antigen presentation may also determine the immune system\u2019s response. Here, we use a simple mathematical model of immune activation to explore the idea of antigen discrimination through dynamics. We propose that antigen presentation is coupled to two nodes, one regulatory and one effecting the immune response, through an incoherent feedforward loop and repressive feedback. This circuit would allow the immune system to effectively estimate the increase of antigens with respect to time, a key determinant of immune reactivity in vivo. Our model makes the prediction that tumors growing at specific rates evade the immune system despite the continuous presence of antigens indicating disease, a phenomenon closely related to clinically observed \u201ctwo-zone tolerance.\u201d Finally, we discuss a plausible biological instantiation of our circuit using combinations of regulatory and effector T cells.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4530, + "tag": "BioModels:BIOMD0000001030" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3487, + "tag": "Vertebrata" + } + ], + "timestamp_created": "2025-01-30 14:45:00.851332+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001030", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3314": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3314, + "name": "Al-Tuwairqi2020 - Dynamics of cancer virotherapy - Phase I treatment", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of cancer virotherapy dynamics is described in the publication:Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,\"Modeling dynamics of cancer radiovirotherapy\",Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,DOI: 10.1016/j.jtbi.2020.110405.Comment:This model is represented by the equations in system (2) of the publication manuscript and describes the cancer-virus interactions for the Phase I virotherapy treatment.Abstract:Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4531, + "tag": "BioModels:BIOMD0000001031" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:01.381670+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001031", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3315": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3315, + "name": "Al-Tuwairqi2020 - Dynamics of cancer radiovirotherapy - Phase II treatment", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of cancer radiovirotherapy dynamics is described in the publication:Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,\"Modeling dynamics of cancer radiovirotherapy\",Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,DOI: 10.1016/j.jtbi.2020.110405.Comment:This model is represented by the equations in system (14) of the publication manuscript and describes the cancer-virus interactions for the Phase II radiovirotherapy treatment.Abstract:Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4532, + "tag": "BioModels:BIOMD0000001032" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:01.914509+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001032", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3316": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3316, + "name": "Almuallem2020 - Virus-macrophage-tumour interactions in oncolytic viral therapies", + "repository_type": "biomodels", + "summary": "
This mathematical model of the role of the innate immune responses generated by macrophages in the context of anti-tumour oncolytic viral therapies is described in the publication:Nada Almuallem, Dumitru Trucu, Raluca Eftimie. \"Oncolytic viral therapies and the delicate balance between virus-macrophage-tumour interactions: A mathematical approach\". Mathematical Biosciences and Engineering, 2021, 18(1): 764-799.doi: 10.3934/mbe.2021041Comment:This model is represented by Equations 2.1a-f of the publication manuscript.Abstract:The success of oncolytic virotherapies depends on the tumour microenvironment, which contains a large number of infiltrating immune cells. In this theoretical study, we derive an ODE model to investigate the interactions between breast cancer tumour cells, an oncolytic virus (Vesicular Stomatitis Virus), and tumour-infiltrating macrophages with different phenotypes which can impact the dynamics of oncolytic viruses. The complexity of the model requires a combined analytical-numerical approach to understand the transient and asymptotic dynamics of this model. We use this model to propose new biological hypotheses regarding the impact on tumour elimination/relapse/persistence of: (i) different macrophage polarisation/re-polarisation rates; (ii) different infection rates of macrophages and tumour cells with the oncolytic virus; (iii) different viral burst sizes for macrophages and tumour cells. We show that increasing the rate at which the oncolytic virus infects the tumour cells can delay tumour relapse and even eliminate tumour. Increasing the rate at which the oncolytic virus particles infect the macrophages can trigger transitions between steady-state dynamics and oscillatory dynamics, but it does not lead to tumour elimination unless the tumour infection rate is also very large. Moreover, we confirm numerically that a large tumour-induced M1\u2192M2 polarisation leads to fast tumour growth and fast relapse (if the tumour was reduced before by a strong anti-tumour immune and viral response). The increase in viral-induced M2\u2192M1 re-polarisation reduces temporarily the tumour size, but does not lead to tumour elimination. Finally, we show numerically that the tumour size is more sensitive to the production of viruses by the infected macrophages.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4533, + "tag": "BioModels:BIOMD0000001033" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4586, + "tag": "Breast cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:02.552363+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001033", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3317": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3317, + "name": "Bunimovich-Mendrazitsky2007 - Mathematical model of BCG immunotherapy", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model, simulating the tumor-immune interactions involved in BCG immunotherapy to treat superficial bladder cancer, is described by the publication:Bunimovich-Mendrazitsky, S., Shochat, E., Stone, L. \"Mathematical Model of BCG Immunotherapy in Superficial Bladder Cancer\". Bull. Math. Biol. 69, 1847\u20131870 (2007). DOI: 10.1007/s11538-007-9195-zComment:This model is based on the system of ODEs given in Equation 4 of the publication manuscript.Reproduction of Figure 4 was achieved by setting p4 = 0.085.Abstract:Immunotherapy with Bacillus Calmette-Gu\u00e9rin (BCG)-an attenuated strain of Mycobacterium bovis (M. bovis) used for anti tuberculosis immunization-is a clinically established procedure for the treatment of superficial bladder cancer. However, the mode of action has not yet been fully elucidated, despite much extensive biological experience. The purpose of this paper is to develop a first mathematical model that describes tumor-immune interactions in the bladder as a result of BCG therapy. A mathematical analysis of the ODE model identifies multiple equilibrium points, their stability properties, and bifurcation points. Intriguing regimes of bistability are identified in which treatment has potential to result in a tumor-free equilibrium or a full-blown tumor depending only on initial conditions. Attention is given to estimating parameters and validating the model using published data taken from in vitro, mouse and human studies. The model makes clear that intensity of immunotherapy must be kept in limited bounds. While small treatment levels may fail to clear the tumor, a treatment that is too large can lead to an over-stimulated immune system having dangerous side effects for the patient.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4534, + "tag": "BioModels:BIOMD0000001034" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4535, + "tag": "Mycobacterium tuberculosis variant bovis BCG" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4663, + "tag": "Urinary bladder cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:03.157372+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001034", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3318": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3318, + "name": "Al-Tuwairqi2020 - Dynamics of cancer virotherapy with immune response", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the role of the immune response in cancer virotherapy dynamics is described in the publication:Al-Tuwairqi, S.M., Al-Johani, N.O.,  Simbawa, E.A. \"Modeling dynamics of cancer virotherapy with immune response.\" Adv Differ Equ 2020, 438 (2020).DOI: 10.1186/s13662-020-02893-6Comment:This model is represented by the system described in Equation 2 of the publication manuscript.Abstract:Virotherapy is a therapeutic treatment for cancer. It uses genetically engineered viruses to selectively infect, replicate in, and destroy cancer cells without damaging normal cells. In this paper, we present a modified model to include, within the dynamics of virotherapy, the interaction between uninfected tumor cells and immune response. The model is analyzed qualitatively to produce five equilibrium points. One of these equilibriums demonstrates the effect observed in virotherapy, where the immune system demolishes infected cells as well as viruses. Moreover, the existence and stability of the equilibrium points are established under certain criteria. Numerical simulations are performed to display the agreement with the analytical results. Finally, parameter analysis is carried out to illustrate which parameters in the model affect the outcome of virotherapy.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4536, + "tag": "BioModels:BIOMD0000001035" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:03.737333+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001035", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3319": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3319, + "name": "Cappuccio2007 - Tumor-immune system interactions and determination of the optimal therapeutic protocol in immunotherapy", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model is the Panetta-Kirschner model of tumor-immune interactions is described in the publication:Cappuccio A, Castiglione F, Piccoli B.\" Determination of the optimal therapeutic protocols in cancer immunotherapy.\" Math Biosci. 2007 Sep;209(1):1-13.doi: 10.1016/j.mbs.2007.02.009. Comment:The model represented in Equations 1-3 is the Panetta-Kirschner model and was used to reproduce Fig. 1.N.B.: Labelling of Fig. 1 graphs in manuscript mismatches with simulation results - CTL and tumor cell plots swapped.Abstract:Cancer immunotherapy aims at eliciting an immune system response against the tumor. However, it is often characterized by toxic side-effects. Limiting the tumor growth and, concurrently, avoiding the toxicity of a drug, is the problem of protocol design. We formulate this question as an optimization problem and derive an algorithm for its solution. Unlike the standard optimal control approach, the algorithm simulates impulse-like drug administrations. It relies on an exact computation of the gradient of the cost function with respect to any protocol by means of the variational equations, that can be solved in parallel with the system. In comparison with previous versions of this method [F. Castiglione, B. Piccoli, Optimal control in a model of dendritic cell transfection cancer immunotherapy, Bull. Math. Biol. 68 (2006) 255-274; B. Piccoli, F. Castiglione, Optimal vaccine scheduling in cancer immunotherapy, Physica A. 370 (2) (2007) 672-680], we optimize both the timing and the dosage of each administration and introduce a penalty term to avoid clustering of subsequent injections, a requirement consistent with the clinical practice. In addition, we implement the optimization scheme to simulate the case of multi-therapies. The procedure works for any ODE system describing the pharmacokinetics and pharmacodynamics of an arbitrary number of therapeutic agents. In this work, it was tested for a well known model of the tumor-immune system interaction [D. Kirschner, J.C. Panetta, Modeling immunotherapy of tumor-immune interaction, J. Math. Biol. 37 (1998) 235-252]. Exploring three immunotherapeutic scenarios (CTL therapy, IL-2 therapy and combined therapy), we display the stability and efficacy of the optimization method, obtaining protocols that are successful compromises between various clinical requirements.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4537, + "tag": "BioModels:BIOMD0000001036" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:04.281413+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001036", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3320": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3320, + "name": "Alharbi2019 - Tumor-normal model (TNM) of the development of tumor cells and their impact on normal cell dynamics", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the interactions between tumor and normal cells is based on the publication:S. A. Alharbi and A. S. Rambely, \"Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells,\" in IEEE Access, vol. 7, pp. 128816-128827, 2019doi: 10.1109/ACCESS.2019.2940060.Comment:This TNM model is described by the system of equations presented in (2), in the publication manuscript.Abstract:The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4538, + "tag": "BioModels:BIOMD0000001037" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:04.776080+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001037", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3321": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3321, + "name": "Alharbi2019 - Tumor-normal-vitamins model (TNVM) of the effects of vitamins on delaying the growth of tumor cells", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model of the interactions between tumor and normal cells, in the presence of a regular rate of vitamins, is based on the publication:S. A. Alharbi and A. S. Rambely, \"Dynamic Simulation for Analyzing the Effects of the Intervention of Vitamins on Delaying the Growth of Tumor Cells,\" in IEEE Access, vol. 7, pp. 128816-128827, 2019doi: 10.1109/ACCESS.2019.2940060.Comment:This TNVM model is described by the system of equations presented in (10), in the publication manuscript.Reproduction of Fig. 11 was achieved by swapping the values of gamma and beta2 stated in the manuscript, i.e. gamma = 0.9817 and beta2 = 0.2291.Abstract:The natural sources of the vitamins, which come from a balanced diet (as recommended by the World Cancer Research Fund and the American Institute for Cancer Research) contribute to protecting the body from advancing progressive of cancer stages. Thus, in this study, we analyze the effect of the intervention of vitamins on delaying the growth of cancer cells based on the dynamics of a normal cell cycle when the tumor cells appear in a tissue as a resulting for progressing abnormal cells due to the weak response of the immune system. We developed a mathematical model, called tumor-normal-vitamins model (TNVM), which is governed by a system of ordinary differential equations and refers to two main populations normal cells and tumor cells. This model considers the intervention of vitamins as a moderating factor within thirty days. The models are discussed analytically and numerically by utilizing the Runge-Kutta method to simulate them. The results of the analysis and simulation of free model illustrate that the model will be stable if the tumor cells succeed in eliminating normal cells in the tissue. Whereas, the analysis and simulation of the TNVM showed a case of coexistence between normal cells and tumor cells occur if an individual consumes a regular rate of vitamins that have been simulated to be 87% per day from a natural food source. Even though the response of the immune system is weak, the daily consumption of enough vitamins can play an essential role in delaying the development of an early stage of cancer. This study contributes to the increasing awareness regarding a healthy diet to reduce the risk of some deadly diseases, especially cancer.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4539, + "tag": "BioModels:BIOMD0000001038" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4206, + "tag": "Response to tumor cell" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:05.263457+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001038", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3322": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3322, + "name": "Zake2021 - PBPK model of metformin in mice: single dose intavenous", + "repository_type": "biomodels", + "summary": "This model is supplementary material of publication \"Physiologically based metformin pharmacokinetics model of mice and scale-up to humans for the estimation of concentrations in various tissues\"by Darta Maija Zake, Linda Zaharenko, JanisKurlovics, Vitalijs Komasilovs, Egils Stalidzans and Janis Klovins.This is a whole-body model representing the pharmacokinetics of metformin in the mouse body. The model is in the form of ordinary differential equations and describes metformin concentration in 20 compartments. The model consists of 20 compartments (\u201cCompartments\u201d in COPASI model) describing various tissues or tissue sub-compartments and body fluids of metformin action (venous and arterial plasma, intestine, kidney, heart, fat, muscle, brain, lungs, stomach, liver, portal vein, remainder urine and feces). Body weight and the weight of all compartments is expressed as a volume in mL and for the calculations it is assumed that 1mL = 1g. The volumes of most compartments are calculated as a fraction of the body weight/volume, and the fractions are determined from literature data, the volumes of the stomach lumen and intestine lumen are fixed and do not change depending on the body weight. Similarly, the volume of external urine and feces is set to 1mL, but those are \u201cvolumeless\u201d compartments as they are only necessary for the calculation of metformin amount, not concentration. The model consists of 20 species (\u201cSpecies\u201d in COPASI model) that correspond to the metformin concentrations in the 20 compartments. The initial concentrations for all the species are 0 nmol/mL as metformin is not produced in the body and can only be detected after dose administration. The model consists of 33 reactions \u2013 they describe the transport processes of metformin in the body. The reactions include local parameters that are involved only in that particular reaction and global parameters \u2013 parameters that are used in multiple reactions or are calculated depending on another parameter e.g. scale-up coefficients. The model consists of 52 global quantities \u2013 parameters involved in multiple reactions or necessary for another parameter calculation:1.Parameters describing metformin dose \u2013 either in peroral (Metformin Dose in Lumen in mg) or intravenous (Metformin Dose in Plasma in mg). 2.Parameter describing mice physiology \u2013 body weight (in mL), cardiac output, blood flow to different compartments described as Q\u201dcompartment_name\u201d (for example Qliver describes blood flow to the liver compartment). Qgfr refers to the glomerular filtration rate. 3.Tissue:plasma partition coefficients (Ktp) that are necessary for the scale-up to humans.4.Parameters involved in the calculation of metformin amount in mg, these parameters are named mg\u201dCompartment_name\u201d (for example mgLiver describes the metformin amount in mg in the liver tissues). The time points of dose release are defined as \u201cevents\u201d in COPASI and can be changed as necessary. Time course simulations can be accessed through the section \u201cTime Course\u201d in this section the time duration and intervals can be changed. When time-course simulations are run three plots are created \u2013 Metformin amount in the 20 compartments, metformin concentrations in the compartments and reaction fluxes of all the reactions (see \u201cOutput Specifications\u201d -> \u201cPlots\u201d to activate or deactivate plots). Also plotting the species result after 0.5 hours will reproduce the literature results.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4540, + "tag": "BioModels:BIOMD0000001039" + }, + { + "id": 4083, + "tag": "Mus sp." + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:05.747580+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001039", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3323": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3323, + "name": "Kurlovics2021 - Metformin partitioning from plasma to RBC, single coefficient", + "repository_type": "biomodels", + "summary": "
This model is a supplementary material of a manuscript\"Diffusion driven metformin exchange transport rates between plasma and red blood cells\"by Janis Kurlovics, Darta Maija Zake, Linda Zaharenko, Kristaps Berzins, Janis Klovins, Egils StalidzansThe setting of the model correspond to Fig.2 for the case with a single coefficient for experimental values of average concentration curve. Parameter estimation can be executed using experimental data file \"average_exp_data.txt\".A=B if metformin concentration is 0 at t=0.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4541, + "tag": "BioModels:BIOMD0000001040" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:06.238555+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001040", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3324": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3324, + "name": "Kimmel2021 - T cell competition and stochastic extinction events in CAR T cell therapy", + "repository_type": "biomodels", + "summary": "
This mathematical model of T cell-tumour interactions considering the roles of T cell competition and stochastic extinction events in CAR T cell therapy is described by the publication:Kimmel GJ, Locke FL, Altrock PM. \"The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy.\" Proc Biol Sci. 2021 Mar 31;288(1947):20210229.doi: 10.1098/rspb.2021.0229Comment:Reproduction of Fig. 2(a) and (b) was simulated by using the fitted model parameter set given in Table 1 of the manuscript's Supplementary Material, however substituting the values of r_N and rho_C for those stated in Table 1 of the publication manuscript, i.e. r_N = 0.17 and rho_C = 0.0251.Abstract:Chimeric antigen receptor (CAR) T cell therapy is a remarkably effective immunotherapy that relies on in vivo expansion of engineered CAR T cells, after lymphodepletion (LD) by chemotherapy. The quantitative laws underlying this expansion and subsequent tumour eradication remain unknown. We develop a mathematical model of T cell\u2013tumour cell interactions and demonstrate that expansion can be explained by immune reconstitution dynamics after LD and competition among T cells. CAR T cells rapidly grow and engage tumour cells but experience an emerging growth rate disadvantage compared to normal T cells. Since tumour eradication is deterministically unstable in our model, we define cure as a stochastic event, which, even when likely, can occur at variable times. However, we show that variability in timing is largely determined by patient variability. While cure events impacted by these fluctuations occur early and are narrowly distributed, progression events occur late and are more widely distributed in time. We parameterized our model using population-level CAR T cell and tumour data over time and compare our predictions with progression-free survival rates. We find that therapy could be improved by optimizing the tumour-killing rate and the CAR T cells' ability to adapt, as quantified by their carrying capacity. Our tumour extinction model can be leveraged to examine why therapy works in some patients but not others, and to better understand the interplay of deterministic and stochastic effects on outcomes. For example, our model implies that LD before a second CAR T injection is necessary.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4542, + "tag": "BioModels:BIOMD0000001041" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4664, + "tag": "Non-Hodgkin lymphoma" + } + ], + "timestamp_created": "2025-01-30 14:45:06.771527+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001041", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3325": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3325, + "name": "Makhlouf2020 - No treatment model of the role of CD4 T cells in tumor-immune interactions", + "repository_type": "biomodels", + "summary": "
This ordinary differential equation model simulating the interactions between tumor and immune cells is detailed in the publication:Ahmed M. Makhlouf, Lamiaa El-Shennawy, Hesham A. Elkaranshawy, \"Mathematical Modelling for the Role of CD4+T Cells in Tumor-Immune Interactions\", Comput Math Methods Med. 2020 Feb 19;2020:7187602.doi: 10.1155/2020/7187602Comment:This no treatment model is described by equations 1-7 of the publication manuscript. Abstract:Mathematical modelling has been used to study tumor-immune cell interaction. Some models were proposed to examine the effect of circulating lymphocytes, natural killer cells, and CD8+T cells, but they neglected the role of CD4+T cells. Other models were constructed to study the role of CD4+T cells but did not consider the role of other immune cells. In this study, we propose a mathematical model, in the form of a system of nonlinear ordinary differential equations, that predicts the interaction between tumor cells and natural killer cells, CD4+T cells, CD8+T cells, and circulating lymphocytes with or without immunotherapy and/or chemotherapy. This system is stiff, and the Runge\u2013Kutta method failed to solve it. Consequently, the \u201cAdams predictor-corrector\u201d method is used. The results reveal that the patient\u2019s immune system can overcome small tumors; however, if the tumor is large, adoptive therapy with CD4+T cells can be an alternative to both CD8+T cell therapy and cytokines in some cases. Moreover, CD4+T cell therapy could replace chemotherapy depending upon tumor size. Even if a combination of chemotherapy and immunotherapy is necessary, using CD4+T cell therapy can better reduce the dose of the associated chemotherapy compared to using combined CD8+T cells and cytokine therapy. Stability analysis is performed for the studied patients. It has been found that all equilibrium points are unstable, and a condition for preventing tumor recurrence after treatment has been deduced. Finally, a bifurcation analysis is performed to study the effect of varying system parameters on the stability, and bifurcation points are specified. New equilibrium points are created or demolished at some bifurcation points, and stability is changed at some others. Hence, for systems turning to be stable, tumors can be eradicated without the possibility of recurrence. The proposed mathematical model provides a valuable tool for designing patients\u2019 treatment intervention strategies.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4543, + "tag": "BioModels:BIOMD0000001042" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:07.304064+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001042", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3326": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3326, + "name": "Wodarz2001 - Viruses as antitumor weapons", + "repository_type": "biomodels", + "summary": "
This mathematical model of the dynamics between tumor, virus and virus-specific CTL populations is described by the publication:Wodarz D. \"Viruses as antitumor weapons: defining conditions for tumor remission\". Cancer Res. 2001 Apr 15;61(8):3501-7.PMID: 11309314Comment:Reproduction of Fig. 3A was achieved by using the initial conditions infected_tumor_cells = 0.01, uninfected_tumor_cells = 0.0001, virus-specific_CTLs = 0, and with modified parameter sets.For Fig. 3A non-cytotoxic virus, k = 17 and beta= 0.5.For Fig. 3A cytotoxic virus, beta = 0.5.These simulation conditions yield plots similar to Fig. 3A in the manuscript.Abstract:Recent research has indicated that viruses specifically infecting tumor cells could be used as an alternative therapeutic approach in cancer patients. A particular example is the adenovirus ONYX-015, which has entered clinical trials in the context of head and neck cancer. Successful therapy crucially requires an understanding about how viral and host parameters influence tumor load. The interactions between the growing tumor, the replicating virus, and possible immune responses are multifactorial and nonlinear. Hence, a complete understanding of how virus and host characteristics influence the outcome of therapy requires mathematical models. In this study, such mathematical models are presented and analyzed. The study investigates three possible scenarios that could be relevant for therapy: (a) viral cytotoxicity alone kills tumor cells; (b) a virus-specific lytic CTL response contributes to killing of infected tumor cells; (c) the virus elicits immunostimulatory signals within the tumor that promote the development of tumor-specific CTL. The models precisely define conditions required for successful therapy. They identify the parameters that need to be measured and modulated to evaluate and refine the existing therapy regimes.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4544, + "tag": "BioModels:BIOMD0000001043" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4603, + "tag": "Cancer" + } + ], + "timestamp_created": "2025-01-30 14:45:07.821706+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001043", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3327": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3327, + "name": "Csikasz-Nagy2006 - Mammalian Cell Cycle model", + "repository_type": "biomodels", + "summary": "

This model originates from the Cell Cycle Database . It is described in:
Analysis of a generic model of eukaryotic cell-cycle regulation. Csik\u00e1sz-Nagy A , Battogtokh D , Chen KC , Nov\u00e1k B , Tyson JJ Biophys. J. [2006 Jun],90(12 ):4361-79
PMID: 16581849
Abstract:
We propose a protein interaction network for the regulation of DNA synthesis and mitosis that emphasizes the universality of the regulatory system among eukaryotic cells. The idiosyncrasies of cell cycle regulation in particular organisms can be attributed, we claim, to specific settings of rate constants in the dynamic network of chemical reactions. The values of these rate constants are determined ultimately by the genetic makeup of an organism. To support these claims, we convert the reaction mechanism into a set of governing kinetic equations and provide parameter values (specific to budding yeast, fission yeast, frog eggs, and mammalian cells) that account for many curious features of cell cycle regulation in these organisms. Using one-parameter bifurcation diagrams, we show how overall cell growth drives progression through the cell cycle, how cell-size homeostasis can be achieved by two different strategies, and how mutations remodel bifurcation diagrams and create unusual cell-division phenotypes. The relation between gene dosage and phenotype can be summarized compactly in two-parameter bifurcation diagrams. Our approach provides a theoretical framework in which to understand both the universality and particularity of cell cycle regulation, and to construct, in modular fashion, increasingly complex models of the networks controlling cell growth and division.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4545, + "tag": "BioModels:BIOMD0000001044" + }, + { + "id": 4546, + "tag": "Cell Cycle" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:08.323269+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001044", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3328": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3328, + "name": "Smith&Moore2004 - The SIR model for the spread of HongKong Flu", + "repository_type": "biomodels", + "summary": "This is the SIR model for disease spread of the Hong Kong flu in New York City in the late 1960's.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4547, + "tag": "BioModels:BIOMD0000001045" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 4548, + "tag": "Influenza A virus (strain A/Hong Kong/1/1968 H3N2)" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4617, + "tag": "Influenza" + } + ], + "timestamp_created": "2025-01-30 14:45:08.869666+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001045", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3329": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "7", + "id": 3329, + "name": "Raman2005 - Mycolic acid pathway of M. tuberculosis", + "repository_type": "biomodels", + "summary": "The mycobacterial cell wall is a distinctive thick layer that protects the tubercle bacillus from general antibiotics and the host\u2019s immune system. Mycolic acids, which are long-chain \u03b1-alkyl-\u03b2-hydroxy fatty acids, are the major constituents of this protective layer, and their synthesis has been shown to be critical for the survival of M. tuberculosis. This model captures the mycolic acid pathway in M. tuberculosis with 197 metabolites participating in 219 reactions catalysed by 28 proteins. The model helps in the rational identification of potential anti-tubercular drug targets.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4549, + "tag": "BioModels:BIOMD0000001046" + }, + { + "id": 3375, + "tag": "Mycobacterium tuberculosis" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:09.362393+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001046", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3330": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3330, + "name": "Collier1996 - Delta Notch intercellular signalling and lateral inhibition", + "repository_type": "biomodels", + "summary": "
ODE model describing how slight variations in Notch and Delta cellular concentrations, through lateral inhibition, lead to cells with different states of differentiation. Lateral inhibition is a process whereby a given cell adopting a given fate prevents its immediate neighbouring cells from doing likewise. Notch and Delta are interacting transmembrane proteins and according to this model, lateral inhibition is due to a process where the inhibited cells (where notch has been activated by Delta) loose their ability to inhibit other cells (by synthesizing Delta). This process creates a feedback loop where cells with more delta proteins on their surface inhibit their immediate neighbours and adopt a different cell fate than those neighbours.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4550, + "tag": "BioModels:BIOMD0000001047" + }, + { + "id": 132, + "tag": "Drosophila" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:09.916530+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001047", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3331": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3331, + "name": "Siddhartha2002 - Kinetic modelling of cancer therapies", + "repository_type": "biomodels", + "summary": "Siddhartha Jain. Kinetic model for designing a cancer therapy. Cancer Cell International 2, 1 (2002).

A kinetic model has been developed to study cancer growth. Cancer growth has been considered as interaction between various independent but interacting compartments. The model considers cell growth and metastasis resulting in the formation of new tumor masses. Using certain representative parameter values, cell growth has been modeled in the absence and the presence of various cancer therapies. Based on this analysis, the critical parameters involved in cancer development have been identified. This model may thus be useful in studying and designing a cancer therapy using the data obtained from specific in vitro experiments.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4551, + "tag": "BioModels:BIOMD0000001048" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:10.466213+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001048", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3332": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3332, + "name": "Alharbi2020 - Tumor and immune system competition", + "repository_type": "biomodels", + "summary": "Sana Abdulkream Alharbi & Azmin Sham Rambely. A New ODE-Based Model for Tumor Cells and Immune System Competition. Mathematics 8, 8 (2020).

Changes in diet are heavily associated with high mortality rates in several types of cancer. In this paper, a new mathematical model of tumor cells growth is established to dynamically demonstrate the effects of abnormal cell progression on the cells affected by the tumor in terms of the immune system\u2019s functionality and normal cells\u2019 dynamic growth. This model is called the normal-tumor-immune-unhealthy diet model (NTIUNHDM) and governed by a system of ordinary differential equations. In the NTIUNHDM, there are three main populations normal cells, tumor cell and immune cells. The model is discussed analytically and numerically by utilizing a fourth-order Runge\u2013Kutta method. The dynamic behavior of the NTIUNHDM is discussed by analyzing the stability of the system at various equilibrium points and the Mathematica software is used to simulate the model. From analysis and simulation of the NTIUNHDM, it can be deduced that instability of the response stage, due to a weak immune system, is classified as one of the main reasons for the coexistence of abnormal cells and normal cells. Additionally, it is obvious that the NTIUNHDM has only one stable case when abnormal cells begin progressing into early stages of tumor cells such that the immune cells are generated once. Thus, early boosting of the immune system might contribute to reducing the risk of cancer.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4552, + "tag": "BioModels:BIOMD0000001052" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:11.057042+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001052", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3333": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3333, + "name": "Garde2020 - metabolic oscillations in Bacillus subtilis biofilms", + "repository_type": "biomodels", + "summary": "Ravindra Garde, Bashar Ibrahim & Stefan Schuster. Extending the minimal model of metabolic oscillations in Bacillus subtilis biofilms. Scientific Reports 10, 1 (2020).

Biofilms are composed of microorganisms attached to a solid surface or floating on top of a liquid surface. They pose challenges in the field of medicine but can also have useful applications in industry. Regulation of biofilm growth is complex and still largely elusive. Oscillations are thought to be advantageous for biofilms to cope with nutrient starvation and chemical attacks. Recently, a minimal mathematical model has been employed to describe the oscillations in Bacillus subtilis biofilms. In this paper, we investigate four different modifications to that minimal model in order to better understand the oscillations in biofilms. Our first modification is towards making a gradient of metabolites from the center of the biofilm to the periphery. We find that it does not improve the model and is therefore, unnecessary. We then use realistic Michaelis-Menten kinetics to replace the highly simple mass-action kinetics for one of the reactions. Further, we use reversible reactions to mimic the diffusion in biofilms. As the final modification, we check the combined effect of using Michaelis-Menten kinetics and reversible reactions on the model behavior. We find that these two modifications alone or in combination improve the description of the biological scenario.", + "tags": [ + { + "id": 3412, + "tag": "Bacillus subtilis" + }, + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4553, + "tag": "BioModels:BIOMD0000001053" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:11.658985+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001053", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3334": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3334, + "name": "Pearce2021 - Fibrin Polymerization", + "repository_type": "biomodels", + "summary": "Here is an ode model for in vitro fibrin matrix polymerization reproducing interactions among fibrinogen, fibrin and other proteins involved in the homeostatic phase of wound healing.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4554, + "tag": "BioModels:BIOMD0000001054" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:12.247239+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001054", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3335": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3335, + "name": "Jeon2018 - Enzyme clustering in Glucose metabolism", + "repository_type": "biomodels", + "summary": "Miji Jeon, Hye-Won Kang & Songon An. A Mathematical Model for Enzyme Clustering in Glucose Metabolism. Scientific Reports 8, 1 (2018).

We have recently demonstrated that the rate-limiting enzymes in human glucose metabolism organize into cytoplasmic clusters to form a multienzyme complex, the glucosome, in at least three different sizes. Quantitative high-content imaging data support a hypothesis that the glucosome clusters regulate the direction of glucose flux between energy metabolism and building block biosynthesis in a cluster size-dependent manner. However, direct measurement of their functional contributions to cellular metabolism at subcellular levels has remained challenging. In this work, we develop a mathematical model using a system of ordinary differential equations, in which the association of the rate-limiting enzymes into multienzyme complexes is included as an essential element. We then demonstrate that our mathematical model provides a quantitative principle to simulate glucose flux at both subcellular and population levels in human cancer cells. Lastly, we use the model to simulate 2-deoxyglucose-mediated alteration of glucose flux in a population level based on subcellular high-content imaging data. Collectively, we introduce a new mathematical model for human glucose metabolism, which promotes our understanding of functional roles of differently sized multienzyme complexes in both single-cell and population levels.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4555, + "tag": "BioModels:BIOMD0000001055" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:12.773384+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001055", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3336": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3336, + "name": "Chulian2021 - feedback signalling in B lymphopoeisis", + "repository_type": "biomodels", + "summary": "Salvador Chuli\u00e1n, \u00c1lvaro Mart\u00ednez-Rubio, Anna Marciniak-Czochra, Thomas Stiehl, Cristina Bl\u00e1zquez Go\u00f1i, Juan Francisco Rodr\u00edguez Guti\u00e9rrez, Manuel Ram\u00edrez Orellana, Ana Castillo Robleda, V\u00edctor M. P\u00e9rez-Garc\u00eda & Mar\u00eda Rosa. Dynamical properties of feedback signalling in B lymphopoiesis: A mathematical modelling approach. Journal of Theoretical Biology 522 (2021).

Haematopoiesis is the process of generation of blood cells. Lymphopoiesis generates lymphocytes, the cells in charge of the adaptive immune response. Disruptions of this process are associated with diseases like leukaemia, which is especially incident in children. The characteristics of self-regulation of this process make them suitable for a mathematical study.

In this paper we develop mathematical models of lymphopoiesis using currently available data. We do this by drawing inspiration from existing structured models of cell lineage development and integrating them with paediatric bone marrow data, with special focus on regulatory mechanisms. A formal analysis of the models is carried out, giving steady states and their stability conditions. We use this analysis to obtain biologically relevant regions of the parameter space and to understand the dynamical behaviour of B-cell renovation. Finally, we use numerical simulations to obtain further insight into the influence of proliferation and maturation rates on the reconstitution of the cells in the B line. We conclude that a model including feedback regulation of cell proliferation represents a biologically plausible depiction for B-cell reconstitution in bone marrow. Research into haematological disorders could benefit from a precise dynamical description of B lymphopoiesis.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4556, + "tag": "BioModels:BIOMD0000001056" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:13.293987+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001056", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3337": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3337, + "name": "Nikolov2020 - p53-miR34 model", + "repository_type": "biomodels", + "summary": "Svetoslav Nikolov, Olaf Wolkenhauer, Julio Vera & Momchil Nenov. The role of cooperativity in a p53-miR34 dynamical mathematical model. Journal of Theoretical Biology 495 (2020).

The objective of this study is to evaluate the role of cooperativity, captured by the Hill coefficient, in a minimal mathematical model describing the interactions between p53 and miR-34a. The model equations are analyzed for negative, none and normal cooperativity using a specific version of bifurcation theory and they are solved numerically. Special attention is paid to the sign of so-called first Lyapunov value. Interpretations of the results are given, both according to dynamic theory and in biological terms. In terms of cell signaling, we propose the hypothesis that when the outgoing signal of a system spends a physiologically significant amount of time outside of its equilibrium state, then the value of that signal can be sampled at any point along the trajectory towards that equilibrium and indeed, at multiple points. Coupled with non-linear behavior, such as that caused by cooperativity, this feature can account for a complex and varied response, which p53 is known for. From dynamical point of view, we found that when cooperativity is negative, the system has only one stable equilibrium point. In the absence of cooperativity, there is a single unstable equilibrium point with a critical boundary of stability. In the case with normal cooperativity, the system can have one, two, or three steady states with both, bi-stability and bi-instability occurring.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4557, + "tag": "BioModels:BIOMD0000001057" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4665, + "tag": "Signal transduction by p53 class mediator" + } + ], + "timestamp_created": "2025-01-30 14:45:13.811021+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001057", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3338": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3338, + "name": "Novak2022 - Mitotic kinase oscillation", + "repository_type": "biomodels", + "summary": "Bela Novak & John J. Tyson. Mitotic kinase oscillation governs the latching of cell cycle switches. Current Biology 32, 12 (2022).

In 1996, Kim Nasmyth1 proposed that the eukaryotic cell cycle is an alternating sequence of transitions from G1 to S-G2-M and back again. These two phases correlate to high activity of cyclin-dependent kinases (CDKs) that trigger S-G2-M events and CDK antagonists that stabilize G1 phase. We associated these \u201calternative phases\u201d with the coexistence of two stable steady states of the biochemical reactions among CDKs and their antagonists. Transitions between these steady states (G1-to-S and M-to-G1) are driven by \u201chelper\u201d proteins. The fact that the transitions are irreversible is guaranteed by a \u201clatching\u201d property of the molecular switches, as we have argued in previous publications. Here, we show that if the latch is broken, then the biochemical reactions can swing back-and-forth across the transitions; either G1-S-G1-S \u2026 (periodic DNA replication without mitosis or cell division) or M-(G1)-M-(G1) \u2026 (periodic Cdc14 release, without fully exiting mitosis). Using mathematical modeling of the molecular control circuit in budding yeast, we provide a fresh account of aberrant cell cycles in mutant strains: endoreplication in the clb1-5\u0394 strain and periodic release and resequestration of Cdc14 (an \u201cexit\u201d phosphatase) in the CLB2kd\u0394 strain.7,8 In our opinion, these \u201cendocycles\u201d are not autonomous oscillatory modules that must be entrained by the CDK oscillator but rather inadvertent and deleterious oscillations that are normally suppressed by the CDK latching-gate mechanism.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4558, + "tag": "BioModels:BIOMD0000001058" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:14.353365+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001058", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3339": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3339, + "name": "Stucki2005 - caspase-3 metabolism", + "repository_type": "biomodels", + "summary": "J\u00f6rg W. Stucki & Hans-Uwe Simon. Mathematical modeling of the regulation of caspase-3 activation and degradation. Journal of Theoretical Biology 234, 1 (2005).

Caspases are thought to be important players in the execution process of apoptosis. Inhibitors of apoptosis (IAPs) are able to block caspases and therefore apoptosis. The fact that a subgroup of the IAP family inhibits active caspases implies that not each caspase activation necessarily leads to apoptosis. In such a scenario, however, processed and enzymically active caspases should somehow be removed. Indeed, IAP\u2013caspase complexes covalently bind ubiquitin, resulting in degradation by the 26S proteasome. Following release from mitochondria, IAP antagonists (e.g. second mitochondrial activator of caspases (Smac)) inactivate IAPs. Moreover, although pro-apoptotic factors such as irradiation or anti-cancer drugs may release Smac from mitochondria in tumor cells, high cytoplasmic survivin and ML-IAP levels might be able to neutralize it and, consequently, IAPs would further be able to bind activated caspases. Here, we propose a simple mathematical model, describing the molecular interactions between Smac deactivators, Smac, IAPs, and caspase-3, including the requirements for both induction and prevention of apoptosis, respectively. In addition, we predict a novel mechanism of caspase-3 degradation that might be particularly relevant in long-living cells.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4559, + "tag": "BioModels:BIOMD0000001059" + }, + { + "id": 4141, + "tag": "Mus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:14.870844+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001059", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3340": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3340, + "name": "Frank2021 - Macrophage polarization", + "repository_type": "biomodels", + "summary": "The model describes the mechanisms by which macrophages differentiate into a given phenotype. The model shows that both extracellular and intracellular signalling are both important for that process. More specifically, STAT1 activity favors macrophages polarization towards M1 phenotype and STAT6 activity favors macrophage polarization towards M2 phenotype. However, these polarizations are can be reversed by molecular signalling.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4560, + "tag": "BioModels:BIOMD0000001060" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:15.594471+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001060", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3341": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3341, + "name": "Canto-Encalada2022-FBA of simultaneous degradation of ammonia and pollutants", + "repository_type": "biomodels", + "summary": "The ammonia-oxidizing bacterium Nitrosomonas europaea has been widely recognized as an important player in the nitrogen cycle as well as one of the most abundant members in microbial communities for the treatment of industrial or sewage wastewater. Its natural metabolic versatility and extraordinary ability to degrade environmental pollutants enable it to thrive under various harsh environmental conditions. This model of N. europaea (iGC535) is the most accurate metabolic model for a nitrifying organism to date, reaching an average prediction accuracy of over 90% under several growth conditions. The manually curated model can predict phenotypes under chemolithotrophic and chemolithoorganotrophic conditions while oxidating methane and wastewater pollutants. It is the first upload of the model.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4561, + "tag": "BioModels:BIOMD0000001061" + }, + { + "id": 4562, + "tag": "Nitrosomonas europaea" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:16.105234+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001061", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3342": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3342, + "name": "Kim2021 - Development of a Genome-Scale Metabolic Model and Phenome Analysis of the Probiotic Escherichia coli Strain Nissle 1917", + "repository_type": "biomodels", + "summary": "Escherichia coli Nissle 1917 (EcN) is an intestinal probiotic that is effective for the treatment of intestinal disorders, such as inflammatory bowel disease and ulcerative colitis. EcN is a representative Gram-negative probiotic in biomedical research and is an intensively studied probiotic. However, to date, its genome-wide metabolic network model has not been developed. Here, we developed a comprehensive and highly curated EcN metabolic model, referred to as iDK1463, based on genome comparison and phenome analysis. The model was improved and validated by comparing the simulation results with experimental results from phenotype microarray tests. iDK1463 comprises 1463 genes, 1313 unique metabolites, and 2984 metabolic reactions. Phenome data of EcN were compared with those of Escherichia coli intestinal commensal K-12 MG1655. iDK1463 was simulated to identify the genetic determinants responsible for the observed phenotypic differences between EcN and K-12. Further, the model was simulated for gene essentiality analysis and utilization of nutrient sources under anaerobic growth conditions. These analyses provided insights into the metabolic mechanisms by which EcN colonizes and persists in the gut. iDK1463 will contribute to the system-level understanding of the functional capacity of gut microbes and their interactions with microbiota and human hosts, as well as the development of live microbial therapeutics.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4563, + "tag": "BioModels:BIOMD0000001062" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:16.705215+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001062", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3343": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3343, + "name": "Lu2019 - Genome scale metabolic model for Saccharomyces cerevisiae - yeastGEM8.5.0", + "repository_type": "biomodels", + "summary": "yeast-GEM: The consensus genome-scale metabolic model of Saccharomyces cerevisiae. Further curations of this model will be tracked in the GitHub repository:https://github.com/SysBioChalmers/yeast-GEM For you use yeast-GEM, please cite the yeast8 paper: Lu, H. et al. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications 10, 3586 (2019). https://doi.org/10.1038/s41467-019-11581-3. The FROG analysis was performed with the yeastGEM_rich_medium.mat, which is a rich medium setup.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4564, + "tag": "BioModels:BIOMD0000001063" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:17.221367+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001063", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3344": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "9", + "id": 3344, + "name": "Kim2011 - the genome scale reconstruction of the Vibrio vulnificus metabolic network - VvuMBEL943", + "repository_type": "biomodels", + "summary": "

This is a model of the genome scale reconstruction of the Vibrio vulnificus metabolic network, VvuMBEL943, described in the article:
Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery
Hyun Uk Kim, Soo Young Kim, Haeyoung Jeong, Tae Yong Kim, Jae Jong Kim, Hyon E Choy, Kyu Yang Yi, Joon Haeng Rhee, and Sang Yup Lee. Molecular Systems Biology 7:460 Jan 2011 doi: 10.1038/msb.2010.115

Abstract:
Although the genomes of many microbial pathogens have been studied to help identify effective drug targets and novel drugs, such efforts have not yet reached full fruition. In this study, we report a systems biological approach that efficiently utilizes genomic information for drug targeting and discovery, and apply this approach to the opportunistic pathogen Vibrio vulnificus CMCP6. First, we partially re-sequenced and fully re-annotated the V. vulnificus CMCP6 genome, and accordingly reconstructed its genome-scale metabolic network, VvuMBEL943. The validated network model was employed to systematically predict drug targets using the concept of metabolite essentiality, along with additional filtering criteria. Target genes encoding enzymes that interact with the five essential metabolites finally selected were experimentally validated. These five essential metabolites are critical to the survival of the cell, and hence were used to guide the cost-effective selection of chemical analogs, which were then screened for antimicrobial activity in a whole-cell assay. This approach is expected to help fill the existing gap between genomics and drug discovery.

This metabolic network model has been thoroughly validated by the authors. VvuMBEL943 is a stoichiometric model that contains the metabolic information of the microbial pathogen, Vibrio vulnificus CMCP6, at genome-scale. The SBML version was generated by Hyun Uk Kim using MetaFluxNet.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4565, + "tag": "BioModels:BIOMD0000001064" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4566, + "tag": "Vibrio vulnificus" + } + ], + "timestamp_created": "2025-01-30 14:45:17.743999+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001064", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3345": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3345, + "name": "vonDassow2000 - Segment Polarity Network model on 1x4 grid of cells", + "repository_type": "biomodels", + "summary": "This is the segment polarity network model described by von Dassow et al. (2000). It represents a toroidal hexagonal array of cells (1x4), where each cell can express various genes (winglessengrailed, hedgehog, cubitus interruptus, and patched) and where their protein products interact within a cell, and across neighboring cells.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4567, + "tag": "BioModels:BIOMD0000001065" + }, + { + "id": 4568, + "tag": "Rhodococcus ruber" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:18.285749+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001065", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3346": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3346, + "name": "Chowell2022 - Random Forest model to predict efficacy of immune checkpoint blockade across multiple cancer patient cohorts", + "repository_type": "biomodels", + "summary": "This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be aware that the column names in the GitHub code and the downloaded dataset from the publication may vary. Users are advised to make minor adjustments to either the code or the dataset to ensure compatibility. The curated version of the model has modified the column names in the training code to align with the dataset.GitHub repository: https://github.com/CCF-ChanLab/MSK-IMPACT-IO", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4569, + "tag": "BioModels:BIOMD0000001066" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:18.787223+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001066", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3347": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3347, + "name": "Deshpande2019 - Random Forest model to predict long non-coding RNAs from coding RNAs in Zea Mays plant transcriptomic data", + "repository_type": "biomodels", + "summary": "This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. The model assigns 1 for coding sequences and 2 for long non-coding sequences. The prediction is performed using a combination of Open Reading Frame (ORF) based, Sequence-based and Codon-bias features. Users need to download the curated ONNX model and also need to convert the sequences into feature matrix as mentioned in PLIT paper (Deshpande et al. 2019) to make predictions on sequences from Zea Mays sequence data.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4571, + "tag": "BioModels:BIOMD0000001067" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:19.345200+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001067", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3348": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3348, + "name": "Nassar2022 - Metagenomics Classification Task for Scientific Literature Text", + "repository_type": "biomodels", + "summary": "This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image. This conversion ensures interoperability and open access. The ONNX format utility can perform the following essential tasks: model conversion, inference, inspection, and optimization. Reference: 1) https://github.com/elixir-europe/biohackathon-projects-2022/tree/main/9 2) https://www.ebi.ac.uk/biomodels/search?query=Maaly+Nassar&domain=biomodels 3) https://gitlab.com/maaly7/emerald_metagenomics_annotations 4) This model is built upon the model of the following publication: Maaly Nassar, Alexander B Rogers, Francesco Talo', Santiago Sanchez, Zunaira Shafique, Robert D Finn, Johanna McEntyre, A machine learning framework for discovery and enrichment of metagenomics metadata from open access publications, GigaScience, Volume 11, 2022, giac077, https://doi.org/10.1093/gigascience/giac077", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4572, + "tag": "BioModels:BIOMD0000001068" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:19.852980+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001068", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3349": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3349, + "name": "Alam2019 - Machine learning approach of automatic identification and counting of blood cells", + "repository_type": "biomodels", + "summary": "This model is used for automatic identification and counting of three types of blood cells: Red Blood Cells (RBC), White Blood Cells (WBC) and Platelet (Platelets) using the \u2018you only look once\u2019 (YOLO) object detection and classification algorithm with some additions to remove overannotation. The YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Postprocessing with k-nearest neighbor (KNN) and intersection over union (IOU) approach reduces issues with multiple annotation of platelets. The original code was extended to save the trained YoloV2 network state into the protobuf format. This is then used to generate the ONNX model, containing the weigths. Additional code was added to implement the inference step for image annotation based on the ONNX model, as well as the post-processing logic as used on the original model output. Dependencies have been documented explicitly using a conda environment.yml file to simplify reproducibility.Original GitHub repository: https://github.com/MahmudulAlam/Automatic-Identification-and-Counting-of-Blood-CellsGitHub repository: https://github.com/nilshoffmann/Automatic-Identification-and-Counting-of-Blood-Cells", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4573, + "tag": "BioModels:BIOMD0000001069" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:20.362087+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001069", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3350": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3350, + "name": "Kong2022 - Conditional Antibody Design as 3D Equivariant Graph Translation", + "repository_type": "biomodels", + "summary": "Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4574, + "tag": "BioModels:BIOMD0000001070" + }, + { + "id": 4575, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-01-30 14:45:20.927615+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001070", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3351": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3351, + "name": "Wang2022 - Scaffolding protein functional sites using deep learning", + "repository_type": "biomodels", + "summary": "Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, \u201cconstrained hallucination,\u201d optimizes sequences such that their predicted structures contain the desired functional site. The second approach, \u201cinpainting,\u201d starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4576, + "tag": "BioModels:BIOMD0000001071" + }, + { + "id": 4575, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-01-30 14:45:21.453698+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001071", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3352": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "8", + "id": 3352, + "name": "Phillips2013 - physiologically based modeling explaining Mammalian rest/activity patterns", + "repository_type": "biomodels", + "summary": "The model provides a framework for understanding rest/activity patterns effected by the circadian rhythm and relating them to underlying diverse phenotypes.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4577, + "tag": "BioModels:BIOMD0000001072" + }, + { + "id": 3114, + "tag": "Mammalia" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:21.979196+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001072", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3353": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3353, + "name": "Patterson2022 - Tumour mutation data driven Random Forest model to predict immune checkpoint inhibitor therapy benefit in metastatic melanoma", + "repository_type": "biomodels", + "summary": "A Random Forest model is developed to incorporate tumor mutation data within the context of the biological process known as leukocyte proliferation regulation. This model aims to predict a patient's response to anti-PD1 treatment.The authors conducted experiments using four different types of classifiers: Random Forest, Gradient Boosting, Feed Forward Neural Network, and Long Short-Term Memory (LSTM) recurrent neural network. Among these classifiers, the Random Forest algorithm yielded the best predictive performance when modeling gene mutation data associated with the 'leukocyte proliferation regulation' biological process. Hence, this curated version of the model focuses on the Random Forest model trained specifically on the 'Leukocyte Proliferation Regulation' process.In this model, a value of '0' is assigned to NonResponders, while a value of '1' is assigned to Responders. Please note that to obtain predictions, users should provide mutation data containing only the genes corresponding to the 'GO_REGULATION_OF_LEUKOCYTE_PROLIFERATION' process keyword, as specified in the 'GO_test_genes_dict_intersection' dictionary.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4578, + "tag": "BioModels:BIOMD0000001073" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:22.484999+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001073", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3354": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3354, + "name": "Liu2023 - Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data", + "repository_type": "biomodels", + "summary": "Immunotherapy has improved the prognosis of patients with advanced non-small cell lungcancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4579, + "tag": "BioModels:BIOMD0000001074" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:23.023177+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001074", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3355": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3355, + "name": "Sammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy", + "repository_type": "biomodels", + "summary": "In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection. Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-MLFor more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4580, + "tag": "BioModels:BIOMD0000001075" + }, + { + "id": 4575, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-01-30 14:45:23.548847+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001075", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3356": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3356, + "name": "L\u00f3pez-Cort\u00e9s2020 - Prediction of Breast Cancer (BC) proteins involved in cancer immunotherapy using molecular descriptors and Multi Layer Perceptron (MLP) neural network", + "repository_type": "biomodels", + "summary": "This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Area Under the Receiver Operating Characteristics (AUROC) score of 0.984 through 3-fold cross-validation. Notably, the model identified top-ranked cancer immunotherapy proteins associated with breast cancer that should be studied for further biomarker discovery and therapeutic targeting.Please note that in this model, the output '0' means BC non-related protein and '1' means BC related protein. The original GitHub repository can be accessed at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4581, + "tag": "BioModels:BIOMD0000001076" + }, + { + "id": 4570, + "tag": "ONNX" + } + ], + "timestamp_created": "2025-01-30 14:45:24.042593+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001076", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3357": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3357, + "name": "Adlung2021 - Cell-to-cell variability in JAK2/STAT5 pathway", + "repository_type": "biomodels", + "summary": "

A mathematical model for cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4582, + "tag": "BioModels:BIOMD0000001077" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:24.576569+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001077", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3358": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3358, + "name": "Hammaren-Geissen2022_PPToP_Model12", + "repository_type": "biomodels", + "summary": "The model encodes the general biological process of protein synthesis and post-translational modification (PTM, such as protein phosphorylation), viewed through the eyes of a specific, widely-used experimental method. Specifically, we model measurements of pulsed stable isotope labelling of amino acids in cell culture (pSILAC), which is a method often used to quantify protein turnover (i.e. the degradation of old and replacement with new) of proteins in a cell.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4583, + "tag": "BioModels:BIOMD0000001078" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:25.124056+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001078", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3359": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3359, + "name": "DeBoeck2021 - Modular approach to modeling the cell cycle, simple cell cycle model", + "repository_type": "biomodels", + "summary": "

Models the production and degradation of cyclin B that drives the early embryonic cell cycle.Cyclin B is degraded by APC/C. The activity of APC/C is modeled not through biochemical interactions, but through a 'functional response curve'. This can be ultrasensitive (with the parameter alpha=0). in this case the system does not oscillate. Importantly the response can be made bistable and the form of the bistability can be easily prescribed. With a bistable response, the system oscillates.The uploaded file corresponds to the model used for Figs.3H, I in the publication.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4584, + "tag": "BioModels:BIOMD0000001079" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:25.617110+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001079", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3360": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3360, + "name": "DeBoeck2021 - Modular approach to modeling the cell cycle, 5 ODE model with 3 bistable switches", + "repository_type": "biomodels", + "summary": "

Model of the mammalian cell cycle as a chain of bistable switches. There are three bistable responses: response of E2F to Cyclin D, of Cdk1 to Cyclin B and of APC/C to Cdk1 activity. The model for the given parameters admits a complex limit cycle characterized by transitions through the bistable switches. The bistable responses are modeled directly using a functional motif, not through biochemical interactions. This modular approach allows to easily modify the properties of the bistable response curves. This version of the model correspond to Fig. 7 in the publication. We illustrated how, using this model, the system can be coupled to the circadian clock, by periodically modifying thresholds of one of the switches. We also illustrated how to implement the restriction point checkpoint using this model (those applications are not coded in the associated sbml file and can be seen in Fig. 8 of the publication). A related, simpler model that illustrates the bistable motif is MODEL2212060001

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4585, + "tag": "BioModels:BIOMD0000001080" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-01-30 14:45:26.117634+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001080", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3361": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "develop", + "id": 3361, + "name": "test", + "repository_type": "github", + "summary": "", + "tags": [], + "timestamp_created": "2025-03-12 11:14:12.387157+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/OSBv2", + "user": { + "email": "filippo+8@metacell.us", + "first_name": "Filippo", + "id": "a2514035-c47f-4d8a-b22b-081d91a5ce6b", + "last_name": "Ledda", + "username": "a" + }, + "user_id": "a2514035-c47f-4d8a-b22b-081d91a5ce6b" + }, + "3362": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "main", + "id": 3362, + "name": "Worm2D", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 559, + "tag": "C. elegans" + }, + { + "id": 2935, + "tag": "Cplusplus" + }, + { + "id": 609, + "tag": "Nervous system" + }, + { + "id": 4622, + "tag": "Neurons" + }, + { + "id": 540, + "tag": "OSBv1" + }, + { + "id": 549, + "tag": "OpenWorm" + } + ], + "timestamp_created": "2025-04-14 15:17:44.038970+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/openworm/CE_locomotion", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3363": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3363, + "name": "Fuss2006_MitoticActivation", + "repository_type": "biomodels", + "summary": "

The model was curated with XPP. The figure 3 was successfully reproduced.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3106, + "tag": "BioModels:BIOMD0000000069" + }, + { + "id": 3107, + "tag": "Cell Cycle, Mitotic" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 2978, + "tag": "Mitotic cell cycle" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 13:55:21.069407+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000069", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3364": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3364, + "name": "Goldbeter2006_weightCycling", + "repository_type": "biomodels", + "summary": "

This model is according to the paper of A model for the dynamics of human weight cycling by A. Goldbeter 2006.The figure3 (A) and (B) have been reproduced by Copasi 4.0.19(development) and SBMLodeSolver.The writer of the paper did not specify any units for the metabolites, so the creator of the model did not define the units as well.Both Q and R are normalized to vary between 0 and 1.


To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not.


To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3123, + "tag": "BioModels:BIOMD0000000079" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 3124, + "tag": "Regulation of multicellular organism growth" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 13:55:25.007882+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000079", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3365": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3365, + "name": "Jenkinson2011_EGF_MAPK", + "repository_type": "biomodels", + "summary": "

This is a model described in the article:
Thermodynamically Consistent Model Calibration in Chemical Kinetics.
Garrett Jenkinson and John Goutsias, BMC Systems Biology 2011 May 6;5(1):64.; PMID:21548948.

ABSTRACT:
BACKGROUND:
The dynamics of biochemical reaction systems are constrained by the fundamental laws of thermodynamics, which impose well-defined relationships among the reaction rate constants characterizing these systems. Constructing biochemical reaction systems from experimental observations often leads to parameter values that do not satisfy the necessary thermodynamic constraints. This can result in models that are not physically realizable and may lead to inaccurate, or even erroneous, descriptions of cellular function.
RESULTS:
We introduce a thermodynamically consistent model calibration (TCMC) method that can be effectively used to provide thermodynamically feasible values for the parameters of an open biochemical reaction system. The proposed method formulates the model calibration problem as a constrained optimization problem that takes thermodynamic constraints (and, if desired, additional non-thermodynamic constraints) into account. By calculating thermodynamically feasible values for the kinetic parameters of a well-known model of the EGF/ERK signaling cascade, we demonstrate the qualitative and quantitative significance of imposing thermodynamic constraints on these parameters and the effectiveness of our method for accomplishing this important task. MATLAB software, using the Systems Biology Toolbox 2.1, can be accessed from www.cis.jhu.edu/~goutsias/CSS lab/software.html. An SBML file containing the thermodynamically feasible EGF/ERK signaling cascade model can be found in the BioModels database.
CONCLUSIONS:
TCMC is a simple and flexible method for obtaining physically plausible values for the kinetic parameters of open biochemical reaction systems. It can be effectively used to recalculate a thermodynamically consistent set of parameter values for existing thermodynamically infeasible biochemical reaction models of cellular function as well as to estimate thermodynamically feasible values for the parameters of new models. Furthermore, TCMC can provide dimensionality reduction, better estimation performance, and lower computational complexity, and can help to alleviate the problem of data overfitting.

This model is a thermodynamically feasible version of a previous modelin the BioModels database,BIOMD0000000019, described in Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Schoeberl et al (2002), PMID:11923843.
The only difference between the present model and the model listed under BIOMD0000000019 are the values of the parameters.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2012 The BioModels.net Team.
For more information see the terms of use.
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3683, + "tag": "BioModels:BIOMD0000000399" + }, + { + "id": 3014, + "tag": "Epidermal growth factor receptor signaling pathway" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3015, + "tag": "Signaling by EGFR" + } + ], + "timestamp_created": "2025-05-07 14:17:33.632208+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000399", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3366": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3366, + "name": "Vizan2013 - TGF pathway long term signaling", + "repository_type": "biomodels", + "summary": "Pedro Viz\u00e1n, Daniel S. J. Miller, Ilaria Gori, Debipriya Das, Bernhard Schmierer & Caroline S. Hill. Controlling long-term signaling: receptor dynamics determine attenuation and refractory behavior of the TGF-\u03b2 pathway. Science Signaling 6, 305 (2013).

Understanding the complex dynamics of growth factor signaling requires both mechanistic and kinetic information. Although signaling dynamics have been studied for pathways downstream of receptor tyrosine kinases and G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors, they have not been investigated for the transforming growth factor-\u03b2 (TGF-\u03b2) superfamily pathways. Using an integrative experimental and mathematical modeling approach, we dissected the dynamic behavior of the TGF-\u03b2 to Smad pathway, which is mediated by type I and type II receptor serine/threonine kinases, in response to acute, chronic, and repeated ligand stimulations. TGF-\u03b2 exposure produced a transient response that attenuated over time, resulting in desensitized cells that were refractory to further acute stimulation. This loss of signaling competence depended on ligand binding, but not on receptor activity, and was restored only after the ligand had been depleted. Furthermore, TGF-\u03b2 binding triggered the rapid depletion of signaling-competent receptors from the cell surface, with the type I and type II receptors exhibiting different degradation and trafficking kinetics. A computational model of TGF-\u03b2 signal transduction from the membrane to the nucleus that incorporates our experimental findings predicts that autocrine signaling, such as that associated with tumorigenesis, severely compromises the TGF-\u03b2 response, which we confirmed experimentally. Thus, we have shown that the long-term signaling behavior of the TGF-\u03b2 pathway is determined by receptor dynamics, does not require TGF-\u03b2-induced gene expression, and influences context-dependent responses in vivo.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3848, + "tag": "BioModels:BIOMD0000000499" + }, + { + "id": 3002, + "tag": "Homo sapiens" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 3849, + "tag": "SMAD protein signal transduction" + }, + { + "id": 3158, + "tag": "Transforming growth factor beta receptor signaling pathway" + } + ], + "timestamp_created": "2025-05-07 14:18:08.906419+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000499", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3367": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3367, + "name": "Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer model", + "repository_type": "biomodels", + "summary": "
Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer model

The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely \"single GAS polymer model\" and \"double GAS polymer model\" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the \"single GAS polymer model\".

This model is described in the article:

Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U
Nat Immunol. 2014 Feb;15(2):168-76.

Abstract:

STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon.

This model is hosted on BioModels Database and identifiedby: BIOMD0000000500 .

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resourcefor published quantitative kinetic models .

To the extent possible under law, all copyright and related orneighbouring rights to this encoded model have been dedicated to the publicdomain worldwide. Please refer to CC0 Public DomainDedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 3850, + "tag": "BioModels:BIOMD0000000500" + }, + { + "id": 3851, + "tag": "Interferon-gamma-mediated signaling pathway" + }, + { + "id": 3090, + "tag": "Mus musculus" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:18:09.588707+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000500", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3368": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3368, + "name": "Mager2005 - Quasi-equilibrium pharmacokinetic model for drugs exhibiting target-mediated drug disposition", + "repository_type": "biomodels", + "summary": "
This model was developed with the aim of constructing an equilibrium model of the pharmacokinetic behaviour of a drug exhibiting target-mediated drug disposition (TMDD). TMDD involves the inclusion of drug-target interactions within a pharmacokinetic description, something which is usually considered negligible and subsequently excluded. Two approaches were used, one of which involved a quasi-equilibrium method to describe the kinetics of TMDD.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4648, + "tag": "BioModels:BIOMD0000000765" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4649, + "tag": "Unknown" + } + ], + "timestamp_created": "2025-05-07 14:20:45.714544+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000000765", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3369": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3369, + "name": "Naizabekov2020 - Methylosinus trichosporium OB3b - iMsOB3b", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports compiled for the metabolic model, iMsOB3b. The model can be found in the Supplementary Section of the Naizabekov et al, 2020 paper cited here.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4666, + "tag": "BioModels:BIOMD0000001081" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:07.052799+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001081", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3370": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3370, + "name": "Charusanti2011 - Y. pestis (iPC815)", + "repository_type": "biomodels", + "summary": "

FROG and miniFROG Reports for the organism Yersinia pestis.
These models originate from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4668, + "tag": "BioModels:BIOMD0000001082" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:07.542623+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001082", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3371": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3371, + "name": "Broddrick2016 - Synechococcus elongatus PCC 7942 (iJB785)", + "repository_type": "biomodels", + "summary": "

FROG and miniFROG Reports for the organism Synechococcus elongatus PCC 7942.
These models originate from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4669, + "tag": "BioModels:BIOMD0000001083" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:08.062464+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001083", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3372": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3372, + "name": "Mol2021 - P. thermoglucosidasius NCIMB 11955 (p_thermo)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports are provided for p_thermo genome-scale metabolic model. p_thermo is the model for Parageobacillus thermoglucosidasius NCIMB 11955. An analysis of the metabolism and internal fluxes of P. thermoglucosidasius is done in this study and the model can be found in the Supplementary data of Mol et al, 2021.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4670, + "tag": "BioModels:BIOMD0000001084" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:08.588348+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001084", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3373": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3373, + "name": "Norsigian2018 - A. baumannii (iCN718)", + "repository_type": "biomodels", + "summary": "

FROG and miniFROG Reports for the organism A. baumannii.
The model (iCN718) originates from BiGG Models Database: A Database of Genome-Scale Metabolic Models (http://bigg.ucsd.edu/). Copyright \u00a9 2019 The Regents of the University of California

To cite BiGG Models Database, please use: King ZA, Lu JS, Dr\u00e4ger A, Miller PC, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, and Lewis NE. BiGG Models: A platform for integrating, standardizing, and sharing genome-scale models (2016) Nucleic Acids Research 44(D1):D515-D522. doi:10.1093/nar/gkv1049

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4671, + "tag": "BioModels:BIOMD0000001085" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:09.206108+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001085", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3374": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3374, + "name": "Jensen2020 - Streptococcus oralis (iCJ415)", + "repository_type": "biomodels", + "summary": "We have presented FROG and miniFROG reports for the first genome-scale model, iCJ415, for Streptococcus oralis SK141. The model can be found in the Supplementary Material of the publication by Jensen et al, 2020 cited here.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4672, + "tag": "BioModels:BIOMD0000001086" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:09.670080+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001086", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3375": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3375, + "name": "Kumelj2019 - Streptomyces coelicolor (iKS1317)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports compiled for iKS1317, a reconstructed genome-scale metabolic model (GEM) for Streptomyces coelicolor. The model reconstruction is available in the Supporting Information of Kumelj et al, 2019 cited here.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4673, + "tag": "BioModels:BIOMD0000001087" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:10.137170+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001087", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3376": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3376, + "name": "Nouri2020 - Zymomonas mobilis (iHN446)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports are compiled for the reconstructed genome-scale metabolic model (iHN446) for Z. mobilis. The model can be found in the article Nouri et al, 2020 cited here.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4674, + "tag": "BioModels:BIOMD0000001088" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:10.659180+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001088", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3377": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "3", + "id": 3377, + "name": "\u00d6zcan2019 - Leuconostoc mesenteroides subsp. cremoris (iLM.c559)", + "repository_type": "biomodels", + "summary": "FROG and miniFROG reports have been compiled for iLM.c559, which is a GSM of the bacterium Leuconostoc mesenteroides subsp. cremoris. The model can be found in the \u00d6zcan et al, 2019 paper cited here.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4675, + "tag": "BioModels:BIOMD0000001089" + }, + { + "id": 4667, + "tag": "OMEX" + } + ], + "timestamp_created": "2025-05-07 14:31:11.253071+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001089", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3378": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3378, + "name": "Dias2019 - GSM model of S. pneumoniae R6", + "repository_type": "biomodels", + "summary": "Phenotypically Reconciled Genome-scale Metabolic Model for the Metabolism of Streptococcus pneumoniae Strain R6.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4676, + "tag": "BioModels:BIOMD0000001090" + }, + { + "id": 704, + "tag": "SBML" + }, + { + "id": 4677, + "tag": "Streptococcus pneumoniae (strain ATCC BAA-255 / R6)" + } + ], + "timestamp_created": "2025-05-07 14:31:11.741324+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001090", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3379": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3379, + "name": "Kulyashov2020 - Genome-Scale Metabolic Model for Geobacillus icigianus", + "repository_type": "biomodels", + "summary": "Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4678, + "tag": "BioModels:BIOMD0000001091" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:12.237328+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001091", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3380": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3380, + "name": "Gautam2020-Metabolic network of Thermotoga sp. Strain RQ7", + "repository_type": "biomodels", + "summary": "As in DOI: 10.1007/s12010-020-03470-z", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4679, + "tag": "BioModels:BIOMD0000001092" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:12.688885+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001092", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3381": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3381, + "name": "Nogales2020 - Genome-scale metabolic network of Pseudomonas putida (iJN1462)", + "repository_type": "biomodels", + "summary": "iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4680, + "tag": "BioModels:BIOMD0000001093" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:13.169816+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001093", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3382": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3382, + "name": "Liao2011 - Genome-scale metabolic reconstruction of Klebsiella pneumoniae (iYL1228)", + "repository_type": "biomodels", + "summary": "
Liao2011 - Genome-scale metabolicreconstruction of Klebsiella pneumoniae (iYL1228)

This model is described in the article:

Liao YC, Huang TW, Chen FC, Charusanti P, Hong JS, Chang HY, Tsai SF, Palsson BO, Hsiung CA.
J. Bacteriol. 2011 Apr; 193(7): 1710-1717

Abstract:

Klebsiella pneumoniae is a Gram-negative bacterium of the family Enterobacteriaceae that possesses diverse metabolic capabilities: many strains are leading causes of hospital-acquired infections that are often refractory to multiple antibiotics, yet other strains are metabolically engineered and used for production of commercially valuable chemicals. To study its metabolism, we constructed a genome-scale metabolic model (iYL1228) for strain MGH 78578, experimentally determined its biomass composition, experimentally determined its ability to grow on a broad range of carbon, nitrogen, phosphorus and sulfur sources, and assessed the ability of the model to accurately simulate growth versus no growth on these substrates. The model contains 1,228 genes encoding 1,188 enzymes that catalyze 1,970 reactions and accurately simulates growth on 84% of the substrates tested. Furthermore, quantitative comparison of growth rates between the model and experimental data for nine of the substrates also showed good agreement. The genome-scale metabolic reconstruction for K. pneumoniae presented here thus provides an experimentally validated in silico platform for further studies of this important industrial and biomedical organism.

This model is hosted on BioModels Database and identified by: MODEL1507180054.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4681, + "tag": "BioModels:BIOMD0000001094" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:13.617840+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001094", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3383": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3383, + "name": "Jamshidi2007 - Genome-scale metabolic network of Mycobacterium tuberculosis (iNJ661)", + "repository_type": "biomodels", + "summary": "
Jamshidi2007 - Genome-scale metabolic networkof Mycobacterium tuberculosis (iNJ661)

This model is described in the article:

Jamshidi N, Palsson B\u00d8.
BMC Syst Biol 2007; 1: 26

Abstract:

BACKGROUND: Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. RESULTS: We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints. CONCLUSION: Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets.

This model is hosted on BioModels Database and identified by: MODEL1507180001.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4682, + "tag": "BioModels:BIOMD0000001095" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:14.087579+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001095", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3384": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3384, + "name": "Irani2015 - Genome-scale metabolic model of P.pastoris N-glycosylation", + "repository_type": "biomodels", + "summary": "
Irani2015 - Genome-scale metabolic model ofP.pastoris N-glycosylation

This model is described in the article:

Irani ZA, Kerkhoven E, Shojaosadati SA, Nielsen J.
Biotechnol. Bioeng. 2015 Oct;

Abstract:

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better predictions of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. This article is protected by copyright. All rights reserved.

This model is hosted on BioModels Database and identified by: MODEL1510220000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4683, + "tag": "BioModels:BIOMD0000001096" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:14.637683+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001096", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3385": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3385, + "name": "Benedict2011 - Genome-scale metoblic network of Methanosarcina acetivorans (iMB745)", + "repository_type": "biomodels", + "summary": "
Benedict2011 - Genome-scale metoblic networkof Methanosarcina acetivorans (iMB745)

This model is described in the article:

Benedict MN, Gonnerman MC, Metcalf WW, Price ND.
J. Bacteriol. 2012 Feb; 194(4): 855-865

Abstract:

Methanosarcina acetivorans strain C2A is a marine methanogenic archaeon notable for its substrate utilization, genetic tractability, and novel energy conservation mechanisms. To help probe the phenotypic implications of this organism's unique metabolism, we have constructed and manually curated a genome-scale metabolic model of M. acetivorans, iMB745, which accounts for 745 of the 4,540 predicted protein-coding genes (16%) in the M. acetivorans genome. The reconstruction effort has identified key knowledge gaps and differences in peripheral and central metabolism between methanogenic species. Using flux balance analysis, the model quantitatively predicts wild-type phenotypes and is 96% accurate in knockout lethality predictions compared to currently available experimental data. The model was used to probe the mechanisms and energetics of by-product formation and growth on carbon monoxide, as well as the nature of the reaction catalyzed by the soluble heterodisulfide reductase HdrABC in M. acetivorans. The genome-scale model provides quantitative and qualitative hypotheses that can be used to help iteratively guide additional experiments to further the state of knowledge about methanogenesis.

This model is hosted on BioModels Database and identified by: MODEL1507180040.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4684, + "tag": "BioModels:BIOMD0000001097" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:15.093688+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001097", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3386": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "6", + "id": 3386, + "name": "Feist2006_methanogenesis_OptiMethanol", + "repository_type": "biomodels", + "summary": "

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..

To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Nov\u00e8re N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4685, + "tag": "BioModels:BIOMD0000001098" + }, + { + "id": 4686, + "tag": "Methanogenesis" + }, + { + "id": 4687, + "tag": "Methanosarcina barkeri" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:15.565854+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001098", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3387": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3387, + "name": "Richards2016 - Genome-scale metabolic reconstruction of Methanococcus maripaludis (iMR539)", + "repository_type": "biomodels", + "summary": "
Richards2016 - Genome-scale metabolicreconstruction of Methanococcus maripaludis (iMR539)

This model is described in the article:

Richards MA, Lie TJ, Zhang J, Ragsdale SW, Leigh JA, Price ND.
J. Bacteriol. 2016 Dec; 198(24): 3379-3390

Abstract:

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus on Methanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes of M. maripaludis strain S2. Our reconstructed metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process.Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas. Although a significant portion of biological methane is generated through this environmentally ubiquitous pathway, existing methanogen models portray the more traditional energy conservation mechanisms that are found in other methanogens. We have constructed a genome scale metabolic network of Methanococcus maripaludis that explicitly accounts for all major reactions involved in hydrogenotrophic methanogenesis. Our reconstruction demonstrates the importance of electron bifurcation in central metabolism, providing both a window into hydrogenotrophic methanogenesis and a hypothesis-generating platform to fuel metabolic engineering efforts.

This model is hosted on BioModels Database and identified by: MODEL1607200000.

To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4688, + "tag": "BioModels:BIOMD0000001099" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:16.058317+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001099", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3388": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "2", + "id": 3388, + "name": "Mohr2022 - Rabbit Purkinje Cardiac Electrophysiology", + "repository_type": "biomodels", + "summary": "we performed a comparative benchmark and investigated a variety of mathematical cardiac AP models, including a newly developed minimalistic model specifically tailored to the AP of rabbit Purkinje cells, for their ability to substitute experiments. The simulated changes in AP duration (dAPD90) at increasing drug concentrations were compared to experimental results from 588 internal Purkinje fiber studies covering 555 different drugs with diverse modes of action. Using our minimalistic model, 80% of the Purkinje experiments could be quantitatively reproduced.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4689, + "tag": "BioModels:BIOMD0000001100" + }, + { + "id": 4575, + "tag": "UNKNOWN" + } + ], + "timestamp_created": "2025-05-07 14:31:16.601930+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001100", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3389": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3389, + "name": "Zhou2024 - Post Infarction Myocyte Electromechanics", + "repository_type": "biomodels", + "summary": "A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets. Abnormalities caused by scar and border zone ionic remodeling are introduced in varying degrees as reported in experimental data obtained in acute and chronic infarction. Simulations enabled reproducing and explaining clinical phenotypes post-MI, from ionic remodelling to ECGs and pressure-volume loops.", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4690, + "tag": "BioModels:BIOMD0000001101" + }, + { + "id": 4691, + "tag": "matlab" + } + ], + "timestamp_created": "2025-05-07 14:31:17.059965+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001101", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3390": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "5", + "id": 3390, + "name": "Burbano2023 - HGFsignaling_in_FattyLiverDisease", + "repository_type": "biomodels", + "summary": "

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, non-alcoholic fatty liver disease (NAFLD) is gaining importance. As the disease progresses it can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in NAFLD development, but the molecular mechanisms responsible for disease development remained unresolved. Here, we uncover a strong downregulation of the PI3K-AKT pathway and an upregulation of the MAPK pathway in primary hepatocytes from a preclinical model fed with a Western diet (WD). Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveals a patient-specific variability in basal MET phosphorylation, which correlates with the outcome of patients after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4692, + "tag": "BioModels:BIOMD0000001102" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:17.513696+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001102", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3391": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "4", + "id": 3391, + "name": "Palaniappan2021 - Cell free modelling of second generation Toehold switches", + "repository_type": "biomodels", + "summary": "
Cervical cancer is a global public health subject as it affects women in the reproductive ages, and accounts for the second largest burden among cancer patients worldwide with an unforgiving 50% mortality rate. Poor awareness and access to effective diagnosis have led to this enormous disease burden, calling for point-of-care, minimally invasive diagnosis methods. Here, an end-to-end quantitative approach for a new kind of diagnosis has been developed, comprising identification of optimal biomarkers, design of the sensor, and simulation of the diagnostic circuit. Using miRNA expression data in the public domain, we identified circulating miRNA biomarkers specific to cervical cancer using multi-tier screening. Synthetic riboregulators called toehold switches specific for the biomarker panel were then designed. To predict the dynamic range of toehold switches for use in genetic circuits as biosensors, we developed a generic grammar of these switches, and built a multivariate linear regression model using thermodynamic features derived from RNA secondary structure and interaction. The model yielded predictions of toehold efficacy with an adjusted R2 = 0.59. Reaction kinetics modelling was performed to predict the sensitivity of the second-generation toehold switches to the miRNA biomarkers. Simulations showed a linear response between 10nM and 100nM before saturation. Our study demonstrates an end-to-end workflow for the efficient design of genetic circuits geared towards the effective detection of unique genomic signatures that would be increasingly important in today\u2019s world. The approach has the potential to direct experimental efforts and minimise costs. All resources are provided open-source (https://github.com/igem2019) under GNU GPLv3 licence.
", + "tags": [ + { + "id": 2936, + "tag": "BioModels" + }, + { + "id": 4693, + "tag": "BioModels:BIOMD0000001103" + }, + { + "id": 704, + "tag": "SBML" + } + ], + "timestamp_created": "2025-05-07 14:31:17.962443+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/BIOMD0000001103", + "user": { + "email": "info@opensourcebrain.org", + "first_name": "OSB", + "id": "095e311e-336f-47d6-b4f6-16f6dd771a8d", + "last_name": "Admin", + "username": "osbadmin" + }, + "user_id": "095e311e-336f-47d6-b4f6-16f6dd771a8d" + }, + "3392": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "feat-add-local-script", + "id": 3392, + "name": "OSBv2-test-local-script", + "repository_type": "github", + "summary": "The osbv2 repository but from a different branch", + "tags": [ + { + "id": 3, + "tag": "osbv2" + } + ], + "thumbnail": "repositories/3392/thumbnail.bin", + "timestamp_created": "2025-06-10 11:23:04.653517+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenSourceBrain/OSBv2", + "user": { + "email": "c@ab.com", + "first_name": "a", + "id": "6f47a287-9640-4c0f-89b9-50e637729deb", + "last_name": "a", + "username": "ca" + }, + "user_id": "6f47a287-9640-4c0f-89b9-50e637729deb" + }, + "3393": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "draft", + "id": 3393, + "name": "test-dandi-001361", + "repository_type": "dandi", + "summary": "sdfasdfsadf **asdfsdf**", + "tags": [ + { + "id": 7, + "tag": "human" + } + ], + "timestamp_created": "2025-06-10 11:29:05.095570+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/001361", + "user": { + "email": "c@ab.com", + "first_name": "a", + "id": "6f47a287-9640-4c0f-89b9-50e637729deb", + "last_name": "a", + "username": "ca" + }, + "user_id": "6f47a287-9640-4c0f-89b9-50e637729deb" + }, + "3394": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "0.211014.0808", + "id": 3394, + "name": "duplicate-dandi-repositoryy", + "repository_type": "dandi", + "summary": "", + "tags": [], + "timestamp_created": "2025-06-10 11:30:08.895201+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000008/0.211014.0809", + "user": { + "email": "c@ab.com", + "first_name": "a", + "id": "6f47a287-9640-4c0f-89b9-50e637729deb", + "last_name": "a", + "username": "ca" + }, + "user_id": "6f47a287-9640-4c0f-89b9-50e637729deb" + }, + "3395": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "0.211014.0808", + "id": 3395, + "name": "duplicate-again!", + "repository_type": "dandi", + "summary": "", + "tags": [], + "timestamp_created": "2025-06-10 11:31:21.216663+00:00", + "timestamp_updated": "---", + "uri": "https://dandiarchive.org/dandiset/000008/0.211014.0809", + "user": { + "email": "c@ab.com", + "first_name": "a", + "id": "6f47a287-9640-4c0f-89b9-50e637729deb", + "last_name": "a", + "username": "ca" + }, + "user_id": "6f47a287-9640-4c0f-89b9-50e637729deb" + }, + "3396": { + "auto_sync": true, + "content_types": "modeling", + "content_types_list": [ + "modeling" + ], + "default_context": "1", + "id": 3396, + "name": "biomodels-test", + "repository_type": "biomodels", + "summary": "", + "tags": [], + "timestamp_created": "2025-06-10 12:17:33.476868+00:00", + "timestamp_updated": "---", + "uri": "https://www.ebi.ac.uk/biomodels/MODEL8459127548", + "user": { + "email": "c@ab.com", + "first_name": "a", + "id": "6f47a287-9640-4c0f-89b9-50e637729deb", + "last_name": "a", + "username": "ca" + }, + "user_id": "6f47a287-9640-4c0f-89b9-50e637729deb" + }, + "3397": { + "auto_sync": true, + "content_types": "experimental", + "content_types_list": [ + "experimental" + ], + "default_context": "main", + "id": 3397, + "name": "Data Skills for Neuroscientists", + "repository_type": "github", + "summary": "", + "tags": [ + { + "id": 653, + "tag": "Tutorial" + }, + { + "id": 9, + "tag": "Test" + } + ], + "timestamp_created": "2025-07-25 13:26:28.306412+00:00", + "timestamp_updated": "---", + "uri": "https://github.com/OpenNeuroAI/DataSkillsforNeuroscientists", + "user": { + "email": "p.gleeson@gmail.com", + "first_name": "Padraig", + "id": "7089f659-90ad-4ed9-9715-2327f7e2e72f", + "last_name": "Gleeson Admin", + "username": "pgleeson" + }, + "user_id": "7089f659-90ad-4ed9-9715-2327f7e2e72f" } } } \ No newline at end of file diff --git a/libraries/client/info_all.sh b/libraries/client/info_all.sh index 07d315ce3..8321db625 100755 --- a/libraries/client/info_all.sh +++ b/libraries/client/info_all.sh @@ -3,17 +3,32 @@ set -ex ## A script to refresh all the cached info json files +quick=0 + +if [[ ($# -eq 1) && ($1 == '-q') ]]; then + quick=1 +fi + ruff format *.py ruff check *.py +# Update the cached info for OSBv1 python osbv1_info.py -python osb_info.py +# Update the cached info for OSB projects on GitHub +python osb_gh_info.py + +# Update the cached info for OSBv2 repositories +python osb_info.py -v2dev python osb_info.py -v2 -python osb_gh_info.py -python loadddandi.py -dry -python modeldb_info.py +if [ "$quick" == 0 ]; then + + python loadddandi.py -dry + python biomodels_info.py + + python modeldb_info.py +fi diff --git a/libraries/client/loadbiomodels.py b/libraries/client/loadbiomodels.py index d4c3a0ce3..9e9737b29 100644 --- a/libraries/client/loadbiomodels.py +++ b/libraries/client/loadbiomodels.py @@ -1,10 +1,17 @@ -import requests - -import pprint +import workspaces_cli +from pprint import pprint, pformat +from workspaces_cli.api import rest_api, k8s_api +import logging +import json import sys -API_URL: str = "https://www.ebi.ac.uk/biomodels" -out_format = "json" +from utils import get_tags_info +from utils import known_users, lookup_user + +from workspaces_cli.models import ( + OSBRepository, + RepositoryContentType, +) # Take from the accessToken cookie after login TOKEN = "EDITME" @@ -25,38 +32,237 @@ else: dry_run = False # dry_run = True +BIOMODELS_URL: str = "https://www.ebi.ac.uk/biomodels" + +index = 0 +min_index = 0 +max_index = 10000 + +verbose = True # +verbose = False + +configuration = workspaces_cli.Configuration( + host="https://workspaces.%s.opensourcebrain.org/api" % v2_or_v2dev, + access_token=TOKEN, +) + +owner_user_id = known_users["OSBAdmin_v2"] +if v2_or_v2dev == "v2dev": + owner_user_id = known_users["OSBAdmin_v2dev"] + +# Enter a context with an instance of the API client +with workspaces_cli.ApiClient(configuration) as api_client: + # Create an instance of the API class + api_instance = k8s_api.K8sApi(api_client) + + try: + # Test if application is healthy + api_response = api_instance.live() + pprint(api_response) + except workspaces_cli.ApiException as e: + print("Exception when calling K8sApi->live: %s\n" % e) + +filename = "cached_info/biomodels.json" +biomodels_info = json.load(open(filename)) +biomodels_info.pop("0", None) + +print("Loaded info on %s biomodels models" % len(biomodels_info)) + +all_updated = [] +all_added = [] +multi_matches = [] +all_errors = [] + + +with workspaces_cli.ApiClient(configuration) as api_client: + api_instance = rest_api.RestApi(api_client) + + def add_biomodels_model(biomodels_model, index): + if "publicationId" not in biomodels_model: + print("Not adding, probably uncurated entry...") + return False + + if biomodels_model["curationStatus"] != "CURATED": + print( + " Not adding, as curationStatus = %s" + % biomodels_model["curationStatus"] + ) + return False + biomodels_model_id = biomodels_model["publicationId"] + name = biomodels_model["name"] + + print( + "\n================ %i: %s, %s ================\n" + % (index, biomodels_model_id, name) + ) + + biomodels_uri = f"{BIOMODELS_URL}/{biomodels_model_id}" + search = f"uri__like={biomodels_uri}" + + found = api_instance.osbrepository_get(q=search) -def get_model_identifiers(): - response = requests.get(API_URL + "/model/identifiers?format=" + out_format) - response.raise_for_status() - output = response.json() - return output + if found.osbrepositories: + matching_repos = [] + matches = [] + for r in found.osbrepositories: + if r.uri == biomodels_uri: + matching_repos.append( + "M: URL to OSBv2 repo: https://%s.opensourcebrain.org/repositories/%i (%s) - %s\n" + % (v2_or_v2dev, r.id, r.uri, biomodels_uri) + ) + matches.append(r) -def get_model_info(model_id): - response = requests.get(API_URL + "/" + model_id + "?format=" + out_format) - response.raise_for_status() - output = response.json() - return output + if len(matching_repos) > 1: + print("Matching: %s" % matching_repos) + err_info = " More than one match for [%s] (search: %s):\n" % ( + biomodels_uri, + search, + ) + for r in found.osbrepositories: + err_info += ( + " - URL to OSBv2 repo: https://%s.opensourcebrain.org/repositories/%i (%s)\n" + % (v2_or_v2dev, r.id, r.uri) + ) + err_info += " - Owner %s\n" % (lookup_user(r.user_id, "")) + print(err_info) + exit() + if verbose: + print( + "\n ------------ Current OSB %s repo info: ---------" + % v2_or_v2dev + ) + print(" %s" % found) + print(" ------------ OSB API info: ---------") + print(" %s" % biomodels_model) -def main(): - model_ids = get_model_identifiers()["models"] - max_count = 10 - count = 0 + multi_matches.append(err_info) + return False - for model_id in model_ids: - if count < max_count: - if "BIOMD" in model_id: - model_link = f"[{model_id}](https://www.ebi.ac.uk/biomodels/{model_id})" - info = get_model_info(model_id) - # model_name = info["name"] - print("\n===============================================") + existing_repo = matches[0] + url_info = ( + " URL to OSBv2 repo: https://%s.opensourcebrain.org/repositories/%i" + % (v2_or_v2dev, existing_repo.id) + ) + try: print( - f" {model_id} ({model_link}): \n{pprint.pformat(info['name'])}--" + " %s already exists (owner: %s); updating..." + % (biomodels_model_id, lookup_user(existing_repo.user_id, url_info)) + ) + except Exception: + exit(-1) + print(url_info) + all_updated.append(url_info) + + if verbose: + print( + "\n ------------ Current OSB %s repo info: ---------" + % v2_or_v2dev + ) + print(" %s" % found) + print(" ------------ OSB API info: ---------") + print(" %s" % biomodels_model) + + tags = get_tags_info(biomodels_info=biomodels_model) + + if not dry_run: + desc = biomodels_model["description"] + print(" Description: %s..." % desc[:150]) + latest_version = biomodels_model["history"]["revisions"][-1] + print(" Latest version: %s" % latest_version) + + return api_instance.osbrepository_id_put( + existing_repo.id, + OSBRepository( + uri=biomodels_uri, + name=name, + summary=desc, + tags=tags, + default_context=existing_repo.default_context, + content_types_list=[RepositoryContentType(value="modeling")], + content_types="modeling", + user_id=owner_user_id, + repository_type="biomodels", + auto_sync=True, + ), + ) + else: + print(f"Search failed: {search}") + print( + " **********************************************************************" + ) + print(" **** Adding %s: %s" % (biomodels_model_id, name)) + print( + " **********************************************************************" + ) + + tags = get_tags_info(biomodels_info=biomodels_model) + + all_added.append(" %i) %s: %s" % (index, biomodels_model_id, name)) + desc = biomodels_model["description"] + print(" Description: %s..." % desc[:150]) + latest_version = biomodels_model["history"]["revisions"][-1] + print(" Latest version: %s" % latest_version) + + if not dry_run: + return api_instance.osbrepository_post( + OSBRepository( + uri=biomodels_uri, + name=name, + summary=desc, + tags=tags, + default_context=str(latest_version["version"]), + content_types_list=[RepositoryContentType(value="modeling")], + content_types="modeling", + user_id=owner_user_id, + repository_type="biomodels", + auto_sync=True, + ) ) - count += 1 + url_info = ( + " URL to OSBv2 repo: https://%s.opensourcebrain.org/repositories/%s" + % (v2_or_v2dev, "???") + ) # found.osbrepositories[0].id) + print(url_info) + + for biomodels_model_id in biomodels_info: + biomodels_model = biomodels_info[biomodels_model_id] + if index >= min_index and index < max_index: + try: + added = add_biomodels_model(biomodels_model, index) + except Exception as e: + print("----------") + logging.exception("Error adding %s" % pformat(biomodels_model)) + print("----------") + print("Error: %s" % str(e)) + print("----------") + if "context_resources" not in str(e): + print("Exiting due to unknown error...") + exit() + else: + print("Known error...") + + index += 1 + + # print(added) + +print( + "\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + + "\n\nDone! All updated (%i total; dry_run: %s):" % (len(all_updated), dry_run) +) +for m in all_updated: + print(m) + +print("\nAll added (%i total):" % len(all_added)) +for m in all_added: + print(m) + +print("\nMultiple matches found (%i total):" % len(multi_matches)) +for m in multi_matches: + print(m) -if __name__ == "__main__": - main() +print("\nErrors found (%i total):" % len(all_errors)) +for de in all_errors: + print(de) diff --git a/libraries/client/loadmodeldb.py b/libraries/client/loadmodeldb.py index fadca49ab..91ef5cd3c 100644 --- a/libraries/client/loadmodeldb.py +++ b/libraries/client/loadmodeldb.py @@ -36,7 +36,7 @@ index = 0 min_index = 0 -max_index = 2000 +max_index = 10000 verbose = True # verbose = False @@ -64,6 +64,7 @@ filename = "cached_info/modeldb.json" modeldb_info = json.load(open(filename)) +modeldb_info.pop("0", None) print("Loaded info on %s modeldb models" % len(modeldb_info)) @@ -80,7 +81,7 @@ def add_modeldb_model(modeldb_model, index): modeldb_model_id = modeldb_model["id"] if "osbv2_gh_repo" not in modeldb_model or not modeldb_model["osbv2_gh_repo"]: all_errors.append( - " %i, %s (%s) doesn't have a Github repo..." + " %i, %s (%s) doesn't have a GitHub repo... Use modeldb_info.py to fork it to OSB" % (index, modeldb_model["name"], modeldb_model_id) ) return @@ -194,7 +195,7 @@ def add_modeldb_model(modeldb_model, index): tags = get_tags_info(modeldb_info=modeldb_model) - all_added.append("%s, index %i" % (modeldb_github, index)) + all_added.append(" %s, index %i" % (modeldb_github, index)) if not dry_run: desc = ( diff --git a/libraries/client/modeldb_info.py b/libraries/client/modeldb_info.py index 695a1fdff..63d94b098 100644 --- a/libraries/client/modeldb_info.py +++ b/libraries/client/modeldb_info.py @@ -1,10 +1,10 @@ """ -Script to get OSBv1 project info +Script to get info on all models on ModelDB via their API, and potentially +add forks of the ModelDB GitHub repos on github.com/OpenSourceBrain """ import sys import json - import pprint from utils import get_github @@ -12,7 +12,6 @@ verbose = True # verbose = False - info = {} with_gitrepo = 0 @@ -30,7 +29,7 @@ if __name__ == "__main__": min_index = 0 - max_index = 2000 + max_index = 10000 index = 0 from osb.utils import get_page @@ -41,6 +40,7 @@ pprint.pprint(models, compact=True) selection = models[min_index:max_index] + for model in selection: print( "\n-------- Model (%i/%i, order %i): %s:\n" @@ -87,7 +87,7 @@ info[model]["osbv2_gh_branch"] = repo_to_use.default_branch except Exception: print( - " Missing fork: %s, forking now: %s" + " **** Missing fork: %s, forking now: %s" % (possible_osbgh_repo, fork_if_missing) ) if fork_if_missing: @@ -118,14 +118,15 @@ print(msg) many_forks.append(msg) - except Exception: - msg = " Problem locating repo for: %i (%i/%i) %s" % ( + except Exception as e: + msg = " Problem with model: %i (%i/%i) %s" % ( info[model]["id"], index, len(selection), info[model]["name"], ) print(msg) + print(e) errors.append(msg) index += 1 @@ -137,10 +138,6 @@ filename = "cached_info/modeldb.json" -strj = json.dumps(info, indent=" ", sort_keys=True) -with open(filename, "w") as fp: - fp.write(strj) - print( "\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + "\n\nDone!" @@ -154,9 +151,14 @@ for m in forked_now: print(m) +info[0] = {} +info[0]["to_be_forked"] = [] +info[0]["errors"] = [] + print("\nStill to be forked (%i total):" % len(to_be_forked)) for m in to_be_forked: print(m) + info[0]["to_be_forked"].append(m.strip()) print("\nMany forks (%i total):" % len(many_forks)) for m in many_forks: @@ -165,5 +167,10 @@ print("\nErrors (%i total):" % len(errors)) for m in errors: print(m) + info[0]["errors"].append(m.strip()) + +strj = json.dumps(info, indent=" ", sort_keys=True) +with open(filename, "w") as fp: + fp.write(strj) print("Data on ModelDB (%i models) written to %s" % (len(info), filename)) diff --git a/libraries/client/osb_gh_info.py b/libraries/client/osb_gh_info.py index 081b36bd1..0520c1c1d 100644 --- a/libraries/client/osb_gh_info.py +++ b/libraries/client/osb_gh_info.py @@ -14,9 +14,11 @@ gh = get_github() +count = 0 for repo in gh.get_user().get_repos(): if "OpenSourceBrain" in repo.url: - print("=============== %s ============" % repo.url) + count += 1 + print("=============== %i: %s ============" % (count, repo.url)) if verbose: pprint.pprint(repo.__dict__, compact=True) diff --git a/libraries/client/utils.py b/libraries/client/utils.py index b719664ce..fb47b5b87 100644 --- a/libraries/client/utils.py +++ b/libraries/client/utils.py @@ -21,10 +21,32 @@ def lookup_user(uid, url): def get_tags_info( - dandi_api_info=None, dandishowcase_info=None, osbv1_info=None, modeldb_info=None + dandi_api_info=None, + dandishowcase_info=None, + osbv1_info=None, + modeldb_info=None, + biomodels_info=None, ): tags = [] + if biomodels_info is not None: + tags.append("BioModels") + tags.append("BioModels:%s" % biomodels_info["publicationId"]) + tags.append(biomodels_info["format"]["identifier"]) + if "modelLevelAnnotations" in biomodels_info: + for mla in biomodels_info["modelLevelAnnotations"]: + if mla["qualifier"] == "bqbiol:hasTaxon": + if "name" in mla: + tags.append(mla["name"]) + elif mla["qualifier"] == "bqbiol:isVersionOf": + if "name" in mla: + n = mla["name"] + tags.append(n[0].upper() + n[1:]) + elif "resource" in mla and mla["resource"] == "Human Disease Ontology": + if "name" in mla: + n = mla["name"] + tags.append(n[0].upper() + n[1:]) + if modeldb_info is not None: tags.append("ModelDB") tags.append("ModelDB:%s" % modeldb_info["id"]) @@ -77,12 +99,12 @@ def get_tags_info( tags.append("%s" % dandishowcase_info["species"]) tags_list = [] - tags = sorted(tags) + tags = sorted(list(dict.fromkeys(tags))) # sort and remove duplicates for tag in tags: tags_list.append({"tag": tag}) print(" ------------ Tags: ---------") - print(" %s" % tags) + print(" %s" % tags) # print(" %s"%tags_list) return tags_list